Gender Differences in Scientific Literacy of HKPISA 2006:
A Multidimensional Differential Item Functioning and
Multilevel Mediation Study
WONG, Kwan Yin
A Thesis Submitted in Partial Fulfillment
of the Requirements for the Degree of
Doctor of Education
in
Education
The Chinese University of Hong Kong
February 2012
Thesis Assessment Committee
Professor CHUNG, Yue-ping Stephen
(Chair)
Professor HO, Sui-chu Esther
(Thesis Supervisor)
Professor CHEUNG, Sin-pui Derek
(Committee Member)
Professor EPSTEIN, Joyce L.
(External Examiner)
ABSTRACT
The aim of this study is to investigate the effect of gender differences of 15-year-old students on
scientific literacy and their impacts on students’ motivation to pursue science education and
careers (Future-oriented Science Motivation) in Hong Kong.
The data for this study was collected from the Program for International Student Assessment in
Hong Kong (HKPISA). It was carried out in 2006. A total of 4,645 students were randomly
selected from 146 secondary schools including government, aided and private schools by
two-stage stratified sampling method for the assessment.
HKPISA 2006, like most of other large-scale international assessments, presents its assessment
frameworks in multidimensional subscales. To fulfill the requirements of this multidimensional
assessment framework, this study deployed new approaches to model and investigate gender
differences in cognitive and affective latent traits of scientific literacy by using
multidimensional differential item functioning (MDIF) and multilevel mediation (MLM).
Compared with mean score difference t-test, MDIF improves the precision of each subscales
measure at item level and the gender differences in science performance can be accurately
estimated. In the light of Eccles et al (1983) Expectancy-value Model of Achievement-related
Choices (Eccles’ Model), MLM examines the pattern of gender effects on Future-oriented
Science Motivation mediated through cognitive and affective factors.
As for MLM investigation, Single-Group Confirmatory Factor Analysis (Single-Group CFA)
was used to confirm the applicability and validity of six affective factors which was, originally
prepared by OECD. These six factors are Science Self-concept, Personal Value of Science,
Interest in Science Learning, Enjoyment of Science Learning, Instrumental Motivation to Learn
Science and Future-oriented Science Motivation. Then, Multiple Group CFA was used to verify
measurement invariance of these factors across gender groups.
The results of Single-Group CFA confirmed that five out of the six affective factors except
Interest in Science Learning had strong psychometric properties in the context of Hong Kong.
Multiple-group CFA results also confirmed measurement invariance of these factors across
gender groups.
i
The findings of this study suggest that 15-year-old school boys consistently outperformed girls
in most of the cognitive dimensions except identifying scientific issues. Similarly, boys have
higher affective learning outcomes than girls. The effect sizes of gender differences in affective
learning outcomes are relatively larger than that of cognitive one.
The MLM study reveals that gender effects on Future-oriented Science Motivation mediate
through affective factors including Science Self-concept, Enjoyment of Science Learning,
Interest in Science Learning, Instrumental Motivation to Learn Science and Personal Value of
Science. Girls are significantly affected by the negative impacts of these mediating factors and
thus Future-oriented Science Motivation. The MLM results were consistent with the
predications by Eccles’ Model.
Overall, the CFA and MLM results provide strong support for cross-cultural validity of Eccles’
Model. In light of our findings, recommendations to reduce the gender differences in science
achievement and Future-oriented Science Motivation are made for science education
participants, teachers, parents, curriculum leaders, examination bodies and policy makers.
ii
從 PISA 2006 探討香港學生科學素養之性別差異:
多維試題功能及多層中介變項研究
摘要
這項研究的目的旨在探討香港 15 歲學生在科學素養上的性別差異及這些差異如何影響男
女生在選擇以科學作為升學及職業的動機。
本研究的數據取自 2006 年在本港舉行的香港學生能力國際評估計劃(Programme for
International Student Assessment)。該計劃的 4645 學生樣本取自 146 所學校，包括：官立、
資助及私立學校，以兩階段分層隨機抽樣的方法選取。
學生能力國際評估計劃如其他大型國際評估一樣，其評估框架採用多維試題架構。本研究
採用配合該試題架構及樣本結構的多維試題功能（MDIF）及多層中介變項（MLM）兩個
研究方法，去了解 15 歲男女學生在科學素養(認知和情感)上的性別差異及這些差異如何
影響男女生在選擇以科學作為升學及職業的動機。比較常用的均差 t-檢定，MDIF 具備提
高各次級量尺的精確度特質，因而可以更有效和準確地計算出男女學生在科學素養上的性
別差異。MLM 則以 Eccles (1983) 的成功期望價值理論為學理基礎去分析和了解這些性別
差異如何影響男女生在選取與科學相關的升學途徑和擇業的動機。
要完成 MLM 的研究，我們必須先使用單組驗證性因子分析(Single-Group CFA)驗證經濟
合作與發展組織(OECD)所建構的六項情意因素，包括：「科學上的自我概念」、「科學的
個人價值」
、
「科學的興趣」
、
「對科學的喜好」
、
「學習科學的工具性動機」和「將來工作而
學習科學的動機」，以便了解使用這些源自西方社會的情意因素在本土研究的可行性及效
度。接着使用本土數據去調整這六項情意因素結構。最後利用多組驗證性因子分析
(Multiple-Group CFA) 去確定這些因素結構對男女生是否都適用 ( 即測量等同檢驗
Measurement Invariance Test)。
iii
由單組驗證性因子分析結果得知，六項情意因素，除了要對「科學的興趣」因素作較大幅
度的修改外，其他五項因素都具有良好的心理測量特性。而多組驗證性因子分析的結果亦
顯示，六項情意因素都能通過測量等同檢驗，亦即這六項因素結構對男女生都適用。
研究結果顯示除了「鑑定形成科學議題」能力外，本港 15 歲的男生在「解釋科學現象」
及「科學論證」等科學認知層面上優於女生。在科學情意發展上，男生比女生亦有更好的
發展，其效應值(effect size)更高於認知層面。
MLM 的研究結果與 Eccles 的成功期望價值理論預測結果吻合，也就是說，男女生在面向
未來升學選科和擇業動機上呈現明顯的性別差異，而這些差異主要是透過情意因素(中介
變項)間接影響男女生的選擇意向。就這些因素而言，女生在選取科學作為未來升學途徑
和職業動機明顯地較男生為弱。
整體而言，驗證性因子分析結果和 MLM 的研究結果支持源自西方社會的 Eccles 成功期望
價值理論具備跨文化效度，在香港華人社會的研究結果與西方結果基本吻合。
最後，本文作者將根據本研究的結果，向科學教育的工作者、教師、父母、課程發展人員、
政策的制定者和考核機構提供一些可行的建議，希望藉此改善香港男女生在科學生涯規劃
上的性別差異。
iv
ACKNOWLEDGEMENTS
I would like to thank my supervisor, Professor HO, Sui-chu Esther, and my advisors Professor
CHEUNG, Sin-pui Derek, Professor CHUNG, Yue-ping Stephen, Professor YIP, Din-yan,
Professor EPSTEIN, Joyce L. and Professor TSANG, Wing-kwong for their professional advice
and insightful comments in this course of doctoral studies.
I would also like to thank the Hong Kong Centre for International Student Assessment for
providing me all kinds of assistance in completing this thesis.
I would like express my sincere gratitude to my mentors, Mr. KWONG Tat Hay and Mr. CHOW
King Wah. Without their continuous support and encouragement throughout the course, I would
never have finished this thesis.
Special thanks go to my brother, Mr. WONG Kwan Yeh and my friend, Mr. CHOI Sze Wai, who
spent many hours reading the drafts and making suggestions.
Last but not least, I am grateful to my dearest mother for her love and support. Many thanks for
her care in all these years.
v
TABLE OF CONTENTS
ABSTRACT ……………………………………………………………………….... i
ACKNOWLEDGEMENTS …………………………………………………….…. v
TABLE OF CONTENTS ……………………………………………………….….. vi
LIST OF TABLES ……………………………………………………………….…. xi
LIST OF FIGURES …………………………………………………………..…….. xiii
ABBREVIATIONS …………………………………………………………..………xiv
CHAPTER ONE: INTRODUCTION
1.1 Background of the study …………………………………………………... 1
1.1.1 Gender-equity in global content of education ……………………..… 2
1.1.2 Gender differences in science performance and affective learning
outcomes ……………………………………………………………. 8
1.1.3 Gender differences in variability of science performance……..…….. 11
1.1.4 PISA background ..…………………………………………………... 13
1.2 Weaknesses of previous gender studies ..………………………………….. 13
1.2.1 Weaknesses of measurement models based on total score ………….. 13
1.2.2 Weaknesses of unidimensional measurement models ………….…….14
1.2.3 Strength of multidimensional IRT models…………………………….14
1.2.4 Strength of multilevel models …………………………………..…….14
1.3 Research questions ..……………………………………………………….. 16
1.4 Significance of the study ..………………………………………………....17
1.4.1 For gender-equity educational policies in Hong Kong …………….…17
1.4.2 For local economic growth ………………………………………….. 18
1.4.3 For gender-inclusive science curriculums, assessments
& teachers’ training …………………………………………………. 19
1.4.4 For academic discourse in gender-equity …………………………….20
1.5 Structure of the thesis ………………………………………………………20
1.6 Summary ………………………………………………………….……….. 21
vi
CHAPTER TWO: LITERATURE REVIEW
2.1 Defining scientific literacy by historical review ……………………………. 22
2.1.1 Cognitive domain of scientific literacy ……………………………… 22
2.1.2 Affective domain of scientific literacy ………………………………. 30
2.1.2.1 Taxonomy of affective domain elements in science education 30
2.1.2.2 Science self-concept …………………...…………………….. 31
2.1.2.3 Motivation in science learning ………………………………. 31
2.2 Gender differences in scientific literacy ………………….. ……………… 33
2.2.1 Defining gender: the nature versus nurture debate ………………….. 33
2.2.2 Gender differences in cognitive learning outcomes ………………….33
2.2.3 Gender differences in affective learning outcomes .……….………....38
2.2.4 Gender differences in science educational and occupational choices.. 40
2.3 Factors attributing gender differences …………………………………….. 44
2.3.1 Biological contributions ……………………………………………. 44
2.3.1.1 Evolutionary psychology perspectives ………………………. 44
2.3.1.2 Brain structural perspectives ………………………………… 45
2.3.1.3 Brain functional perspectives ………………………………... 45
2.3.1.4 Hormonal perspectives ………………………………………. 46
2.3.2 Sociocultural contributions ………………………………………….. 46
2.3.2.1 Gender-role ……..………………………………………….… 47
2.3.2.2 Schooling and family conditions …………………………….. 47
2.3.3 Item characteristics attributing to gender differences …………..…… 49
2.3.3.1 Scientific content …………………………………………….. 49
2.3.3.2 Item format ……………………………………………………49
2.3.4 Expectancy-value model of achievement-related choices in science .. 50
2.3.4.1 Self-concept of ability as mediator of gendered choices …..… 51
2.3.4.2 Subjective task values as mediators of gendered choices ……. 52
2.4 Local research on gender differences in scientific literacy …………….….. 52
2.4.1 Gender differences in science performance ……………………..……52
2.4.2 Gender differences in affective domain ……………….……….…..... 56
2.5 Summary …………………………………………………………………. 57
vii
CHAPTER THREE: RESEARCH DESIGN AND METHODS
3.1 PISA 2006 database ……………………………………………………….. 58
3.2 Conceptual framework of present study ……………………………………60
3.3 Conceptualization and operationalization of scientific literacy …………… 62
3.3.1 Cognitive domain ……………………………………………………. 61
3.3.2 Affective domain …………………………………………………….. 63
3.3.2.1 Science Self-concept …………………………………………65
3.3.2.2 Personal Value of Science ……………………………………67
3.3.2.3 Interest and Enjoyment of Science Learning ……………….. 68
3.3.2.4 Motivation to Learn Science …………………………….….. 72
3.4 Conceptualization and operationalization of Parental SES …………….…..74
3.5 Multidimensional Differential Item Functioning (MDIF) …………….……75
3.5.1 The item response (IRT) model ………………………..……………. 75
3.5.1.1 DIF model for gender differences studies ……………………. 77
3.5.1.2 Effect size by DIF ……………………………………………. 79
3.5.1.3 Item fit statistics …………………………………………..….. 79
3.6 Model testing in SEM ………………………………………….………….. 80
3.7 Summary ………………………………………………………….……….. 80
CHAPTER FOUR: GENDER DIFFERENCES IN STUDENTS’ COGNITIVE &
AFFECTIVE LEARNING OUTCOMES
4.1 Gender differences in students’ cognitive outcomes ……………………… 81
4.1.1 Gender differences in science performance dimensions …………….. 81
4.1.1.1 Gender differences in science performance dimensions
measured by MSD …………………………………………….82
4.1.1.2 Gender differences in science performance dimensions
measured by MDIF …………………………………………... 84
4.1.2 Gender differences in content domains ………………………………86
4.1.2.1 Gender differences in content domains measured by MSD …. 86
4.1.2.2 Gender differences in content domains measured by MDIF … 87
4.1.3 Gender differences in item formats …………………………………..89
4.1.4 Gender variability in science performance ………………………….. 90
4.1.4.1 Gender variability measured by variance ratio (B/G) ……..… 90
4.1.4.2 Gender variability measured by number of students
against each ability estimate …………………………………..92
viii
4.2 Gender differences in students’ affective learning outcomes measured by
MSD ……………………………………………………………………….. 95
4.3 Gender differences in science achievement related choices measured by
MSD ………………………………………………………………………. 98
4.4 Gender differences in students’ affective learning outcomes measured by
DIF ……………………………………………………………………….. 99
4.5 Gender differences in science achievement related choices measured by
DIF ………………………………………………………………………… 100
4.6 Summary ……………………………………………………………………101
CHAPTER FIVE: THE FINDINGS BY EXPECTANCY-VALUE MODEL OF
ACHIEVEMENT-RELATED CHOICES
5.1 Pearson correlations between affective factors and gender ……………..….104
5.2 Gender differences by revised Expectancy-value Model ……………..……106
5.2.1 Grouping homogeneity ………………………………………….…....106
5.2.2 Mediation effect of Science Performance …………………………… 106
5.2.3 Mediation effect of Science Self-concept …………………………… 109
5.2.4 Mediation effect of Interest in Science Learning …………………….112
5.2.5 Mediation effect of Enjoyment of Science Learning ……………….. 113
5.2.6 Mediation effect of Interest and Enjoyment of Science Learning ….. 116
5.2.7 Mediation effect of Attainment Value …………………...……………117
5.2.8 Mediation effect of Utility Value …………………………………..…119
5.2.9 Mediation through Attainment Value and Utility Value ………….…..121
5.2.10 Full models of gender effects on Future-oriented Science Motivation 122
5.3 Summary ……………………………………………………….………..… 126
CHAPTER SIX: CONCLUSIONS AND IMPLICATIONS
6.1 Database and data analysis ……………………………………………...….129
6.2 Major findings …………………………………………..………………… 130
6.2.1 Multidimensional DIF model ……………………………………...… 130
6.2.2 Multilevel Mediation using Expectancy-Value Model ……………….134
6.3 Revisiting conceptual model …………………………………………….… 137
6.4 Implications for policy and practice …………………………………….… 139
6.4.1 Implications for policy makers ……………………………….….…...139
ix
6.4.2 Implications for school administrators, teachers and
textbook authors ………………………………………………..…… 140
6.4.3 Implications for parents and students ………………………..……….140
6.5 Limitations and recommendations for future research ………………………143
6.5.1 Limitations of the study ………………………………………………143
6.5.2 Recommendations for future research ………………………………..144
Appendix A: Handling missing values …………………………………………. 146
Appendix B: Booklet effects ……………………………………………………. 149
Appendix C: Wright map for science performance dimensions …………………151
Appendix D: Gender differences in scientific performance measured by MDIF . 152
References ………………………………………………………………………. 158
x
LIST OF TABLES
Table
1.1
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
2.10
2.11
2.12
2.13
2.14
2.15
2.16
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
3.11
3.12
3.13
3.14
Title
Page
Gender differences in Nobel Laureates from 1901 to 2009
Characteristics of Scientific Literacy: The 1960s
Characteristics of Scientific Literacy: The 1970s
Characteristics of Scientific Literacy: The 1980s
A multidimensional and hierarchical model of scientific literacy
Content Summary for the National Science Education Standards and
Benchmarks for Science Literacy
Scientific competency framework of PISA 2006
Klopfer’s taxonomy of affective behaviors in science education
A range of components in affective domains of scientific literacy
Gender differences in science performance at elementary schools
Gender differences in science performance at high schools
Gender differences in affective leaning outcomes at elementary
schools
Gender differences in affective leaning outcomes at high schools
Gender differences in science educational and occupational choices
at high schools
Gender differences in science educational and occupational choices
at universities
Trends in average science performance by gender - 1995 through
2007 (Grade 4)
Trends in average science performance by gender - 1995 through
2007 (Grade 8)
Data collection method
Demographic features of the participating students
Distribution of PISA 2006 Scientific Literacy items (knowledge
domains by competency)
A summary of procedure to conduct multi-group invariance test
across gender groups
Item parameters for Science Self-concept
Model fit for Science Self-concept
Measurement invariance test across the gender group for Science
Self-concept
Item parameters for Personal Value of Science
Model fit for Personal Value of Science
Measurement invariance test across the gender group for model of
Personal Value of Science
Item parameters for Interest in Science Learning
Item parameters and scale reliability for Enjoyment of Science
Learning
Model fit and estimated latent correlations for Interest in and
Enjoyment of Science Learning
Measurement invariance test across the gender group for
two-dimensional model of Interest in and Enjoyment of Science
Learning
12
25
27
27
28
xi
29
29
30
30
35
36
38
39
41
42
54
55
58
59
62
64
65
66
66
67
67
68
69
69
70
71
Table
Title
Page
3.15
3.16
Item parameters for Instrumental Motivation to Learn Science
Item parameters for Future-oriented Science Motivation
Model fit and estimated latent correlations for motivation to learn
science
Measurement invariance test across the gender group for
two-dimensional model of motivation to learn science
Model fit for socioeconomic status
Gender differences in scientific competency
Summary of items showing statistically significant gender DIF for
different science performance dimensions
Gender differences in content domains
Summary of items showing statistically significant gender DIF for
item content
Summary of items showing statistically significant gender DIF for
item format
Gender variance ratio on the PISA scale
Gender differences in affective learning outcomes (WLE scores)
Gender differences in Future-oriented Science Motivation (WLE
scores)
Gender differences in affective learning outcomes (PV scores)
Gender differences in Future-oriented Science Motivation (PV
scores)
Correlations among gender (Girl), affective factors and Science
Performance
Mediation effect of science performance
Mediation effect of Science Self-concept
Mediation effect of Interest in Science Learning (Model 4a),
Enjoyment of Science Learning (Model 4b) and Interest and
Enjoyment of Science Learning (Model 4c)
Mediation effect of Attainment Value (Model 5a), Utility Value
(Model 5b) and Attainment Value and Utility Value (Model 5c):
Full model of gender effects on Future-oriented Science Motivation
EM Correlations matrix of SES
Descriptive Statistics of the results of multiple imputation of SES
Hong Kong estimated booklet effects in logits
Internationally estimated booklet effects
Gender DIF items for Closed Constructed Response (CCR)
Gender DIF items for Multiple Choice (MC)
Gender DIF items for Complex Multiple Choice (CMC)
Gender DIF items for Open Response (OR)
72
72
3.17
3.18
3.19
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
5.1
5.2
5.3
5.4
5.5
5.6
A1
A2
A3
A4
A5
A6
A7
A8
xii
73
73
74
83
85
87
88
90
91
96
98
99
100
105
109
111
115
120
125
146
147
150
150
152
152
154
156
LIST OF FIGURES
Figure
Title
Page
1.1
1.2
Effect of gender equality on global economic competitiveness
OECD Social Institutions and Gender Index (SIGI)
The trend of day school first attempters in science subject choice by
gender in HKCEE 2001-2009
Gender differences in science
Expectancy-value model of achievement-related choices
Revised Expectancy-value Model of Achievement-related Choices
in Science
A second-order CFA model of INTSCIEHKG
A graphical representation of within-item and between-item
multidimensionality.
Item fit statistics for the three science performance dimensions:
Explaining Phenomena Scientifically (EPS), Identifying Scientific
Issues (ISI) and Using Scientific Evidence (USE)
Gender variability on different science performance level
Gender variance ratio on different science performance level
Science performance: Number of boys and girls at each ability
estimate
Explaining Phenomena Scientifically (EPS): Number of boys and
girls at each ability estimate
Identifying Scientific Issues (ISI): Number of boys and girls at each
ability estimate
Using Scientific Evidence (USE): Number of boys and girls at each
ability estimate
Item characteristic curves for Learning advanced science topics
would be easy for me (ST37Q01)
Gender effect on Science Performance and Future-oriented Science
Motivation (Model 1)
Mediation effect of Science Performance (Model 2)
Mediation effect of Science Self-concept (Model 3)
Mediation effect of Interest in Science Learning (Model 4a)
Mediation effect of Enjoyment of Science Learning (Model 4b)
Mediation effect of Interest and Enjoyment of Science Learning
(Model 4c)
Mediation effect of Attainment Value (Model 5a)
Mediation effect of Utility Value (Model 5b)
Mediation effect of Attainment Value and Utility Value (Model 5c)
Full model (Model 6a) of gender differences in Future-oriented
Science Motivation
Full model (Model 6b) of gender differences in Future-oriented
Science Motivation
Revised Conceptual Model for Studying Gendered Educational and
Occupational Trajectories in Science
Missing value pattern of SES and related factors
Wright map for the three dimensions: Explaining Phenomena
Scientifically (EPS), Identifying Scientific Issues (ISI) and Using
Scientific Evidence (USE) in science performance
3
4
1.3
2.1
2.2
3.1
3.2
3.3
3.4
4.1
4.2
4.3
4.4
4.5
4.6
4.7
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
6.1
A1
A2
xiii
19
48
50
61
71
77
79
91
92
93
93
94
95
100
107
108
110
112
114
116
118
119
121
123
124
138
147
151
ABBREVIATIONS
AAI
CCR
CDC
CEDAW
CFA
CFI
CGSS
CMC
DAC
ΔCFI
DFID
DIF
DSS
Δχ2
EAP
EPS
ERGEEA
ESS
FIML
GSES
HKCEE
HKEAA
HKPISA
IEA
INSTSCIE
INTSCIE
IRT
ISI
JOYSCIE
KAS
KOS
LS
MAR
MC
MCAR
MCMC
MDG3
MDIF
MGMI
MI
MML
MNAR
MRCMLM
MSD
MVN
NELS
NLSY
Academic Achievement Index
Closed Constructed Response
Curriculum Development Council
Convention on the Elimination of All Forms of Discrimination against Women
Confirmatory Factor Analysis
Comparative Fit Iindex
Child’s General Self Schemata
Complex Multiple Choice
Development Assistance Committee
Change of Comparative Fit Index
UK Department for International Development
Differential item functioning
Direct Subsidy Scheme
Change of Chi-square
Expected a posterior
Explaining Phenomena Scientifically
Gender Equity Education Act
Earth and Space Systems
Full information maximum likelihood
Guidelines on Sex Education in Schools
Hong Kong Certificate of Education Examination
Hong Kong Examinations and Assessment Authority
Programme for International Student Assessment in Hong Kong
International Association for the Evaluation of Educational Achievement
Instrumental Motivation to Learn Science
Interest in Science Learning
Item Response Theory
Identifying Scientific Issues
Enjoyment of Science Learning
Knowledge About Science
Knowledge Of Science
Living Systems
Missing at random
Multiple Choice
Missing completely at random
Markov chain Monte Carlo
Millennium Development Goals
Multidimensional Differential Item Functioning
Multi-group Measurement Invariance
Measurement Invariance
Marginal Maximum Likelihood
Missing not at random
Multidimensional random coefficients multinomial logit model
Mean Score Difference
Multivariate Normal Distribution
National Educational Longitudinal Study of the Eighth Grade Class
National Longitudinal Study of Youth
xiv
NNFI
NSTA
OECD
OR
PERSCIE
PISA
PS
RMR
RMSEA
SAT
SCIEFUT
SCSCIE
SDT
SEL
SEM
SEQ
SIGI
SOD
SP
SRMR
SSPA
STEM
STS
TIMSS
TLI
TS
UGC
UNDP
UNESCAP
UNESCO
URCMLM
USE
WFMS
Non-Normed Fit Index (NNFI)
National Science Teachers Association
Organisation for Economic Co-operation and Development
Open Response
Personal Value of Science
Programme for International Student Assessment
Physical Systems
Root Mean Square Residual
Root Mean Square Error of Approximation
US Scholastic Aptitude Test
Future-oriented Science Motivation
Science Self-concept
Self-determination theory
Scientific Explanations
Structural Equation Modeling
Scientific Enquiry
OECD Social Institutions and Gender Index
Sex Discrimination Ordinance
Science Performance
Standardized Root Mean Square Residual
Secondary School Places Allocation
Science, technology, engineering and mathematics
Science, technologies and society
Trends in International Mathematics and Science Study
Tucker Lewis Index
Technology Systems
University Grants Committee of the Hong Kong Special Administrative Region
United Nations Development Programme
United Nations Economic and Social Commission for Asia and the Pacific
United Nations Educational, Scientific and Cultural Organization
Unidimensional random coefficients multinomial logit model
Using Scientific Evidence
Weighted Fit Mean Square Error
xv
CHAPTER ONE
INTRODUCTION
1.1 Background of the study
In the past two decades, the number of girls going to university and graduating with bachelor
degree outnumbered that of boys. This trend is similar across all the developed OECD
(Organisation for Economic Co-operation and Development) countries and Hong Kong (OECD,
2009a; UGC, 2009; UGC 2010). International assessment such as TIMSS1 2009 and PISA2
2006 reported that girls are catching up with boys on science performance. Empirical evidences
support that there are no significant gender differences found in science performance at grade 4,
grade 8 and grade 9 (e.g. Ho et al., 2008; Martin et al., 2008).
Yet, the number of girls who took science subjects, including physics, chemistry and biology, in
senior secondary studies and their performance (A-C) of these subjects in two public
examinations namely, Hong Kong Certificate of Education Examination (HKCEE) and Hong
Kong Advanced Level Examination were well below those of boys’ counterparts (HKEAA,
2009a and HKEAA, 2009b). The same pattern persists at university course selection and
occupational choices after secondary school education (Census and Statistics Department, 2006;
UGC, 2009; UGC, 2010). In short, girls tend to select non-science educational programs and
careers.
In conclusion, significant gender differences in science performance are disappearing in these
years while the gender segregated educational and occupational choices in science continue. So,
what are major factors influence girls’ motivation to choose science as their educational and
careers goals? Is there any gender differences in these factors that might lead to the observed
gendered patterns in educational and occupational choices related to science?
To answer these two major questions, we have to find out how and to what extent the gender
differences influencing the students’ science performance and affective learning outcomes in
Hong Kong. The data obtained from the PISA 2006 database will be used to address these two
questions.
PISA is a triennial study to collect data from 15-year-old students about their cognitive and
1
2
Trends in International Mathematics and Science Study
Programme for International Student Assessment
1
affective learning outcomes inside and outside schools. It is a project commenced and
coordinated by the OECD. The third cycle of PISA: PISA 2006, three subject domains were
assessed, with scientific literacy as the major domain, whereas mathematics and reading as
minor domains. The enriched sets of cognitive3 and affective factors about scientific literacy
collected for major domain enable us to examine the gender difference in scientific literacy and
its subsequent impacts on achievement-related choices in science.
In the later sections, the background of gender-equity educational policy and theoretical
perspectives in gender differences in Hong Kong will be discussed.
1.1.1 Gender-equity in global content of education
Gender equity as human right
In 1979, the Convention on the Elimination of All Forms of Discrimination against Women
(CEDAW) provides a comprehensive framework to guide all rights-based action for gender
equity, including that of United Nations Development Programme (UNDP). UNDP supports
capacity building of its national partners to take on measures which could advance women’s
rights and take account of the full range of their contributions to development, as a basic for
MDG3 accomplishment (DFID, 2007 & UNDP, 2007).
“It is impossible to realize our goals while discriminating against half the
human race”
(Kofi Annan, 2006)
The United Nations World Summit of 2005, the Millennium Development Goals (MDG3)
reaffirmed gender equity and women’s empowerment, including indicators and concrete targets
related to girls’ education and gender mainstreaming. The United Nations Educational,
Scientific and Cultural Organization (UNESCO) identify five critical areas of concern in gender
mainstreaming (UNESCO, 2000):
1.
equal access to education for women and girls;
2.
women’s contribution to peace;
3.
women’s access to the media, and their image in the media;
4.
women’s contribution to the management of natural resources and environmental
protection;
5.
3
the girl-child with regard to access to education and literacy.
To distinguish the cognitive components from affective components of scientific literacy, science performance
(SP) is used to signify the cognitive performance of scientific literacy in the subsequent chapters.
2
In brief, the gender-equity in education is one of the main focuses of UN Millennium Project
Task Force on Education and Gender Equality in coming decade (MPTG, 2005).
Gender equity for economic development
OECD focuses more on gender equality as a vital goal for social and economic development
and growth in a global economy which is about effectiveness in improving people’s lives in a
sustainable way. From the economic and social survey of Asia and the Pacific in 2007, it has
been estimated that persistent gender inequality and discrimination against women due to
restrictions on access to employment and education alone cost between USD 58 billion and
USD 77 billion per year in the Asia Pacific (UNESCAP, 2007). Similarly, the correlation study
by World Economic Forum on the Global Gender Gap Index 2009 and the Global
Competitiveness Index 2009-2010 from 134 countries confirmed the correlation between gender
equality and the level of competitiveness of countries (see Figure 1.1). Though correlation does
not imply causality, it is in consistent with human capital theory that women represent half of
human population and her empowerment means a more efficient use of human resources in
national economic development and growth. (Zahidi et al., 2009).
Mahony (1988) argues that achieving a national competitiveness in the global economy through
schooling is the central theme of British educational policy, and national prosperity depends on
high levels of knowledge and skills in an increasingly service-led economy.
Figure 1.1: Effect of gender equality on global economic competitiveness
Gender Equality (0.00 – 1.00 scale)
3
The Development Assistance Committee (DAC) Guiding Principles for Aid Effectiveness on
Gender Equality and Women’s Empowerment (2008) further elaborate the principle of gender
equality to cover cultural, religious and social factors which heavily influence gendered subject
choice, specifically, more emphasis is placed on ‘traditional’ subjects for girls, while more
encouragement for boys to study subjects such as science and mathematics. Gender bias in
curricula at all educational levels reinforces stereotypes about the roles of girls and boys.
To promote and monitor gender equality worldwide, the OECD Social Institutions and Gender
Index (SIGI) (see Figure 1.2) was established and became a new composite measure for gender
equality in 102 non-OECD countries. Among those countries, Hong Kong ranked 20/102 while
China ranked 83/102 in 2009. In other words, Hong Kong has better gender equality than China.
Figure 1.2:
OECD Social Institutions and Gender Index (SIGI)
Promoting gender equity in education
United States
Title IX of the Education Amendments of 1972, an amendment of the original the Civil Rights
Act enacted in 1964, says that
No person in the United States shall, on the basis of sex, be excluded from participation
in, be denied the benefits of, or be subjected to discrimination under any education
program or activity receiving Federal financial assistance
(Mink, 1972)
4
Title IX asserts sex discrimination in the areas of science or math education, or in other aspects
of academic life. However, Title IX gave a very broad description of gender equity in education
only while the National Science Teachers Association (NSTA), the professional body for science
education, in its position statement about the gender equity in science education presented clear
guidelines to science teachers and school administrators (NSTA, 2006) in four areas: teaching,
selecting science curriculum; assessing student performance and in helping students prepare for
further study and careers.
In the classroom, science teachers must
1.
Implement varied and effective research-based teaching and assessment strategies that
align with the learning styles of all students.
2.
Ensure that all students are in a learning environment that encourages them to
participate fully in class discussions and science activities and investigations.
3.
In developing and implementing professional development and teacher preparation
programs, science teachers, administrators, teacher educators, and policy makers must
4.
Ensure that discussions about research-based issues related to the pedagogy of gender
equity are an integral part of professional development and teacher education
programs.
5.
Be aware of their own deep-seated beliefs so that they can ensure that their beliefs do
not interfere with objective science teaching.
In selecting science curriculum, science teachers, administrators, and community members must
6.
Select only those curriculum materials that promote gender inclusiveness through
their text, illustrations, and graphics.
7.
Select only those curriculum materials that present culturally diverse male and female
role models working in all disciplines and at all levels of science.
In developing assessment tools, science teachers, administrators, and evaluators must
8.
Design and implement varied kinds of assessment models so that all students,
regardless of their learning style, can be assessed fairly in science.
9.
Provide administrative support for the development and use of a range of assessment
tools that promote gender equity.
5
In helping students prepare for careers, guidance counselors and science teachers must
10. Encourage all students to consider science and science-related careers by exposing
them to a range of school and community activities.
11. Provide all students with the most recent information about the kinds of opportunities
available in the sciences, as well as the preparation necessary to attain such careers.
Taiwan
The Gender Equity Education Act was promulgated and implemented in 2004. It demanded all
the authorities and schools to promote and adopt gender-inclusive curricula, teaching, and
assessments. All curricula shall cover gender equity education elements, such as affective
education, sex education, and gay and lesbian education, and comply with principle of gender
equity. Teachers shall maintain gender equity consciousness, and “shall encourage students to
take courses in fields that are not traditionally affiliated with their gender” (Ministry of
Education, 2005).In 2005, the Enforcement Rules for the Gender Equity Education Act
(ERGEEA) was further clarified the professional practices in gender-equity in schools.
1. Curricula:
(1) Pre-service training of staff members, orientation training of new staff members,
in-service program and preparation program for candidates of educational administrators
as prescribed in Article 15 of the Act.
(2) Curricula and activities provided to students as prescribed in the first paragraph of
Article 17.
2. Instruction:
(1) Develop innovative teaching methods related to gender equity education.
(2) Enhance teachers’ competence in gender equity education pedagogies.
3. Assessments:
(1) Cognition, affection, and practice of the concept of gender equity.
(2) Diverse and non-gender-biased methods of assessment such as observation, operation
tasks, performances, oral exams, written exams, assignments, learning progress portfolio,
research reports etc.
(Ministry of Education, 2006 p. 1-2)
Hong Kong
Locally, there is no clear policy to address the issue of gender equity in education and the focus
is on the sex education rather than sex-equity in education. The education department and
6
curriculum development council published the Guidelines on Sex Education in Schools (GSES)
in 1997 and advised the secondary schools to infiltrate sex education elements in various
subjects such as General Studies, Science, Biology, Social Studies, Ethics and Religious Studies,
and Home Economics.
