1. health and fitness
2. disorders
3. mental disorder
4. depression

Learning and Individual Differences 22 (2012) 76–82
Contents lists available at SciVerse ScienceDirect
Learning and Individual Differences
journal homepage: www.elsevier.com/locate/lindif
Negative thinking versus positive thinking in a Singaporean student sample:
Relationships with psychological well-being and psychological maladjustment
Shyh Shin Wong ⁎
Nanyang Technological University, Singapore, Republic of Singapore
a r t i c l e
i n f o
Article history:
Received 28 December 2010
Received in revised form 14 November 2011
Accepted 22 November 2011
Keywords:
Negative thinking
Positive thinking
Stress
Anxiety
Depression
Anger
Life satisfaction
Happiness
a b s t r a c t
This study examines the relationships of positive thinking versus negative thinking with psychological wellbeing and psychological maladjustment. Three hundred and ninety-eight undergraduate students from Singapore participated in this study. First, positive thinking were positively correlated with indicators psychological well-being – life satisfaction and happiness, and negatively correlated with indicators of
psychopathology – stress, anxiety, depression, and anger. In contrast, negative thinking were positively correlated with indicators of psychopathology – stress, anxiety, depression, and anger, and negatively correlated
with indicators of psychological well-being – life satisfaction and happiness. Second, hierarchical multiple regression results showed that females were more likely than males to be stressed and anxious at the first step
of entry. However, there were no significant differences between the sexes in terms of depression, anger, life
satisfaction, and happiness. Age did not significantly predict any of the criterion variables. Third, hierarchical
multiple regression results showed that negative thinking accounted for more of the significant incremental
unique variance in depression, stress, anxiety, life satisfaction, anger, and happiness in order of effect size.
This is also found that positive thinking do accounted for a sizable significant incremental unique variance
in happiness and life satisfaction, while a very small percentage of 1% significant incremental unique variance
for stress, depression, anxiety, and anger. Implications and limitations of these findings were discussed.
© 2011 Elsevier Inc. All rights reserved.
1. Introduction
The role of critical and creative thinking has been relatively well
studied in educational research (Abrami et al., 2008; Simonton,
2000). However, with the growing importance of Social and Emotional Learning (SEL) (Elias et al., 1997; Goleman, 1995, Ministry of
Education, 2009) and Emotional Literacy (Goleman, 1995), the role
of other types of thinking in relation to Social and Emotional Learning
and Emotional Literacy should be considered. The present study examined the role of negative thinking versus positive thinking in emotional or psychological maladjustment as expressed in various
psychological problems and emotional well-being subsumed under
the larger research area of psychological well-being.
Negative thinking have been found to play a pivotal role in psychological maladjustment, such as depression (Beck, Rush, Shaw, &
Emery, 1979; Dobson & Breiter, 1983; Harrell & Ryon, 1983; Hollon
& Kendall, 1980; Hollon, Kendall, & Lumry, 1986; Lightsey, 1994a,
1994b; Lightsey & Christopher, 1997; Muris, Mayer, den Adel, Roos,
& van Wamelen, 2009; Olioff, Bryson, & Wadden, 1989), anxiety
(Beck, Emery, & Greenberg, 1985; Heimberg, Acerra, & Holstein,
1985; Szentagotai & Freeman, 2007, Wong, 2008), anger (Beck,
⁎ Psychological Studies Academic Group, National Institute of Education, Nanyang
Technological University, 1 Nanyang Walk, Singapore 637616, Republic of Singapore.
E-mail address: [email protected].
1041-6080/$ – see front matter © 2011 Elsevier Inc. All rights reserved.
doi:10.1016/j.lindif.2011.11.013
1999), and stress (Lightsey, 1994a). Negative thinking has also been
found to be negatively associated with psychological well-being,
such as happiness (Lightsey, 1994b). Positive thinking has also been
found to be negatively related to psychological maladjustment, such
as depression (Ingram, Kendall, Siegle, & Guarino, 1995; Lightsey,
1994a, 1994b; Lightsey & Christopher, 1997) and positively related
to psychological well-being, such as satisfaction with life (Ingram et
al., 1995) and happiness (Lightsey, 1994b).
However, the issue of the relative importance of negative thinking
versus positive thinking arises with the acceptance of positive thinking as a construct worthy of scientific investigation recently (Ingram
et al., 1995). Ingram and Wisnicki (1988) proposed that there are at
least two possible positions on this issue. First, the presence of positive thinking may be less important in determining psychological
maladjustment than the absence of negative thinking (Kendall,
1984; Kendall & Hollon, 1981). This is the traditional position taken
by theorists and researchers who emphasize the importance of psychopathological processes in influencing psychological maladjustment. For example, in the “vulnerability hypothesis” (Lightsey,
1994a) of the cognitive theory of depression, negative thinking and
underlying negative schemas are hypothesized to contribute to depression (Beck, 1967; Beck et al., 1979). However, with the recent
focus on the positive psychology movement (Seligman &
Csikszentimihalyi, 2000; Simonton & Baumeister, 2005); a second position proposes that the presence of positive thinking rather than the
S.S. Wong / Learning and Individual Differences 22 (2012) 76–82
77
presence of negative thinking may be more important in determining
psychological maladjustment as well as psychological well-being
(Heimberg et al., 1985; Ingram, Smith, & Brehm, 1983). Lightsey
(1994a, 1994b) proposed that this constitutes the “buffer hypothesis”, a logical corollary of the “vulnerability hypothesis”.
consists of three hundred and twenty-nine (82.7%) Chinese, thirtyseven (9.3%) Malays, twenty-three (5.8%) Indians, nine (2.3%) identified with other racial groups (which includes all other ethnic groups
not listed). Participation was voluntary and responses were
anonymous.