With the introduction of curriculum reform in 2001 which placed emphasis on holistic education,
cross-curriculum programs in civic education, moral education, sex education, health education
and environmental education have all been integrated into moral and civic education. Moral and
civic education stress on cultivating students’ positive values and attitudes, helping them
develop a healthy lifestyle, acquire skills in life to face and deal with daily life and social
problems, learn how to face the challenge of growth, and deal with doubts and perplexities
about sex, for example, dating and courtship, gender awareness, and sexual harassment but does
not include gender equity.
Currently, under law, the gender equity in education is administrated by anti-discrimination
ordinances in Sex Discrimination Ordinance (SDO). The SDO only ensure that boys and girls
have equal chance of assessing educational resources at system level, for example, through
Secondary School Places Allocation (SSPA) system, boys’ score were scaled up while that of
girls were scaled down systematically in order to accommodate a pre-set allocation system
before year 2001. This limited girls’ access to the prestigious schools territory-wide and
produced negative impact on girls’ educational outcomes (Tang, 2006). The High Court found
the system in violation of the SDO in June 2001 and the gender-disparity practice was abolished
and amended to allow boys and girls to have equal access of these top schools.
Apart from the GSES and SDO, in practice, there are no clear gender equity policies and
guidelines implemented at school curricula, school-based assessment, initial teachers’ education
and individual student. The local activist groups for instance, the Association Concerning
Sexual Violence Against Women, the Gender Research Centre at the Chinese University of
Hong Kong and commentators keep complaining that the Hong Kong Special Administrative
Region Government puts inadequate concerns and efforts to adopt gender-equity education in
local schools: kindergartens, primary schools, secondary schools and tertiary institutes.
7
1.1.2 Gender differences in science performance and affective learning outcomes
Gender differences have been a hot and contentious issue in Western education since the
Women’s Liberation Movement in the United States in 1960s and early 1970s. Gender has been
well realized as a key social identity that influences an individual’s educational experiences and
achievements. The mostly influential and cited research findings by Maccoby and Jackin in
1974 suggested that girls have lower self-esteem, achievement motivation and a better rote
learner than boys whereas boys have higher-level of cognitive processing than that of girls.
They also concluded that gender differences are well-established in these domains: girls have
higher verbal ability than boys while boys have greater visual-spatial and mathematical abilities
than girls.
However, in Hyde’s “The Gender Similarities Hypothesis” published in 2005, she highlighted
the evidence for gender similarities by meta-analysis of 5000 psychological gender differences
studies covering approximately 7 million people that 80% of the effect sizes4 were small or
close to zero in the United States.
Contradicting evidence from Britain GCSE results has suggested that boys are significantly
underachieving in comparison with girls in all subjects (Department for Education and Skills,
2004). The boys’ underperformance hit the headlines firstly in the mid-1990s after the
introduction of mandatory national curriculum in England and Wales in 1988 and boys and girls
were forced to take the same core subjects for the first time. Girls very quickly caught up with
and even slightly outstripped boys at GCSE science examinations (Epstein et al., 1998; Francis
& Skelton, 2005; Department for Education and Skills, 2009). This trend applies up to degree
level and more girls gain good degree awards than that of boys for a decade. The zero sum game
version of education becomes the moral panic among British journalists, commentators and
policy-makers. It justifies a greater focus and disbursement on meeting boys’ needs at the
expense of girls and vice versa. It prompted an acknowledgment of prevalent gender inequities
and disparities in relation to science (Calabrese, 1998).
The two IEA (International Association for the Evaluation of Educational Achievement) studies
in the 1970s and 1980s also reported that the gender differences in science performance were
consistently favouring boys and the differences intensified with age and grade levels of
4
Effect size of gender differences was evaluated with Cohen d. Cohen d = 0.2 is small, d = 0.5 is medium and d ≥
0.8 is large. Cohen d smaller than 0.2 is considered as negligible effect size in the social science context and
conventional clinical practices (Cohen, 1988).
8
schooling (Keeves, 1986, 1992). Similar patterns were observed in many participating countries
in the Trends in International Mathematics and Science Study (TIMSS), and that girls’
underachievement in science education is particularly distinct towards the end of the education
system (Law, 1996a, Mullis, et al., 1998; Robitaille & Beaton, 2002).
Likewise, the same complication persists in the local conditions that Yung et al. (2006) reported
a consistent trend of boys outperforming girls with significant differences from TIMSS 1995 to
TIMSS 2003 in science while this trend was not found in TIMSS 2007 and PISA 2000 to PISA
2006 (Ho et al., 2003, 2005, 2008; Martin et al., 2008).
Previous studies indicated that the gender differences can be underestimated if boys and girls
are each given gender-biased items and the gender effect gets cancelled. The gender differences
at item level can be distorted by just looking at the overall performance of boys and girls (Cole,
1997). McCrae (2009), the principal research fellow and leader of the Mathematics and Science
test development team at the Australian Council for Educational Research, who managed
framework and item development for the PISA 2006 science assessment, admitted that
Percentage of score points to be assigned to the knowledge of science components of
the assessment was determined by the PISA Governing Board (PGB) before the field
trial, in June 2004, to be about 60%. This decision had a far-reaching consequence in
terms of overall gender differences in the PISA 2006 science results, as boys generally
outperformed girls on knowledge of science items and the situation was reversed for
knowledge about science items.5
(OECD, 2009b p. 44)
From the analysis6 of competencies domains in scientific literacy in PISA 2000 and PISA 2006:
boys perform better than girls in explaining phenomena scientifically and using scientific
evidence, but perform less satisfactorily in identifying scientific issues. Yip et al (2004) alerts us
to consider a test dominated gendered items may lead to a different outcome about gender effect
on science performance.
5
6
Knowledge of science refers to knowledge about the natural world and knowledge about science refers to
scientific enquiry and scientific explanations.
PISA 2006 includes cognitive and affective learning outcomes of scientific literacy in the assessment framework
while PISA 2000 and PISA 2003 include cognitive domain only.
9
Though gender differences in various content areas are fluctuating over the years because of
difference test design, boys are generally dominant in physical and earth sciences while girls
equally well in biology and chemistry (Law, 1996a; Yip et al., 2004 & Yung, 2006). Girls
usually perform less well than boys in the areas which are not commonly covered in the formal
school science curricula despite they are more industrious in the school subject (Mullis et al.,
2000). Research findings also suggest that there is a large gender gap in terms of
multiple-choice and true-false questions and boys outperformed girls statistical significantly
(Yip et al., 2003 & Yung et al., 2006). The gender differences in guessing tendencies are robust
that males tend to guess while girls do not attempt and skip the items (Gershon & Sinai, 1991).
It hypothesized that boys attribute partially to their superior performance than girls in
multiple-choice questions because of their higher guessing tendency. Several research findings
also support that the multiple-choice format favours boys whereas the open-response format
favours girls (Liu, 2009; Bolger & Kellaghan, 1990; Bell & Hay, 1987; Murphy, 1982). Arnot et
al. (1998, p.28) suggest that boys show greater adaptability to more traditional approaches to
learning which require the memorisation of abstract, unambiguous facts and rules that have to
be acquired quickly. They also appear to be more willing to sacrifice deep understanding which
requires effort, for correct answers achieved at speed. While girls appear to prefer open-ended
tasks which are related to real situations and tend to respond in ways that are collaborative and
provide a broader context (Francis & Skelton, 2005). This gendered learning style differences
attribute significantly the item level gender bias in assessment.
The education system in Hong Kong is characterized as examination-led where the materials
transpire in the classroom are largely dictated by what happen in the public examination hall.
Despite the competition for tertiary education has dropped considerably in recent years, the
emphasis on examination for selection purpose is still much more weighted than many other
countries (Yung, 2002). The differential performance of boys and girls in science subject
domains thus has strong implications for the equity of assessments in public examinations and
subsequent educational opportunities.
Though the gender differences in science performance reduce in the past few decades, the
percentages of girls select science and engineering courses, in particular physical sciences in
both higher secondary and university levels remains substantially lower than that of boys
(Halpern et al., 2007). However, the direct social and economic impact on the overall
underrepresentation of females in science, technology, engineering and mathematics (STEM)
has not been fully explored. So, why not many females enter the STEM workforce after
10
graduation? Ho et al. (2008) suggested that instrumental motivation to learn science was one of
the key affective measures that decide students’ course selection, career choice and academic
performance. Kelly’s (1988) three-year trajectory research on 1472 British secondary students’
course choice showed a strong correlation between subject choice and their attitude to science in
junior secondary. In the United States, Tai et al. (2006) analyzed eighth-grade (about
13-year-olds) students about career expectation in STEM for years 1988 through 2000 using
National Education Longitudinal Study of 1988 (NELS:88). Their results indicated that students
with expectations for a science-related career were 3.4 times more likely to earn physical
science and engineering degrees than students without similar expectations. From these findings,
affective learning outcomes are critical components to assess one’s achievement in scientific
literacy.
In sum, the paradox of gender differences studies from Western literatures demonstrates that the
gender differences in education outcomes might be reduced substantially over the past few
decades; however, the gender differences of future career orientation in the field of science has
not yet been well testified in local context .
1.1.3 Gender differences in variability of science performance
For countries and regions with technology driven economy, the economic development and
growth is highly dependent on individuals who having high-level of scientific competencies (i.e.
Levels 5 and 6 in PISA proficiency levels on science performance) to bring new technology and
innovation while having basic competence (i.e. Level 2) are essential to enable individuals to
absorb and adopt new technology to their workplaces. Communities with large number of
scientifically literate citizens will benefit the economic growth than one with less literate
individuals (Hanushek & Woessmann, 2007).
Historically, Ellis (1934) and Galton (1969) demonstrated that boys were intellectually and
educationally more variable than girls. Modern comments from Noddings (1992) and Feingold
(1992) come to the same conclusion that gender differences in science variation do exist. The
variability hypothesis has tried to explain why men were more often than women found in the
ranks of genius and idiot. For example, from 1901 to 2009, out of all 187 Nobel Laureates in
Physics, 157 Nobel Laureates in Chemistry and 195 Nobel Laureates in Physiology or Medicine,
2 (1.1%), 4 (2.5%) and 10 (5.1%) are women respectively (see Table 1.1).
11
Table 1.1: Gender differences in Nobel Laureates from 1901 to 2009
Nobel Prize
Nobel Laureates
Natural Science
Women
Men
No. of Prize
Physics
2 (1.1%)
185 (98.9%)
187
Chemistry
4 (2.5%)
153 (97.5%)
157
Physiology / Medicine
10 (5.1%)
185 (94.9%)
195
Sub-total
16 (3.0%)
523 (97.0%)
539
Social Science
Women
Economic Sciences
1 (1.6%)
63 (98.4%)
64
Peace
12 (10.0%)
85 (70.8%)
120*
Literature
12 (11.3%)
94 (88.7%)
106
Sub-total
25 (5.5%)
242 (83.4%)
290
Total
41 (4.9%)
765 (92.3%)
829
Men
No. of Prize
*
Remark: Out of 120 Nobel Laureates in Peace, 97 times were awarded to individuals and 23
times to organizations. (Source: Nobel Foundation, 2009)
To explain bunching together of males in the top ranks, Hedges and Nowell (1995) found that
males were dominant at higher end of the distributions in educational attainment of science
literacy and mathematics. The pattern was observed in the US Scholastic Aptitude Test (SAT),
National Longitudinal Study of Youth (NLSY) and National Educational Longitudinal Study of
the Eighth Grade Class (NELS). Machin and Pekkarinen (2008) extended these gender variance
studies to cover wider sample of countries participating in PISA 2003 and the results suggested
that boys having larger learning diversity is a robust phenomenon.
The same phenomenon was observed in Hong Kong PISA studies. The boys in the 95th
percentile outperformed girls with statistically significance except in 2003. At the 5th percentile,
girls outperformed boys from PISA 2000 to 2006. (Yip et al., 2004, Ho et al., 2003; Ho et al.,
2008).
12
1.1.4 PISA background
There have been a number of large scale international assessments launched since 1960s for
example, the IEA’s Third International Mathematics and Science Study and the OECD’s PISA.
The IEA survey, TIMSS, focus on the trends on learning outcomes of students about knowledge
and skills broadly aligned with science curricula of participating countries.
PISA on other hand focus on measures how well students can apply their knowledge and skills
of science to real-life problems. PISA is thus designed to represent the learning outcomes at age
15, rather than a direct measure of attained curriculum knowledge (OECD, 2006).
OECD started the first cycle of PISA in 2000 and repeated every three years. However, the main
focus, of the first two cycles in 2000 and 2003, was on reading and mathematics. The scope of
and number of items on cognitive domain of scientific literacy was very limited and not
comprehensive enough in these two cycles of assessment while the major domain of PISA 2006
was on scientific literacy and the total number of relevant items had increased from 35 in 2000
to 108 in 2006. In addition, affective learning outcomes, such as: Enjoyment of Science
Learning (JOYSCIE), Future-oriented Science Motivation (SCIEFUT), Interest in Science
Learning (INTSCIE), Instrumental Motivation to Learn Science (INSTSCIE), Personal Value of
Science (PERSCIE) and Science Self-concept (SCSCIE) also had been included as part of
assessment in PISA 2006 (OECD, 2006). The assessment framework of PISA 2006 is more
complete and valid in evaluating students’ overall performance in scientific literacy.
1.2 Weaknesses of previous gender studies
1.2.1 Weaknesses of measurement models based on total score
As mentioned by Cole (1997), the gender differences in achievement can be distorted using total
score or mean score comparison method. Furthermore, Liu (2006) elaborated that the gender
differences could be underestimated if boys and girls are each favored by some items in the
assessment and thus the gender effect will be cancelled out when the total score or mean score is
compared. Secondly, such comparisons did not take into account the assessment structure, for
example, the dimensionality and components of the scientific literacy. Therefore, the relative
strengths and weaknesses of boys and girls in certain areas of science remain unclear.
13
1.2.2 Weaknesses of unidimensional measurement models
Educational and psychological measurements in large-scale assessments such as PISA and
TIMSS tend to cover a large variety of latent traits in a short period of time by having multiple
short subtests for each distinct latent trait. Unfortunately, such approach suffers from
imprecision because of short test lengths and unidimensional assumptions of measurement
models (Cheng et al, 2009; Wu, 2008). The problem is well known as bandwidth-fidelity
dilemma (Cronbach & Gleser, 1965). Bandwidth refers to the amount of information that can be
contained in a message, while fidelity refers to the precision of the information conveyed. In
short, the higher the precision of a given test, the less the extent of the information it can be
gathered in a session of limited time and items and vice versa. Murphy (1993) suggested that
there was an inevitable trade-off between attaining a high degree of precision in measurement
of any one attribute (or characteristic) and obtaining information about a large number of
characteristics.
1.2.3 Strength of multidimensional IRT models7
In most of measurement models, a set of items are used to measure distinct latent traits which
follows the unidimensionality assumption. The distinct latent traits in each subtest are then
analyzed separately. However, this ‘‘composite’’ unidimensional approach often violates the
test’s claim of subtest structure when the traits are highly correlated (Wang 1994; Adams,
Wilson & Wang, 1997). Even the subtests with the underlying dimensions are not highly
correlated; the unidimensional model can introduce bias in ability estimation (Folk & Green,
1989). The person measures8 are also not reliable and attenuated since the correlations between
latent traits are always underestimated (precision issue) in unidimensional models (Cheng et al.,
2009).
A number of studies worked around the problem of precision in subtest measurement using
unidimensional models, for example, Wainer, Sheehan and Wang (2000) deployed other subtest
scores to improve the individual subtest score measurement. Davey and Hirsch (1991) call this
method as consecutive approach which ignores the multidimensionality of the dataset and failed
to take advantage of the correlation between latent traits to improve the reliability.
More importantly, modern assessments like PISA not only aim at measuring the students’
7
8
Details of multidimensional IRT models will be discussed in Chapter 3.
The person measure refers to the ability estimate by IRT model. The precision of person measure increases with
number of items in a given test.
14
achievement but also the conceptual understanding demonstrated by the performances at each
dimension (Adams, Wilson & Wang, 1997). Understanding of students’ performance in each
domain of science is critical in science education and remedial actions can be taken to address
the relative weak areas of the curriculum. Unidimensional models do not provide solution to the
above issues while create problem in reporting students’ achievement when the test score is a
summative results of multiple subtests.
The multidimensional model resolves the problem above by taking the advantage of the
correlations between latent traits. Thus, assessments consist of larger number of subtests
enhance measurement precision (Wang, Chen & Cheng, 2004). Using multidimensional model,
Wu (2008) even demonstrated that the precision of reading scores estimation at individual
student level improved using mathematics and science scores together. Apart from improving
measurement precision, multidimensional models also eliminate the validity issue by modeling
the multidimensionality properties of the subtests, for example, a test having several
unidimensional subtests can be modeled with “between” item multidimensionality 9 in
ConQuest software (Adams, Wilson & Wang, 1997).
1.2.4 Strength of multilevel models
Individual-level random sampling is not always possible for practical or ethical reasons in the
field experiments. Practically, social and behavior science researches usually collect data with
nested structure or multilevel or hierarchical in nature. Therefore, any attempt to understanding
the individual-level learning outcomes and behavior patterns can severely handicap one’s ability
to elucidate the underlying social processes which are constantly shaping students’ behaviors
patterns. These social processes work at social groupings where people are changing
simultaneously over time (Heck et al., 2010). Individuals from the same cluster, for example
same classroom or school, may resemble each other not only in terms of outcomes, but also in
terms of compliance behavior (Jo et al., 2008). Modeling and analysis of the multilevel data
with single level techniques will be misleading.
Conventionally, intraclass correlation (ICC) is used to estimate the proportion of variance that
exists between clusters compared to the total variance ( σ b2 + σ w2 ) in multilevel data. The
intraclass correlation coefficient in outcome Y is:
9
Refer to chapter 3 for details.
15
σ b2
ICCY = 2
σ b + σ w2
where σ b2 is the between-cluster variance and σ w2 is the within-cluster variance. The larger the
ICC values, the higher the homogeneity of the groups and can be quite different from each other.
Simulation studies demonstrate that the larger the ICC values, the standard errors are more
likely to be underestimated if the clustering effects of sampling are ignored at estimation (e.g. Jo
et al., 2008). In other words, the statistical model underlying the multistage stratified sampling
violates the key assumptions (e.g. single random sampling provides independent errors) of
single level multiple regression models. The variances and standard errors are underestimated
and thus bias and erroneous conclusions.
To recap, multidimensional and multilevel techniques are essential in processing the PISA 2006
datasets that the science performance items were built upon multidimensional framework and
the two-stage sampling was deployed. The data collected is by nature multidimensional and
multilevel and unidimensional and single level multiple regression models are not applicable to
current research.
1.3 Research questions
The gender equity in education is not only a key issue in human development and social justice,
it is also related to economic development and growth in the long run (Mahony, 1988; Tse, 1998;
Zahidi et al., 2009). In order to achieve better gender-equity in science education, this study
looks at these key questions to figure out the current status of gender-equity education in Hong
Kong: Are boys and girls doing equally well in scientific literacy? In particular, what kinds of
items in terms of item format and content display gender differential item functioning10 (DIF)?
How large of gender variability in Hong Kong secondary schools? To what extent and how
gender effects are mediated through cognitive and affective domains of scientific literacy on
achievement related choice? What can we do to reduce gender inequity in terms of curriculum
design, classroom practices, initial professional teachers’ training, assessment and educational
policies?
In sum, the purpose of this study is to explore the gender effects on learning outcomes, educational
and occupational choices related to science of 15-year-olds (S.1-S.4) in Hong Kong secondary
schools. The following are the research questions to be addressed for this study:
10
Differential item functioning (DIF) occurs when people from different groups, for example gender, with the
same latent trait have a different probability of giving a certain response on a questionnaire or test.
16
1.
Is there any gender differences in students’ cognitive outcomes?
1.1 Is there any gender difference in science performance?
1.2 Is there any gender variability in science performance?
1.3 Which item content domains show substantial gender difference?
1.4 What type of item formats shows DIF and gender item interaction?
2.
Is there any gender differences in students’ affective outcomes?
2.1 Is there any gender differences in students’ self-concept?
2.2 Is there any gender differences in students’ interest and enjoyment values?
2.3 Is there any gender differences in students’ attainment value and utility value towards
science?
2.4 Is there any gender differences in future-oriented science motivation?
3.
To what extent and how gender effects are mediated through cognitive and affective
domains of science on achievement related choice?
3.1 Is there any mediation effect of student science self-concept to their achievement
related choice after controlling parental SES and science performance?
3.2 Is there any mediation effect of student interest-enjoyment value to their achievement
related choice after controlling parental SES and science performance?
3.3 Is there any mediation effect of student attainment value and utility value to their
achievement related choice after controlling parental SES and science performance?
3.4 Is there any mediating effect of student cognitive and affective domains of science on
students’ achievement related choice?
1.4 Significance of the study
1.4.1 For gender-equity educational policies in Hong Kong
In both TIMSS and PISA assessments, gender differences are always one of the top areas of
studies and in international reports and citations. This indicates a strong faith of Western world
that gender-equity education is a critical belief in basic human rights and integrity, social justice
and progression, man-power development and economic growth to face the challenge of
globalization in 21st century. In contrast, the gender equity in Hong Kong, at present, is realized
by legislation and the gender-equity policies in education are still at very preliminary stage of
development. The local activists, professionals and journalists criticize that the present
government gender-equity policies is obsolete and only tackle the elementary issues of gender
disparities in terms of education accessibility. While most Hong Kong people are satisfied with
17
9-year free, compulsory and universal basic education offered since 1978, the veracity of
unfairness under universal basic education is essentially ignored (Tse, 1998). The most recent
report on review of 9-year compulsory education by the Board of Education in 1997 once again
placed key focus on “formal equality of opportunity” (Coleman, 1968): (1) education for all, (2)
assessment and (3) allocation systems rather than educational inequity.
The sub-committee recommends that Hong Kong should continue with its present
policy of offering free and compulsory education to all children of the relevant age for
nine years.
Board of Education, 1997
As mentioned by the Development Assistance Committee (DAC) Expert Group on Women in
Development of OECD, monitoring, reporting and evaluation are critical processes for assessing
and improving development practices and impacts (DAC, 1999 p.35). The current study
presents a unique opportunity to investigate the gender effects on scientific literacy and provide
grounds for gender-equity policies, gender-differential policies, non-gender biased curriculum
development and assessment in local schools.
1.4.2 For local economic growth
Klasen’s (1999) cross-country regression analysis demonstrated that gender inequity in
education had a direct impact on economic growth through lowering the average quality of
human capital. The empowerment of women and participation in STEM is vital to sustainable,
people-centred development and uphold respect for women’s human rights (OECD, 2006 &
DAC, 1999). According to the 2006 population by-census in Hong Kong, however, the field of
education of highest level attended by girls in pure science was about 20% less than that of boys
(Census and Statistics Department, 2006). Although the statistics from PISA 2006 showed that
the overall science performance of both sexes were similar (Ho et al., 2008), the same trend can
also be detected in the HKCEE subject choices in the past decade from 2001 to 2009 (see Figure
1.3). The results pinpointed the fact that the performance of affective domains in scientific
literacy is crucial to future-oriented motivation in subject choices and career-orientation.
18
Figure 1.3: The trend of day school first attempters in science subject choice by gender
in HKCEE 2001-2009
55
Subject choice (%)
50
Biology (Girls)
Biology (Boys)
Chemistry (Girls)
Chemistry (Boys)
Physics (Girls)
Physics (Boys)
45
40
35
30
2000
2002
2004
2006
2008
2010
Examination year
To the career masters at schools and government officers in man-power and economic
development areas, the discourse in gender-equity educational policy can be collapsed into
economy policy (Ball and Gewirtz, 1997; Ball, 1999). The research findings will be valuable for
both educational and economic policy makers to arise girls’ involvement in science learning in
post-compulsory education and science careers which is considered imperative in the
increasingly technological workplace in the 21st century.
1.4.3 For gender-inclusive science curriculums, assessments & teachers’ training
Currently, the major curriculum documents and assessment guides, for example, the Science
Education Key Learning Area Curriculum Guide (Primary 1 - Secondary 3) and Combined
Science Curriculum and Assessment Guide (Secondary 4 - 6) are gender-blinded. The gender
effect on classroom learning and school-based assessment in science domains are basically
ignored and the gender-equity courses for professional initial teachers’ training are often offered
as electives (Chan et al., 2009).
Without adequate and persuasive evidence from empirical research, the current situation of
gender-insensitive curriculum design and implementation in science education and formal
teachers’ training is hard to be revised, improved and monitored continuously. For the benefits
of boys and girls, the results of this study attempt to alert the participants in science education
19
such as school heads, science teachers and curriculum officers to pay attention to gender
disparity issues in their daily works.
1.4.4 For academic discourse in gender-equity research
The Rasch model and DIF is usually employed to study the effect of item position and item
format on students’ performance. The applications of multidimensional item response modeling
to study gender effect on PISA 2003 Mathematics performance was led by Professor Mark
Wilson at the University of California, Berkeley and his student, Lydia Ou Liu at the
Educational Testing Service, Princeton. Such innovative application is new in the field of
educational assessment and gender study.
For the mediation study, the mediators were selected based on the well-developed Eccles’ (1983)
expectancy-value model of achievement-related choices which was seldom used outside North
America to study the gender differences of Asian population.
This study deploys similar methodology to study the gender effect on scientific literacy of PISA
2006 and that will benefit the local research communities by studying the applicability of such
method and model to Hong Kong situation.
1.5 Structure of the thesis
This thesis is divided into six chapters. Chapter one has provided an overview of the research
backgrounds, limitations of previous gender studies and possible solution. Then, research
questions of the present study and its significances have been stated.
Chapter two conducts literature review on gender differences of science performance and
affective learning outcomes. It also endeavors to cast the theoretical standpoints in connection
with gender and achievement, from sex role theory to psychoanalytic theory; and from
evolutionary-biological to social constructionist. The chapter attempts to summarize the gender
differences in scientific literacy and some common theories in explaining the gender differences
from the literature.
Chapter three presents the conceptual frameworks and methodology to address the research
questions. More specifically, Mean Score Difference (MSD), Multidimensional Differential
Item Functioning (MDIF) and Multilevel Mediation (MLM) are used to examine the gender
differences at mean score level, item level and system level respectively. The MDIF is for
20
gender biased item analysis. While MLM investigates the underlying relationship between
gender differences in affective learning outcomes and gendered educational and occupational
choices related to science.
Chapter four discusses the results of the gender differences of scientific literacy in terms of the
cognitive and affective outcomes using MSD and MDIF. Chapter five looks into the gender
differences of achievement related choices using Eccles et al’s (1983) model and MLM.
Chapter six summarizes all the major findings of the study, examines the implications for policy
and practice at school, families, examination bodies and education authorities, reflects
limitations of the present study, and recommends for future research.
1.6 Summary
Chapter one starts with an overview of the research backgrounds of the gender equity issues in
the field of science education. Then, the limitations of previous gender studies and the possible
solution to these limitations were discussed. Lastly, the research questions and significances of
the study were listed. In the next chapter, the related literatures and theoretical frameworks
about gender differences in science will be examined.
21
CHAPTER TWO
LITERATURE REVIEW
To assess gender differences in scientific literacy, two fundamental but controversial concepts
“scientific literacy” and “gender” have to be clarified. The purpose of this chapter is to examine
these two controversial concepts and to review critically the factors contributing to gender
differences in scientific literacy.
2.1 Defining scientific literacy by historical review
2.1.1 Cognitive domain of scientific literacy
1950s Scientific literacy has been widely recognized as the goal for science education since
1950s (Hurd, 1958; Hurd, 1970; McCurdy, 1958; Rockefeller, 1958). However, its definition is
not universally accepted in either the science or education community. It swings like a
pendulum in science education reform in the United States and the Western world (Deboer,
2000). The differences in its meanings and interpretations may give the public a general
impression that scientific literacy is an ill-defined, diffuse and controversial concept
(Champagne & Lovitts, 1989).
To define scientific literacy, the meaning of “literacy” has to be clarified first. The common
understanding of “literacy” is the ability to read and write, but its meaning changes over time.
Beingliterate refers to people who can master the process required to interpret culturally
important information (deCastell & Luke, 1986) while OECD (2003) define “literacy” as the
capacity of students to analyze, reason and communicate effectively as they pose, solve and
interpret problems in a variety of subject matter areas.
Literacy can be further classified into inert literacy and liberating literacy. Inert literacy refers to
the capacity of people to read a passage or sign a document whereas liberating literacy means
people can read freely and widely in search of whatever information and knowledge they choose
(Cremin, 1988). The liberating literacy is therefore more widely accepted in a society that
offers free access to materials that open people’s minds and allows them to explore new ideas
and aspirations (Bybee, 1997b).
Based on this perspective, John Dewey (1916) framed the early goal of science education as
instrumental in nuturing independence of thought of all students and to act independentally of
arbitrary authority to enable them to participate more fully and effectively in an open
22
democratic society. The function of schooling is to prepare students for adulthood in a
democratic society, and therefore enabling students to carry out independent scientific inquiries
and investigations in the laboratories becomes an essential component of scientific literacy in
the school curriculum.
Whatever natural science may be for the specialist, for edu cational purposes it is
knowledge of the conditions of human action.
(Dewey, 1916, p. 228).
This functionalist perspective in the sociological account of scientific literacy is still prevalent
in most European Countries today; the European Commission (1995) White Paper on Education
and Training argued that “the importance of adequate scientific awareness – not simply in the
mathematical sense – to ensure that democracy can function properly. Democracy functions by
majority decision on major issues which, because of their complexity, require an increasing
amount of background knowledge. … At the moment, decisions in this area are all too often
based on subjective and emotional criteria, the majority lacking the general knowledge to make
an informed choice. Clearly this does not mean turning everyone into a scientific expert, but
enabling them to fulfill an enlightened role in making choices which affect their environment
and to understand in broad terms the social implications of debates between experts. There is
similarly a need to make everyone capable of making considered decisions as consumers. (pp.
11-12)”
Some science educators suggest that without critical public engagement and debate of possible
applications and implications in scientific advances, such as stem cell research and avian flu
vaccination programs, the public distrust scientific expertise and place unnecessary restrictions
on future research and technological development. Such restrictions will in turn hinder the
advancement of potential scientific and technological innovations and breakthroughs which may
bring solutions to plethora of issues our contemporary society facing now (Osborne, 2007;
Millar, 1996a).
23
1960s
In 1960s, the focus of scientific literacy in the Western world shifted to content knowledge of
various scientific academic disciplines. Most curriculum content concentrated on teaching
abstract models and scientific concepts of the natural world that were organized by scientists.
The tremendous driving force of such science curriculum reform in this period was a result of
direct competition between the United States and former Soviet Union, in particular in areas
strongly related to science and technology, such as nuclear weapons and space exploration. The
key stimulus was former Soviet Union’s successful launch of Sputnik I in October 1957.
In 1961, President John F. Kennedy declared a goal of landinga man on the Moon and returning
him safely to the Earth. The US national goal was translated into support of science programs
that encouraged students to enter careers in science and engineering. The goal of science
education in the period was thus to prepare young people to enter the field of science and
engineering and the linkage between science and daily applications of science in society were
seldom mentioned. Diane Ravitch (1983) opined that it was pedagogically imprudent to focus so
heavily on the structure of the disciplines at the expense of the interests and developmental
needs of learners. The characteristics of the scientific literacy in 1960s can be found in Table
2.1.
24
Table 2.1 Characteristics of Scientific Literacy: The 1960s (Source: Bybee, 1997b p. 53-54)
National Science Teachers
Association Theory into
Practice (1964)
Conceptual Schemes
(1)
(2)
(3)
(4)
(5)
(6)
(7)
All matter is composed of
units called fundamental
particles; under certain
conditions these particles
can be transformed into
energy and vice versa.
Matter exists in the form
of units that can be
classified into hierarchies
of organizational levels.
The behavior of matter in
the universe can be
described on a statistical
basis.
Units of matter interact.
The basis of all ordinary
interactions are
electro-magnetic,
gravitational, and nuclear
forces.
All interacting units of
matter tend toward
equilibrium states in
which the energy content
(enthalpy) is a minimum
and the energy
distribution (entropy) is
most random. In the
process of attaining
equilibrium, energy
transformations or matter
transformations occur;
nevertheless, the sum of
energy and matter in the
universe remains
constant.
One of the forms of
energy is the motion of
units of matter. Such
motion is responsible for
heat and temperature and
for the states of matter:
solid, liquid, and gaseous.
All matter exists in time
and space, and since
interactions occur among
its units, matter is subject
in some degree to
changes with time. Such
changes may occur at
various rates and in
various patterns.
Paul Hurd and
James Callagher
(1966)
(1)
(2)
(3)
(4)
National Science
Teachers Association
Theory into Practice
(1964)
Processes of Science
Appreciate the
socio/historical
development of
science.
Aware of the ethos
of modern science.
Understand and
appreciate the
social and cultural
relationships of
science.
Recognise the
social
responsibility of
science.
(1)
(2)
(3)
(4)
(5)
25
Science proceeds
on the assumption,
based on centuriesold experience, that
the universe is not
capricious.
Scientific
knowledge is based
on observation of
samples of matter
that are accessible
to public
investigation in
contrast to purely
private inspection.
Science proceeds in
a piecemeal
manner, even
though it also aims
at achieving a
systematic and
comprehensive
understanding of
various sectors or
aspects of nature.
Science is not, and
will probably never
be, a finished
enterprise, and there
remains much more
to be discovered
about how things in
the universe behave
and how they are
interrelated.
Measurement is an
important feature of
most branches of
modern science
because the
formulation, as well
as the
establishment, of
laws are facilitated
through the
development of
quantitative
distinctions.
Milton Pella (1967)
(1)
(2)
(3)
(4)
(5)
(6)
Interrelationships
between science and
society
Ethics of science
Nature of science
Conceptual
knowledge
Science and
technology
Science in the
humanities
1970s – 1980s
Until 1970s, however, the science, technologies and society (STS) movement revised the goal of
science education ‘to develop scientifically literate individuals who understand how science,
technology, and society influence one another and who are able to use this knowledge in their
everyday decision-making’ (NSTA, 1982; Solomon, 1993; Solomon & Aikenhead, 1994) (see
Table 2.2 and Table 2.3). The National Science Teachers Association (NSTA) (1991) suggested
that a scientifically and technologically literate person can demonstrate both intellectual
capability and attributes in science:
Intellectual
1. uses concepts of science and of technology, as well as an informed reflection of ethical
values, in solving everyday problems and making responsible decisions in everyday life,
including work and leisure;
2. locates, collects, analyses, and evaluates sources of scientific and technological
information and uses these sources in solving problems, making decisions, and taking
actions;
3. distinguishes between scientific and technological evidence and personal opinion and
between reliable and unreliable information;
4. offers explanations of natural phenomena testable for their validity;
5. applies scepticism, careful methods, logical reasoning, and creativity in investigating the
observable universe;
6. defends decisions and actions using rational argument based on evidence; and
7. analyses interactions among science, technology and society.
Attitudinal
8. displays curiosity about the natural and human-made world;
9. values scientific research and technological problem solving;
10. remains open to new evidence and the tentativeness of scientific/technological
knowledge; and
11. engages in science/technology for excitement and possible explanations.
Societal
12. recognizes that science and technology are human endeavours;
13. weighs the benefits/burdens of scientific and technological development;
14. recognizes the strengths and limitations of science and technology for advancing human
welfare; and
15. engages in responsible personal and civic actions after weighing the possible
consequences of alternative options.
Interdisciplinary
16. connects science and technology to other human endeavours e.g. history, mathematics,
the arts, and the humanities; and
17. considers the political, economic, moral and ethical aspects of science and technology as
they relate to personal and global issues.
26
Table 2.2: Characteristics of Scientific Literacy: The 1970s (Source: Bybee, 2008 p. 87)
Michael Agin
(1974)
Victor Showalter
(1974)
(1) Science and Society
(1) Nature of Science
(2) Ethics of Science
(3) Nature of Science
(4) Knowledge of the
Concepts of Science
(2) Concepts in Science
(3) Processes of Science
Benjamin Shen
(1974)
(1) Practical Science
Literacy
(2) Civic Science Literacy
(3) Cultural Science Literacy
(4) Values of Science
(5) Science and Society
(5) Science and Technology (6) Interest in Science
(6) Science and the
(7) Skills Associated with
Humanities
Science
Table 2.3: Characteristics of Scientific Literacy: The 1980s (Source: Bybee, 2008 p. 88)
National Science
National Commission Improving
American Association
Teachers Association, on Excellence in
Indicators of the
for the Advancement
Science-Technology- Education, A Nation Quality of Science of Science, Science for
Society: Science
at Risk (NCEE, 1983)and Mathematics All Americans
Education for the
Education in Grades (AAAS, 1989)
1980s (NSTA, 1982)
K-12, (Murnane St
Raizen, 1988)
(1) Scientific and 1. Concepts, laws, 1. The nature of 1. The nature of
technological
and processes of
the scientific
science
process and
physical and
worldview
2. The nature of
inquiry skills
biological
2. The nature of
mathematics
(2) Scientific and
sciences
the scientific 3. The nature of
technological
2. Methods of
enterprise
technology
knowledge
scientific inquiry3. Scientific
4. The physical
(3) Skills and
and reasoning
habits of mind
setting
knowledge of
3. Applications of 4. Science and
5. The living
science and
knowledge to
human affairs
environment
technology in
everyday life
6. The human
personal and
4. Social and
organism
social decisions
environmental
7. Human society
(4) Attitudes, values,
implications of
8. The designed
and appreciation
scientific and
world
of science and
technological
9. The
technology
development
mathematical
(5) Interactions
world
among
10. Historical
science-technolo
perspectives
gy- society via
11. Common themes
context of
12. Habits of mind
science-related
societal issues
27
It is clear that a more realistic framework and long term developmental goal of scientific
literacy can be visualized with a single comprehensive model. Bybee (1997a) achieved this
developmental goal by proposing a model that consists of four dimensions: nominal, functional,
conceptual and procedural and multidimensional scientific literacy (see Table 2.4).
Table 2.4: A multidimensional and hierarchical model of scientific literacy
Nominal Scientific Literacy
(1) Identifies terms, questions, as scientific but demonstrates incorrect topics, issues,
information, knowledge, or understanding.
(2) Has misconceptions of scientific concepts and processes.
(3) Gives inadequate and inappropriate explanations of scientific phenomena.
(4) Expresses scientific principles in a naive manner.
Functional Scientific Literacy
(1) Uses scientific vocabulary.
(2) Defines scientific terms correctly.
(3) Memorizes technical words.
Conceptual and Procedural Scientific Literacy
(1) Understands conceptual schemes of science.
(2) Understands procedural knowledge and skills of science.
(3) Understands relationships among the parts of a science discipline and the
conceptual structure of the discipline.
(4) Understands organizing principles and processes of science.
Multidimensional Scientific Literacy
(1) Understands the unique qualities of science.
(2) Differentiates science from other disciplines.
(3) Knows the history and nature of science disciplines.
(4) Understands science in a social context.
The first two dimensions focus on the ability of using scientific language to describe and
explain observation while the third dimension requires higher cognitive skills to understand
scientific concepts and processes. The last and highest dimension, multidimensional scientific
literacy, consists of sociocultural dimensions of science and technology: history, nature and
social context of science. The attractive features of Bybee’s multidimensional and hierarchical
model are able to capture the key ideas of earlier presented frameworks for scientific literacy at
right dimensions (Pella, O’Hearn, & Gala, 1966, Agin, 1974; Showalter, 1974; Murnane &
Raizen, 1988). Moreover, it incorporates two essential standards for measuring achievements in
scientific literacy, National Science Education Standards and Benchmarks for Science Literacy.
(see Table 2.5)
28
Table 2.5: Content Summary for the National Science Education Standards and
Benchmarks for Science Literacy (Source: Bybee, 2008 p. 88)
National Science Education Standards
Benchmarks for Science Literacy
(1) Unifying Concepts and Processes Science (1) The Nature of Science
as Inquiry Physical Science Life Science (2) The Nature of Mathematics
(2) Earth and Space Science
(3) The Nature of Technology
(3) Science and Technology
(4) The Physical Setting
(4) Science in Personal and Social
(5) The Living Environment
Perspectives
(6) The Human Organism Human
(5) History and Nature of Science
Society
(7) The Designed World
(8) The Mathematical World Historical
Perspectives Common Themes
Habits of Mind
From the review above, the assessment framework of PISA 2006 science is in line with Bybee’s
multidimensional definition (see Table 2.6), and put strong emphasis on the second view of
science education: to prepares future literate citizens with abilities to apply their scientific
knowledge to daily life and make informed decision in real life context rather than specialist
education making them very proficient within their specialist domain but with no broad
education about science.
Table 2.6: Scientific competency framework of PISA 2006 (OECD, 2006)
Dimensions11
Sub-dimensions
(1) Recognising issues that it is possible to
investigate scientifically
Identifying scientific issues
(2) Identifying keywords to search for
(Conceptual and Procedural
scientific information
Scientific Literacy)
(3) Recognising the key features of a scientific
investigation
11
Explaining phenomena scientifically
(Nominal Scientific Literacy)
(1) Applying knowledge of science in a given
situation
(2) Describing or interpreting phenomena
scientifically and predicting changes
(3) Identifying appropriate descriptions,
explanations, and predictions
Using scientific evidence
(Multidimensional Scientific
Literacy)
(1) Interpreting scientific evidence and
making and communicating conclusions
(2) Identifying the assumptions, evidence and
reasoning behind conclusions
(3) Reflecting on the societal implications of
science and technological developments
The terms in bracket are components of Bybee’s (1997a) multidimensional and hierarchical model.
29
2.1.2 Affective domain of scientific literacy
2.1.2.1 Taxonomy of affective domain elements in science education
In the past 30 years, the affective learning outcomes of science education has been considered as
one of the key components of scientific literacy among science educators and substantial work
has been done in the science education research community (Baker & Doran, 1975, Choppin &
Frankel, 1976, Osborne et al., 2003). However, its content is not well defined. A typical
taxonomy of affective behaviors in science was worked out by Klopfer (1971) (see Table 2.7).
Table 2.7: Klopfer’s taxonomy of affective behaviors in science education
(1) the manifestation of favourable attitude toward science and scientists;
(2) the acceptance of scientific enquiry as a way of thought;
(3) the adoption of scientific attitudes;
(4) the enjoyment of science learning experiences;
(5) the development of interest in science and science-related activities; and
(6) the development of an interest in pursuing a career in science or science related work.
Later studies extended Klopfer’s (1971) taxonomy to cover a wider range of components such
as attitudes, values, beliefs, interests and motivation in affective measurements (see Table 2.8,
Simpson et al., 1994 and Osborne et al., 2003). The following section reviews some key
affective factors that are used in this study.
Table 2.8: A range of components in affective domains of scientific literacy
(1) the perception of the science teacher;
(2) anxiety toward science;
(3) the value of science;
(4) self-esteem in science;
(5) motivation towards science;
(6) enjoyment of science;
(7) attitudes of peers and friends towards science;
(8) attitudes of parents towards science;
(9) the nature of the classroom environment;
(10) achievement in science; and
(11) fear of failure on course.
30
2.1.2.2 Science self-concept
According to Wigfield and Karpathian (1991), self-concept is defined as individuals’ affective
reactions to their characteristics, and overall evaluation of themselves as persons. Shavelson et
al (1976) studied a number of definitions about self-concept. They came to the conclusion that
self-concept is a continual process of reinforcement by evaluative inferences and that it reflects
both cognitive and affective responses.
Scheirer and Kraut (1979) pointed out that self-concept is a complex construct composed of
descriptive, evaluative, comparative, and affective elements. Pajares (1996) maintained that
self-concept includes competence judgments coupled with evaluative reactions and feelings of
self-worth. Markus and Nurius (1986) viewed self-concept as “a system of affective-cognitive
structures about the self that lends structure and coherence to the individuals’ self-relevant
experiences”.
Modem theorists argued that self-concept is a multidimensional and subject specific construct
rather than a global measurement of self-related experiences (Harter, 1990; 1998; Marsh, 1993).
For example, if a researcher is interested in relations of self-concept to science performance,
then he or she should measure self-concept in this domain, rather than just using a general
self-concept measure. Students should be asked to give response to the item “I learn school
science topics quickly” rather than “I learn school subjects quickly”.
In summary, science self-concept consists of two components, affective and cognitive
evaluation of self-experiences. Second, self-concept is a multidimensional and domain specific
construct.
2.1.2.3 Motivation in science learning
The word “motivation” is derived from the Latin “movere” which means “to move”. Motivation
refers to an internal state that arouses, directs, and sustains students’ behaviour. Motivation can
be further divided into intrinsic motivation and extrinsic motivation. According to
Self-determination theory (SDT), intrinsic motivation is only possible when individuals freely
choose their own actions; that is, they are self-determined (Ryan & Deci, 2000). On the other
hand extrinsic motivation, such as receiving a reward, works for individuals with purpose.
31
Interest in science learning
Interest is more specific than intrinsic motivation, which is a broader motivational characteristic
(Hidi & Harackiewicz, 2001). For practical reasons, interest can be divided into individual and
situational interest. Individual interest is a relatively stable evaluation of certain domains while
situational interest is an emotional state aroused by specific features of an activity or a task.
Feeling-related and value-related valences are two distinguishable aspects of individual interest
(Schiefele, 2001). Feeling-related valences refer to the feelings that are associated with an
object or an activity itself - feelings like involvement, stimulation, or flow. Value-related
valences refer to the attribution of personal significance or importance to an object.
Enjoyment of science learning
According to Csikszentmihalyi (1990, 1996), enjoyment refers to “flow” activities that provide
students a feeling of creative accomplishment and satisfaction. Flow is a state of deep
absorption in an activity that is intrinsically enjoyable. So, enjoyment of science learning is
defined as total engagement in the science learning activity that is intrinsically enjoyable
(Shernoff et al., 2003).
Personal value of science
The personal value of science is defined as students’ beliefs about the value of science in terms
of the relevance and importance of science around them, both now and in the future (PISA,
2006). This definition of personal value is identical to attainment value in Eccles et al (1983)
Expectancy-value Model for Achievement-related Choices. Attainment value in Eccles’ (1983)
model refers to the needs, personal values, and explicit motives that an activity fulfills. As they
grow up, individuals develop an image of who they are and what they would like to be.
Instrumental motivation to learn science
Instrumental motivation is defined as the motivation that derives from the future goals. It is a
type of extrinsic motivation to learn and get good grades of school science for future careers and
studies. The activities engaged in have a utility value when they are perceived as instrumental
for achieving other goals in the near or distant future (Eccles & Wigfield, 2002).
32
2.2 Gender differences in scientific literacy
2.2.1 Defining gender: the nature versus nurture debate
The complexity of using the terms ‘sex’ versus ‘gender’ in educational and social research is in
connection with the ‘nature/nurture’ argument. The use of ‘sex’ or ‘gender’ depend on political
standpoints and briefs about the origin of sex differences which are inherent and biological, or
socially produced or a mixture of both (Francis & Skelton, 2005). Traditionally, ‘Sex’ is defined
as biological distinction between males and females based upon their genetic composition
(males have XY chromosomes while females have two XX chromosomes), reproductive
anatomy and physiology. However, many research findings from feminist psychologists and
social constructionists demonstrate that gender difference is nurtured through a complex system
of social classification and hierarchy (Crawford & Unger, 1994; Crawford et al., 1995; Unger &
Crawford, 1996). In between, the proponents of ‘brain sex’ differences and socio-biologists
argue that it is extremely difficult to isolate biological influences from sociocultural and
environmental factors and the two influences are reciprocal in nature (Halpern, et al., 2007).
These authors choose to use a biopsychosocial model (Halpern, 2000, 2004) to explain the ‘sex’
differences due to complex interaction of biological and sociocultural/environmental variables.
Socialization practices are undoubtedly important, but there is also good evidence that
biological sex differences play a role in establishing and maintaining cognitive sex
differences.
(Halpern, 2000 p. xvii)
In short, the distinction between ‘sex’ and ‘gender’ is intertwined in the debate of nature or
nurture as a key factor to determine the differences of cognitive and affective learning outcomes
between boys and girls. In this thesis, the influence of sociocultural factors on gender
differences in science literacy was evaluated. The term “gender” has been chosen for
emphasizing sociocultural effects on gender differences.
2.2.2 Gender differences in cognitive learning outcomes
A typical technique used in gender difference studies is meta-analysis which is a statistical
method for aggregating research findings across all the available research findings with the
same question (Hedges & Becker, 1986). If there is a gender difference in science abilities, it is
able to indicate which gender achieves more highly and the magnitude of the difference. For
each gender study, effect size by Cohen (1988) d for gender difference is calculated with the
following formula:
33
M boys − M girls
2
2
( SDboys
+ SDgirls
)/2
where Mboys is the mean score for boys, Mgirls is the mean score for girls, and
2
2
( SDboys
+ SDgirls
) / 2 is the pooled standard deviation. The value of d measures how far apart the
male and female means are, in standardized units and indicates the magnitude (effect size) of
gender difference across all the research findings (Hyde, 2005). A positive value of d indicates
males score higher and vice versa. According to Cohen (1988), the effect size is small, medium
and large if d is less than or equal to 0.2, equal to 0.5 and equal to or larger than 0.8
respectively.
Hyde (1990) suggested that meta-analysis across all the available studies is a better alternative
in investigatingscepticism scepticism gender differences because such research findings are
notoriously inconsistent across studies and it overcomes this problem by aggregating many
studies involving tens of thousands and even millions of participants. This provides more
reliable research findings than any individual study.
Using the meta-analysis method, Hyde (2005) found out that out of 128 valid effect sizes, 124
(about 97%) were similar. The effect sizes for science related gender studies were ranging from
+0.19 (spatial visualization) to +0.73 (mental rotation). Based on these findings, Hyde (2005)
proposed the gender similarities hypothesis:
The gender similarities hypothesis holds that males and females are similar on most, but
not all, psychological variables. That is, men and women, as well as boys and girls, are
more alike than they are different.
(Hyde, 2005 p.581)
Halpern (2000), however, argued that some cognitive tasks did show sex differences and some
of these differences were lost in aggregated results of meta-analysis. Halpern disagreed with
Hyde in assigning values to small and large effect sizes, affirming that small differences might
result in accumulating very big differences in the long run. In Valian’s (1998) analysis of
females’ slow advancement in academia and other professions, she also showed how small
differences were compounded over time to create big differences.
Gender differences in science performance
Using the same meta-analysis method of Hyde, this section summarizes the gender studies of
science in the past few decades.
34
Table 2.9 summarizes the gender studies of science performance in elementary school education.
Out of these twenty four studies, thirteen (54%) show that boys had an advantage in biological
science and physics. Significant gender differences were not found in general science, geology
or earth sciences and chemistry (Becker, 1989). According to Cohen (1988), the effect sizes of
gender differences in these studies were relatively small (Cohen’s d = 0.02 to 0.43)12.
Table 2.9: Gender differences in science performance at elementary schools
Study
Region Grade Area of study
Effect size
Results
Ashbaugh (1968)
Georgia
5
Geology
+ve
Boys outperformed girls
Ashbaugh (1968)
Georgia
4
Geology
0.43
Boys outperformed girls
Ashbaugh (1968)
Georgia
6
Geology
0.26
Boys outperformed girls
Boys outperformed girls
Law (1997)
HK
4
General science
+ve
significantly; Greatest gender
difference internationally
Keeves & Kotte
IEA
Boys outperformed girls
4
General science
+ve
(1996)
Science
significantly
Tamir & Amir (1975) Israel
1
Physical science
0.36
Boys outperformed girls
Tamir & Amir (1975) Israel
2
Physical science
0.16
Boys outperformed girls
Hsu (2008)
Taiwan
6
General science
NA
No significant gender differences
Marjoribanks (1976)
UK
6
Physical science
-0.12
Girls outperformed boys
Allen (1970)
USA
1
Physical science
-ve
Girls outperformed boys
Allen (1975)
USA
5
Biology
+ve
Boys outperformed girls
Significant gender differences for
the high ability students; No
significant gender differences at
Dimitrov (1999)
USA
5
Physical science
+ve
the low and medium ability level;
Significance of the interaction,
gender x format x ability.
Anderson & Butts
USA
6
Electricity
+ve
Boys outperformed girls
(1980)
Allen (1973)
USA
3
Physical science
0.42
Boys outperformed girls
Bridgham (1969)
USA
3
Electrostatics
0.42
Boys outperformed girls
Fuller, May & Butts
USA
3
Life cycles
0.37
Boys outperformed girls
(1979)
Boys outperformed girls; No
Shrigley (1972)
USA
6
Earth science
0.24
significant differences in scores of
boys and girls
Scott & Siegel (1965) USA
6
Science concepts
0.12
Boys outperformed girls
Skinner (1967)
USA
5
Geo1ogy
0.02
Boys outperformed girls
Wallach & Kogan
USA
5
General science
-0.03
Girls outperformed boys
(1966)
Scott & Siegel (1965) USA
4
Science concepts
-0.05
Girls outperformed boys
Scott & Siegel (1965) USA
5
Science concepts
-0.18
Girls outperformed boys
Bowyer & Linn
USA
6
Science literacy
-0.15
Girls outperformed boys
(1978)
Note: The codes for effect size are as follows: positive (+ve) difference, boys outperformed girls;
negative (-ve) difference, girls outperformed boys. Effect size cannot be determined is
denoted as “NA”.
12
Absolute values of Cohen’s d are reported here.
35
Table 2.10 summarizes the gender studies of science performance at high schools. Out of twenty
eight studies, twenty two (79%) show that boys consistently outperformed girls in general
science and science subject domains from grade 8 to 12. Girls outperformed boys in three
studies (11%). The effect sizes of gender differences in these studies were all relatively small
(Cohen’s d = 0.12 to 0.42).
Table 2.10: Gender differences in science performance at high schools
Study
Region
Keeves (1975)
Australia
Keeves & Kotte
(1996)
Walberg (1969)
IEA
Science
Canada
Hart (1978)
Canada
Grade Area of study
Science
7
performance
General
8
science
12
Physics
BSCS
12
Biology
Yip et al (2004)
HK
10
Ho et al (2005)
HK
10
Ho et al (2008)
HK
10
General
science
General
science
General
science
Effect size
0.32
0.42
0.12
Boys outperformed girls
+ve
Boys outperformed girls; Boys
scored higher than girls at the
higher percentiles (75th and
above)
+ve
Boys outperformed girls;
+ve
Boys outperformed girls;
Law (1997)
HK
8
Mullis et al
(2000)
HK
8
General
science
+ve
Yung et al (2006)
HK
8
General
science
+ve
Martin et al
(2008)
HK
8
General
science
+ve
Tamir & Kempa
(1975)
Israel
10
Tamir (1974)
Tamir (1974)
Israeli
Israeli
12
12
Tamir (1976)
Israeli
12
36
Boys outperformed girls
Boys outperformed girls
significantly
Boys outperformed girls
+ve
General
science
Phys,
Chemistry &
Biology
Botany
Zoology
BSCS
Biology
Results
+ve
Boys achieve significantly
more than girls; Greatest
gender difference
internationally
Boys outperformed girls; No
significant differences in
achievement of boys and girls;
Girls improved significantly
from 1995 to 1999 while boys
showed non-significant
improvement
Boys outperformed girls
significantly; Girls improved
significantly from 1995 to
2003 while boys showed no
improvement
Boys outperformed girls; Both
girls and boys improved
significantly from 1995 to
2007
NA
No significant gender
differences
+ve
-ve
Boys outperformed girls
Girls outperformed boys
0.12
Boys outperformed girls
Hsu (2008)
Taiwan
7-8
Hsu (2008)
Taiwan
9-10
Strope &
Braswe11 (1966)
USA
13
Kruglak (1970)
USA
13
USA
6-8
USA
11
USA
11
USA
8
McDuffie &
Beehier (1978)
Field & Cropley
(1969)
Ogden &
Brewster (1977)
Babikian (1971)
General
science
General
science
NA
NA
Astronomy
concepts
Freshman
physics
Science
performance
General
science
Science
performance
Physical
science
Science
performance
+ve
No significant gender
differences
No significant gender
differences
Men did better than women in
learning astronomy facts and
concepts.
+ve
Boys outperformed girls
-ve
Girls outperformed boys
0.38
Boys outperformed girls
0.36
Boys outperformed girls
0.36
Boys outperformed girls
Ogden &
USA
11
0.28
Boys outperformed girls
Brewster (1977)
Doran & Sellers
USA
10
Biology
0.22
Boys outperformed girls
(1978)
Lynch et al
Physical
0.22
Boys outperformed girls
USA
8
(1979)
science
Marek (1981)
USA
10
Biology
0.18
Boys outperformed girls
Thomas & Snider
USA
8
Chemistry
0.13
Boys outperformed girls
(1969)
Sieveking &
College
USA
13
-0.12
Girls outperformed boys
Savitsky (1969)
chemistry
Note: The codes for effect size are as follows: positive (+ve) difference, boys outperformed girls;
negative (-ve) difference, girls outperformed boys. Effect size cannot be determined is
denoted as “NA”.
To sum up, boys had better science performance than girls. Boys tended to outperformed girls in
biology and physics at elementary level and boys’ advantages in physics (Cohen’s d = 0.36 to
0.42) persisted high school. However, there were no significant gender differences found in
general science and chemistry at either elementary or high schools. It should be noted that all
the effect sizes obtained from these studies were relatively small (Cohen’s d = 0.02 to 0.43).
37
2.2.3 Gender differences in affective learning outcomes
In the past two decades, numerous researches related to gender differences in affective learning
outcomes have been conducted. Patrick et al (2009) investigated the gender differences of
kindergarten boys and girls in the United States and found that boys in regular classrooms like
science more than girls. Boys also found to have higher science self-concept than girls in
American elementary schools and German high schools (e.g. Andre et al., 1999; Rudasill &
Callahan, 2010; Häussler & Hoffmann, 2002).
Table 2.11: Gender differences in affective leaning outcomes at elementary schools
Study
Region Grade Area of study Effect size
Results
Patrick,
Boys in regular classrooms
Interest in
Mantzicopoulos &
+ve
reported liking science more
USA
5
science learning
Samarapungavan
than did girls
(2009)
Boys reported significantly
more interest in learning
Interest in
Jones, Howe & Rua
+ve
USA
6
about the listed science topics
science
(2000)
than girls
Andre, Whigham,
Boys had higher science
Science
+ve
Hendrickson, &
USA
4-6
self-concepts than girls
self-concept
Chambers (1999)
Self-perception
Boys had higher
Rudasill &
USA
5-12 of ability in
+ve
self-perceptions of ability in
Callahan (2010)
science
science than girls
Note: The codes for effect size are as follows: positive (+ve) difference, boys outperformed girls;
negative (-ve) difference, girls outperformed boys.
In terms of motivation to learn science, personal value of science and enjoyment of science
learning, various research findings suggested that boys had higher values in this affective
domain than girls at higher schools (e.g. Salta & Tzougraki, 2004; Weinburgh, 2000; Cheung,
2008; Cheung, 2009b; Salta & Tzougraki, 2004). A summary of these research findings can be
found in Table 2.11 and Table 2.12.
Out of these thirty studies, twenty five (83%) show that boys had higher interest and enjoyment
in science learning, personal value of science, science self-concept and motivation to learn
science than girls. Girls showed higher motivation than boys to learn science in three (10%)
international studies.
Unlike cognitive development in science, the results in Table 2.11 and Table 2.12 also suggest
that boys on average develop better affective learning outcomes in science than girls early in
elementary school. The same pattern persists throughout higher school education. The effect
sizes of these studies range from small (Cohen’s d = 0.08) to medium (Cohen’s d = 0.60).
38
Table 2.12: Gender differences in affective leaning outcomes at high schools
Study
Region Grade Area of study Effect size
Results
Boys were significantly higher
Physics related
Häussler &
+ve
than girls in physics related
Germany
7
self-concept
Hoffmann (2002)
self-concept
Boys were significantly higher
Physics-related
Häussler &
+ve
than girls in physics related
Germany
7
interest
Hoffmann (2002)
interest
No significant difference in the
level of interest, usefulness,
Importance of
Salta & Tzougraki
0.08 and importance attributed to
Greece
11
chemistry
(2004)
chemistry between boys and
girls
No significant difference in the
level of interest, usefulness,
Usefulness of
Salta & Tzougraki
0.10 and importance attributed to
Greece
11
chemistry
(2004)
chemistry between boys and
girls
No significant difference in the
level of interest, usefulness,
Interest in
Salta & Tzougraki
0.11
and importance attributed to
Greece
11
chemistry
(2004)
chemistry between boys and
girls
Boys had significantly higher
Personal value
0.11 personal value of science than
Cheung (2008)
HK
10
of science
0.33
girls
Boys had significantly higher
Self-concept in
0.45 self-concept in science than
Cheung (2008)
HK
10
science
0.59
girls
Interest in
0.30 - Boys had significantly higher
Cheung (2008)
HK
10
science
0.43 interest in science than girls
Boys had significantly higher
Enjoyment of
0.39 enjoyment of science learning
Cheung (2008)
HK
10
science learning
0.52
than girls
Boys had significantly higher
Instrumental
0.20
instrumental motivation to
Cheung (2008)
HK
10
motivation to
0.37
learn science than girls
learn science
Attitude toward
Boys like chemistry theory
Cheung (2009b)
HK
10-12 chemistry
+ve
lessons more than girls at
lesson
grade 10-11
Girls were significantly more
Motivation to
Steinkamp &
International NA
-0.60
motivated to learn botany
learn botany
Maehr (1984)
Girls were significantly more
Motivation to
Steinkamp &
International NA
-0.31
motivated to learn chemistry
learn chemistry
Maehr (1984)
Girls were significantly more
Motivation to
Steinkamp &
International NA
-0.28
motivated to learn biology
learn biology
Maehr (1984)
Boys were significantly higher
Importance of
Steinkamp &
International NA
0.13
value of science than girls
science
Maehr (1984)
Boys had significantly higher
Enjoyment of
Steinkamp &
International NA
0.14 enjoyment of science learning
science learning
Maehr (1984)
than girls
39
Boys were significantly more
motivated to learn geology
Boys were significantly more
Steinkamp &
International NA
0.20 motivated to learn general
Maehr (1984)
science
Boys were significantly more
Steinkamp &
International NA
0.35 motivated to learn physical
Maehr (1984)
science
Boys had higher self-concept
+ve
Weinburgh (2000)
USA
6-8
in science than girls
Boys had significantly higher
Enjoyment of
+ve
enjoyment of science learning
Weinburgh (2000)
USA
6-8
science learning
than girls
Boys had higher motivation to
Motivation to
+ve
Weinburgh (2000)
USA
6-8
learn science than girls
learn science
Intrinsic
Boys had significantly higher
Bryan, Glynn &
USA
9-10 motivation to
0.34 intrinsic motivation to learn
Kittleson (2011)
learn science
science
Boys were more interested in
Interested in
+ve
Lee (1998)
USA
16-18
science than girls
science
7th- and 10th-grade hoys and
National Center for
7 & Enjoyment of
NA
girls were equally likely to
Education Statistics USA
10
science learning
enjoy mathematics and science
(1997)
Note: The codes for effect size are as follows: positive (+ve) difference, boys outperformed girls;
negative (-ve) difference, girls outperformed boys. Effect size cannot be determined is
denoted as “NA”.
Steinkamp &
Maehr (1984)
International
NA
Motivation in
geology
Motivation to
learn general
science
Motivation to
learn physical
science
Science
self-concept
0.14
2.2.4 Gender differences in science educational and occupational choices
Eccles and co-workers had conducted a large number of gender researches on science
educational and occupational choices in the past thirty years. They found girls’ disadvantages in
science related achievement choices were highly correlated to the affective learning outcomes in
science (e.g. Frome, Alfeld, Eccles & Barber, 2006). High school boys were also more likely
than girls to choose careers in science-related fields in the United States, Israeli and Hong Kong
(Jacobs, 2006, Friedler & Tamir, 1999; Nagy et al., 2006; Census and Statistics Department,
2006). European Commission (2009) reported that females in scientific research remained a
minority, accounting for 30% of researchers in the European Union in 2006.
Similarly, girls had lower motivation to choose advanced science courses at high schools and
universities. The University Grants Committee of Hong Kong (UGC, 2011) reported a
consistently lower course enrolment rate of girls in science disciplines than boys from
sub-degree levels to postgraduate levels except at taught postgraduate levels. A summary of the
findings can be found in Table 2.13 and Table 2.14.
40
Table 2.13: Gender differences in science educational and occupational choices at high schools
Study
Region Grade
Area of study
Effect size
Results
Nagy,
Effects of academic
More girls than boys
Trautweina,
self-concept and
selected advanced
Baumerta,
Germany 10-12 intrinsic value of
-ve
biology course in grade
Köllerb &
biology in course
12
Garrettc (2006)
choices
Boys had significantly
Future-oriented
higher future-oriented
motivation to learn
+ve
motivation to learn
Ho et al (2008)
HK
10
science and have
science and have science
science careers
careers than girls
Boys selected more
Science course taking
science courses, and
Friedler & Tamir
Israeli
5-12 pattern at elementary
+ve
displayed greater interest
(1990)
and secondary levels
in science careers than
girls
Males were more likely to
Factors that influence
persist in science and
persistence in science
+ve
Mau (2003)
USA
8
engineering career
and engineering career
aspirations than females
aspirations
A gap in career
Boys were more likely to
National Center
aspirations of boys and
+ve
aspire to be scientists and
for Education
USA
8
girls in science or
engineers
Statistics (1997)
engineering
Female students were just
as likely as male students
National Center
Science course taking
NA
to take advanced science
for Education
USA
9-12
pattern in high school
courses in high school;
Statistics (1997)
physics is exception.
Dunteman,
Sex differences in
Boys were more likely
Wisenbaker &
USA
12 college science
+ve
than their girls to major in
Taylor (1978)
program participation
science
Effect of intrinsic value
Females were more likely
Frome, Alfeld,
of physical science on
than males to drop out
Eccles & Barber
USA
12 young women’s
+ve
occupations in
(2006)
occupational
traditionally
aspirations
male-dominated fields.
Boys were more likely
Parents’ expectations
than girls to choose
and in young adult
Jacobs, Chhin &
+ve
USA
12
careers in science-related
children’s gender-typed
Bleeker (2006)
fields
occupational choices
Note: The codes for effect size are as follows: positive (+ve) difference, boys outperformed girls;
negative (-ve) difference, girls outperformed boys. Effect size cannot be determined is
denoted as “NA”.
Out of nine gender studies related to science educational and occupational choices at high
schools, seven (78%) show boys more likely than girls to choose careers and studies in
science-related fields (see Table 2.13). Only one study (11%) in Germany shows that more girls
than boys tended to enroll in advanced biology courses at grade 12.
41
Table 2.14: Gender differences in science educational and occupational choices at universities
Effect
Study
Region Grade
Area of study
Results
size
Science course
Boys were more likely to take
Subtaking pattern at
+ve
UGC (2011)
HK
science course than girls
degree
sub-degree level
Science course
Boys were more likely to take
taking pattern at
+ve
UGC (2011)
HK undergrad
science course than girls
postsecondary
level
Science course
taking pattern at
Girls were more likely to take
UGC (2011)
HK
postgrad taught
-ve taught postgraduate science
postgraduate
course than boys
level
Science course
taking pattern at
Boys were more likely to take
UGC (2011)
HK
postgrad research
+ve research postgraduate science
postgraduate
course than girls
level
Females in scientific research
European
remain a minority, accounting
Females in
NA
Commission
Europe postgrad
for 30% of researchers in the
scientific research
(2009)
EU in 2006
Interest in
Girls were significantly more
Lee (1998)
USA undergrad becoming a
-0.50 interest to become biologist
biologist
than boys
Interest in
Boys were significantly more
Lee (1998)
USA undergrad becoming a
0.18 interest to become chemists
chemist
than girls
Interest in
Boys were significantly more
Lee (1998)
USA undergrad becoming a
0.32 interest to become scientists
scientist
than girls
Interest in
Boys were significantly more
Lee (1998)
USA undergrad becoming a
0.63 interest to become physicists
physicist
than girls
Webb, Lubinski,
Course taking
More girls took medical
& Benbow
USA undergrad pattern of
-0.39
science as majors than boys
(2005)
medical science
Webb, Lubinski,
Course taking
More girls took biological
& Benbow
USA undergrad pattern of
-0.26
science as majors than boys
(2005)
biological science
Webb, Lubinski,
Course taking
More girls took chemistry as
& Benbow
USA undergrad pattern of
-0.01
majors than boys
(2005)
chemistry
Webb, Lubinski,
Course taking
No gender difference in taking
& Benbow
USA undergrad pattern of earth
0.01
Earth science as majors
(2005)
science
Webb, Lubinski,
Course taking
More boys took physical
& Benbow
USA undergrad pattern of
0.34
science as majors than girls
(2005)
physical science
42
National Science
Foundation
(2010)
National Center
for Education
Statistics (1997)
USA
Female share of
science and
engineering
undergrad
bachelor’s
degrees, by field
in 2007
NA
USA
Science course
enrollment
undergrad pattern at
postsecondary
level
NA
Males earned a majority of
bachelor’s degrees awarded in
engineering, computer
sciences, and physics. Females
earned half or more of
bachelor’s degrees in
psychology, agricultural
sciences biological sciences
chemistry.
At postsecondary level,
women were less likely than
men to earn a degree in
physical sciences, and
computer sciences.
exception is in life science
degrees
Number of women in science
and engineering occupations
rising from 12% to 27%
between 1980 and 2007.
Representation of
females in
NA
USA undergrad
science and
engineering
Science course
National Center
enrollment
Males were more likely than
for Education
USA postgrad pattern at
+ve women to earn master’s
Statistics (1997)
postgraduate
degrees in science
level
Note: The codes for effect size are as follows: positive (+ve) difference, boys outperformed girls;
negative (-ve) difference, girls outperformed boys. Effect size cannot be determined is
denoted as “NA”.
National Science
Foundation
(2010)
Similar to the high school situation, nine out of eighteen studies (50%) show that boys were
more likely than girls to enroll in science programs at sub-degree and degree levels in Hong
Kong, Europe and the United States (see Table 2.14). However, five out of eighteen studies
(28%) suggest that girls were more likely than boys to take science programs in Hong Kong
(taught postgraduate programs) and the United States (undergraduate programs). The effect
sizes of gender differences found in these studies range from small (Cohen’s d = 0.01) to
medium (Cohen’s d = 0.63).
To sum up, boys were more likely than girls to make educational and occupational choices
related to science at both high school and university levels. The effect sizes of gender
differences on these choices fluctuated from small (Cohen’s d = 0.01) to medium (Cohen’s d =
0.63). Hong Kong girls followed the similar gendered educational and occupational choice
pattern of Western counterparts and tended to choose non-science oriented careers and
educational programs.
43
2.3 Factors attributing gender differences
The debate of gender differences as a phenomenon of natural inherence by genetic and natural
selection or socioculturally constructed (or a mixture of the two paradigms) (Halpern, 2000) is
long. These two complete different views of gender differences are referred to as the nature
versus nurture debate. For pure academic studies, the debate depends on research findings
which can demonstrate whether gender is socioculturally constructed or the gender differences
are inherent and inevitable. The following session provides a review of possible explanations for
gender differences from the two camps, social constructionists and socio-biologists. It serves
two purposes here: (1) to provide an overview of the problem and its underlining factors
influencing the gender differences which may be in strong connection with Hong Kong
educational context and (2) to provide a theoretical starting point which guides us the selection
or development of an appropriate model out of all these possible perspectives for this study.
2.3.1 Biological contributions
In contrast to sociocultural theories, researchers in cognitive and biological sciences look into
the scientific aspects of ‘sex’ differences in cognitive performance in terms of structural,
functional, psychological, evolutionary, developmental differences of male and female brains as
well as the hormonal effects on the cognitive developments of boys and girls.
The regular clinical approaches deployed in these studies include magnetic resonance imaging
(MRI), X-ray computed tomography (CT scan) and positron emission tomography (PET) which
allow cognitive and behavior psychologists and neurobiologists to map and distinguish the
structural and physiological differences, such as physical brain size, blood flow rate and glucose
metabolic patterns of neurons in various brain areas, between brains of opposite sex.
2.3.1.1 Evolutionary psychology perspectives
Darwin (1871) proposed the ‘theory of natural selection’ with ‘sexual selection’ as the subset of
the theory to explain the natural mechanism involving competition among the members of same
sex over mates and selective choice of mating partners. Evolutionary psychologists suggest that
the male-male competition for reproduction, dominance hierarchies, controlling ecological rich
territories and hunting behaviours in evolutionary history support the brain development for
large-scale navigation in males (Chagnon, 1988). This is why
mental rotation tasks, which
require simultaneously maintaining a three-dimensional object in working memory while
transforming it, display very large gender differences with Cohen (1988) d range from 0.63 to
0.77 (Loring-Meier & Halpern, 1999; Masters & Sanders, 1993). The contemporary male
44
visuospatial abilities are the result of their roles as hunters and fighters which require abilities to
construct and track projectile movement in three-dimensional space. The hypothesis is
consistent with the predication of Darwin’s theory of sexual selection (Halpern et al., 2007).
The cognitive capacity and behavior differences between the sexes were therefore well
developed millions of years ago to ensure the survival and propagation of the human race
(Francis & Skelton, 2005).
2.3.1.2 Brain structural perspectives
By the mid 1800s, the most popular argument for superior cognitive abilities of males is
because of having larger and heavier brains than females. The absolute size of the brain as a
direct indicator of intellectual capacity was problematic since it implied that animals with larger
brains were more clever than human beings. However, the weight of the brain to weight of
female’ body ratio was actually larger than that of the male, so that provided opposite evidence.
Similar hypotheses to explain males’ superior spatial abilities in science and mathematics, such
as males having a larger cortex surface, more convolutions (Mosedale, 1978), higher volumes of
white matter and cerebrospinal fluid in the male brain (Blatter et al., 1995; Gur et al., 1999) all
failed to produce significant and consistent empirical evidences (Halpern et al., 2007) and so
bigger isn’t better and size is not related to intelligence. From Gur et al’s (1999) spatial tests,
females may achieve higher level of spatial performance using different strategies than males.
In contrast to controversial evidence of brain size differences between males and female,
substantial evidence suggests that females have larger corpus callosum which supports the idea
of greater connectivity between the two hemispheres for faster language processing in females
(Halpern et al., 2007). This might partially explain females outperforming males in open-ended
response items in TIMSS and PISA assessments that demand better language ability.
2.3.1.3 Brain functional perspectives
Other researchers attempted to explain the differences by examining the functional differences
of the brain. Two direct estimations of brain regional activities are to measure blood flow rate
and glucose consumption rate changes in response to scientific tasks. Gur et al (2000) suggested
that the males’ better performance in difficult items of visuospatial tasks was in strong
connection with more focal activation of right visual-association areas that results in more
lateralization of cognitive abilities (i.e. rely on one side of hemisphere to process the
visuospatial tasks e.g. mental rotation). Females handle similar tasks, recruit additional brain
regions on both sides of the hemispheres for distributed processing. As a result, there is the more
45
distributed and bilateral recruitment of brain regions in females than in males as the complexity
of the task increase (Kucian et al., 2005). A similar situation was demonstrated in 3-D virtual
maze, females deploying parietal and prefrontal activation, which takes more effort, whereas
males relied upon automatic retrieval of geometric-navigation cues in hippocampus only (Grön
et al., 2000). The blood flow rate and glucose metabolic rates in females are higher than that of
males and the magnetic resonance imaging (MRI) pattern confirm more bilateralization of
cognitive abilities for females (Gur et al., 1995; Murphy et al., 1996).
2.3.1.4 Hormonal perspectives
Administration of male hormone, testosterone to female-to-male transsexuals before sex-change
surgery improved their spatial cognition (Van Goozen, Cohen-Kettenis, Gooren, Frijda, & Van
de Poll, 1994, 1995). The pattern of improvement was later confirmed by 3-D spatial-ability test
for these individuals (Voyer et al., 1995; Slabbekoorn et al., 1999)
The magnitude of testosterone effect on female-to-male transsexuals improved the spatial
cognition significantly for the first three months (Cohen’s d = 0.56), but no further improvement
after seven months of treatment. For male-to-female transsexuals, androgen suppression did not
decrease spatial performance of these individuals, suggesting prenatal effects of androgen on
these abilities. The results were consistent with postnatal activation theory (Voyer et al., 1995).
2.3.2 Sociocultural contributions
Compared with evolutionary psychologists and neurobiologists, social scientists’ attention is
drawn into the sociocultural factors in seeking to explain observed cognitive and affective
differences of females and males (Unger, 1981). Blickenstaff (2005) argued strongly against the
hypotheses of inborn genetic differences to explain the gender differences of students. Based on
the socialization theory, Blickenstaff (2005) counter-proposed an evaluation of eight alterative
hypotheses for gender differences in science and STEM from females’ perspective (p. 371-372):
1. Girls’ lack of academic preparation for a science major/career.
2. Girls’ poor attitude toward science and lack of positive experiences with science in
childhood.
3. The absence of female scientists/engineers as role models.
4. Science curricula are irrelevant to many girls.
5. The pedagogy of science classes favors male students.
6. A ‘chilly climate’ exists for girls/women in science classes.
7. Cultural pressure on girls/women to conform to traditional gender roles
8. An inherent masculine worldview in scientific epistemology.
46
2.3.2.1 Gender-role
Traditionally, perception of science and scientists is widely stereotyped as masculine or male
dominated.
Gender role sterertyping is an attempt to explain gendered patterns on
achievement in the socialization process. Children acquire their knowledge of gender by
observing and mimicking their behavior on same-sex members of their family, friends and local
communities as well as gender stereotype messages from the public media (Ngai, 1995; Sharpe,
1976). Steele and Aronson (1995) opined that stereotype or stereotype threat occurs when a
person’s belief that he or she belongs to a group stereotyped as inferior in a given ability in the
presence of certain contextual cues. The contextual cues could discourage females from aspiring
to and pursuing science education and careers and from taking leadership roles (Schmader,
2004). As a long term effect, stereotype threats can decrease females’ opportunities of being
accepted into science educational programs whose admission requirements emphasize test
scores.
According to Rosenthal and Rubin (1982), an effect accounting for only 4% variance of scores
is associated with a difference of 60% versus 40% of a group’s performance above average. For
example, to get admission to medical program at university, an individual must attain at least an
average score at public examination to qualify for admission. 4% variability in score will result
in 60% of one group and 40% of the other group qualifying for admission. Therefore, though
stereotype threat may sometimes seem ‘‘small”, they can have substantial real-world
consequences.
2.3.2.2 Schooling and family conditions
Simpson and Oliver (1985) demonstrated that school was a significant origin of variation with
respect to science performance and affective outcomes. Students’ academic achievement could
also be mediated strongly by their family background such as socioeconomic status (SES) and
parental involvement (Ho and Willms, 1996).
Yang (1996) from Taiwan integrated the views of others and came up with a model with six
dimensions to explain gender differences in science interest: social culture (sex role), individual
cognition, individual affect, education environment (school), family background and nature of
science (see Figure 2.1).
47
Figure 2.1: Gender differences in science (Source: Yang, 1996 p. 56)
Social culture
Sex role
Family background
Schooling
Gender differences
in science interest
Science
knowledge
Nature of
science
Attitude toward science
Individual
affect
Self-efficacy
Science achievement
Ability
Individual
cognition
1. Social culture (of gender) includes sex role formation process through different views
and evaluation of gender behavior from public media, laws, history, custom, thought
and philosophy.
2. Individual cognition includes students’ science ability and performance.
3. Individual affect includes attitude toward science, interest in science and personal value
of science.
4. Education environment (school) includes students’ gender, school type, classroom
activities, teacher (ability and gender), curriculum and teaching kits.
5. Family background includes parental socioeconomic status (SES), parents’ careers,
parents’ expectation on child’s success and child-rearing style.
6. Nature of science includes scientific method, science knowledge, logical thinking in
science, experimentation, scientific analysis, scientists.
48
2.3.3 Item characteristics attributing to gender differences
2.3.3.1 Scientific content
Gender differences in science assessments can vary a lot, in terms of content knowledge, in
favour of either boys or girls (Kahle & Lake, 1983, Johnson, 1987; Murphy, 1991). Many
studies (e.g. Walding, 1994) pinpointed that content, both conceptual understanding of science
and practical aspects of science, has implication for gender differences in science attainment.
Kelly (1978) examined gender differences in the 14-year-old population in the first IEA science
survey conduced in the developed countries. The results suggested gender differences within
branches of science, showing that boys have clear advantages on physical systems, intermediate
on chemical systems and smaller for biological ones. Similar conclusion are observed in other
researches (e.g. Erickson & Ericken, 1984; Jovanovic, Solano-Flores & Shavelson, 1994)
2.3.3.2 Item format
With the same test length, multiple-choice assessments often show a higher reliability over ones
with open-ended formats (Mazzeo et al., 1992) and this explains why multiple-choice tests are
commonly used in
public examinations (e.g. HKCEE) and large scale international
assessments such as TIMSS and PISA. However, consistently over the years, boys are reported
to be favored by multiple-choice tests while girls are favored by open-ended items (Hoste, 1982;
Stobart, Elwood & Quinlan, 1992; Walding, et al., 1994). Tests consisting of mainly
multiple-choice or open-ended formats can generate test bias on either sex (Shepard, 1993).
After examining the results of General Certificate of (GCE) examinations in UK, Murphy (1978,
1982) explained that females are favored by items of open-response (e.g. essay) since these
items demand higher verbal ability; whereas close-response items (e.g. multiple-choice) do not
require it, but focus solely on problem solving, an area in which males can do better.
Another common claim over girls’ poorer performance in multiple-choice tests is their lack of
confidence and willingness to take risks and give responses in novel situations by guessing. In
the USA, the National Assessment of Educational Progress (NAEP) includes an ‘I Don’t Know’
(IDK) alternative option in the multiple-choice items of science domains to estimate more
accurately the knowledge of respondents from different group. Sherman (1974) examined the
results of NAEP and found out that girl candidates tended to choose IDK as the item responses
more often than their male counterpart.
Overall, Gipps and Murphy (1994) stated that factors within the assessment itself, for example,
item format and response mode, were one of influential sources of invalidity in assessment.
49
They recommended those responsible for designing public examinations ensure that a range of
assessment techniques are used so that there is no bias against one particular group of
candidates.
2.3.4 Expectancy-value model of achievement-related choices in science
Development of an interest in pursuing a career in science or science related work has been well
recognized as one of the important aspects of affective behaviors in science education (Klopfer,
1971). To understand gender differences in educational and occupational tendency and choices,
one must understand the sociocultural influences to those choices. A well-developed model of
achievement-related choices has been proposed by Eccles et al (1983) (see Figure 2.2).
Figure 2.2: Expectancy-value model of achievement-related choices (Eccles et al., 1983).
As indicated by the model, an individual student chooses his or her future academic
programmes and careers based on two criteria namely, (1) expectations for success and (2)
subjective task values. Eccles and her colleagues conceptualize expectation for success as one’s
belief in achieving the tasks when he or she takes on the challenges. Subject task values were
defined in terms of four essential motivational components:
50
(1) the utility value of task in achieving either one’s short or long range goals or
obtaining external rewards;
(2) the intrinsic interest in, and enjoyment of, the task;
(3) the attainment value – the needs, personal values, and explicit motives that an
activity fulfills; and
(4) the cost of engaging in the activity.
How can Eccles et al (1983) model help us to understand gendered choices of academic courses
in high schools, major in universities, and careers? If boys and girls hold different expectations
for success in science-related tasks and they attach different values to success at these tasks,
then gender differences in achievement-related choices in science occur.
In the past two decades, researches findings indicated that self-concept of ability and subjective
task values acted as major mediators of gendered choices in the Eccles et al (1983) model (see
Jacobs, 2005 for review). The next two sections will review these two mediators.
2.3.4.1 Self-concept of ability as mediator of gendered choices
The effect of self-concept of ability on achievement-related choices has been discussed
extensively (e.g. Eccles et al., 1983; Nagy et al., 2006; Meece et al., 2006). These authors
suggested that self-concepts of ability are a critical predictor and mediator of gendered choices.
Nagy et al (2006) found that at grade 10, German boys outperformed girls on the mathematics
and biology achievement tests, and reported higher math self-concepts and intrinsic values.
Girls scored higher on the biology self-concept and intrinsic value scales. Boys were twice more
likely to enroll in advanced mathematics at grade 12 than girls. The reverse pattern was found
for biology. The gender differences at Grade 10 mediated the gender effect on course enrollment
in Grade 12.
Jacobs (2005) reported that gender differences in both self-concepts and career aspirations along
traditional gender-typed lines were found. Girls were underrepresented in the high science
ability self-concept cluster in the United States. As a result, they were less likely than boys to
aspire to careers in fields related to physical science. However, they were more likely than boys
to aspire to health- and biology-related careers.
In short, current literatures support the theory that science self-concept acts as a mediator of
gendered choices in science education and careers.
51
2.3.4.2 Subjective task values as mediators of gendered choices
Evidence of subjective task values as mediators of gendered educational choices can be found in
Dunteman, Wisenbaker, and Taylor’s (1978) longitudinal study in the United States. They
investigated the link between personal values of science and selection of university major in
science using the National Longitudinal Study of over 20,000 high school seniors from 1200
high schools with 18 seniors per school. They concluded that students who were high on
thing-orientation and low on person-orientation were more likely to select a math or a science
major. Boys tended to be thing-oriented while girls were more likely to be person-oriented. This
gendered tendency reflected in their personal value of science. Hence, boys were more likely
than girls to select science majors in college studies.
Eccles et al (1999) conducted a longitudinal study of about 1,000 European-American, middle
and working class adolescents from southeastern Michigan in the United States. The study
provided additional evidence to show that subjective task values mediated the gendered
occupational choices. They assessed how high school seniors attach different values to a wide
array of occupations and occupational characteristics. Their results indicated that individuals
who valued helping others were predicted not entering a physical science-related profession.
They concluded that males and females differed, in a gender stereotypic fashion, in the value
they attached to the different career characteristics and self-perceptions. These differences could
explain a sizeable amount of the gendered variance in career choices.
In other words, the above findings suggested that subjective task values are the important
mediators of gender differential choices in science.
2.4 Local research on gender differences in scientific literacy
2.4.1 Gender differences in science performance
After extensive Internet search using Scholar.google, Eric, Sagepub, Wiley, JSTOR,
Informaworld, Springerlink, PsycINFO, ProQuest and Hong Kong Academic Library Link
(HKALL), only three research articles (Keyes, 1983; Lin, 2009; Yip et al., 2004) and six reports
(Law 1996a, 1996b, 1997; Ho et al., 2003, 2005, 2008; Yung et al., 2006) directly address the
gender differences of cognitive performance of science domains in Hong Kong. The findings of
these four local gender studies and six reports are summarized as follows:
Keyes (1983) conducted a gender research on Hong Kong Chinese Adolescents to test the
hypothesis of sex differences in patterns of cognitive ability by variation in sex-role
52
identification. She discovered that males had better spatial ability while females were better
confluent production. Based on these results, she came to a conclusion that biological sex
differences were the best predictor of a male or female pattern of performance.
Lin (2009) from Taiwan used structural equation modeling (SEM) to investigate three factors,
family, school and self, in accounting for the gender differences of PISA 2006 science
performance of students from Taiwan, Japan, South Korea and Hong Kong. She reported that
both family and school factors influence less than student self on science performance.
Out of all local studies about gender differences in science performance, the most
comprehensive one was carried out by Yip et al (2004). The study covers gender differences in
various domains of science knowledge, practical and communication skills and item formats.
Boys were reported to outperform girls on items with earth and physical science, understanding
of scientific knowledge and closed response format. Girls on the other hand tended to perform
better on items with recognizing questions and identifying scientific evidence. In terms of
gender diversity, boys in higher ability groups consistently achieved higher than girls.
For TIMSS assessments, Hong Kong students had relatively large gender differences in science
performance at fourth grade (Law, 1997). Law (1996) also reported that the gender differences
in science performance were the largest amongst those participating countries meeting the
sampling and participation requirements at eighth grade. In both cases, boys outperformed girl
counterparts with significant differences. Gender differences were found to be the least in life
sciences and largest in earth and physical sciences. Girls tended to perform better in open
response items than boys.
In 2006, Yung et al. reported that boys outperformed girls in science content domains.
Significant gender differences were consistently found in Earth Science and physics. Among the
ability areas, factual knowledge, conceptual understanding, analysis and reasoning, boys
outperformed girls in all the areas with significant differences. However, a significant reduction
in gender differences was found from 1995 to 2003 assessment period. During this period, girls’
performance has improved faster than that of males. The same trend of improvement happened
between 2003 and 2007 and by TIMSS 2007, no significant difference between boys’ and girls’
achievement in science was reported (see Table 2.15 & Table 2.16) (Martin et al., 2008).
53
Table 2.15: Trends in average science performance by gender - 1995 through 2007 (Grade 4)
(Source: TIMSS 2007 International Science Report, Martin et al., 2008 p. 60)
Combining the three PISA reports (Ho et al., 2003, 2005, 2008), there are no statistically
significant gender differences reported in the overall scores of the 15-year-olds at schools.
However, boys tended to do better in understanding concepts and explaining phenomena
scientifically while girls were better at recognizing questions and identifying evidence. Boys did
better in closed items than girls while girls outperformed boys on the open-response items. For
the content knowledge, the results indicated that there was no consistent support that boys can
do better on physical science and biological science than girls. However, the variability of boys’
science scores was always larger than females apparently at upper and lower extremes (Machin
& Pekkarinen, 2008).
54
Table 2.16: Trends in average science performance by gender – 1995 through 2007 (Grade 8)
(Source: TIMSS 2007 International Science Report, Martin et al., 2008 p. 61)
The insistency in reporting gender differences from TIMSS and PISA over the decade may be
caused by how the assessments are being constructed. The tests with higher proportion of
open-ended items or dominated with the closed items may lead to an opposite conclusion. The
composition of the tests at item level is so crucial to the final conclusion of gender differences.
The validity of the outcome measure and suggest that the conclusions about group differences
and about correlates of achievement depend heavily on specific features of the items that make
up the test (Hamilton, 1998). Still more sophisticated techniques such as DIF are seldom used to
investigate the item response pattern of boys and girls. Similarly, the causes, contextual factors,
of the differences are also not yet fully explored at system levels though the gender differences
seem to be disappearing in recent years.
55
2.4.2 Gender differences in affective domain
The development of affective learning outcomes has become more important in local schools.
The Curriculum Development Council (CDC, 1988) stated clearly that one of the broad aims of
secondary 1 to 3 science syllabus was ‘to develop curiosity and interest in science”. Recently,
more emphasis was put on affective learning outcomes of science education that the Science
Education Key Learning Area Curriculum Guide for Primary 3 - Secondary 3 (CDC, 2002)
recommended schools adopt strategies to nurture students’ interest in school science learning:
It is important to nurture students’ interest in science learning. Students are generally
intrigued by new things. They are interested in problems that puzzle them and have a
natural urge to find solutions to settle them. Organizing the curriculum around problems or
phenomena that puzzle students helps motivate students to learn.
Rather than relying solely on textbooks, teachers of General Studies and science subjects
are encouraged to make use of hands-on exploratory learning activities to develop
students’ interest in science. It is essential that students participate in a wide range of
activities to develop enjoyment in the process of science learning.
(Science Education KLA Guide, p.7)
In response to this trend in curriculum development and evaluation, Cheung (2009a) used
Attitude toward Chemistry Lessons Scale (ATCLS) to investigate the interaction effect between
grade level and gender at secondary 4 and 5. The result indicates boys in secondary 4 and
secondary 5 like chemistry theory lessons more than that of girls. In contrast, girls’ attitude
toward chemistry laboratory work remains more or less the same from secondary 4 to secondary
7 with boys’ attitude declining over the same period (Cheung, 2009b).
The gender differences in affective domains are usually not the focus of the local reports. Even
some researches indicated that the correlation between attitude toward science and achievement
can be as high as 0.50 for boys and 0.55 for girls which accounted for 25-30% of the variance in
science performance (Weinburgh, 1995). The only gender differences study in affective domain
was from Cheung (2009b). The correlation between affective outcomes and science
performance was positively correlated by gender at each grade level from secondary 1 to 5.
56
2.5 Summary
In chapter two, scientific literacy in cognitive and affective domains commonly used in the
science education community and its evolutionary nature of scientific literacy definition have
been discussed. Basically, there is no consensus about scientific literacy definition in the science
education community. PISA 2006 adopted Bybee’s multidimensional definition of scientific
literacy for cognitive domain and Klopfer’s taxonomy of affective behaviours in science
education for affective domain (OECD, 2006; Bybee, 1997b; Klopfer, 1976). “Gender”
differences were then defined in respect to the sociocultural and biological contributions.
From the literature review, there were clear gender differences in science. The differences in
science performance had been narrowed in the past few decades. However, the gender gaps in
affective learning outcomes remained large, in particular, the educational and occupational
trajectories related to science. Girls tended to choose non-science oriented careers and
educational programs.
Secondly, factors attributing gender differences i.e. biological contributions and sociocultural
contributions were included as part of the literature review. Again, there is no agreement about
the “nature and nurture” of gender differences in science. Girls’ disadvantages in science
performance were reduced and became insignificant in recent years. However, the gender
differences in affective learning outcomes and future-oriented motivation remained large and
significant. Eccles et al (1983) Expectancy-value Model of Achievement-related Choices was
used to illustrate the gendered pattern in educational and occupational choices in science.
Thirdly, other factors attributing to gender differences, for example, item characteristics were
revised. Previous literatures suggested that closed response items tended to favor boys while
open response items tended to favor girls.
Finally, a brief literature review on local gender studies in science was conduced. Form this
review; we might conclude that there is a limited understanding of the relationship between
science performance, affective learning outcomes and gendered choices in science careers and
education in local context. In the coming chapters, we attempt to answer this question “How,
and to what extent, the gender differences in students’ science performance and affective
learning outcomes influence their intention to choose science-oriented careers and educational
opportunities. In the coming chapter 3, the major factors will be conceptualized for later
investigations in chapter 4 and chapter 5.
57
CHAPTER THREE
RESEARCH DESIGN AND METHODS
The study deployed quantitative research methods, mainly Multidimensional
Differential Item Functioning (MDIF) and Multilevel Mediation (MLM), to explore the
gender differences at item level and system level. The following sections will discuss
the data collection method, research framework, research methods and analysis
techniques.
3.1 PISA 2006 database
To increase the quality of the data collected, the PISA survey used two-stage stratified
sampling procedure. The first stage for the main study PISA 2006 was conducted
between May and June 2006 in Hong Kong to have stratified sample of 150 local
schools, namely, government, aided, international and independent under direct subsidy
scheme (DSS) with three achievement level of students, high, medium and low. The
level of achievement is determined by a composite index, Academic Achievement
Index (AAI), derived from individual student’s school performance and territory-wide
assessment. The sample represents 5.7% of the 15-year-old school students of the target
population in 2006 (Ho et al., 2008). The overall distribution of the participating
schools is shown in Table 3.1.
Table 3.1: Participating school distribution in PISA 2006 in Hong Kong. (Source:
Ho et al., 2008, P. 7)
Type of School Student Academic Intake Total Number of Schools Number of Schools Participated
Government
High Ability
Medium Ability
Low Ability
N/A
High Ability
Medium Ability
Low Ability
N/A
Local/DSS
International
17
7
10
2
Aided
128
125
126
1
#
Independent
43
27
Total
486
#
There is no intake information about independent schools.
58
6
2
3
0
46
46
35
0
7
1
146
Table 3.2 shows the demographic features of the 4645 students in the sample. Most of
the students were from secondary four or grade 10 (64.1%) and the second largest
proportion was from secondary three or grade 9 (24.4%) which was account for 90% of
the sample in total. The female (50.6%) to male (49.4%) ratio in the sample was about
in equal proportion. Most of the students were born in Hong Kong (75.5%) while the
rest were from other areas (23.4%). In terms of immigrant status, students who were not
born in Hong Kong but at least of the parents born in Hong Kong were classified as
native. Students were classified as first generation if his or her parents and the student
were not born in Hong Kong. The third group of students was classified as second
generation if the student were born in Hong Kong while the parents were born in other
places. According to this PISA definition, 55.6% of the students were natives and first
generation and second generation were 18.7 % and 24.4% respectively.
Table 3.2: Demographic features of the participating students (Source: Ho et al., 2008,
P. 9)
Number of Participating Students
Proportion (%)
Grade/Form
7/ S1
8/ S2
9/ S3
10/ S4
11/S5
Total
107
421
1134
2978
5
4645
2.3
9.1
24.4
64.1
0.1
100
Gender
Female
Male
Total
2351
2294
4645
50.6
49.4
100
Place of Birth
Hong Kong
Non-Hong Kong
Data Missing
Total
3509
1089
47
4645
75.5
23.4
1.0
100*
Immigrant Status
Native
2581
55.6
Second-Generation
1134
24.4
First-Generation
869
18.7
Data Missing
61
1.3
Total
4645
100
*
The sum of “place of birth” category is not 100 because of round up error at
decimal place.
59
3.2 Conceptual framework of present study
To answer the research question concerning to what extent and how gender effects are
mediated through cognitive and affective domains of science on achievement related
choice, a new model has been constructed for the present study based on the
Expectancy-value Model of Achievement-related Choices well-developed by Eccles et
al (1983) (see Figure 3.1). Eccles et al (1983) model was chosen for present study since
it is intentionally designed to analyze the gender-segregated choices in the STEM areas
(Halpern et al., 2007). Secondly, it captures the major affective factors in the model
which are well known to be socially, culturally, and psychologically influenced (Eccles,
2011).
There are four types of variables in the model: independent variable, mediators,
dependent variable and control variable. The independent variable consists of gender
(Girls). The mediators include Science Self-concept (SCSCIE), Enjoyment of Science
Learning (JOYSCIE), Interest in Science Learning (INTSCIE), Instrumental Motivation
to Learn Science (INSTSCIE), Personal Value of Science (PERSCIE) and Science
Performance (SP)13. The dependent variable is Future-oriented Science Motivation
(SCIEFUT). Parental SES acts as a control variable in the model.
SCSCIE is placed under Child’s general self schemata, to reflect one’s perceptions and
expectations for success in scientific literacy. JOYSCIE and INTSCIE are grouped
under Subjective Task Value to reflect one’s motivation to learn school science.
PERSCIE is put under Attainment Value to reflect one’s needs, personal values, and
explicit motives that an activity fulfills. INSTSCIE is placed under Utility Value to
reflect the usefulness of a task in facilitating the achievement of goals or in obtaining
any immediate or long-term rewards. JOYSCIE, INTSCIE, PERSCIE and INSTSCIE
are thus grouped under Subjective Task Value. Eccles (2011) mentioned that
gender-role socialization could lead males and females to place different Subjective
Task Values on various long-range goals and activities. If one place success in one’s
gender role as the key component of his or her identity, then activities that fulfill this
role have higher value and vice versa.
General value of science and self-efficacy in science are two affective factors available
in the PISA 2006 survey, which are not enclosed in the revised model so as to keep the
13
Science performance (SP) refers to the cognitive performance of scientific literacy.
60
original psychometric properties of the measurement models in Eccles et al (1983)
model14.
Parental SES and Science Performance are two new components included in the model
which is not found in Eccles et al (1983) Expectancy-value Model of
Achievement-related Choices. However, these two components are important control
variables for estimating the mediated effects more actually (Bradley & Corwyn, 2002;
Meece et al, 2006).
Figure 3.1: Revised Expectancy-value Model of Achievement-related Choices in
Science
Independent
Mediators
Dependent
variable:
variable:
Child’s general self
schemata
Science
Self-concept
(SCSCIE)
Interest in Science
Learning
(INTSCIE)
Stable child
characteristic
Girl
(STF Gender)
Control
variable:
Cultural milieu
Parental
SES
14
Enjoyment of
Science Learning
(JOYSCIE)
Attainment Value
(PERSCIE)
Achievement related
choice
Future-oriented
Science
Motivation
(SCIEFUT)
Utility Value
(INSTSCIE)
Subjective task value
Science
Performance
(SP)
The definition of self-efficacy in science in PISA 2006 is different from self-concept of one’s abilities in
Eccles et al (1983) model. General value of science is not a part of subjective task value in the model.
61
3.3 Conceptualization and operationalization of scientific literacy
3.3.1 Cognitive domain
As stated in chapter two, the assessment framework of cognitive domains in scientific
literacy focus on the practical aspects of the scientific knowledge, skills, competencies
and other attributes embodied in individuals that are relevant to personal, social and
economic well-being rather than the national curriculum of different participating
countries and areas. The criteria for item selection for PISA 2006: (1) Scientific
situations or contexts in which scientific knowledge and the use of scientific processes
are applied. The framework identifies three main areas: Science in Life and Health,
Science in Earth and Environment, and Science in Technology. (2) Scientific
knowledge or concepts, which constitute the links that aid understanding of related
phenomena. Scientific processes, centered on the ability to acquire, interpret and act
upon evidence. Three such processes present in PISA relate to: i) describing, explaining
and predicting scientific phenomena, ii) understanding scientific investigation, and iii)
interpreting scientific evidence and conclusions (OECD, 2006). After trial study, 108
assessment items were kept for the main study with reference to the following
assessment framework (see Table 3.3).
Table 3.3: Distribution of PISA 2006 science performance items (knowledge domains
by competency). (Source: PISA 2009b)
scientific competency
(cognitive dimensions)
Identifying Explaining
Using
scientific
phenomena scientific
issues
scientifically evidence
physical
systems
living systems
15
2
17 (13%)
24
1
25 (23%)
12
0
12 (11%)
2
6
8
(7%)
24
1
25 (23%)
0
21
21 (19%)
31
(29%)
108
knowledge
of science earth and
space systems
technology
systems
scientific
knowledge enquiry
about
scientific
science
explanations
Total
Sub-total
24
(22%)
62
53
(49%)
62
(57.4%)
46
(42.6%)
3.3.2 Affective domain
This section outlines the procedure to conceptualize and operationalize the affective
domain factors used in this study. For each factor, four stringent steps are used verify
its suitability for multilevel mediation study. Firstly, the internal consistency,
Cronbach’s α of affective factors (or measurement models in SEM terminology) was
examined. The criteria for Cronbach’s α value15 equal to or above 0.7 which indicate
data collected of high reliability (Henson, 2001).
Secondly, multiple-imputation was used to impute the missing values. The detail
procedure of handling missing values can be found in Appendix A.
Thirdly, confirmatory factor analysis (CFA) of structural equation modeling (SEM) was
used to (1) confirm theoretically expected item dimensionality of the data collected
from student questionnaire, and (2) make necessary adjustment of dimensional structure
to fit Hong Kong context (Kaplan, 2000; Brown, 2006). The CFA will produce a series
of model fit indices to validate the goodness-of-fit between the model and the data
collected. The most common index used to check the model fit is the χ2 goodness-of-fit
tests. The χ2 goodness-of-fit tests which compare the expected and observed values to
determine how well an experimenter’s predictions fit the data. The limitation of the test
is its sensitivity to sample size, for tests involving large samples such as TIMSS and
PISA which results in a rejection of the null hypothesis, even when the factor model is
appropriate (DeCoster, 1998). Since the sample size is very large (N=4645) in the
present study, χ2 goodness-of-fit will be reported for the affective factors but it is not be
used for assessing the model fits of the affective factors. Rather, the degree of model fit
is assessed against the following four indexes: Root Mean Square Error of
Approximation (RMSEA), Root Mean Square Residual (RMR), Comparative Fit Index
(CFI) and Tucker Lewis Index (TLI) or Non-Normed Fit Index (NNFI). RMSEA values
below 0.05 indicate a close model fit whereas values over 0.10 are usually interpreted
as unacceptable model fit. RMR values should be less than 0.05. Both values of CFI
and NNFI between 0.90 and 0.95 indicate an acceptable model fit, and values greater
than 0.95 indicating a good model fit.
Finally, multiple-group CFA is used to confirm measurement invariance (MI) across
15
The range of Cronbach’s α is 0.0 to 1.0. The higher Cronbach’s α, the better the internal consistency.
63
gender groups i.e. same measurement scale is comparable across boys and girls. Reise
et al (1993) mentioned that it is misleading to make comparisons across the group if
measurement scale and trait scores are not comparable. The procedure to conduct MI
across gender groups is: (1) Pattern invariance (2) Unconstrained (baseline model) (3)
Measurement weights (factor loadings) (4) Structural covariances (5) Measurement
residuals (or uniqueness) (Byrne, 1994; Vandenberg & Lance, 2000). A summary of these
model tests is presented in Table 3.4.
Step
1
Table 3.4: A summary of procedure to conduct multi-group invariance test across
gender groups
Comparative
Model
Description
Purpose
model (MI?)
Whole sample
Model fit with all sample
Localization of measurement
models i.e. fitting local data
--
Pattern invariance
2
(1a) Boys’ sample
Model fit with boys’ sample
3
(1b) Girls’ sample
Model fit with girls’ sample
Confirming pattern
invariance of boys’ sample
Confirming pattern
invariance of girls’ sample
---
Gender invariance
(constrained
model)
4
(2) Unconstrained
Model fit with boys’ and
girls’ sample with no
constraint
5
(3) Measurement
weights
Factor loadings are set
invariants across gender
6
(4) Structural
covariances
Factor loadings and
covariances are set
invariants across gender
7
(5) Measurement
residuals
Factor loadings,
covariances and
uniqueness are set
invariants across gender
Acting as a baseline model
for model comparison with
(3), (4) and (5).
Checking gender equivalence
of factor loadings of
measurement models
Checking gender equivalence
of factor loadings and
covariances of measurement
models
Checking gender equivalence
of factor loadings covariances
and uniqueness of
measurement models
-(3) versus (2)
(4) versus (3)
(5) versus (4)
&
(5) versus (2)
As pointed out by Cheung and Rensvold (2002), the most commonly used
goodness-of-fit of SEM is the χ2 statistic. χ2 statistic is problematic because of the
statistic’s functional dependence on sample size N. Cheung and Rensvold (2002)
proposed to use the change of CFI (ΔCFI) rather than the χ2 difference (Δχ2) to evaluate
MI for practical reasons. Empirical evidence supported that ΔCFI might be more
accurate than the Δχ2 test because of its insensitivity to sample size but more sensitive
64
to lack of invariance16 (Meade, Johnson, & Braddy, 2008). Cheung and Rensvold
(2002) recommended ΔCFI=0.01 as a cutoff point for MI test. In other words, ΔCFI ≤
0.01 suggest an invariant situation and the null hypothesis of invariance should not be
rejected.
As the sample size of the current study is large (N=4645), Δχ2 represents an excessively
stringent test of MI (Cudeck & Brown, 1983; MacCallum, Roznowski & Necowitz,
1992). ΔCFI is therefore more practical to test for multi-group MI (Cheung and
Rensvold, 2002) and will be reported for subsequent measurement models in this study.
3.3.2.1 Science Self-concept
CFA on Science Self-concept
In Table 3.5, six items for on SCSCIE were used to assess students’ perceptions of
ability in science. The items were inverted for IRT scaling so that more positive WLE
scores on this index indicate higher levels of SCSCIE (OECD, 2009b). The value of
Cronbach’s α was 0.929 indicating that the data were of high reliability.
Table 3.5: Item parameters for Science Self-concept
Item parameters for science self-concept
(SCSCIE)
Model
16
Item
ST37Q01
ST37Q02
How much do you agree with the
statements below? (Strongly
Agree/Agree/
Disagree /Strongly disagree)
a) Learning advanced <school
science> topics would be easy for
me
b) I can usually give good answers
to <test questions> on <school
science> topics
ST37Q03
c) I learn <school science> topics
quickly
ST37Q04
d) <School science> topics are easy
for me
ST37Q05
ST37Q06
e) When I am being taught <school
science>. I can understand the
concepts very well
f) I can easily understand new
ideas in <school science>
The measurement model is not applicable across groups.
65
Scale reliability
(Cronbach’s α)
0.929
Table 3.6 shows the results of CFA for model of SCSCIE. The model fit indices,
RMSEA (0.048), RMR (0.005), CFI (0.996) and TLI (0.992) were satisfactory for
SCSCIE.
Table 3.6: Model fit for Science Self-concept
Model
χ2 (df)
p
RMSEA
RMR
CFI
TLI
Accept model
SCSCIE
83.327 (7)
0.000
0.048
0.005
0.996
0.992
Yes
Model fit
(criteria)
---
>0.050
<0.080
<0.050
>0.900
>0.900
---
Multiple-group CFA on Science Self-concept
Table 3.7 shows that ΔCFI was less than the cutoff (0.01) for the constrained models,
boys and girls had similar measurement weights (factor loadings), structural
covariances and measurement residuals for the model. The results suggest that the
measurement scale for SCSCIE was comparable across the gender groups.
Table 3.7: Measurement invariance test across gender groups for
Science Self-concept
Model
Whole sample
Pattern invariance
(1a) Boys’ sample
(1b) Girls’ sample
Gender invariance
(constrained model)
(2) Unconstrained
(3) Measurement
weights
(4) Structural
covariances
χ2
(Δ)
83.327
df
(Δ)
7
CFI
(Δ)#
0.996
52.570
32.443
7
7
0.996
0.998
85.013
101.171
(16.158)**
111.968
(10.797)**
14
19
(5)
20
(5)
TLI
RMSEA
(ΔCFI ≤ 0.01)
0.005
0.992
0.048
--
0.007
0.004
0.990
0.995
0.053
0.039
---
0.997
0.006 0.993
0.006
0.996
0.011 0.994
0.011
(0.001)
0.996
0.028 0.993
0.028
(0.001)
0.992
192.445
26
(5) Measurement
(0.004) 0.032 0.991
0.032
residuals
(80.477)*** (6)
(0.005)
--Model fit (criteria)
>0.900 <0.050 >0.900 <0.080
**
***
#
Note: p<0.01, p<0.001; cutoff for (Δ) =0.01 (Cheung and Rensvold, 2002)
66
Invariant?
RMR
-Yes
(3) versus (2)
Yes
(4) versus (3)
Yes
(5) versus (4)
(5) versus (2)
--
3.3.2.2 Personal Value of Science
CFA on Personal Value of Science
In Table 3.8 five items for PERSCIE were used to assess the extent to which students
value the contribution of science to their own personal development. The items were
inverted for IRT scaling so that positive WLE scores on this index indicate students’
positive perceptions of PERSCIE (OECD, 2009b). The value of Cronbach’s α was
0.795 indicating that the data were of high reliability.
Table 3.8: Item parameters for Personal Value of Science
Item parameters for personal
value of science (PERSCIE)
OECD
model
Item
ST18Q03
ST18Q05
ST18Q07
ST18Q08
ST18Q10
How much do you agree with the
statements below? (Strongly
Agree/Agree/
Disagree /Strongly disagree)
c) Some concepts in <broad
science> help me see how I
relate to other people
e) I will use <broad science> in
many ways when I am an adult
g) <Broad science> is very
relevant to me
h) I find that <broad science>
helps me to understand the
things around me
j) When I leave school there will
be many opportunities for me to
use <broad science>
Scale reliability
(Cronbach’s α)
0.795
Table 3.9 shows the results of CFA for one-dimensional model of PERSCIE items. The
model fit indices, RMSEA (0.057), RMR (0.010), CFI (0.992) and TLI (0.979) were
satisfactory for PERSCIE.
Table 3.9: Model fit for Personal Value of Science
0.992
NNFI
(TLI)
0.979
Accept
model
Yes
<0.050 >0.900
>0.900
---
Model
χ2 (df)
p
RMSEA
RMR
CFI
PERSCIE
63.452 (4)
0.000
0.057
0.010
Model fit
(criteria)
---
>0.050
<0.080
67
Multi-group CFA on Personal Value of Science
Table 3.10 shows that ΔCFI was less than the cutoff (0.01) for the constrained models;
therefore boys and girls had similar measurement weights (factor loadings), structural
covariances and measurement residuals for the model. The results suggest that the
measurement scale for PERSCIE was comparable across the gender groups.
Table 3.10: Measurement invariance test across gender groups for model of Personal
Value of Science
Invariant?
df
CFI
χ2
RMR
TLI
RMSEA
Model
#
(ΔCFI ≤ 0.01)
(Δ)
(Δ)
(Δ)
Whole sample
63.452
4
0.992
0.010 0.979
0.057
-Pattern invariance
(1a) Boys’ sample
32.154
4
0.991
0.012 0.979
0.055
-(1b) Girls’ sample
30.745
4
0.993
0.008 0. 982
0.053
-Gender invariance
(constrained model)
(2) Unconstrained
62.899
8
0.992
0.010 0.981
0.038
-68.206
Yes
12
0.992
(3) Measurement
0.012 0.987
0.032
)
(5.307
(3) versus (2)
(4) (0.000)
weights
Yes
(4) Structural
68.223
13
0.992
0.012 0.988
0.030
(4) versus (3)
(0.017)
(1) (0.000)
covariances
0.983
Yes
135.465
19
(5) Measurement
(0.009)
0.020 0.983
0.036
(5) versus (4)
(67.242)*** (6)
residuals
(0.009)
(5) versus (2)
---Model fit (criteria)
>0.900 <0.050 >0.900
<0.080
***
#
Note: p<0.001; cutoff for (Δ) =0.01 (Cheung and Rensvold, 2002)
3.3.2.3 Interest and Enjoyment of Science Learning
CFA on Interest and Enjoyment of Science Learning
In Table 3.11, eight items for INTSCIE were used to assess the extent to which students
are interested in learning topics in different science disciplines. The items were inverted
for IRT scaling so that more positive WLE scores on this index indicate higher levels of
INTSCIE (OECD, 2009b). The value of Cronbach’s α was 0.833 indicating that the
data were of high reliability.
68
Table 3.11: Item parameters for Interest in Science Learning
Item parameters for interest in
science learning (INTSCIEHKG)
Model
HK
model
Physical
science
(PHYSCIHKG)
Biological
science
(BIOSCIHKG)
Deleted
ST21Q01
How much interest do
you have in learning
about the following
<broad science>
topics?
a) Topics in physics
ST21Q02
b) Topics in chemistry
Item
ST21Q03
ST21Q04
ST21Q05
ST21Q06
Scientific
method
ST21Q07
(SCIMETHKG)
ST21Q08
c) The biology of
plants
d) Human biology
e) Topics in astronomy
f) Topics in geology
g) Ways scientists
design experiments
h) What is required for
scientific explanations
Scale reliability
(Cronbach’s α)
0.833
In Table 3.12, five items in JOYSCIE were used assess the extent to which students like
doing specific scientific tasks. The items were inverted for IRT scaling so that positive
WLE scores on this index indicate higher levels of JOYSCIE. (OECD, 2009b). The
value of Cronbach’s α was 0.904 indicating that the data were of high reliability.
Table 3.12: Item parameters and scale reliability for Enjoyment of Science Learning
Item parameters for enjoyment
of science learning (JOYSCIE)
Model
Item
How much do you agree with the
statements below? (Strongly
Agree/Agree/
Disagree /Strongly disagree)
ST16Q01
a) I generally have fun when I am
learning <broad science> topics
ST16Q02
b) I like reading about <broad
science>
ST16Q03
c) I am happy doing <broad
science> problems
ST16Q04
d) I enjoy acquiring new knowledge
in <broad science>
ST16Q05
e) I am interested in learning about
<broad science>
69
Scale reliability
(Cronbach’s α)
0.904
Table 3.13 shows the result of CFA for two-dimensional model of INTSCIE and
JOYSCIE. The model fit indices, RMSEA (0.104), RMR (0.049), CFI (0.899) and TLI
(0.857) were not satisfactory for Hong Kong sample. The lack of fit was mostly due to
correlated error terms between interest items about similar topics (e.g. biology of plants
and human biology) (OECD, 2009b. From the CFA analysis of Hong Kong sample,
astronomy (factor loading = 0.45) and geology (factor loading = 0.43) were two items
being deleted from the final model as they were not part of local school science
curriculum for 15-year-old students in Hong Kong. Adding these two items caused the
misfit of the model which reflected in RMSEA value (0.170) and other model fit indices
(e.g. TLI = 0.718).
Figure 3.2 shows a second-order CFA model of INTSCIEHKG17 which grouped item
ST21Q01 and ST21Q02 into physical science (PHYSCIHKG), ST21Q03 and ST21Q04
into biological science (BIOSCIHKG), ST21Q07 and ST21Q08 into scientific method
(SCIMETHKG) in response to correlated error terms between interest items of similar
topics. The final model shows a second-order factor, INTSCIEHKG which accounts for
the hierarchical factor structure of the multiple factors. The estimated latent correlation
between the INTSCIEHKG & JOYSCIE was high (0.85) and significant.
Table 3.13: Model fit and estimated latent correlations for Interest in and Enjoyment of
Science Learning
Latent
correlations
Model
p
RMSEA
RMR
CFI
TLI
between:
χ2 (df)
GENSCIE
/PERSCIE
INTSCIE
2697.940 (20) 0.000
--0.170
0.074
0.798
0.718
INTSCIEHKG
62.685 (40)
0.012
0.011
0.007
0.999
0.999
--JOYSCIE
179.003 (11)
0.000
0.057
0.009
0.994
0.991
--INTSCIE
0.049
0.81**
0.104
0.899
0.857
& JOYSCIE 3248.604 (64) 0.000
Accept
model
No
Yes
Yes
No
INTSCIEHKG
& JOYSCIE
424.927 (40)
0.000
0.046
0.015
0.987
0.985
0.85**
Yes
Model fit
(criteria)
---
>0.050
<0.080
<0.050
>0.900
>0.900
---
---
Note: **Correlation is significant at the 0.01 level (2-tailed).
Figure 3.2: A second-order CFA model of INTSCIEHKG.
17
HKG subscript is used to denote localized measurement model for Hong Kong context.
70
Multi-group CFA on Interest and Enjoyment of Science Learning
Table 3.14 shows that ΔCFI was less than the cutoff (0.01) for the constrained models;
therefore boys and girls had similar measurement weights, structural covariances and
measurement residuals for the model. The results suggest that the measurement scale for
two-dimensional model of INTSCIE and JOYSCIE was comparable across the gender
groups.
Table 3.14: Measurement invariance test across gender groups for two-dimensional
model of Interest in and Enjoyment of Science Learning
Invariant?
CFI
df
χ2
Model
RMR
TLI
RMSEA
#
(ΔCFI ≤ 0.01)
(Δ)
(Δ)
(Δ)
Whole sample
424.927
40
0.987
0.015
0.985
0.046
-Pattern invariance
(1a) Boys’ sample
211.726
40
0.987
0.015
0.983
0.043
-(1b) Girls’ sample
251.040
40
0.986
0.015
0.980
0.047
-Gender invariance
(constrained
model)
(2) Unconstrained
462.766
80
0.987
0.015
0.981
0.032
-Yes
(3) Measurement
514.473
87
0.985
0.020
0.981
0.033
(3) versus (2)
(51.707)
(7) (0.002)
weights
518.843
Yes
(4) Structural
92
0.985
0.021
0.981
0.032
(4.37)
(4) versus (3)
(5) (0.000)
covariances
0.985
Yes
619.123
106
(5) Measurement
(0.000)
0.021
0.982
0.032
(5) versus (4)
residuals
(100.28)*** (14)
(0.002)
(5) versus (2)
Model fit (criteria)
--->0.900 <0.050 >0.900
<0.080
***
#
Note: p<0.001; cutoff for (Δ) =0.01 (Cheung and Rensvold, 2002)
71
3.3.2.4 Motivation to Learn Science
CFA on Motivation to Learn Science
In Table 3.15, five items for INSTSCIE were used to assess the extent to which
students believe that learning science in school is useful and worthwhile for future
studies or job prospects. The items were inverted for IRT scaling so that positive WLE
scores on this index indicate higher levels of INSTSCIE (OECD, 2009b). The value of
Cronbach’s α was 0.937 indicating that the data were of high reliability.
Table 3.15: Item parameters for Instrumental Motivation to Learn Science
Item parameters for instrumental motivation
to learn science (INSTSCIE)
Model
Item
ST35Q01
ST35Q02
ST35Q03
ST35Q04
ST35Q05
How much do you agree with the
statements below? (Strongly
Agree/Agree/Disagree /Strongly
disagree)
a) Making an effort in my <school
science> subject(s) is worth it
because this will help me in the work I
want to do later on
b) What I learn in my <school science>
subject(s) is important for me because I
need this for what I want to study later
on
c) I study <school science> because I
know it is useful for me
d) Studying my <school science>
subject(s) is worthwhile for me because
what I learn will improve my career
prospects
e) I will learn many things in my
<school science> subject(s) that will
help me get a job
Scale reliability
(Cronbach’s α)
0.937
In Table 3.16, four items for future-oriented science motivation (SCIEFUT) were used
to assess the extent to which students intend to continue to learn science and take up a
science-related career. The items were inverted for IRT scaling so that positive WLE
scores on this index indicate higher levels of motivation to learn science and take up a
science-related career in the future (OECD, 2009b). The value of Cronbach’s α was
0.931 indicating that the data were of high reliability.
72
Table 3.16: Item parameters for Future-oriented Science Motivation
Item parameters for
future-oriented science
motivation (SCIEFUT)
Model
Item
How much do you agree with the
statements below? (Strongly
Agree/Agree/Disagree /Strongly
disagree)
ST29Q01
a) I would like to work in a career
involving <broad science>
ST29Q02
b) I would like to study <broad
science> after <secondary
school>
ST29Q03
c) I would like to spend my life
doing advanced <broad science>
ST29Q04
d) I would like to work on <broad
science> projects as an adult
Scale reliability
(Cronbach’s α)
0.931
Table 3.17 shows the results of CFA for a two-dimensional model of motivation to
learn science. The fit was satisfactory for Hong Kong sample (RMSEA = 0.074). The
latent correlation between the two factors is 0.71.
Table 3.17: Model fit and estimated latent correlations for motivation to learn science
Model
χ2 (df)
p
RMSEA
RMR
CFI
NNFI
(TLI)
INSTSCIE
57.702 (4)
0.000
0.054
0.005
0.997
0.993
Latent correlations
between:
INSTSCIE/
SCIEFUT
---
SCIEFUT
22.025 (1)
0.000
0.069
0.004
0.999
0.992
---
Yes
691.651 (26)
0.000
0.074
0.019
0.983
0.976
0.71**
Yes
---
>0.050
<0.080
<0.050 >0.900 >0.900
---
---
INSTSCIE
& SCIEFUT
Model fit
(criteria)
Note: **Correlation is significant at the 0.01 level (2-tailed).
Multi-group CFA on Motivation to Learn Science
Table 3.18 shows that ΔCFI was less than the cutoff (0.01) for the constrained models;
therefore boys and girls had similar measurement weights, structural covariances and
measurement residuals for the model. The results suggest that the measurement scale for
the model of motivation to learn science was comparable across the gender groups.
73
Accept
model
Yes
Table 3.18: Measurement invariance test across the gender group for two-dimensional
model of motivation to learn science
Model
Whole sample
Pattern invariance
(1a) Boys’ sample
(1b) Girls’ sample
Gender invariance
(constrained model)
(2) Unconstrained
(3) Measurement
weights
(4) Structural
covariances
χ2
(Δ)
691.651
df
(Δ)
26
CFI
(Δ)#
0.983
358.780
360.789
26
26
0.982
0.983
Invariant?
RMR
TLI
RMSEA
(ΔCFI ≤ 0.01)
0.019
0.976
0.074
--
0.019
0.017
0.975
0.976
0.075
0.074
---
-0.982
0.018
0.975
0.053
0.982
Yes
0.021
0.978
0.050
(0.000)
(3) versus (2)
0.981
Yes
0.026
0.978
0.049
(0.001)
(4) versus (3)
0.976
Yes
982.677
71
(5) Measurement
(0.005) 0.030
0.975
0.053
(5) versus (4)
residuals
(224.647)*** (9)
(0.006)
(5) versus (2)
--- >0.900 <0.050 >0.900
-Model fit (criteria)
<0.080
***
#
Note: p<0.001; cutoff for (Δ) =0.01 (Cheung and Rensvold, 2002)
719.570
750.289
(30.719)***
758.030
(7.741)
52
59
(7)
62
(3)
3.4 Conceptualization and operationalization of Parental SES
The parental SES of the students was modeled by three factors, highest parental
occupational status (HISEI), educational level of mother (MISCED) and educational
level of father (FISCED). These factors were extracted from the students’ questionnaire.
The CFA result in Table 3.19 suggest that the parental SES was in perfect fit of the
collected data (RMSEA = 0.000).
Table 3.19: Model fit for socioeconomic status
Model
χ2 (df)
p
RMSEA
RMR
CFI
TLI
Accept model
SES
0.000 (0)
0.000
0.000
0.000
1.000
1.000
Yes
Model fit (criteria)
---
>0.050
<0.080
<0.050
>0.900
>0.900
---
74
3.5 Multidimensional Differential Item Functioning (MDIF)
This session describes the method used to address the research questions in two levels,
item level of gender differences in science performance and affective learning
outcomes.
3.5.1 The item response (IRT) model
To study gender differences in science performance at the item level. The IRT model
should be flexible enough to handle the PISA dichotomously and polytomously scored
items which are arranged in a multidimensional structure. Second, it should be able to
isolate the items displaying differential item functioning (DIF).
Adams and Wilson (1996) proposed a generalized approach to fit Rasch models, such
as simple logit model (Rasch, 1960), the scale and partial credit models (Andrich, 1978;
Masters, 1982), the linear logistic test model (Fisher, 1983), multifaceted models
(Linacre, 1994), into single generalized logit model called the unidimensional random
coefficients multinomial logit model (URCMLM). Provided that θ is the latent variable,
the item response probability j in item i can be modeled as:
P (X ij = 1; A, b , ξ | θ )=
exp (bijθ + a ′ij )
Ki
,
∑ exp (b θ + a′ )
ik
ij
k =1
where Ki is the possible responses to item i. Thus, URCMLM is flexible enough to take
care of PISA dichotomous and polytomous scored items and DIF with the design matrix
A and scoring matrix bik allow to handle the rating scale and partial credit models for
example, the category indicators 0, 1, 2 and 3 may be used to indicate respondents’
choices or a different level of performance.
However, as discussed in chapter two, assuming the items in PISA 2006 follows
unidimensionality is invalid since the assessment is made up of several unidimensional
sub-scales and the multidimensional version of URCML has to be used (see Wilson &
Hoskens, 2005). It is a direct extension of URCML to cover a set of D-latent traits from
the respondents allocated into D-dimensional latent space. The multidimensional
random coefficients multinomial logit model (MRCMLM) is formulated as follows:
75
P (X nik = 1 | θ n , ξ )=
exp (bik θ n + a ′ik ξ )
Ki
∑ exp (b θ
ik
n
+ a ′ik ξ )
,
k =1
where Xnik= 1 if person n’s response to item i is in category k or 0 otherwise (1≤ i ≤ I, 1
≤ k ≤ Ki, 1≤ n ≤ N, and Xnik , is fixed to zero as a reference category for model
identification); θn is a d x 1 ability parameter vector of a person n (1 < d (dimension) <
D); b'ik is a 1 x d scoring vector for category k of item i; ξ is a p x 1 item parameter
vector; and a'ik is a 1 x p vector to specify linear combination of p elements of ξ for
each response category. ξ is a fixed unknown parameter vector while θn is a random
parameter vector. The parameters of θn are assumed to follow a multivariate normal
distribution (MVN): (Liu, 2006 p.48-49). Wu, Adams & Wilson (1997) implemented
MRCMLM in ConQuest IRT software which makes multidimensional scale and partial
credit models analysis possible.
In this study, ConQuest IRT software was used to estimate the parameters in MVN by
marginal maximum likelihood (MML) method and person’s ability by expected a
posterior (EAP) estimation.
A three dimensional model was used to analyze science performance based on its three
competency domains. For each domain of items, they are assigned to the three
dimensions with reference to between-item multidimensionality model (Wang, Wilson
& Adam, 1997) (see Figure 3.3).
76
Figure 3.3: A graphical representation of within-item and between-item
multidimensionality.
ITEMS
LATENT
DIMENSIONS
ITEMS
1
2
1
1
3
4
4
2
2
5
6
6
7
7
8
1
2
3
5
LATENT
DIMENSIONS
3
3
8
9
9
Between-item
Multidimensionality
Within-item
Multidimensionality
3.5.1.1 DIF model for gender differences studies
Differential item functioning (DIF) is a method in IRT to test the item bias favouring
different groups of examinees, after controlling their underlying abilities.
According to Peck (2002) and Liu (2006), the possibility of getting a correct item
without gender based DIF, can be defined as:
P ( X | θ , g = male) = P( X | θ , g = girls )
for all the values of θ
where X is the observed item response, θ is the unobserved latent trait to be measured
and g is the group member indicator. However, if gender based DIF exits, the
URCMLM model for DIF estimation of dichotomous item i will be:
logit [( X ni = 1 | θ n , g ) ] = θ n − β i + γ i G
where Xni is the response of person n to item i, βi is the item parameter indicating the
77
item difficulty for item i and γi is the item DIF parameter. If G is 1 then g is the control
group while G is 0 then g is the experimental group.
To make the DIF more comparable, -1 and 1 is assigned for the control group and
experimental group respectively. Hence, γi can be inferred as the differences in the item
difficulty between the two gender groups.
logit male = θ n − β i + γ i
logit female = θ n − β i − γ i
The gender DIF effect can then be modeled as:
exp(θ n − β i − γ g X ik Ζ g )
P (X = 1 | θ n , β i , γ g )=
.
1 − exp(θ n − β i − γ g X ik Ζ g )
where θn is the unobserved latent ability of student n under normal distribution. βi is the
item difficulty for item i. Xik is an item variable with value 1 for k is 1 and 0 otherwise.
Zg is the group indicator for male and female students and γg is the random variable to
estimate DIF. Both γg and θn follow the bivariate normal distribution and μcontrol is
assumed to be (0,0) if there is no DIF. If μexpt is significantly different from zero, there
is a mean DIF effect for two genders (Liu, 2006).
ConQuest models the above equation and computes DIF γg for each item. The model
statement (command statement for calculation) for performing the DIF analysis is:
model item –gender + item*gender + item*step;
The model statement contains four terms will result in the estimation of four sets of
parameters. The term ‘item’ results in the estimation of a set item difficulty parameters,
the second term ‘–gender’ results in the mean ability estimation of male and female
students across all the items, the third term ‘item*gender’ results in the estimation of
DIF and the last item ‘item*step’ results in deployment of the partial credit model in the
estimation. The negative sign in front of the gender term is to ensure that the gender
parameters will be presented more naturally with a higher number corresponding to a
higher mean ability and vice versa.
78
3.5.1.2 Effect size by DIF
Paek (2002) conducted a DIF research on the relationship between traditional DIF size
and the DIF estimates by ConQuest. The effect size of DIF of an item i is classified as
negligible (Class A DIF) if the absolute size is twice of DIF estimated by ConQuest i.e.
|2γi|<.426; intermediate DIF (Class B DIF) for .426≤|2γi|<.638 and large DIF (Class C
DIF) for |2γi| ≥.638. So, by looking at the classification of all the items in science
performance, we can get some idea of the size of gender differences across the items
and competency dimensions.
3.5.1.3 Item fit statistics
If an item fit the expected model, its in weighted fit mean square error (WFMS) statistic
provided by ConQuest has an expected value of 1 (Wright and Masters, 1982).
Statistically, the MNSQ statistic is a χ2 statistic divided by its degrees of freedom. For
partial credit model and rating scaling, a range of WFMS values (0.7 to 1.3) is often
considered as critical range for item fit (Wright, Linacre, Gustafson, & Martin-Löf,
1994). In this study, when an item with a WFMS beyond the critical range is considered
as poor fitting. Figure 3.4 below shows that all the 108 items for cognitive domain of
scientific literacy have good item fit statistics.
Figure 3.4: Item fit statistics for the three science performance dimensions: Explaining
Phenomena Scientifically (EPS), Identifying Scientific Issues (ISI) and
Using Scientific Evidence (USE)
1.3
EPS
ISI
Fit stastistics (WFMS)
1.2
USE
1.1
1
0.9
0.8
0.7
79
3.6 Model testing in SEM
Single mediation models
The MLM SEM model testing followed similar procedure used by MacKinnon
(2008). A MLM SEM model is a path model that specifies a hypothesized causal
chain between independent (X), dependent (Y), and mediating (M) variables with
consideration of nested effect of multilevel data: X→Mx→Y where X is Girl, Y
is the Future-oriented Science Motivation and Mx is the individual mediator in this
study.
A statistical mediation analysis tests the X→Mx→Y relation, gender effect was
said to be mediated if (i) X had a statistically significant effect on the hypothesized
dependent (Y) in the absence of the mediator (Mx), (ii) X had a statistically
significant effect on the hypothesized mediator (Mx), (iii) the hypothesized
mediator had a statistically significant effect on the dependent (Y), and (iv) the
mediated effect was statistically significant after controlling for the parental SES
(MacKinnon, 2008).
Multiple mediator models
The revised Expectancy-value Model of Achievement-related Choices in Science
(see Figure 3.1) incorporates all the mediator models above in a stepwise manner.
Their mediated gender effects in the multiple mediation model were calculated
using αM*βM for each of the six mediators. The RMSEA, RMR, CFI and TLI were
used to evaluate the overall fit of the models. MacKinnon (2008) suggested that
initial mediation model testing should start with single mediator models and then
with more complicated mediator models will help researchers to understand what
is truly going on with the mediation.
3.7 Summary
In chapter three, the data collection method was mentioned. Secondly, the
conceptual framework and the procedure to conceptualize cognitive and affective
factors were stated. Thirdly, the research methods, MDIF at item level and
multilevel mediation at system level, were put forwarded. Finally, the internal
consistency and model fit for CFA of affective factors was validated for boys and
girls.
80
CHAPTER FOUR
GENDER DIFFERENCES IN STUDENTS’
COGNITIVE & AFFECTIVE LEARNING OUTCOMES
In this chapter, the gender differences in students’ cognitive and affective learning
outcomes will be examined. Firstly, gender differences in cognitive outcomes including:
the overall score and the three competencies in science performance (SP), Explaining
Phenomena Scientifically (EPS), Identifying Scientific Issues (ISI) and Using Scientific
Evidence (USE) will be investigated by two methods: Mean Score Difference (MSD)18
and Multidimensional Differential Item Functioning (MDIF)19.
Secondly, SP content domains and item formats will be investigated by MDIF. Thirdly,
gender variability will be analyzed using gender variance ratio and distribution pattern of
boys and girls at each ability estimate.
Finally, the gender differences in affective learning outcomes including: Enjoyment of
Science Learning (JOYSCIE), Future-oriented Science Motivation (SCIEFUT), Interest
in Science Learning (INTSCIE), Instrumental Motivation to Learn Science (INSTSCIE),
Personal Value of Science (PERSCIE) and Science Self-concept (SCSCIE) will be
analyzed using MSD.
4.1 Gender differences in students’ cognitive outcomes
4.1.1 Gender differences in science performance dimensions
To study the gender differences in students’ cognitive outcomes, section 4.1.1.1 will use
18
A SPSS macro program provided by the PISA 2006 data analysis manual (OECD, 2009d) was used to
measure the mean gender difference and the standard error of the difference.
19
MDIF is for measuring gender differences at item level.
81
MSD to investigate the gender differences of mean score. Section 4.1.1.2 will use MDIF
to exam the gender differences at item level.
4.1.1.1 Gender differences in science performance dimensions measured by MSD
Table 4.1 below shows the overall performance as well as the performance in each
dimension of SP for boys and girls using mean score difference method. There was no
significant gender difference in the overall achievement of SP.
However, boys performed significantly better than girls on Explaining Phenomena
Scientifically (EPS) (20.79, p<0.001) while girls outperformed boys on Identifying
Scientific Issues (ISI) with statistical significance (14.66, p<0.05). Boys performed
slightly better than girls on Using Scientific Evidence (USE) but there was no statistical
significant difference found in this area of competency. The results deviated from Hong
Kong PISA 2006 Report published in 2008. Ho et al (2008) suggested that there was no
significant gender differences found in EPS, ISI and USE of PISA 2006 by comparing
the mean percentage correct between boys and girls. However, the comparisons neither
captured the measurement error nor the sampling error at the two-stage sampling process.
As a result, biased estimates in standard errors could be produced.
The current study follows the data analysis procedure (i.e. Fay’s Balanced Repeated
Replication (BRR) method) of PISA 2006, to compute the standard errors on the
differences among plausible values (OECD, 2009b p.157-160); therefore, the attenuation
in mean differences due to measurement error and sampling error could been avoided
(Wu, 2005).
82
Table 4.1: Gender differences in scientific competency
Boys(N=2294)
Girls(N=2351)
Difference
Mean
(SE/SD)
Mean
(SE/SD)
Boys-Girls
(SE)
545.61
(3.46/95.12)
538.91
(3.47/88.73)
Explaining
phenomena
scientifically
559.79
(3.50/97.29)
Identifying
scientific issues
Using scientific
evidence
Competency
Science performance
z
d
6.70
(4.85)
1.38
0.07
539.00
(3.27/88.60)
20.79
(4.55)
4.57***
0.22
520.37
(4.07/103.17)
535.03
(4.48/97.98)
-14.66
(5.90)
-2.48*
0.15
543.58
(3.76/102.93)
541.18
(3.98/94.32)
2.40
(5.49)
0.44
0.02
Note: *p<.05; ***p<.001; z statistic was calculated by dividing the difference by its
standard error. The effect size d of the difference was calculated with the Cohen’s
equation
Meanboys − Meangirls
2
2
( SDboys
+ SDgirls
)/2
(Cohen, 1988).
Results from the Table 4.1, indicates that most of the effect sizes20 Cohen d were below
0.20 except “Explaining phenomena scientifically”. However, small effect sizes can lead
to long term systematic implications (Cole, 1997a, 1997b). A relatively large effect size
found in the EPS competency domain (Cohen’s d = 0.22) demands students to
(1) apply science knowledge in a given situation
(2) describe or interpret phenomena scientifically and predict changes
(3) make appropriate descriptions, explanations, and predictions.
The above results were consistent with Yip et al’s (2004) findings, where Hong Kong
PISA 2000 dataset was used. Using mean difference method, Yip et al (2004) found that
20
Cohen’s d = 0.2 is small, d = 0.5 is medium and d ≥ 0.8 is large. Cohen d smaller than 0.2 is considered
as negligible effect size in the social science context and conventional clinical practices (Cohen, 1988).
83
items assessing students’ understanding of scientific knowledge or concepts (i.e. EPS)
were in favor of boys. The result was also same as Le’s (2009) findings from PISA 2006
where all the data of the 50 participating countries was used. Le found that EPS items are
clearly in favor of boys while ISI and USE items tend to favor girls.
4.1.1.2 Gender differences in science performance dimensions measured by MDIF
The next analysis was to use the method of MDIF to investigate the gender differences
where the overall abilities of students were taken into account. Table 4.2 shows the items
with statistically significant gender DIF for the three science performance dimensions
after controlling the overall abilities of boys and girls (see Appendix D). In the first
column, the items were classified according to the category system21 proposed by Peak
(2002). Out of 108 science performance items, 52 items (48%) showed statistically
significant gender differences. There were 29 items favoring girls and 23 items favoring
boys. Although more items favored girls, but most of these items had negligible effect
sizes (Class A items). The number of items with intermediate effect sizes favored boys
(Class B items) was equal to that of girls. Likewise, the number of items with large effect
sizes (Class C items) favored either sex were equal.
21
The item DIF γi estimated by ConQuest software has negligible effect size for |2γi|<0.426 (Class A DIF);
intermediate effect size (Class B DIF) for 0.426≤|2γi|<0.638 and large effect size (Class C DIF) for |2γi|
≥0.638.
84
Table 4.2: Summary of items showing statistically significant gender DIF for different
science performance dimensions
Number of items
Classification
Item Favouring Sub-total Favouring Sub-total
Total
of DIF items
dimension
girls
for girls
boys
for boys
(Effect size)
EPS
12
ISI
USE
3
9
Class B
(Intermediate)
EPS
ISI
USE
2
0
0
Class C
(Large)
EPS
ISI
USE
2
0
1
Class A
(Small)
11
24
Total
18
42
2
2
0
0
2
4
3
1
0
2
3
6
23
52
2
5
29
If the items displaying gender DIF are sorted according to science performance
dimensions, the number of items with intermediate effect size and large effect size
favored either sex were the same.
In contrast to the mean score difference method, the Hong Kong results estimated by
MDIF had shown that large portion of the items (52 items out of 108 items or 48%)
showing statistically significant gender differences at item level (see the sixth and
seventh columns of tables in Appendix D). This result is consistent with a number of
earlier studies. For example, Hamilton (1999) revealed that American boys had
significant advantage in 12th-grade constructed-response science DIF items in the
National Education Longitudinal Study of 1988. Using gender DIF method and 50
participating countries sample, Le (2009) found that there were large number of items
showing significant gender DIF in PISA 2006 science items.
85
4.1.2 Gender differences in content domains
This section examine gender differences on each content domains of science performance
including Knowledge About Science (KAS) which includes Scientific Enquiry (SEQ),
Scientific Explanations (SEL) and Knowledge Of Science (KOS) which cover Earth and
Space Systems (ESS), Living Systems (LS), Physical Systems (PS), Technology Systems
(TS). Section 4.1.2.1 will use MSD to investigate the gender differences at mean score.
Section 4.1.2.2 will use MDIF to exam the gender differences at item level.
4.1.2.1 Gender differences in content domains measured by MSD
Table 4.3 shows the students’ performance by content domains including KAS and KOS.
Girls scored barely higher than boys without any statistical significance (p=0.596) in
KAS. On the other hand, boys achieved significantly higher statistical score than girls in
KOS sub-domains, in particular the PS (p<0.001). The results followed the traditional
rule of thumb that boys tended to perform better than girls in earth sciences and physical
sciences (Schmidt et al., 1997, Yung et al., 2006) and that it displayed the largest gender
differences in physical sciences among all the content domains (Cohen’s d = 0.35).
Surprisingly, the results also suggested that boys significantly outperformed girls on LS
(p<0.05). This result deviated from many previous findings that girls performed better
than boys or had no statistical gender difference in LS (Jovanovic et al., 1994; Mullis,
Martin, Fierros, Goldberg & Stemler, 2000; Yip et al., 2004).
86
Table 4.3: Gender differences in content domains
Boys(N=2294)
Girls(N=2351)
Difference
Content domain22
Mean
(SE/SD)
Mean
(SE/SD)
Boys-Girls
(SE)
Knowledge About
Science (KAS)
540.27
(3.44/101.23)
542.83
(3.48/94.61)
Earth & Space
Systems (ESS)
532.78
(3.65/97.29)
Living Systems
(LS)
Physical
Systems (PS)
z
d
-2.55
(4.79)
-0.53
0.03
517.63
(3.21/93.54)
15.15
(4.86)
3.12*
0.16
563.56
(3.37/98.71)
551.90
(3.23/91.82)
11.66
(4.81)
2.42*
0.12
562.65
(3.35/100.03)
528.93
(3.41/93.14)
33.72
(4.77)
7.08***
0.35
Knowledge Of
Science (KOS)
Note: *p<.05; ***p<.001.
4.1.2.2 Gender differences in content domains measured by MDIF
Table 4.4 shows the number of items with statistically significant gender DIF by item
content after controlling the overall abilities of boys and girls (see Appendix D). Then,
the items were classified according to the category system proposed by Peak (2002). If
the items displaying gender DIF are sorted according to Knowledge Of Science (KOS),
more items from PS favored boys while more items from LS, ESS and TS favored girls.
However, most of ESS and TS items were from Class A with small effect sizes. In terms
of KAS, the number of items displaying gender DIF favored either sex were similar.
With respect to item content, in general, our results agreed with prior findings on gender
differences. Becker (1989) conducted a meta-analysis of gender differences in science
content and showed that males had significant advantages in studies of biology, general
science, and physics, but significant differences were not found for studies of mixed
science content, and geology and earth sciences. Similarly, advantages were found for
22
PISA 2006 database does not provide plausible values for Scientific Enquiry, Scientific Explanations and
Technology Systems.
87
8th-grade and 10th-grade boys on physical science. Small gender differences were also
divulged in life science test that favored boys in the United States (Burkam, 1997).
Table 4.4: Summary of items showing statistically significant gender DIF for item
content
Number of items
Classification
Content Item23 Favoring Sub-total Favoring Sub-total
Total
of DIF items
domain content
girls
for girls
boys
for boys
(Effect size)
KAS
Class A
(Small)
KOS
KAS
Class B
(Intermediate)
KOS
KAS
Class C
(Large)
Total
23
KOS
SEQ
4
SEL
4
ESS
LS
PS
TS
5
8
0
3
SEQ
0
SEL
0
ESS
LS
PS
TS
0
1
1
0
SEQ
0
SEL
0
ESS
LS
PS
TS
0
2
0
1
KAS
8
KOS
21
8
16
0
2
0
3
29
2
4
0
4
8
0
0
0
0
1
1
0
0
1
1
0
0
1
7
16
6
42
12
0
4
2
1
6
2
23
52
Knowledge About Science: Scientific Enquiry (SEQ), Scientific Explanations (SEL); Knowledge Of
Science: Earth and Space Systems (ESS), Living Systems (LS), Physical Systems (PS), Technology
Systems (TS)
88
4.1.3 Gender differences in item formats
This section examines the gender differences in item formats including Multiple Choice
(MC), Complex Multiple Choice (CMC), Closed Constructed Response (CCR) and Open
Response (OR). In section 4.1.3.1 MDIF was used to exam the gender differences at item
level.
Table 4.5 shows the number of items with statistically significant gender DIF for item
formats after controlling the overall abilities of boys and girls (see Appendix D). The first
column classifies items according to the category system proposed by Peak (2002). The
results indicated that boys were generally favored by closed constructed response items
while girls were favored by Open Response (OR) items. The results were consistent with
previous studies on gender differences with respect to item formats (Le, 2009; Yip et al.,
2004; Bolger & Kellaghan, 1990; Mazzeo et al., 1993; Cole, 1997b; Hamilton, 1999;
Zenisky et al., 2004). Multiple choice and closed response items tended to favor boys
while open-response and contextualized items tended to favor girls (Le, 2009; Yip et al.,
2004).
89
Table 4.5: Summary of items showing statistically significant gender DIF for item format
Number of items
Classification Item Favoring Sub-total Favoring Sub-total
Total
of DIF items format
girls
for girls
boys
for boys
MC
7
CMC
CCR
OR
5
0
12
MC
Class B
CMC
(Intermediate) CCR
OR
2
0
0
0
MC
CMC
CCR
OR
0
1
0
2
Class A
(Small)
Class C
(Large)
7
24
4
2
5
18
42
2
2
0
0
0
2
4
3
0
2
1
0
3
6
Note: Multiple Choice (MC), Complex Multiple Choice (CMC),
Closed Constructed Response (CCR) and Open Response (OR)
4.1.4 Gender variability in science performance
4.1.4.1 Gender variability measured by variance ratio (B/G)
The gender variance for each science performance level as defined by PISA scale, were
computed for boys and girls. The results are shown in Table 4.6. In the last column of
Table 4.6, the pattern of gender variability between boys and girls were compared in form
of variance ratio (B/G), where the trend was presented in Figure 4.1. The results indicated
that girls’ variance was always smaller than that of boys at every level of science
performance and it kept fairly constant around 1400 units for all science performance
levels, except at level 0. The largest variance for girls was found at level 0. The boys’
variance was more volatile at all levels and reached the peaks at level 0 and level 6.
90
Table 4.6: Gender variance ratio on the PISA scale
Science performance level
Boys’ variance#
Girls’ variance#
2505.3
1619.0
1606.8
1479.5
1449.9
1534.9
1845.3
1752.7
1376.9
1455.1
1416.3
1449.5
1359.7
1337.6
Level 0 - below 335
Level 1 - 335.0 to 409.4
Level 2 - 409.5 to 484.0
Level 3 - 484.1 to 558.6
Level 4 - 558.7 to 633.2
Level 5 - 633.3 to 707.9
Level 6 - Above 707.9
Variance ratio (B/G)
1.4
1.2
1.1
1.0
1.0
1.1
1.4
Remark#: Average value of PV1 to PV5 variances.
Figure 4.1 and Figure 4.2 shows the gender variability and gender variance ratio on
different science performance levels respectively. The results show that boys always had
higher gender variability than those of girls. Using several American national norms of
standardized test batteries, Feingold (1992) found that males were consistently more
varied than females in intellectual abilities. Our results were thus in line with Feingold’s
(1992) hypothesis on gender variability.
Figure 4.1: Gender variability on different science performance level
3000
Boys’ variance
2500
Girls’ variance
Variance in PVs
2000
1500
1000
500
0
0
1
2
3
4
Science performance level
91
5
6
7
Figure 4.2: Gender variance ratio on different science performance level
1.5
Variance ratio (Boys/Girls)
1.4
1.3
1.2
1.1
1
0.9
0
1
2
3
4
5
6
7
Science performance level
4.1.4.2 Gender variability measured by number of students against each ability estimate
This section examines the gender variability by measuring the number of students against
each ability estimate. Figure 4.3 shows the number of boys and girls at each ability
estimate for science performance. More boys allocated at the left tail of the distribution
between -0.5 logits to -2.5 logits. Girls outnumbered boys ranging from -0.5 logits to 0.5
logits and at about 1.0 logit. However, boys outnumbered girls between 2.3 logits and 2.6
logits.
This shows that more boys did poorly than girls for the bottom of the distribution. In the
middle part of the distribution, girls tended to outnumber boys. At the upper portion of
the distribution, more boys did better than girls. This finding is consistent with the greater
variability of boys than girls on different science performance levels (see section 4.1.4.1).
92
Figure 4.3: Science performance: Number of boys and girls at each ability estimate
60
Girls
Boys
Number of students
50
40
30
20
10
0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
Ability estimate (logits)
Figure 4.4, Figure 4.5 and Figure 4.6 show the number of boys and girls at each ability
estimate for EPS, ISI and USE. For the dimension of EPS, the number of girls dominated
between -0.5 logit and 1.5 logits while boys outnumbered girls between 1.5 logits and 2.0
logits. More boys achieved higher mark than girls, except the highest ability zone with
ability estimate bigger than 2.0 logits. This shows that more boys could demonstrate a
higher level of competency in EPS dimension.
Figure 4.4: Explaining Phenomena Scientifically (EPS): Number of boys and girls at each
ability estimate
70
Girls
Boys
Number of students
60
50
40
30
20
10
0
-3.0
-2.0
-1.0
0.0
1.0
Ability estimate (logits)
93
2.0
3.0
4.0
For the dimension of ISI, it was obvious that girls almost outnumbered boys from -1.2
logits to 3.2 logits except the regions near 1.8 logits. Consistent with Yip’s previous
studies, the domination of girls in ISI was reproduced. Girls demonstrated higher
scientific skills (Yip et al., 2004), whereas boys had better conceptual understanding in
science. (e.g. Machin & Pekkarinen, 2008).
Figure 4.5: Identifying Scientific Issues (ISI): Number of boys and girls at each ability
estimate
70
Girls
Boys
Number of students
60
50
40
30
20
10
0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
Ability estimate (logits)
For the dimension of USE, number of boys dominated in the upper regions (1.0 logit or
above) while number of girls subjugated in lower to middle regions (from -1.2 logits to
1.0 logit). Such results suggest that more boys displayed a higher level of competency in
USE dimension.
94
Figure 4.6: Using Scientific Evidence (USE): Number of boys and girls at each ability
estimate
70
Girls
Boys
Number of students
60
50
40
30
20
10
0
-3.0
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
Ability estimate (logits)
Overall, the four distributions show multimodal distribution with large degree of
overlapping between number of boys and girls at each ability estimate. Our results did
not support the Machin and Pekkarinen’s (2008) hypothesis that more male higher
achievers than females occupied at the right tails of the distributions.
In short, apart from the greater ability diversity of boys than that of girls at left tails and
right tails of the distributions mentioned above, the numbers of lower achievers of male
outnumbered females in all the dimensions of science performance. This pattern of boys’
underachievement at the left tails of the distribution is increasingly identified as an
international trend and issues. From this point of view, the gender differences appear to
be growing in favor of girls rather than diminishing (Francis & Skelton, 2005).
4.2 Gender differences in students’ affective learning outcomes measured by MSD
This section examines the gender differences in students’ affective learning outcomes in
science. Table 4.7 shows the gender differences in Science Self-concept (SCSCIE),
95
Interest in Science Learning (INTSCIE), Enjoyment of Science Learning (JOYSCIE),
Personal Value of Science (PERSCIE), Instrumental Motivation to Learn Science
(INSTSCIE) and Future-oriented Science Motivation (SCIEFUT).
Table 4.7: Gender differences in affective learning outcomes (WLE scores)
Affective factor
SCSCIE
INTSCIE
JOYSCIE
PERSCIE
INSTSCIE
Note:
Boys(N)
Girls (N)
Difference
Mean (SE/SD)
Mean (SE/SD)
Boys-Girls (SE)
2280
2346
-0.03 (0.02/0.02)
-0.47 (0.03/0.02)
2282
2346
0.33 (0.02/0.03)
0.06 (0.03/0.02)
2282
2346
0.54 (0.02/0.89)
0.21 (0.02/0.87)
2283
2346
0.50 (0.02/0.91)
0.44 (0.02/0.86)
1811
1905
0.28 (0.02/0.94)
0.05 (0.02/0.91)
z
d
0.44 (0.04)
12.23***
0.47
0.27 (0.04)
7.21***
0.28
0.33 (0.03)
10.14***
0.37
0.16 (0.03)
4.75***
0.18
0.23 (0.03)
7.85***
0.25
***p<0.001. z statistic was calculated by dividing the difference by its
standard error
Overall, boys’ SCSCIE was significantly higher than that of girls (0.44, p<0.001). The
effect size, (Cohen’s d =0.47) of SCSCIE was medium. The results were consistent with
previous findings in other countries. Häussler and Hoffmann’s (2000) found that boys’
physics-related self-concept was higher than their general school-related self-concept
while the opposite was true for girls in Germany. In Reis and Park (2001) study using
data from the National Education Longitudinal Study of 1988, they found that
high-achieving 12th-grade boys had higher SCSCIE than their female counterparts in the
United States.
96
Similarly, Boys’ INTSCIE was significantly higher than girls (0.27, p<0.001). The effect
size of INTSCIE was small (Cohen’s d = 0.28). The results were line with previous
studies. For example, Evans (2002) conducted a gender study of interest and knowledge
acquisition in science learning in the United States, Taiwan and Japan. Girls’ scores were
found to be lower than those of boys on every interest item regardless of their different
cultural background.
In general, boys had significant higher JOYSCIE than girls (0.33, p<0.001). The effect
size (Cohen’s d = 0.37) was small. Our findings in JOYSCIE were in good agreement
with earlier studies. Weinburgh (1994, 2000) found boys more positive in their JOYSCIE,
motivation in science, and self-concept of science in the United States.
Although boys had statistically significant higher PERSCIE (or Attainment Value in
Eccles’ model) than that of girls (0.16, p<0.001), though the effect size (Cohen’s d =
0.18) was relatively smaller than other affective domains. The results suggested that girls,
tended to isolate “science identity” from their conception of own identity, ideals or
competence in science domain (Wigfield, 1994). These results were aligned with prior
findings. Using data from the 2004 Student Achievement Indicators Programme,
Adamuti-Trache and Sweet (2009) found that more Canadian boys than girls possessed a
high self-concept in science. Boys were likely than girls to place a higher value on math
and science. Simpkins and Davis-Kean’s (2005) also found that American boys had
significantly higher PERSCIE than girls at high schools.
The results also indicated that boys had significantly higher INSTSCIE (or utility value in
Eccles’ model) than girls (0.23, p<0.001). In other words, girls were less motivated than
boys to learn science in order to improve their career prospects. However, the effect size
97
(Cohen’s d = 0.22) was rather small in social science context. Our results were consistent
with the Australian study by Ainley et al (2008). They reported that boys had higher
values than girls in JOYSCIE, instrumental motivation, future orientation to study or
work in science, science self efficacy and science self concept.
4.3 Gender differences in science achievement related choices measured by MSD
Table 4.8 shows gender differences in students’ SCIEFUT. Boys’ overall mean
SCIEFUT were significant higher than girls (p<0.001). Lau (1997) found that the gender
differences on subject choices became explicit when S.3 students were asked to opt the
stream of studies.
Table 4.8: Gender differences in Future-oriented Science Motivation (WLE scores)
Boys(N=2279)
Girls(N=2344)
Difference
Mean (SE/SD)
Mean (SE/SD)
Boys-Girls (SE)
0.46 (0.017/0.87)
0.12 (0.02/0.91)
0.34 (0.03)
Note:
z
d
12.96***
0.38
***p<0.001; z statistic was calculated by dividing the difference by its
standard error
Though the gender differences in science have declined over the years, it continues to be
a gulf between the number of boys and girls to pursue university degrees in engineering,
physical sciences and computer sciences (UGC, 2009; Stumpf & Stanley, 1996; Bae &
Smith, 1996). The differential gender differences in this aspect lead to gender-segregated
career paths and further studies in science (Eccles, 2011). This phenomenon is so-called
“I can but I don’t want to” (Jacobs, et al., 2005). The continuum of gender differences on
science educational and career choices suggests that affective learning outcomes are
much more important than cognitive achievement in science (Linver, Davis-Kean, &
Eccles, 2002).
98
4.4 Gender differences in students’ affective learning outcomes measured by
DIF
This section examines the gender differences in affective outcomes estimated by DIF and
the results are presented in Table 4.9. The results were consistent with that estimated by
MSD (see Table 4.7) and Cheung’s (2008) findings. Boys had significantly (p<0.001)
higher learning outcomes in all science-related affective domains.
Table 4.9: Gender differences in affective learning outcomes (PV scores)
Affective
factor
SCSCIE
INTSCIE
JOYSCIE
PERSCIE
INSTSCIE
Boys(N)
Girls (N)
Difference
Estimate
Estimate
Boys-Girls
2280
2346
0.61
-0.61
2282
2346
0.16
-0.16
2282
2346
0.47
-0.47
2283
2346
0.18
-0.18
1811
1905
0.31
-0.31
SE
z
d
1.23
0.04
16.57***
0.48
0.32
0.02
8.94***
0.29
0.94
0.03
14.27***
0.41
0.36
0.03
6.52***
0.22
0.62
0.04
7.92***
0.23
Note: *** p < 0.001; z statistic was calculated by dividing the estimate by its standard
error
To illustrate how DIF provide us more information about gender differences in affective
domains than traditional MSD (e.g. Cheung, 2008), a typical item characteristic curve of
the SCSCIE item “Learning advanced science topics would be easy for me (ST37Q01)”
was examined (see Figure 4.7). Given that a boy and a girl have the same overall SCSCIE
level; girls are likely to report 0.5 logits lower than boys in this item. In other words, it
was significantly harder for girls to believe that “they can learn advanced science topics
at schools”.
99
Figure 4.7: Item characteristic curves for Learning advanced science topics would be
easy for me (ST37Q01)
4.5 Gender differences in science achievement related choices measured by
DIF
Table 4.8 shows gender differences in students’ SCIEFUT estimated by DIF. Boys’
mean estimate in SCIEFUT were significant higher than girls (p<0.001). The results
were consistent with that estimated by MSD (see Table 4.8) and Cheung’s (2008)
findings.
Table 4.10: Gender differences in Future-oriented Science Motivation (PV scores)
Boys(N=2279)
Girls(N=2344)
Difference
Estimate
Estimate
Boys-Girls
0.54
-0.54
1.09
SE
z
d
0.04
15.08***
0.40
Note: *** p < 0.001; z statistic was calculated by dividing the estimate by its standard
error
In short, the results obtained from DIF method were in good agreement with MSD.
However, it also provided us the item response behaviour of different affective
factors.
100
4.6 Summary
To sum up, six major findings emerged from the present study. First of all, methods
based on multidimensional differential item functioning (MDIF) provided a more
sensitive method to exam gender differences than the mean difference method.
Overall, significant gender differences were found at item level of science
performance but not at mean score level.
Secondly, significant gender differences were found in two dimension of science
performance: Explaining Phenomena Scientifically (EPS) and Identifying Scientific
Issues (ISI). EPS favored boys while ISI favored girls. However, the effect sizes
were small in these science performance dimensions. Our results were not consistent
with two Hong Kong PISA reports published in 2005 and 2008. Based on the PISA
2003 and PISA 2006 datasets, the reports suggested that there were no significant
gender differences in science performance in Hong Kong. However, our results
indicated that there were significant gender differences in two dimensions of science
performance. The discrepancies were originated from the varied sensitivity of the
methods as used in the estimations. Mean score differences and standard error, based
on replicate procedure24 will be a more reliable alternative in gender studies.
Thirdly, the gender variance ratio (B/G) was always greater than one at all science
performance levels. It highlighted the fact that boys’ science performance varied
more than those of girls at low, medium and high achieving levels. In addition, for
low achievers, there were more boys than girls found at left tails of the EPS, ISI and
USE distributions. However, for high achievers, there was no evidence to support
24
PISA 2006 used Fay’s Balanced Repeated Replication (BRR) replication method to produce the
repeated subsamples or replicate samples so as to overcome the overestimation of standard errors of
mean differences.
101
that more boys than girls found at the right tails of the EPS, ISI and USE
distributions.
Fourthly, there was significant gender differences found in three domains of
“Knowledge Of Science” but not for “Knowledge About Science”. Boys
outperformed girls on Knowledge Of Science namely, Earth and Space Systems,
Living Systems and Physical Systems and the largest effect size was found for
Physical Systems. There was no significant gender difference on “Knowledge About
Science”.
Fifthly, regarding to the item format, more items with gender DIF favored girls, in
particular the Open Response items. On the contrary, Closed Constructed Response
items favored boys.
Sixthly, regarding the affective domain of scientific literacy, boys had significant
higher values than girls in all affective factors including Enjoyment of Science
Learning, Interest in Science Learning, Instrumental Motivation to Learn Science,
Personal Value of Science and Science Self-concept. Moreover, significant gender
difference was found in Future-oriented Science Motivation. The effect size of
affective domain was larger than cognitive domain. They were all in favor of boys.
Overall, the results indicated that boys have higher cognitive and affective learning
outcomes than girls. Moreover, the gender differences in science-related educational
and career choices of boys and girls were significant. In particular, the current
cognitive and affective learning outcomes of scientific literacy might affect students’
career choices. The gender differences of science performance in Hong Kong follow
102
the trends of most Western countries such as US, UK, Germany and Australia.
However, for high achievers, there was no evidence to support that more boys than
girls found at the right ends of the distributions.
In the next chapter, the Eccles et al (1983) Expectancy-value Model of
Achievement-related Choices will be used to investigate the sociocultural effects on
gender differences of future-oriented educational and career choices.
103
CHAPTER FIVE
THE FINDINGS BY EXPECTANCY-VALUE MODEL OF
ACHIEVEMENT-RELATED CHOICES
In this chapter, gender effect on achievement-related choices and performance will be
examined. The expectancy-value model for achievement-related choices will be
deployed to scrutinize to what extent and how cognitive factors and affective factors
including: Enjoyment of Science Learning, Future-oriented Science Motivation,
Interest in Science Learning, Instrumental Motivation to Learn Science, Personal
Value of Science and Science Self-concept mediated gender effects and affected
students’ achievement-related choice.
5.1 Pearson correlations between affective factors and gender
Table 5.1 shows the correlations among Girls, Science Performance and affective
factors, namely Enjoyment of Science Learning (JOYSCIE), Future-oriented Science
Motivation (SCIEFUT), Interest in Science Learning (INTSCIE), Instrumental
Motivation to Learn Science (INSTSCIE), Personal Value of Science (PERSCIE) and
Science Self-concept (SCSCIE). The results indicated that girls were negatively
correlated with all the affective factors (correlation: -0.094 to -0.235, p<0.01) and SP
(correlation: -0.041, p<0.01). In other words, girls had relatively lower affective
learning outcomes and cognitive outcome than boys.
104
Table 5.1: Correlations among gender (Girl), affective factors and Science Performance
SES
Girl
SP
INSTSCIE
INTSCIE
PERSCIE
SCSCIE
SCIEFUT
JOYSCIE
-
SCIEFUT
-
0.634**
-
0.534**
0.563**
-
0.365**
0.449**
0.511**
-
0.461**
0.507**
0.541**
0.692**
-
0.516**
0.509**
0.546**
0.676**
0.574**
-
0.200***
0.311***
0.214***
0.238***
0.234***
0.353***
-
-0.041**
-0.124**
-0.134**
-0.094**
-0.235**
-0.190**
-0.182**
0.029
0.220**
0.011
0.097**
0.072**
0.085*
0.030*
0.100**
SCSCIE
PERSCIE
INTSCIE
INSTSCIE
SP
Girl
SES
JOYSCIE
-
Note: **p<0.01, ***p<0.001.
Abbreviation:
Enjoyment of Science Learning (JOYSCIE); Future-oriented Science Motivation (SCIEFUT); Gender (Girl); Interest in Science Learning
(INTSCIE); Instrumental Motivation to Learn Science (INSTSCIE); Plausible value for Science Performance (SP); Personal Value of Science
(PERSCIE); Science Self-concept (SCSCIE), Parental Social Economic Status (SES)
105
5.2 Gender differences by revised Expectancy-value Model
The following sections deploy the revised Eccles et al (1983) model to address one key
question: To what extent and how gender effects on achievement-related choice are
mediated through cognitive and affective domains of science? Eccles (1987) argued that
successful intervention of gender-inequality required a thorough knowledge of the
gender role socialization processes linked to these psychological variables. Therefore,
the mediation effects of Science Performance, Science Self-concept, Enjoyment of
Science Learning, Interest in Science Learning, Instrumental Motivation to Learn
Science and Personal Value of Science will be examined.
5.2.1 Grouping homogeneity
The intra-class correlation (ICC) is a measure of grouping homogeneity. It approaches
“1” when there is small variation between groups. ICC approaches “0” when
within-groups variance equals between-groups variance, indicating that the clustering
effect is negligible. The bigger the number of clusters, the smaller (or better) the
grouping homogeneity as the sampling is close to random sampling. The ICC value
(0.007) for SCIEFUT is statistically insignificant for the schools25. This result suggests
that SCIEFUT does not vary much between schools and therefore factors at the student
level will be our focus of further analysis.
5.2.2 Mediation effect of Science Performance
For Model 1, Girl, parental SES and SP were included in the estimation. Model 1
examines the gender effect on SCIEFUT and SP after controlling parental SES. The
model fit indices as discussed in chapter 3, RMSEA (0.067), CFI (0.979), TLI (0.961)
and SRMR (0.017) were reasonable for a good-fitting model26 (See Figure 5.1).
25
The two-staged sampling with complex data structure was handled with ‘TYPE IS COMPLEX’ in
Mplus. It specifies that the data is complex and clustered into groups of schools.
26
The most commonly reported fit indicator is χ2. The χ2 statistic is not reported for the model 1 and
106
Figure 5.1: Gender effect on Science Performance and Future-oriented Science
Motivation (Model 1)
Independent
variable:
Dependent variable:
Achievement related choice
(A)
Girl
Future-oriented
Science
Motivation
(SCIEFUT)
-0.191***
(B)
Parental
SES
0.045**
-0.048*
Control
variable:
(D)
0.223***
(C)
Science
Performance
(SP)
Note: *p<0.05, **p<0.01, ***p<0.001; RMSEA=0.067, CFI=0.979, TLI=0.961,
SRMR=0.017
As the model fit is deemed to be adequate, the estimates are presented in Table 5.2
where parental SES shows significant positive effects on SP (0.223, p<0.001) and their
Future-oriented Science Motivation (0.045, p<0.01). Girl, however, had significant
negative effect on SP (-0.048, p<0.05) and SCIEFUT (-0.191, p<0.001).
Building on Model 1, Model 2 included SP as a mediator between Girl and SCIEFUT
(see Figure 5.2). The model fit indices are the same as model 1 (see Figure 5.1).
Therefore, the Model 2 is also an adequate-fitting model. After adding SP as a mediator
between Girl and SCIEFUT, the direct effect of parental SES on SCIEFUT became
insignificant (-0.010, p=0.542). The effect of parental SES mediated totally through SP
to act on SCIEFUT (0.055, p<0.01 see Table 5.2). In addition, SP also mediated
partially the negative effect of Girl on SCIEFUT (-0.012, p<0.05 see Table 5.2).
Compared with boys, girls were essentially less motivated (-0.179, p<0.001) toward
subsequent models because of its over-sensitivity to large sample sizes.
107
participating in science-related education and employment in the future. Their cognitive
performance in science (-0.048, p<0.05) were also not as good as boys.
Figure 5.2: Mediation effect of Science Performance (Model 2)
Independent
variable:
Mediator
Dependent variable:
Achievement related choice
(A)
Girl
Future-oriented
Science
Motivation
(SCIEFUT)
-0.179***
(B)
Control
variable:
Parental
SES
-0.010(ns)
-0.048*
(C)
0.000(ns)
-0.049*
(E)
(D)
0.223***
Science
Performance
(SP)
0.248***
(E)
Note: *p<0.05, ***p<0.001; RMSEA=0.067, CFI=0.979, TLI=0.961, SRMR=0.017
The disadvantage for girls in their future education and career choices in the field of
science can be explained partly by their lower performance in science. Consistent with
Eccles (2011), gendered socialization is one of the possible causes to explain why fewer
girls select science-related educational programmes and vocations. Overall, our results
are in agreement with previous findings that parental SES, have strong and positive
effects on students’ cognitive ability and their future career choice (Ho, 1997;
Davis-Kean, 1999).
108
Table 5.2: Mediation effect of Science Performance
Model 1
Model 2
Estimate
(SE)
Estimate
(SE)
0.223***
0.045**
(0.019)
(0.017)
0.223***
-0.010 (ns)
(0.019)
(0.016)
Mediator
Science Performance
(B) GirlÆ SP
(E) SPÆ SCIEFUT
-0.048*
0.248***
(0.024)
(0.017)
Indirect Effect of SES
SESÆSPÆ SCIEFUT
0.055***
(0.006)
-0.012*
-0.012*
(0.006)
(0.006)
-0.179***
-0.191***
(0.014)
(0.014)
Student Background
(D) SESÆ SP
(C) SESÆ SCIEFUT
Indirect Effect of Girl
GirlÆSPÆSCIEFUT
Total Indirect Effect of Girl
Direct Effect of Girl
(B) GirlÆSP
(A) GirlÆSCIEFUT
Total Effect of Girl
Model fit (criteria)
RMSEA (<0.080)
CFI
(>0.900)
TLI
(>0.900)
SRMR (<0.080)
-0.048*
-0.191***
-0.191***
0.067
0.979
0.961
0.017
(0.024)
(0.014)
(0.014)
0.067
0.979
0.961
0.017
Note: *p<0.05, **p<0.01, ***p<0.001.
5.2.3 Mediation effect of Science Self-concept
Model 3 included SCSCIE as a mediator between Girl and SCIEFUT. The model fit
indices, RMSEA (0.048), CFI (0.978), TLI (0.972) and SRMR (0.033) were reasonable
for a good-fitting model (see Figure 5.3).
109
Figure 5.3: Mediation effect of Science Self-concept (Model 3)
Independent
variable:
Mediators
Dependent
variable:
Child’s General Self
Schemata (CGSS)
Science
Self-concept
(SCSCIE)
Stable child
characteristic
(A)
Girl
(G)
Achievement related
choice
0.356***
-0.121***
-0.019(ns)
Future-oriented
Science
Motivation
(SCIEFUT)
(C)
-0.048*
0.219***
Control variable:
Parental
SES
(B)
(D)
0.223***
Science
Performance
(SP)
(E)
Note: *p<0.05, *** p<0.001; RMSEA=0.048, CFI=0.978, TLI=0.972, SRMR=0.033
The results in Table 5.3 indicate that the direct effect of Girl was reduced by 32% from
-0.179 to -0.12127 when SCSCIE was included in the analysis. SCSCIE is a significant
mediating factor for the gender effects on SCIEFUT (-0.060, p<0.001). Its mediating
effect ((F)x(G) = -0.060) was about six-fold of SP ((B)x(E) = -0.011). These results
suggest that SCSCIE as a component of Child’s General Self Schemata (CGSS)28 is a
critical mediator of gender effects on SCIEFUT.
27
28
Negative sign indicated relatively smaller values of girls than boys. Absolute values were used for
calculating the percentage change of direct effect of girl before and after adding affective mediator(s).
Child’s General Self Schemata (CGSS) is a component of Eccles et al (1983) model; please refer to
chapter 3 for details.
110
Table 5.3: Mediation effect of Science Self-concept
Student Background
(D) SESÆSP
(C) SESÆSCIEFUT
Mediator
Science Self-concept
(F) GirlÆSCSCIE
(G) SCSCIEÆSCIEFUT
Science Performance
(B) GirlÆSP
(E) SPÆSCIEFUT
Indirect Effect of SES
SESÆSPÆSCIEFUT
Indirect Effect of Girl
GirlÆSCSCIEÆSCIEFUT
GirlÆSPÆSCIEFUT
Total Indirect Effect of Girl
Direct Effect of Girl
(A) GirlÆSCIEFUT
Total Effect of Girl
Model fit (criteria)
RMSEA (<0.080)
CFI
(>0.900)
TLI
(>0.900)
SRMR (<0.080)
Note: *p<0.05, ***p<0.001.
Model 2
Estimate
(SE)
Model 3
Estimate
(SE)
0.223***
0.045**
0.223***
-0.019(ns)
(0.017)
(0.014)
-0.168***
0.356***
(0.015)
(0.018)
(0.019)
(0.017)
-0.048*
0.248***
(0.024)
(0.017)
-0.048*
0.219***
(0.024)
(0.016)
0.055***
(0.006)
0.049***
(0.006)
-0.012*
-0.012*
(0.006)
(0.006)
-0.060***
-0.011*
-0.071***
(0.007)
(0.005)
(0.009)
-0.179***
-0.191***
(0.014)
(0.014)
-0.121***
-0.192***
(0.015)
(0.014)
0.067
0.979
0.961
0.017
0.049
0.979
0.971
0.033
Our results were compatible with previous findings that the higher science self-concept,
the higher future-oriented science motivation. Nagy et al (2006) found that subject
based self-concept at grade 10 could predict subsequent course choices at grade 12 in
Germany. Similarly, Simpkins and Davis-Kean (2005) found that European American
students with higher self-concepts in science were more likely than those with moderate
or low self-concepts, to take physical science courses in high school. So, girls’
disadvantage in future choices of science studies and careers can also be explained
partially by their lower SCSCIE in the Hong Kong context.
111
5.2.4 Mediation effect of Interest in Science Learning
Model 4a included INTSCIE as a mediator between Girl and SCIEFUT. The model fit
indices, RMSEA (0.062), CFI (0.955), TLI (0.939) and SRMR (0.071) were reasonable
for a good-fitting model (see Figure 5.4).
The results in Table 5.4 indicate that the direct effect of Girl was reduced by 68% from
-0.179 to -0.057 when INTSCIE was included in the analysis for SCIEFUT. This was
because INTSCIE mediated the gender effects on SCIEFUT partially (-0.131, p<0.001).
The indirect effect of Girl on SCIEFUT through SP became insignificant (-0.003,
p=0.075) after adding INTSCIE as the mediator of gender differences.
Figure 5.4: Mediation effect of Interest in Science Learning (Model 4a)
Independent
Mediators
Dependent
variable:
variable:
Subjective task value
Stable child
characteristic
(H)
-0.193***
Science
Interest
(INTSCIE)
Achievement related
choice
(I)
0.680***
(A)
-0.057***
Girl
Future-oriented
Science
Motivation
(SCIEFUT)
-0.048*
Control
variable:
Parental
SES
(B)
(D)
0.223***
(C)
Science
Performance
(SP)
-0.033**
0.068***
(E)
Note: *p<0.05,**p<0.01,***p<0.001; RMSEA=0.062, CFI=0.955, TLI=0.939, SRMR=0.071
112
The results suggest that INTSCIE alone could explain 31% (-0.060/-0.192×100%) of
the gender effects on SCIEFUT. Secondly, the mediating effect of SP was insignificant
in the presence of INTSCIE. In other words, enhancement of girls’ cognitive
performance in science will not improve girls’ inclination to choose science-related
education programmes and careers in the future as we take student science interest into
account. Therefore, improvement of girls’ INTSCIE appears to be more important for
overcoming the disadvantage of girls in the SCIEFUT.
5.2.5 Mediation effect of Enjoyment of Science Learning
Model 4b included JOYSCIE as a mediator between Girl and SCIEFUT. The model fit
indices, RMSEA (0.067), CFI (0.963), TLI (0.948) and SRMR (0.085) were reasonable
for model fit though SRMR was slightly higher than the criterion (SRMR<0.080) (See
Figure 5.5).
The results in Table 5.4 indicate that the direct effect of Girl was reduced by 65% from
-0.179 to -0.063 when JOYSCIE was included in the analysis for SCIEFUT. This was
because JOYSCIE mediated the gender effects on SCIEFUT partially (-0.125, p<0.001).
As a result of the addition of JOYSCIE, the indirect effect of Girl through SP became
insignificant (-0.002, p=0.095). This pattern is similar to that of the indirect effect of
interest in science learning.
113
Figure 5.5: Mediation effect of Enjoyment of Science Learning (Model 4b)
Independent
variable:
Mediators
Dependent
variable:
Achievement related
choice
Stable child
characteristic
Subjective task value
(A)
Girl
-0.189***
(J)
Science
Enjoyment
(JOYSCIE)
Future-oriented
Science
Motivation
(SCIEFUT)
-0.063***
0.664***
(K)
-0.048*
Control
variable:
Parental
SES
(B)
(D)
0.223***
(C)
Science
Performance
(SP)
-0.026*
0.046**
(E)
Note: *p<0.05,**p<0.01,***p<0.001; RMSEA= 0.067, CFI=0.963, TLI=0.948, SRMR=0.085
114
Table 5.4:Mediation effect of Interest in Science Learning (Model 4a), Enjoyment of
Science Learning (Model 4b) and Interest and Enjoyment of Science Learning (Model 4c)
Model 2
Model 4a
Model 4b
Model 4c
Estimate
Estimate
Estimate
Estimate
(SE)
(SE)
(SE)
(SE)
Student Background
0.223***
0.223***
0.223***
0.223***
(D) SESÆSP
(0.019)
(0.019)
(0.019)
(0.019)
0.045**
-0.033**
-0.026*
-0.033**
(C) SESÆSCIEFUT
(0.017)
(0.012)
(0.012)
(0.004)
Mediator
Interest in Science
Learning
-0.193***
-0.190***
(H) GirlÆINTSCIE
(0.020)
(0.020)
0.680***
0.490***
(I) INTSCIEÆSCIEFUT
(0.012)
(0.036)
Enjoyment of Science
Learning
-0.189***
-0.184***
(J) GirlÆ JOYSCIE
(0.015)
(0.015)
0.664***
0.243***
(K) JOYSCIEÆSCIEFUT
(0.011)
(0.036)
Science Performance
-0.048*
-0.048*
-0.048*
-0.048*
(B) GirlÆ SP
(0.024)
(0.024)
(0.024)
(0.024)
(E) SPÆ SCIEFUT
0.248***
0.068***
0.046**
0.013(ns)
(0.017)
(0.015)
(0.015)
(0.015)
Indirect Effect of SES
0.055***
0.015***
0.007*
0.003(ns)
SESÆ SPÆ SCIEFUT
(0.006)
(0.004)
(0.003)
(0.003)
Indirect Effect of Girl
GirlÆ INTSCIE Æ
-0.131***
-0.093***
SCIEFUT
(0.014)
(0.012)
GirlÆ JOYSCIE Æ
-0.125***
-0.045***
SCIEFUT
(0.010)
(0.008)
-0.012*
-0.003(ns)
-0.002(ns)
-0.001(ns)
GirlÆ SPÆ SCIEFUT
(0.006)
(0.002)
(0.001)
(0.001)
Total Indirect Effect of
-0.012*
-0.135***
-0.128***
-0.139***
Girl
(0.006)
(0.014)
(0.011)
(0.013)
Direct Effect of Girl
-0.179***
-0.057***
-0.063***
-0.051***
(A) GirlÆSCIEFUT
(0.014)
(0.014)
(0.012)
(0.012)
-0.191***
-0.192***
-0.191***
-0.190***
Total Effect of Girl
(0.014)
(0.014)
(0.015)
(0.014)
Model fit (criteria)
0.067
0.055
RMSEA (<0.080)
0.067
0.062
0.963
0.955
CFI
(>0.900)
0.979
0.955
0.948
0.945
TLI
(>0.900)
0.961
0.939
0.085
0.078
SRMR (<0.080)
0.017
0.071
Note: *p<0.05, ***p<0.001.
115
5.2.6 Mediation effect of Interest and Enjoyment of Science Learning
Model 4c included both INTSCIE and JOYSCIE as mediators between Girl and
SCIEFUT. The model fit indices, RMSEA (0.055), CFI (0.955), TLI (0.945) and SRMR
(0.078) were reasonable for an adequate-fitting model (see Figure 5.6).
Figure 5.6: Mediation effect of Interest and Enjoyment of Science Learning (Model 4c)
Independent
variable:
Mediators
Dependent
variable:
Subjective task value
Stable child
characteristic
(H)
-0.190***
Girl
-0.184***
(J)
Science
Interest
(INTSCIE)
Science
Enjoyment
(JOYSCIE)
Achievement related
choice
(I)
0.490***
(A)
-0.051***
0.243***
(K)
Future-oriented
Science
Motivation
(SCIEFUT)
-0.048*
Control
variable:
Parental
SES
(B)
(D)
0.223***
(C)
-0.033**
Science
Performance
(SP)
0.013(ns)
(E)
Note: *p<0.05, *** p<0.001; RMSEA=0.055, CFI=0.955, TLI=0.945, SRMR=0.078
The results in Table 5.4 indicate that the direct effect of Girl was reduced by 72% from
-0.179 to -0.051 when INTSCIE and JOYSCIE were included in the analysis for
SCIEFUT. This was because INTSCIE and JOYSCIE mediated the gender effects on
SCIEFUT partially (-0.139, p<0.001). Besides, the indirect effect of Girl through SP
was found insignificant (-0.001, p=0.414). The indirect effect of parental SES through
SP also was also found insignificant (0.003, p=0.362).
116
The results were consistent with past research findings that the disadvantage of girls’
motivation in science careers and studies might originate from lower INTSCIE and
JOYSCIE. Consistent with Kelly and Smail’s (1986) gender study on 11-year-old UK
students, they also found that girls who endorsed sex stereotypes showed less interest
than boys in learning science, which might affect their long-term planning to work in a
career related to science or doing advanced science in the future.
The results also indicate that children from families with higher parental SES were less
motivated than their poorer counterparts to choose science-oriented education
programmes and careers after secondary school education. In other words, the higher
the parental SES is, the lower the children’s SCIEFUT in Hong Kong. In a global sense,
although rich societies like Hong Kong have extra economic and social resources to
support their 15-old-children to achieve more highly in science, it does not necessarily
encourage more adolescents to participate in science-oriented programmes and careers
in the future in the context of Hong Kong.
To recap, our findings show that gender differences due to the affective domain
override the cognitive domain, and infer that gender equity programmes focusing on
improving females’ cognitive achievement might not able to resolve the problem of
gender-segregated achievement-related choices in science. Conversely, gender equity
programmes targeted at rising girls’ affective domains of INTSCIE as well as JOYSCIE
are likely to solve the problem of girls’ disadvantage in their future careers and
education opportunities in science.
5.2.7 Mediation effect of Attainment Value
Model 5a included PERSCIE (or Attainment Value in Eccles et al model, 1983) as a
mediator between Girl and SCIEFUT. The model fit indices, RMSEA (0.050), CFI
(0.971), TLI (0.962) and SRMR (0.059) were reasonable for a good-fitting model (see
117
Figure 5.7).
The results in Table 5.5 indicate that the direct effect of Girl was reduced by 11% from
-0.179 to -0.159 when PERSCIE was included in the analysis for SCIEFUT.
PERSCIE is a significant mediating factor for the gender effects on SCIEFUT (-0.029,
p<0.001). As a result, PERSCIE alone could explain 15% (-0.029/-0.196×100%) of the
gender effects on SCIEFUT.
Figure 5.7: Mediation effect of Attainment Value (Model 5a)
Independent
variable:
Mediators
Dependent
variable:
Subjective task value:
Attainment value
Stable child
characteristic
Achievement related
choice
Personal Value
(L)
(M)
of Science
0.376***
(PERSCIE)
-0.076 ***
(A)
-0.159***
Girl
Future-oriented
Science
Motivation
(SCIEFUT)
(B)
Control
variable:
Parental
SES
-0.048*
-0.022(ns)
(C)
(D) 0.223***
Science
Performance
(SP)
0.159***
(E)
Note: *p<0.05, *** p<0.001; RMSEA=0.068, CFI=0.960, TLI=0.939, SRMR=0.055
118
5.2.8 Mediation effect of Utility Value
Model 5b included INSTSCIE (or Utility Value in Eccles et al model (1983)) as a
mediator between Girl and SCIEFUT. The model fit indices, RMSEA (0.054), CFI
(0.977), TLI (0.968) and SRMR (0.053) were reasonable for a good-fitting model (see
Figure 5.8).
Figure 5.8: Mediation effect of Utility Value (Model 5b)
Independent
variable:
Mediators
Stable child
characteristic
Girl
Dependent
variable:
Subjective task value:
Utility value
(A)
-0.051 ***
(B)
(N)
-0.048*
Control
variable:
Parental
SES
Instrumental
(O)
Motivation to
Learn science
(INSTSCIE)
Achievement related
choice
-0.164***
0.430***
Future-oriented
Science
Motivation
(SCIEFUT)
-0.011(ns)
(C)
(D) 0.223***
Science
Performance
(SP)
0.162***
(E)
Note: *p<0.05, *** p<0.001; RMSEA=0.054, CFI=0.977, TLI=0.968, SRMR=0.053
The results in Table 5.5 indicate that the direct effect of Girl was reduced by 8% from
-0.179 to -0.063 when INSTSCIE was included in the analysis for SCIEFUT.
INSTSCIE is also a significant mediating factor for the gender effects on SCIEFUT
(-0.022, p<0.01).
119
Table 5.5: Mediation effect of Attainment Value (Model 5a), Utility Value (Model 5b)
and
Student Background
(D) SESÆSP
(C) SESÆSCIEFUT
Model 2
Estimate
(SE)
Model 5a
Estimate
(SE)
Model 5b
Estimate
(SE)
Model 5c
Estimate
(SE)
0.223***
(0.019)
0.045**
(0.017)
0.223***
(0.019)
-0.022(ns)
(0.015)
0.223***
(0.019)
-0.011(ns)
(0.015)
0.223***
(0.019)
-0.019(ns)
(0.014)
Mediator
Attainment Value
-0.076***
(0.020)
0.375***
(0.018)
(L) GirlÆPERSCIE
(M) PERSCIEÆSCIEFUT
-0.076***
(0.020)
0.273***
(0.020)
Utility Value
(N) GirlÆINSTSCIE
(O) INSTSCIEÆSCIEFUT
-0.051***
(0.015)
0.430***
(0.023)
-0.051**
(0.015)
0.343***
(0.023)
Science Performance
(B) GirlÆSP
(E) SPÆSCIEFUT
-0.048*
(0.024)
0.248***
(0.017)
-0.048*
(0.024)
0.159***
(0.018)
-0.048*
(0.024)
0.162***
(0.016)
-0.048*
(0.024)
0.107***
(0.016)
0.055***
(0.006)
0.035***
(0.005)
0.036***
(0.005)
0.023***
(0.004)
-0.021***
(0.006)
-0.017**
(0.006)
-0.005(ns)
(0.003)
-0.043***
(0.010)
Indirect Effect of SES
SESÆSPÆSCIEFUT
Indirect Effect of Girl
GirlÆPERSCIE Æ
SCIEFUT
GirlÆINSTSCIEÆ
SCIEFUT
GirlÆSPÆ SCIEFUT
Total Indirect Effect of
Girl
Direct Effect of Girl
(A) GirlÆSCIEFUT
Total Effect of Girl
-0.029***
(0.008)
-0.012*
(0.006)
-0.012*
(0.006)
-0.008*
(0.004)
-0.036***
(0.010)
-0.022**
(0.007)
-0.008*
(0.004)
-0.030**
(0.009)
-0.179***
(0.014)
-0.191***
(0.014)
-0.159***
(0.014)
-0.196***
(0.014)
-0.164***
(0.014)
-0.193***
(0.014)
-0.152***
(0.013)
-0.195***
(0.014)
0.067
0.979
0.961
0.017
0.068
0.960
0.939
0.055
0.054
0.977
0.968
0.053
0.050
0.971
0.961
0.059
Model fit (criteria)
RMSEA (<0.080)
CFI
(>0.900)
TLI
(>0.900)
SRMR (<0.080)
Note: *p<0.05, ***p<0.001.
120
5.2.9 Mediation through Attainment Value and Utility Value
Model 5c included both PERSCIE and INSTSCIE as mediators between Girl and
SCIEFUT. The model fit indices, RMSEA (0.050), CFI (0.971), TLI (0.961) and SRMR
(0.059) were reasonable for an adequate-fitting model (see Figure 5.9).
Figure 5.9: Mediation effect of Attainment Value and Utility Value (Model 5c)
Independent
Mediators
Dependent
variable:
variable:
Subjective task value:
Attainment value
Stable child
characteristic
(L)
-0.051 ***
(B)
Control
variable:
Parental
SES
(M)
of Science
0.273 ***
(PERSCIE)
-0.076 ***
Girl
Achievement related
choice
Personal Value
(A)
-0.152 ***
(N) Instrumental (O)
Motivation to
Learn science
(INSTSCIE)
0.343 ***
-0.048*
Utility value
Future-oriented
Science
Motivation
(SCIEFUT)
-0.019(ns)
(C)
0.107***
(E)
(D) 0.223***
Science
Performance
(SP)
Note: *p<0.05, *** p<0.001; RMSEA=0.050, CFI=0.971, TLI=0.961, SRMR=0.059
The results in Table 5.5 indicate that the direct effect of Girl was reduced by 15% from
-0.179 to -0.152 when PERSCIE and INSTSCIE were included in the analysis for
SCIEFUT. This was because PERSCIE and INSTSCIE mediated the gender effects on
SCIEFUT partially (-0.017, p<0.001). The indirect effect of Girl through SP was also
found insignificant (-0.005, p=0.053).
The results suggest that PERSCIE and INSTSCIE could explain about 20%
((-0.021-0.017)/-0.195×100%) of the gender effects on SCIEFUT. Secondly, the
mediating effect of SP was found insignificant in the presence of PERSCIE and
INSTSCIE.
121
Our findings were consonant with Chow and Salmela-Aro’s results (2011). They stated
that Finn boys had higher task value, Attainment Value and Utility Value and dominated
the high-math-and-science group, while girls dominated the low-math-and-science
group at secondary school. The high-math-and-science group had reported a stronger
tendency to enroll in science-related programmes after the completion of compulsory
education.
In sum, our findings support that girls’ disadvantage in their future careers and
education opportunities in science can be accounted for in part by their lower
Attainment Value and Utility Value in science. However, the effect size might be
slightly less than the “general self schemata”.
5.2.10 Full models of gender effects on Future-oriented Science Motivation
Model 6a and Model 6b included all the five affective mediators, SCSCIE, JOYSCIE,
INTSCIE, INSTSCIE and PERSCIE. Presumably, Model 6a represents the revised
Eccles et al (1983) model and had no direct effect of Girl (GirlÆSCIEFUT) while
Model 6b was proposed based on the empirical evidence of current study (i.e. had
direct effect of Girl on SCIEFUT). The model fit indices, RMSEA (0.044), CFI (0.947),
TLI (0.940/0.941) and SRMR (0.057) were basically the same for the two models. The
fit indices were also reasonable for good-fitting models (see Figure 5.10 and Figure
5.11).
The results in Table 5.6 (Model 6a) indicate that SCSCIE, JOYSCIE, INTSCIE,
INSTSCIE and PERSCIE are significant mediating factors for gender effects on
SCIEFUT (-0.142, p<0.001). Since there is no direct effect of Girl (GirlÆSCIEFUT) in
Model 6a, the total mediating effect of affective factors is slightly higher than that in
Model 6b (-0.135, p<0.001).
122
Figure 5.10: Full model (Model 6a) of gender differences in Future-oriented Science
Motivation
Independent
variable:
Mediators
Dependent
variable:
Child’s general self
schemata
Science
Self-concept
(SCSCIE)
(F)
0.050***
Interest in Science
Learning
(INTSCIE)
-0.167***
(H)
-0.201***
(J)
Stable child
characteristic
-0.185***
(L)
Girl
(STF Gender)
(G)
-0.095***
(N)
Enjoyment of
Science Learning
(JOYSCIE)
Attainment Value
(PERSCIE)
(I)
0.392***
Achievement related
choice
(K)
0.194***
Future-oriented
Science
Motivation
(SCIEFUT)
(M)
0.092***
(O)
0.168***
-0.063***
Utility Value
(INSTSCIE)
Control
variable:
(B)
-0.049*
Subjective task value
0.000(ns)
(E)
-0.034***
Cultural milieu
Parental
SES
(D) 0.223***
Science
Performance
(SP)
(C)
Note: *p<.05, *** p<0.001; RMSEA=0.044, CFI=0.947, TLI=0.940, SRMR=0.057
123
Figure 5.11: Full model (Model 6b) of gender differences in Future-oriented Science
Motivation
Independent
Mediators
Dependent
variable:
variable:
Child’s general self
schemata
(F)
-0.167***
(H)
-0.191***
Stable child
characteristic
(J)
-0.184***
Girl
(STF Gender)
Science
Self-concept
(SCSCIE)
Interest in Science
Learning
(INTSCIE)
Enjoyment of
Science
(JOYSCIE)
(A) -0.055***
(L)
-0.094***
(N)
Attainment Value
(PERSCIE)
-0.062***
(G)
0.045**
(I)
0.377***
Achievement related
choice
(K)
Future-oriented
Science
Motivation
(SCIEFUT)
0.194***
(M)
0.096***
(O)
0.172***
Control
Variable:
(B)
-0.048*
Utility Value
(INSTSCIE)
Subjective task value
0.000(ns)
(E)
-0.031**
Cultural milieu
Parental
SES
(D) 0.223***
Science
Performance
(SP)
(C)
Note: *p<.05, **p<.01, *** p<.001; RMSEA=0.044, CFI=0.947, TLI=0.941,
SRMR=0.057
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Table 5.6: Full model of gender effects on Future-oriented Science Motivation
Model 2
Model 6a
Model 6b
Estimate
(SE)
Estimate
(SE)
Estimate
(SE)
Student Background
(D) SESÆSP
(C) SESÆSCIEFUT
0.223***
0.045**
(0.019)
(0.017)
0.223***
-0.034**
(0.019)
(0.010)
0.223***
-0.031**
(0.019)
0.010
Direct Effect of Girl
GirlÆSP
GirlÆSCIEFUT
-0.048*
-0.179***
(0.024)
(0.014)
-0.049*
(0.024)
-0.048*
-0.055***
(0.024)
(0.011)
-0.008**
(0.001)
-0.007**
(0.003)
-0.036***
(0.007)
-0.036***
(0.007)
-0.079***
(0.011)
-0.072***
(0.010)
-0.009**
(0.001)
-0.009**
(0.003)
-0.011***
(0.003)
-0.011***
(0.003)
Indirect Effect of Girl
GirlÆ SCSCIEÆ
SCIEFUT
GirlÆJOYSCIE Æ
SCIEFUT
GirlÆINTSCIEÆ
SCIEFUT
GirlÆ PERSCIEÆ
SCIEFUT
GirlÆINSTSCIEÆ
SCIEFUT
Total Indirect Effect of
Girl
Total Effect of Girl
Model fit (criteria)
RMSEA (<0.080)
CFI
(>0.900)
TLI
(>0.900)
SRMR (<0.080)
-0.012*
(0.006)
-0.142***
(0.012)
-0.135***
(0.012)
-0.191***
(0.014)
-0.142***
(0.012)
-0.189***
(0.014)
0.067
0.979
0.961
0.017
0.044
0.947
0.940
0.057
0.044
0.947
0.941
0.057
Note: *p<0.05, **p<0.01, ***p<0.001. Model 6a and Model 6b were tested with 5
plausible values (PV1SCIE to PV5SCIE) provided by PISA 2006 and PV1SCIE was
used to for the final analysis.
The results in Table 5.6 (Model 6b) indicate that the direct effect of Girl was reduced by
69% from -0.179 to -0.055 when SCSCIE, JOYSCIE, INTSCIE, INSTSCIE and
PERSCIE were included in the analysis for SCIEFUT. SCSCIE, JOYSCIE, INTSCIE,
INSTSCIE and PERSCIE are significant mediating factors for the gender effects on
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SCIEFUT (-0.135, p<0.001). This explains over 71% (-0.135 / -0.189 x 100%) of
gender effects on Future-oriented Science Motivation. The results also show that the
mediating effect of SCSCIE, INTSCIE, JOYSCIE significantly overrides that of
PERSCIE and INSTSCIE29 (-0.156, p<0.001).
5.3 Summary
In sum, six major findings emerged from this mediation study. Firstly, children from
richer families were less motivated than the poorer counterparts to choose
science-oriented education programmes and careers after secondary school education.
In other words, the higher the parental SES was, the lower the children’s
Future-oriented Science Motivation.
Secondly, Figure 5.10, Figure 5.11 and Table 5.6 have demonstrated a method for
testing multiple mediations simultaneously. The multiple mediation study shown in
model 6b makes it possible for us to compare the relative magnitudes of the specific
indirect effects associated with the mediators. The order of the magnitude was:
INTSCIE (-0.072), JOYSCIE (-0.036), SCSCIE (-0.011), PERSCIE (-0.009) and
INSTSCIE (-0.007). This finding provides us with a clue that gender effects mediate
through Interest and Enjoyment of Science Learning and Science Self-concept more
than Attainment Value and Utility Value. In fact, “Interest and Enjoyment of Science
Learning” was the most influential affective mediator followed by “Science
Self-concept” which provided us some hints on the order and magnitude of intervention
to be followed for educators.
29
The difference between (INTSCIE, JOYSCIE) mediation and (PERSCIE, INSTSCIE) was estimate
with MODEL CONSTRAINT statement in Mplus: Difference = (F*J + H*I + J*K) – (L*M + N*O)
where F to O is the path coefficients of Model 6b.
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Thirdly, from model 6b, both direct and indirect paths were significant (p<0.001) and
the results partially supported Halpern’s (2000, 2004) biopsychosocial hypothesis. The
hypothesis recognizes the mixing effects of biological, psychological and social impact
on gender development. However, total indirect effect (psychological and social effects)
(-0.135, p<0.001) prevailed over the direct effect (biological effect) (-0.055, p<0.001)
for gender differences and explains over 71% of gender effects on Future-oriented
Science Motivation. Sociocultural conditions of individuals were still clearly the
dominant
factors
influencing
the
development
of
gender
differences
in
achievement-related choices in science education and careers.
Fourthly, all the indirect effects for girls on affective learning outcomes were significant
and negative (p<0.001). It is a paradox in late modern societies that social literacy
allows females more freedom to choose their future life while persistent gender role
stereotype obstruct their educational and occupational trajectories related to science,
and consequently restrict their social mobility. As suggested by Leung (2011), the major
task of career guidance for girls is not to “help her to make a career decision”, but to
“empower her to overcome the various social structural barriers limiting her choices”
and to promote “social justice in gender-biased educational and career choices”.
Fifthly, gender differences in Future-oriented Science Motivation cannot be diminished
by improving female science cognitive achievement. The mediation effect of Science
Literacy is insignificant when the affective factors were taken into the estimation.
Sixthly, parental SES is like a double-edged sword, it helped students to improve their
science performance because of extra family resources. At the same time, it
discouraged the students from families with higher parental SES from choosing science
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as their future studies and careers. For parents and teachers, they should reflect on
“Why our wealthy students tend not to choose science as their future studies and
careers”.
All the models studied so far are in line with the revised Eccles et al (1983) model
expectancy-value model of achievement-related choices and the results provided us
with more insight into the problem “gender-segregated science related educational and
career choices” in Hong Kong context.
In the next chapter, I will sum up the major findings of the study, examine the
implications for policy and practice at school, families, examination bodies and
education authorities, discuss limitations of the study, and recommend future research.
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CHAPTER SIX
CONCLUSIONS AND IMPLICATIONS
This chapter sums up the major findings of the study, examines the implications for
policy and practice at school, families, examination bodies and education authorities,
discusses limitations of the study, and makes recommendations for future research.
6.1 Database and data analysis
The study focus on three major questions: (1) Are there any gender differences in
science performance? (2) Are there any gender differences in affective learning
outcomes of scientific literacy? (3) What are the mediation effects of gender differences
through cognitive and affective factors on future-oriented science motivation?
The data was obtained from Hong Kong PISA 2006 which consists of 4645 students,
representing 5.7% of the 15-year-old population, selected from 146 local schools with
two-staged randomly sampling. All the student demographic and affective variables
were retrieved from the student questionnaire while the students’ cognitive performance
in science was taken from 13 assessment booklets.
The study deployed two key quantitative research methods, Multidimensional
Differential Item Functioning (MDIF) and Multilevel Mediation (MLM). MDIF was
used to investigate gender effects at the item level while MLM was used to study the
mediation effects of affective variables on students’ future-oriented science motivation.
Confirmatory factor analysis (CFA) was firstly used to testify the goodness of fit of the
measurement models. In other words, the OECD measurement models were fitted with
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local data and then the models were adjusted until the model fits were accepted. Next,
the “Multi-group Measurement Invariance” (MGMI) test was conducted to assess the
applicability of all the measurement models across the genders. Once this procedure
had finished, the MLM was conducted. It is important to note that although MLM is the
advanced statistical technique for structural equation modelling, the casual relationship
between the latent variables and gender effects might not be affirmed. In the following
section, the major findings of the current study will be displayed.
6.2 Major findings
6.2.1 Multidimensional DIF model
1.
The multidimensional DIF is both more robust and more accurate in estimating
the gender differences in science performance.
When the gender effect was modeled as a facet in item model, the gender differences in
science performance were statistically significant at item level while no gender
differences could be detected using Mean Score Difference (MSD). One of the reasons
for the robust result is that multidimensional DIF caters test validity and reliability in
the same model. The ConQuest software puts one more step forward to retrieve
information from other dimensions within the same matrix to improve the precision of
measurement. These features supplement traditional MSD.
In short, the MDIF was a method with sufficient sensitivity to detect the gender
differences at the item level.
2.
In general, small effect sizes for gender differences were identified across (i)
competency dimensions, (ii) content and (iii) item formats.
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Overall, small but statistically significant gender differences in science performance
were found. The effect sizes detected in this study, in most of the cases, were less than
0.20 which is considered as small (i.e. little impact) in education and social science
studies. The largest effect size (0.35) was found in physical systems under knowledge
of science.
Findings from this study support gender similarities hypothesis in cognitive
performance as proposed by Hyde (2005) that the effect in sizes of gender differences is
small.
3.
Boys and girls showed differential advantages in science performance
competencies, content domains and item formats
The MSD results on science performance competencies show that boys outperformed
girls on Explaining Phenomena Scientifically (EPS) and Using Scientific Evidence
(USE) while girls surpassed boys on Identifying Scientific Issues.
The MSD results on content domains ascertain that boys outperformed girls on three
domains of “Knowledge Of Science” but not for “Knowledge About Science”. Boys did
better than girls on Earth and Space Systems, Living Systems and Physical Systems and
the largest effect size was found for Physical Systems. There was no significant gender
difference on “Knowledge About Science”.
The MDIF results show that boys did better than girls for most of the item format,
including multiple choice, complex multiple choice, closed constructed response,
except open response. But, the MDIF results indicate that more items were in favor of
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girls, after controlling the average abilities of boys and girls.
In short, the MSD and MDIF results were consistent with most of previous gender
studies in science. Boys showed advantages in EPS and USE competencies and content
domains, in particular, physical systems. However, the effect size for physical systems
(0.35) was small. Regarding the gender differences on item format, girls were favored
by open response items while boys were favored by closed constructed response at item
level.
4.
Boys had higher gender variability than girls’ at all science performance levels
and gender variance ratios (B/G) were the highest at tails of the distributions.
More boys were found to be lower achievers of science than that of girls (left ends
of the distributions).
In contrast to the traditional expectation, the findings did not support the notion that
more boys occupied the right ends of the distributions. The numbers of high achievers
in this region were in fact alike for boys and girls.
However, boys outnumbered girls at the left ends of the distributions, for the three
dimensions “Explaining Phenomena Scientifically”, “Identifying Scientific Issues” and
“Using Scientific Evidence”. Boys’ underachievement is an issue in the Western
countries like UK and US. Hong Kong schools are now facing similar problem.
Besides the concern of underachievement, boys’ cognitive outcomes varied much more
than that of girls at all level of science performance. The gender variance ratio (B/G)
reached the highest at both ends of the distributions. These results were consistent with
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the existing literatures (e.g. Feingold, 1992).
To sum up, there is no apparent evidence to support that the number of boys’ higher
achievers outnumbered girls at the right ends of the distributions. The
underachievement of more boys located at the left ends of the distributions, and also
their higher cognitive variability in science performance, gave some implications for
classroom teaching and learning. For this, we have elaboration in a later session.
5.
Boys had higher affective learning outcomes than girls.
Findings from this study suggested that there were significant gender differences in
affective learning outcomes. Boys showed statistically significant higher levels of
Enjoyment of Science Learning (JOYSCIE), Interest in Science Learning (INTSCIE),
Instrumental Motivation to Learn Science (INSTSCIE), Personal Value of Science
(PERSCIE), Science Self-concept (SCSCIE) and Future-oriented Science Motivation
(SCIEFUT) than girls.
The largest three effect size (Cohen d > 0.20) in affective learning outcomes was
SCSCIE (0.47), SCIEFUT (0.38) and JOYSCIE (0.37). In Hong Kong, it is important
to note that girls tended to report a significantly lower Science Self-concept,
Future-oriented Science Motivation and Interest in Science Learning than boys.
As a whole, compared with boys, girls had clear disadvantages in terms of confidence
and future-oriented motivation to participate in science related educational programmes
or careers after secondary school education.
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6.2.2 Multilevel Mediation using Expectancy-Value Model
Most of the past gender studies focused on regression analyses of gender effects on
science performance and correlation between gender and attitudes toward science. The
current study explored further into the underlying mediation mechanism of gender
effects on Future-oriented Science Motivation using SEM. The following section
summarizes the major findings of the study.
6.
OECD affective constructs in science and Eccles et al (1983) model were both
applicable to local schools upon localization process.
The affective measurement models were all developed based on the OECD
(Organisation for Economic Co-operation and Development) countries and most of
these countries except Japan and South Korea were non-Asian origin. In addition, the
model fits were done with the international calibration sample of OECD countries only
and the deployed measurement models may not reflect the genuine psychometric
characteristics of Hong Kong students.
To apply these measurement models locally, all measurement models or constructs
deployed in this study were firstly validated with CFA using AMOS software. For the
poor-fitting measurement models, such as, “Interest in Science Learning”, two misfit
items, topics in astronomy and topics in geology were removed from the model due to
the fact that local science curriculum does not cover them in science education.
Secondly, the split of general science interest into physical science, biological science
and scientific method by second order CFA confirmed perfect fit of the modified
measurement model with respect to local data.
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The second step was to validate the measurement invariance (suitability) of the
constructs across two gender groups, boys and girls. Results for the multiple-group CFA
confirmed the validity of the measurement models for local males and females.
For the structural models, the revised Eccles et al (1983) model was used to
conceptualize the interactions among subjective expectations of success and the
personal value of available educational and career choices. The final structural model
was a good fit with the local PISA data suggesting that Eccles et al (1983) model is not
only useful to understand gender differences in Western science education, but also
applicable to the Hong Kong context.
Most of the past local adoption of the model, focused on physical education (e.g. Pang
& Ha, 2010). The current study extended and validated the model’s applicability to
study the gendered educational and vocational choices related to science education in
Hong Kong.
To sum up,, CFA results suggested that most of the OECD measurement models are a
good fit with local data. However, modifications were often necessary to ensure that all
the measurement models reflected the actual psychometric properties of local context.
The results of the structural models also confirmed the applicability of Eccles et al
(1983) model for local science education.
Comparison of the mediation effects of cognitive and affective domains
7.
Cognitive achievement was an insignificant mediator of gender differences on
future-related achievement choices while affective factors were important
intervening mediators of gender differences.
135
Multiple mediator models used in this study allowed us to compare directly the relative
magnitudes of specific indirect effects. From this study, cognitive achievement was
found to be a strong mediator for gender differences provided that other affective
mediators were excluded from the model. However, its mediating effect reduced to zero
in a multiple mediator model when the model included affective mediators.
To sum up, the mediating effect of cognitive achievement was negligible in a multiple
mediator model. The gender differences in future-oriented motivation in science can
probably be reduced by rising females’ affective learning outcomes in science rather
than cognitive performance.
Comparison of the mediation effects of self-schema and subject task values
8.
“Interest in Science Learning” and “Enjoyment of Science Learning” were the
most influential intervening mediators of gender differences.
Built upon the Eccles et al (1983) model, the order of strength to mediate gender effects
on future-oriented science motivation are: Interest in Science Learning, Enjoyment of
Science Learning, Science Self-concept (self-schema), Personal Value of Science
(attainment value) and Instrumental Motivation to Learn Science (utility value).
The results suggest that Interest and Enjoyment of Science Learning dominate the
mediation effects of gender differences and have a decisive role in shaping students’
future-orientation of studies and career paths in science. Science self-concept is
considered to be a very strong predictor of gendered course choices and careers
orientation in Western societies. Surprisingly, its effect was overshadowed by Interest
and Enjoyment of Science Learning in Hong Kong context. There is at least one
possible explanation for this unexpected result. Hong Kong students tended to
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underestimate their science self-concept more than their Western counterparts because
of the more demanding science curriculum (Lam, Cheng, Lai, Leung & Tsoi, 1996) and
humble nature of Asian students.
Personal Value of Science and Instrumental Motivation to Learn Science has similar
and weak mediation effect of gender differences. The present junior secondary school
science curriculum put inconsiderable emphasis on the nature of science (or knowledge
about science in PISA terminology) and it is hard for students to build up a clear picture
of how the science enterprise works. Consequently, there is no simple way for junior
secondary school students to recognize the importance of science with their own
identity under existing science curriculum.
To recap, Interest in Science Learning and Enjoyment of Science Learning are the two
most influential intervening mediators of gender differences.
6.3 Revisiting conceptual model
The revised Eccles et al (1983) model, the conceptual model, was used for multilevel
mediation (MLM) study and was displayed in chapter 2. In the model, affective factors
(or psychosocial factors) are the major mediators for gender differences. However, the
empirical evidence of this study partially supports the biopsychosocial model of gender
differences which recognizes the joint impact of biological, psychological and social
influences (Halpern, 2000, 2004). The new model in Figure 6.1 includes the possibility
of direct effect of biological influence on Future-oriented Science Motivation. However,
it is also possible that there are still some affective factors not covered in the present study
which have significant effects. That is worthy to be studied in the future.
137
Figure 6.1: Revised Conceptual Model for Studying Gendered Educational and
Occupational Trajectories in Science
Independent
variable:
Mediators
Child’s general self
schemata
Dependent
variable:
Science
self-concept
(SCSCIE)
Interest in Science
Learning
(INTSCIE)
Stable child
characteristic
Enjoyment of
Science Learning
(JOYSCIE)
Gender
Attainment value
(PERSCIE)
Control
variable:
Cultural milieu
SES
Achievement related
choice
Future-oriented
Science
Motivation
(SCIEFUT)
Utility value
(INSTSCIE)
Subjective task value
Science
Performance
(SP)
Key: Solid line (⎯) indicates significant path coefficients; dotted line (----) lines signify
insignificant path coefficients.
138
6.4 Implications for policy and practice
This section will discuss the implications for policy makers, school leaders, teachers,
parents and students:
6.4.1. Implications for policy makers
As of today, there is no clear gender equity educational policy in Hong Kong. The
enforcement of gender equity at school level is still limited to formal equality of
opportunity of access to education in 70’s.
The research findings suggested that girls were significantly less motivated than boys to
study science and work in science field after secondary education, However, it did not
support the view that improving girls’ performance in science will encourage more girls
entering science careers and enrolling in science programmes after secondary school
education. It appears that the problem of “leaking science pipeline” cannot simply be
resolved by offering equal opportunity of schooling. One of the possible reasons is
implementation of science curricula at school level. Quihuis (2002) found that female
high achievers in science class were more likely to suffer from negative affect and to
worry about confirming stereotypes, while male high achievers were more likely to
experience positive affect and enjoy curriculum in science classes.
Moreover, textbook and teaching materials were found to transmit gender role
ideologies at schools (EOC, 1999). More male role models, such as male scientist,
appearing more frequently in the science textbooks than female ones is a common
phenomenon. Gender role beliefs and stereotypes are believed to influence the
development of children’s self-concepts, their perceptions of the value of various
activities an also expectations for success in science (Eccles, 2011).
139
In fact, practical and comprehensive intervention strategies can be found in Taiwan.
Since 2005, the Enforcement Rules for the Gender Equity Education Act clarify the
professional practices, including pre-service teacher training, school curricula, teaching
materials, assessments as well as classroom instruction, for gender-equity in Taiwan
schools. The local government officials, Legislative Council members and education
leaders from local institutions should consider proposing and implementing similar
schemes.
As a short term measure, the Education Bureau could incorporate subject level affective
domain measurement by gender into the School Development and Accountability
Framework. This will alert the local school administrators about the importance of
gender equity policy at school level.
6.4.2. Implications for school administrators, teachers and textbook authors
Consistent with previous studies, our results indicate that interest and enjoyment in
science learning are the two most influential factors to motivate girls to learn science and
select science-related careers in the future. Science teachers should bring more hands-on
laboratory experiences and projects with real life context into daily lessons, helping
students not only to learn but also to enjoy and become interested in science. Girls were
found to enjoy learning science when they could apply what they had learnt to their
lives or to the world around them (Quihuis, 2002). Recent research evidence has
demonstrated that a context-based science-technology-society (STS) instruction
approach can cultivate positive attitudes toward science for both girls and boys and
reduce the gender difference in affective learning outcomes (Bennett, Lubben, &
Hogarth, 2007)
140
Science teachers can inspire students, particularly girls, about science by sharing their
own interest in science and providing students with a captivating curriculum. Girls are
more likely to become interested in science when they can establish a good relationship
with their science teachers (AAUW, 1991). Friendly teachers, who are easy to approach
with science questions, also help girls in learning science and encourage them to
consider science for their future studies and careers.
Our findings point out that students,, in particular girls, reported a significantly lower
Science Self-concept than boys. Classroom instruction and assessments, therefore,
should focus more on raising students’ Science Self-concept. Formative assessment is
more appealing to girls while competition and grades comparison in the science lessons
will reduce their Science Self-concept and discourage them from learning science.
Science teachers can help promote a cooperative learning atmosphere and discourage
competition in classroom learning.
Curriculum leaders and science panel heads at schools should realize that subject
content coverage and high performance in public examinations are not the only
successful indicators for science curricula. High affective learning outcomes are also a
critical evaluation of the implemented curricula. Yet, many school teachers overlook the
issue of students’ diversity in science affective development. Extra effort is placed on
drilling public examination materials, while time for interest and enjoyment in science
learning is either ignored or reduced.
As the findings indicate that boys have higher diversity in science performance, schools
receiving more boys should be aware of greater learning diversity in classroom learning.
School level science curricula should be enriched with more interesting hands-on
141
experiments and exciting project work. Curriculum time should be increased
accordingly to cater for boys’ greater diversity in learning.
One of reason for underrepresentation of females in the science field was the lack of
instrumental motivation (utility values) to learn science. Careers teachers at schools or
tertiary institutions may help in improving careers information about science. School
level science careers exhibitions and enterprise partnership schemes in science will
provide careers related exposure and experiences for girls and probably retain more
girls in the science stream.
Apart from subject knowledge, science textbook authors are recommended to include
historical and current events and scientists’ stories of major scientific discoveries, for
example, the discovery of polonium and radium by Marie and Pierre Curie, the
discoveries in neurotrophic factors and their potential for the treatment of
neurodegenerative diseases by Nancy Ip, to raise students’ attainment values of science
works. Girls may realize that men and women are equally important in many scientific
discoveries and innovations.
6.4.3 Implications for parents and students
According to Eccles and her colleagues, parents’ gender stereotypes provide boys and
girls with differential socialization experiences. The girls’ self schema and subjective
task values will subsequently be reduced.
Parents can help by not only treating their sons and daughters equally in terms of the
types and frequency of science activities and games, but also encourage their daughters
to participate in male-dominated science to the same extent as their sons. Parents also
142
can act as a positive science role model and teach their children science by reading
science literature and watching or listening to science programmes with their children
and discussing what they have learnt from these activities.
6.5 Limitations and recommendations for future research
6.5.1 Limitations of the study
PISA, like other international assessments such as TIMSS has one natural limitation,
the database collected contain only one wave of data. To understand the educational and
vocational trajectories of students, it is essential to have a longitudinal design to follow
the 15-year-olds for a longer period of time.
The second limitation of the study is its reliance upon career intentions rather than
actual future educational and career choices. It would be an important extension for
future research to cover the actual educational and career choices of the students.
Thirdly, there was no separation of biological and physical sciences in PISA
measurement models. CFA analysis of “interest in science learning” from this study and
other literatures (e.g. Nagy et al., 2006) suggest gender differential properties of
biological and physical sciences. Physical science usually demands higher
mathematical abilities and girls typically presented with more anxiety toward
mathematics than boys (Eccles, 1984).
Given the important of educational and vocational decisions for personal, social and
economic development, it is worthwhile to study the gender related differences in this
aspects in great details. Unfortunately, in Hong Kong, there are limited studies
concerning the gender differences in science, in particular, longitudinal studies about
143
girls’ underrepresentation in STEM careers. The current study can only provide a
snapshot of the problem “I can but I don’t want to” in science education. More future
longitudinal studies and in-depth case studies about gender development in STEM
rather than science are essential to have comprehensive understanding of the problem.
Knowledge base for development, intervention, and policy concerning gender
differences in occupational outcomes in science will definitely lead to a future solution
to the problem.
6.5.2 Recommendations for future research
Provided that the effect sizes for gender differences in affective domains of science are
larger than cognitive domains, it will be meaningful to explore the origin and
development of the gender differences in science affective learning outcomes by
qualitative method such as ethnography. Moreover, the sensitive techniques used in this
study such as MDIF, will be very useful for item level analysis of other PISA
competency domains.
Secondly, the Eccles Expectancy-Value Model has rich sets of psychological and
sociocultural constructs related to gender differences, which have not yet been fully
explored in the study, for example, parental stereotype and gender differential
performance in science education. The information gathered will be very useful for
parents, social psychologists and social educationalists..
Finally, comparative educational studies using Eccles Expectancy-Value Model can be
conducted using cross country PISA data. It will be useful to understand the differential
sociocultural contributions or regional effects of different countries on gender
differences in STEM and to make use of these understandings to reduce the gender
144
differences in science education. Better still if the model was examined and proved to
be feasible in Hong Kong.
145
Appendix A: Handling missing values
Missing values in the PISA datasets can pose challenge and even threat in subsequent
data analysis in confirmatory factor analysis (CFA) and multilevel regression analysis.
It may seriously jeopardize the validity of results, it is important to handle missing data
in an appropriate way. Practically, there is three type of missing values: missing
completely at random (MCAR), missing at random (MAR) and missing not at random
(MNAR) (Rubin, 1976).
The traditional tactics to deal with missing value are listwise deletion, pairwise deletion,
mean substitution, and various regression-based estimations. These tactics are
considered as unacceptable solution because of their biased parameter estimation.
(Heck, Thomas et al., 2010). Up to now, full information maximum likelihood (FIML)
and multiple imputation (MI) are the two approaches considered as acceptable in the
literature (Peugh & Enders, 2004).
Lam (2005) investigated the missing values of SES of HKPISA 2000+ datasets and
found out that the missing values of SES did not follow MCAR pattern. Here comes to
the similar conclusion with reference to the Little’s MCAR test (Little, 1988) on PISA
2006 dataset. The null hypothesis for Little’s MCAR test is that the data are MCAR.
Data are MCAR when the pattern of missing values does not depend on the data values.
The SES missing values are not MCAR candidates since the p value is less than .05.
(See Table A.1). Listwise deletion cannot therefore be applied to the SES missing
values.
Table A1: EM Correlations matrix of SES
MISCED FISCED
HISEI
SES
MISCED
1
FISCED
.593
1
HISEI
.497
.524
1
SES
.840
.853
.803
1
Little’s MCAR test: Chi-Square = 41.950, DF = 9, Sig. = .000
The Little’s MCAR test result suggests that multiple-imputation is essential to recover
and complete the dataset for SES. The same procedure above was used to analyse the
missing value patterns for other factors before conducing multiple-imputation.
146
The second way to look at the pattern of missing values is monotone or nonmonotone
pattern of missing data. The multiple imputation module of IBM SPSS Statistics 19 was
used to find out the pattern of the missing values in SES. The missing pattern of SES is
shown in FigureA1. Pattern 1 represents cases which have no missing values, while
Pattern 2, 3, 4 and 5 represents cases that have missing values on SES. In short, missing
values
of
SES
does
not
display
monotone
pattern
and
nonmonotone
multiple-imputation method should be used.
Figure A1: Missing value pattern of SES and related factors
The first method for the multiple imputations was Markov chain Monte Carlo (MCMC)
which can handle nonmonotone pattern and the output was shown in Table A2.
Table A2: Descriptive Statistics of the results of multiple imputation of SES
Data
Original Data
4385
.0000000
Std.
Minimum
Deviation
1.00000000 -2.0397540
Imputed Values
260
-.2047680
1.05584122 -3.4862281
3.0354896
4645
-.0114617
1.00419286 -3.4862281
3.2021793
Complete Data
After Imputation
N
Mean
147
Maximum
3.2021793
The second method for the multiple imputations was Bayesian imputation which was
used to generate missing values using CFA. However, for this method, the original
PISA 2006 dataset with missing values was used for model fit before missing data
generation.
IBM SPSS Statistics 19 script to conduct multiple imputation of SES
// Impute Missing Data Values.
MULTIPLE IMPUTATION PV1MATH PV2MATH PV3MATH PV4MATH
PV5MATH PV1READ PV2READ PV3READ PV4READ PV5READ PV1SCIE
PV2SCIE PV3SCIE PV4SCIE PV5SCIE PV1INTR PV2INTR PV3INTR PV4INTR
PV5INTR PV1SUPP PV2SUPP PV3SUPP PV4SUPP PV5SUPP PV1EPS PV2EPS
PV3EPS PV4EPS PV5EPS
PV1ISI PV2ISI PV3ISI PV4ISI PV5ISI PV1USE PV2USE PV3USE PV4USE
PV5USE SES
/IMPUTE METHOD=FCS MAXITER= 10 NIMPUTATIONS=1
SCALEMODEL=LINEAR INTERACTIONS=NONE SINGULAR=1E-012
MAXPCTMISSING=NONE
/MISSINGSUMMARIES NONE
/IMPUTATIONSUMMARIES MODELS DESCRIPTIVES
/OUTFILE IMPUTATIONS=ImputedSES.sav.
148
Appendix B: Booklet effects
To adjust the booklet effect (or position effect of items in different booklets), the
booklet difficulty parameters were first estimated like other items in ConQuest. The
calibration model statement is:
Model item + item * step + booklet;
The results of the calibration are presented in last column of Table A3. The item fit is
good (all weighted fit mean square, MNSQ values are close to 1) and the booklet effect
is relatively smaller for most of the booklets due to adoption of the Balanced
Incomplete Block (BIB) test design in PISA booklets. However, the booklet effect is
still significant at p <0.000. A positive value indicates a booklet that is harder than the
average while a negative value indicates a booklet that is easier than the average. For
example, students taking Booklet 2 are 0.194 logit higher than the overall average.
Likewise, the estimated average achievement level of students taking Booklet 3 is 0.131
higher than the overall average, and the average achievement level of students taking
Booklet 12 is 0.203 lower than the overall average. Booklet 2 (-0.194 logit) is the
easiest booklet while Booklet 12 (0.203 logit) is the most difficult one and the
difference is 0.397 logit difference, about half a grade difference. The differences of
estimate on average students’ achievement level are attributed to the booklets
themselves, rather than the differing abilities of the students. To rectify, booklet
difficulty estimates were added to adjust and reflect the proficiencies of students who
responded to certain booklets.
As recommended by PISA technical report (OECD, 2009b), internationally estimated
booklet parameters are more desirable option from the perspective of cross-national
consistency. Table 4 summarizes the booklet effects with international calibration.
By comparing to Table A3 and Table A4, the Hong Kong estimated booklet effects are
closely aligned with the calibrated international booklet effects. The use of students’
ability estimates, the plausible values, directly from PISA database for gender
difference study is valid.
149
Table A3: Hong Kong estimated booklet effects in logits
Booklet
Estimate
Weighted fit
SE
MNSQ
CI
T
1
-0.029
0.052
1.01
( 0.85, 1.15)
0.1
2
-0.194
0.055
0.95
( 0.85, 1.15)
-0.7
3
-0.131
0.055
1.00
( 0.85, 1.15)
0.0
4
0.045
0.054
1.03
( 0.85, 1.15)
0.4
5
0.034
0.051
1.06
( 0.85, 1.15)
0.8
6
0.064
0.055
1.04
( 0.85, 1.15)
0.5
7
-0.136
0.060
0.99
( 0.85, 1.15)
-0.2
8
0.041
0.055
1.08
( 0.85, 1.15)
1.0
9
0.004*
0.053
0.96
( 0.85, 1.15)
-0.5
10
0.198
0.054
1.01
( 0.85, 1.15)
0.2
11
0.078
0.053
0.94
( 0.85, 1.15)
-0.9
12
0.203
0.054
0.99
( 0.85, 1.15)
-0.1
13
-0.177
0.062
1.02
( 0.85, 1.15)
0.2
Note: An asterisk next to a parameter estimate indicates that it is constrained
Separation Reliability = 0.834
Chi-square test of parameter equality = 64.53, df = 12, Sig Level = 0.000
Table A4: Internationally estimated booklet effects
Booklet
Science domain
1
2
3
4
5
6
7
8
9
10
11
12
13
-0.033
-0.214
-0.220
-0.068
0.017
0.072
-0.213
0.189
0.002
0.229
0.130
0.219
-0.112
Source: OECD, 2009b p. 221
150
Appendix C: Wright map for science performance dimensions
The Wright map in Figure A2 shows that the PISA 2006 science items can cover the
students’ ability distributions in the three dimensions quite well, except the upper
portion of the distributions. For example, there is only one item (item 77: S519Q03)
filling up the higher ability region of Identifying Scientific Issues (ISI) distribution. For
a more accurate estimation, more items which demand higher order cognitive ability is
necessary to fill up these gaps in order to achieve a continuum of item difficulty.
Figure A2: Wright map for the three dimensions: Explaining Phenomena Scientifically
(EPS), Identifying Scientific Issues (ISI) and Using Scientific Evidence (USE) in
science performance
Logit
EPS
ISI
USE
4
21
3
2
1
0
-1
X
X
X
XX
XX
XXX
XXXXX
XXXXX
XXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXXXXX
XXXXXXXXXX
XXXXXXXXX
XXXXXXXX
XXXXXXXXX
XXXXXXXX
XXXXXX
XXXXXX
XXXXXX
XXXX
XXX
XXX
XX
XX
XX
X
X
X
30 52
13
1 17 18
11
31 46 49
14 15 40
4 48
20 24 33
53
5
45 47
26 32
27 42
8 50
10 12 29 35
19 22 43
9 23 34
37
6
36
7 16 41
28 4439 51
38
25
2
-2
X
X
XX
XX
XX
XXX
XXXX
XXXX
XXXXX
XXXXXXX
XXXXXXX
XXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXX
XXXXXXX
XXXXXX
XXXXX
XXXXX
XXXX
XXXX
XXX
XXX
XX
X
XX
X
X
X
X
77
70
65
54
57
64
76
71 72
73
67
66
55 74
75
56 61
59
60 68
58
63
62
69
3
-3
Note: Each ‘X’ represents 30.5 cases
151
X
X
X
XX
XXX
XXX
XXXXX
XXXXXX
XXXXX
XXXXXX
XXXXXXXX
XXXXXXX
XXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXXXX
XXXXXXXX
XXXXXXXX
XXXXXXX
XXXXXXX
XXXXX
XXXXX
XXXXXX
XXXX
XXXX
XXX
XXX
XXX
XX
X
X
X
X
X
108
106
82
90
85 97 99 105
81 88 93
79 94
86
101 107
96 100
80 87 89 95
91 102
104
78
84
83
98
103
92
Appendix D: Gender differences in scientific performance measured by MDIF
Table A5: Gender DIF items for Closed Constructed Response (CCR)
Item code
S413Q06
S416Q01
S421Q01
S421Q02
S421Q03
Boys’ DIF
Girls’ DIF
Weighted
| 2DIF |
Estimate
Estimate
Competency
item fit
(DIF class)
(SE)
(SE)
(girls/boys)
0.84
0.017
-0.017
CCR
PS
EPS
0.90
(0.041)
(0.041)
1.02
0.126**
-0.126**
0.25
CCR
SEL
USE
1.09
(0.047)
(0.047)
(Class A)
1.03
0.143***
-0.143***
0.29
CCR
PS
EPS
0.98
(0.042)
(0.042)
(Class A)
1.20
-0.036
0.036
CCR
LS
EPS
1.16
(0.042)
(0.042)
0.90
0.348***
-0.348***
0.70
CCR
ESS
EPS
0.93
(0.044)
(0.044)
(Class C)
Note: *p<0.05, **p<0.01, ***p<0.001; DIF estimates with negative values mean
relatively easier with reference to the opposite gender
Item
format
Item
content
Table A6: Gender DIF items for Multiple Choice (MC)
Item code
Item
format
Item
content
Competency
S213Q02
MC
TS
EPS
S256Q01
MC
PS
EPS
S268Q01
MC
SEQ
ISI
S268Q06
MC
LS
EPS
S304Q02
MC
PS
EPS
S326Q03
MC
SEL
USE
S408Q01
MC
LS
EPS
S408Q05
MC
SEQ
ISI
S413Q05
MC
TS
USE
S415Q02
MC
ESS
EPS
S425Q02
MC
SEL
USE
Weighted
item fit
(girls/boys)
1.07
0.87
1.21
0.95
0.94
1.05
0.97
1.01
0.93
0.86
1.07
1.06
0.93
0.87
0.97
1.00
1.14
1.13
0.95
0.92
0.97
0.93
152
Girls’ DIF
Estimate &
(SE)
0.040
(0.051)
-0.310
(0.052)
0.061
(0.046)
0.315***
(0.042)
0.293***
(0.044)
0.021
(0.047)
-0.103**
(0.044)
0.094**
(0.044)
-0.109**
(0.048)
-0.011
(0.047)
0.065
(0.048)
Boys’ DIF
Estimate &
(SE)
-0.040
(0.051)
0.310
(0.052)
-0.061
(0.046)
-0.315***
(0.042)
-0.293***
(0.044)
-0.021
(0.047)
0.103**
(0.044)
-0.094**
(0.044)
0.109**
(0.048)
0.011
(0.047)
-0.065
(0.048)
| 2DIF |
(DIF class)
0.62
(Class B)
0.63
(Class B)
0.59
(Class B)
0.21
(Class A)
0.19
(Class A)
0.22
(Class A)
-
S425Q05
MC
SEQ
ISI
S426Q03
MC
ESS
EPS
S426Q05
MC
ESS
EPS
S428Q01
MC
SEL
USE
S428Q03
MC
SEL
USE
S437Q01
MC
PS
EPS
S437Q03
MC
PS
EPS
S437Q04
MC
PS
EPS
S438Q02
MC
SEQ
ISI
S447Q02
MC
SEQ
ISI
S447Q03
MC
SEQ
ISI
S447Q04
MC
SEQ
ISI
S456Q02
MC
LS
EPS
S465Q02
MC
ESS
EPS
S465Q04
MC
ESS
EPS
S466Q05
MC
SEL
USE
S476Q01
MC
LS
EPS
S476Q02
MC
LS
EPS
S476Q03
MC
LS
EPS
S477Q02
MC
LS
EPS
S477Q03
MC
LS
EPS
0.98
1.04
0.99
0.98
0.90
0.90
0.92
0.96
0.83
0.84
1.08
0.86
1.06
1.01
0.98
0.96
0.98
1.02
0.97
0.87
1.01
0.98
0.95
1.01
1.22
1.22
1.02
0.98
1.03
1.01
1.05
1.09
1.10
0.96
0.99
0.91
0.97
0.85
1.01
0.90
0.95
0.8
153
-0.051
(0.047)
-0.102**
(0.043)
0.034
(0.044)
0.212***
(0.048)
0.089
(0.056)
0.118**
(0.050)
0.104**
(0.042)
0.050
(0.043)
-0.008
(0.047)
-0.070
(0.044)
0.052
(0.044)
-0.027
(0.044)
-0.227***
(0.044)
-0.101*
(0.042)
-0.103*
(0.041)
0.199***
(0.047)
0.094*
(0.044)
0.206***
(0.043)
-0.031
(0.046)
-0.113*
(0.045)
0.01
(0.049)
0.051
(0.047)
0.102**
(0.043)
-0.034
(0.044)
-0.212***
(0.048)
-0.089
(0.056)
-0.118**
(0.050)
-0.104**
(0.042)
-0.050
(0.043)
0.008
(0.047)
0.070
(0.044)
-0.052
(0.044)
0.027
(0.044)
0.227***
(0.044)
0.101*
(0.042)
0.103*
(0.041)
-0.199***
(0.047)
-0.094*
(0.044)
-0.206***
(0.043)
0.031
(0.046)
0.113*
(0.045)
-0.010
(0.049)
0.20
(Class A)
0.42
(Class A)
0.24
(Class A)
0.21
(Class A)
-
0.45
(Class B)
0.20
(Class A)
0.21
(Class A)
0.40
(Class A)
0.19
(Class A)
0.41
(Class A)
0.23
(Class A)
-
S478Q01
MC
S485Q03
MC
S498Q03
MC
S508Q03
MC
S521Q02
MC
S521Q06
MC
Note:
1.26
-0.098*
0.098*
0.20
1.20
(0.041)
(0.041)
(Class A)
0.83
0.029
-0.029
PS
USE
0.88
(0.053)
(0.053)
1.03
-0.008
0.008
SEQ
ISI
1.01
(0.044)
(0.044)
1.03
0.074
-0.074
SEQ
ISI
1.01
(0.045)
(0.045)
1.18
0.073
-0.073
PS
EPS
1.02
(0.043)
(0.043)
1.02
-0.059
0.059
PS
EPS
0.87
(0.048)
(0.048)
*p<0.05, **p<0.01, ***p<0.001; DIF estimates with negative values mean
relatively easier with reference to the opposite gender
LS
EPS
Table A7: Gender DIF items for Complex Multiple Choice (CMC)
Item code
Item
format
Item
content
Competency
S213Q01
CMC
SEQ
ISI
S269Q04
CMC
PS
EPS
S326Q04
CMC
LS
EPS
S408Q04
CMC
LS
EPS
S413Q04
CMC
TS
USE
S415Q07
CMC
SEQ
ISI
S415Q08
CMC
SEQ
ISI
S426Q07
CMC
SEQ
ISI
S438Q01
CMC
SEQ
ISI
S456Q01
CMC
LS
EPS
S458Q02
CMC
LS
USE
S466Q01
CMC
SEQ
ISI
S466Q07
CMC
SEQ
ISI
Weighted
item fit
(girls/boys)
0.89
0.92
1.08
1.04
0.88
0.89
1.16
1.16
1.07
1.01
0.86
1.00
0.93
1.02
1.1
1.26
0.93
1.17
1.05
1.11
1.11
1.08
1.02
1.12
0.88
1.18
154
Girls’ DIF
Estimate &
(SE)
-0.082
(0.045)
0.201***
(0.034)
0.116**
(0.042)
-0.031
(0.042)
0.077
(0.047)
-0.109*
(0.048)
0.004
(0.047)
-0.021
(0.045)
0.045
(0.052)
0.030
(0.043)
-0.148**
(0.047)
0.051
(0.047)
-0.012
(0.053)
Boys’ DIF
Estimate &
(SE)
0.082
(0.045)
-0.201***
(0.034)
-0.116**
(0.042)
0.031
(0.042)
-0.077
(0.047)
0.109*
(0.048)
-0.004
(0.047)
0.021
(0.045)
-0.045
(0.052)
-0.030
(0.043)
0.148**
(0.047)
-0.051
(0.047)
0.012
(0.053)
| 2DIF |
(DIF class)
0.40
(Class A)
0.23
(Class A)
0.22
(Class A)
0.30
(Class A)
-
S478Q02
S478Q03
S493Q01
S493Q03
S495Q01
S495Q02
S495Q04
S498Q02
S508Q02
S510Q01
S514Q04
S519Q02
S524Q06
S527Q01
S527Q03
S527Q04
1.05
0.005
-0.005
0.99
(0.047)
(0.047)
1.00
-0.125**
0.125**
CMC
LS
EPS
0.93
(0.046)
(0.046)
1.04
-0.355***
0.355***
CMC
LS
EPS
1.02
(0.044)
(0.044)
1.01
-0.072
0.072
CMC
LS
EPS
0.92
(0.047)
(0.047)
1.00
0.009
-0.009
CMC
SEL
USE
1.03
(0.047)
(0.047)
1.09
0.258***
-0.258***
CMC
SEL
USE
1.07
(0.048)
(0.048)
1.08
-0.106*
0.106*
CMC
SEQ
ISI
1.09
(0.044)
(0.044)
1.08
0.116**
-0.116**
CMC
SEQ
ISI
1.11
(0.044)
(0.044)
1.07
0.060
-0.060
CMC
SEQ
ISI
1.00
(0.045)
(0.045)
1.08
0.083*
-0.083*
CMC
PS
EPS
1.12
(0.042)
(0.042)
0.88
-0.146**
0.146**
CMC
TS
USE
0.98
(0.050)
(0.050)
1.15
0.05
-0.050
CMC
PS
EPS
1.13
(0.041)
(0.041)
1.06
0.216***
-0.216***
CMC
TS
USE
1.02
(0.049)
(0.049)
1.07
0.139
-0.139
CMC
SEL
USE
1.03
(0.257)
(0.257)
1.11
0.073
-0.073
CMC
ESS
EPS
1.14
(0.044)
(0.044)
1.07
-0.038
0.038
CMC
ESS
EPS
1.06
(0.316)
(0.316)
Note: *p<0.05, **p<0.01, ***p<0.001; DIF estimates with negative values mean
relatively easier with reference to the opposite gender
CMC
SEL
USE
155
0.25
(Class A)
0.71
(Class C)
0.52
(Class C)
0.21
(Class A)
0.23
(Class A)
0.17
(Class A)
0.29
(Class A)
0.43
(Class C)
-
Table A8: Gender DIF items for Open Response (OR)
Item code
Item
format
Item
content
Competency
S114Q03
OR
SEL
USE
S114Q04
OR
SEL
USE
S114Q05
OR
ESS
EPS
S131Q02
OR
SEL
USE
S131Q04
OR
SEQ
ISI
S268Q02
OR
LS
EPS
S269Q01
OR
ESS
EPS
S269Q03
OR
LS
EPS
S304Q01
OR
PS
USE
S304Q03a
OR
TS
USE
S304Q03b
OR
TS
EPS
S326Q01
OR
SEL
USE
S326Q02
OR
SEL
USE
S408Q03
OR
LS
EPS
S425Q03
OR
LS
EPS
S425Q04
OR
SEQ
USE
S426Q01
OR
ESS
EPS
S428Q05
OR
LS
EPS
S437Q06
OR
PS
EPS
S438Q03
OR
SEQ
ISI
S447Q05
OR
SEL
USE
Weighted
item fit
(girls/boys)
0.86
0.93
1.03
0.98
1.02
1.07
0.94
0.94
0.93
0.98
0.99
0.97
0.86
0.85
0.89
0.83
0.90
0.97
1.05
0.95
0.85
0.8
0.96
0.92
0.89
0.87
1.03
0.98
1.00
1.00
0.95
1.01
0.93
1.19
0.92
0.92
1.03
0.85
0.92
0.95
0.92
156
Girls’ DIF
Estimate &
(SE)
0.026
(0.051)
-0.015
(0.034)
-0.196***
(0.042)
-0.098*
(0.047)
-0.113*
(0.044)
-0.116**
(0.041)
-0.011
(0.045)
-0.171***
(0.046)
0.094*
(0.047)
-0.10*
(0.047)
0.044
(0.043)
-0.201***
(0.053)
-0.189***
(0.052)
-0.133**
(0.043)
0.049
(0.041)
-0.174***
(0.047)
0.016
(0.052)
0.019
(0.041)
-0.037
(0.048)
-0.08
(0.044)
-0.031
Boys’ DIF
Estimate &
(SE)
-0.026
(0.051)
0.015
(0.034)
0.196***
(0.042)
0.098*
(0.047)
0.113*
(0.044)
0.116**
(0.041)
0.011
(0.045)
0.171***
(0.046)
-0.094*
(0.047)
0.10*
(0.047)
-0.044
(0.043)
0.201***
(0.053)
0.189***
(0.052)
0.133**
(0.043)
-0.049
(0.041)
0.174***
(0.047)
-0.016
(0.052)
-0.019
(0.041)
0.037
(0.048)
0.08
(0.044)
0.031
| 2DIF |
(DIF class)
0.39
(Class A)
0.20
(Class A)
0.23
(Class A)
0.23
(Class A)
0.34
(Class A)
0.19
(Class A)
0.20
(Class A)
0.40
(Class A)
0.38
(Class A)
0.27
(Class A)
0.35
(Class A)
-
S458Q01
OR
S465Q01
OR
S477Q04
OR
S485Q02
OR
S485Q05
OR
S493Q05
OR
S495Q03
OR
S498Q04
OR
S508Q04
OR
S510Q04
OR
S514Q02
OR
S514Q03
OR
S519Q01
OR
S519Q03
OR
S524Q07
OR
Note:
0.87
(0.032)
(0.032)
0.96
0.135**
-0.135**
LS
EPS
0.90
(0.041)
(0.041)
1.18
-0.056
0.056
SEL
USE
1.21
(0.035)
(0.035)
1.07
-0.264***
0.264***
LS
EPS
0.92
(0.048)
(0.048)
1.10
0.115***
-0.115***
PS
EPS
1.11
(0.032)
(0.032)
0.96
0.015
-0.015
SEQ
ISI
0.90
(0.036)
(0.036)
0.96
0.003
-0.003
LS
EPS
0.89
(0.042)
(0.042)
0.87
0.109*
-0.109*
SEL
USE
0.87
(0.047)
(0.047)
1.17
-0.165***
0.165***
SEL
USE
1.10
(0.035)
(0.035)
0.90
0.005
-0.005
SEQ
ISI
0.93
(0.044)
(0.044)
0.89
0.156***
-0.156***
PS
EPS
0.92
(0.042)
(0.042)
0.87
-0.22***
0.22***
TS
USE
0.94
(0.057)
(0.057)
0.98
-0.093*
0.093*
ESS
EPS
0.97
(0.041)
(0.041)
1.08
-0.068
0.068
SEL
USE
0.97
(0.035)
(0.035)
1.06
0.108
-0.108
SEQ
ISI
1.01
(0.218)
(0.218)
0.91
0.048
-0.048
SEL
USE
0.87
(0.047)
(0.047)
*p<0.05, **p<0.01, ***p<0.001; DIF estimates with negative values mean
relatively easier with reference to the opposite gender
157
0.27
(Class A)
0.53
(Class C)
0.23
(Class A)
0.22
(Class A)
0.33
(Class A)
0.31
(Class A)
0.44
(Class C)
0.19
(Class A)
-
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