1.1. The present study
2.2. Measures
The purpose of the current study is to identify the relative incremental contributions of negative thinking versus positive thinking accounting for incremental unique variance in several important
psychological outcome variables, that indicate either psychological
maladjustment (e.g., depression, anxiety, anger, stress) or psychological well-being (e.g., life satisfaction and happiness) in an Asian sample. The design of the current study served to add to the knowledge
based as prior research in two main ways. Previous research tends
to predominantly involves European and American participants.
Hence, cross-cultural convergent and divergent criterion-related validity for the constructs of both negative thinking and positive thinking in an Asian sample is not well known. Secondly, the current study
looked at the explanatory powers of both positive thinking and negative thinking in multiple important mental health outcomes in both
the negative as well as positive domains, i.e., both psychological maladjustment and psychological well-being. This is an improvement in
research design since previous research tended to focus on just one
type of thinking or one type of outcome domain, rather than multiple
types concurrently.
A number of hypotheses were examined in the current study.
First, Hypothesis 1A states that negative thinking would be positively
correlated with measures of psychological maladjustment – depression, anxiety, anger, and stress, while Hypothesis 1B states that negative thinking would be negatively correlated with measures of
psychological well-being – life satisfaction and happiness. In contrast,
Hypothesis 2A states that positive thinking would be negatively correlated with measures of psychological maladjustment – depression,
anxiety, anger, and stress, while Hypothesis 2B states that positive
thinking would be positively correlated with measures of psychological well-being – life satisfaction and happiness.
Second, a series of hierarchical multiple regression analyses would
be used to identify the relative unique contributions of negative
thinking versus positive thinking accounting for the incremental variance in various mental health outcome variables after controlling for
either positive thinking or negative thinking respectively in second
step of entry after entry of age and sex at first step of entry
(Tabachnick & Fidell, 2007). These hierarchical multiple regression
results obtained could also be used indirectly to compare the viability
of the “vulnerability hypothesis” and the “buffer hypothesis”. If the
“vulnerability hypothesis” is more viable than the “buffer hypothesis”, we would expect that negative thinking would account for
more incremental unique variance than positive thinking in the selected outcome measures, even after controlling for demographic variables and positive thinking (Hypothesis 3A). Conversely, if the
“buffer hypothesis” is more viable than the “vulnerability hypothesis”, we would expect that positive thinking would account for
more incremental unique variance than negative thinking in the selected outcome measures, even after controlling for demographic variables and negative thinking (Hypothesis 3B).
2.2.1. Automatic Thoughts Questionnaire-Negative (ATQ-N)
The Automatic Thoughts Questionnaire-Negative (ATQ-N; Hollon
& Kendall, 1980) was designed to measure the frequency of negative
thoughts. These negative thoughts are hypothesized to play an important role in the development, maintenance and treatment of various psychological maladjustment, including depression (Beck et al.,
1979) and anxiety (Beck et al., 1985), and anger (Beck, 1999). The
thirty items on the ATQ-N are scored on a five-point Likert scale
format (One = Not at all, Two = Sometimes, Three = Moderately
often, Four = Often, Five = All the time). The total score is a sum of
all thirty items. The ATQ has excellent internal consistency and has
good concurrent validity for depression (Hollon & Kendall, 1980).
2.2.2. Automatic Thoughts Questionnaire-Positive (ATQ-P)
The ATQ-P (Ingram & Wisnicki, 1988) was developed as the theoretical complement to the ATQ-N. The ATQ-P is a thirty-item selfreport instrument that measures the frequency of positive thoughts.
The thirty items on the ATQ-N are scored on a five-point Likert scale
format (One = Not at all, Two = Sometimes, Three = Moderately
often, Four = Often, Five = All the time). The total score is a sum of
all thirty items. The ATQ-P is reported to be reliable, able to discriminate between psychopathological and non-psychopathological
states, unaffected by social desirability influences, has good convergent and discriminant validity, and is sensitive to change in affective
states (Ingram et al., 1995).
2.2.3. Beck Depression Inventory-II (BDI-II)
The BDI-II (Beck, Steer, & Brown, 1996) is a twenty-one-item selfreport measure that assesses depressive symptoms. Each item contains four statements reflecting varying degrees of symptom severity.
Respondents are instructed to circle the number (ranging from zero
to three, indicating severity) that corresponds with the statement
that best describes them. Total BDI-II score can range from zero to
sixty-three. The BDI-II has demonstrated high internal consistency,
good test-retest reliability, and good construct and concurrent validity with other common measures of depression in clinical and nonclinical samples (Beck et al., 1996; Whisman, Perez, & Ramel, 2000).
2.2.4. Beck Anxiety Inventory (BAI)
The BAI (Beck, Epstein, Brown, & Steer, 1988) is a twenty-one-item,
self-report inventory for measuring severity of anxiety. Respondents
are instructed to circle the number representing one of the four alternatives, which ranges from no anxiety (One= Not at all) to severe anxiety (Four = Severely or “I could barely stand it”). Total BAI score can
range from twenty-one to eighty-four. The BAI has demonstrated
high internal consistency and test-retest reliability and good concurrent and discriminant validity (Beck & Steer, 1991; Beck et al., 1988;
Fydrich, Dowdall, & Chambless, 1992; Riskind, Beck, Brown, & Steer,
1987). Moreover, the BAI was carefully constructed to avoid confounding with depression.
2. Method
2.1. Participants
Three hundred and ninety-eight college students from Singapore
participated in this study. The mean age was 25.53 years
(SD = 3.68). There were two hundred and fifty-three (63.6%) females
and one hundred and forty (35.2%) males. Ethnic composition
2.2.5. Trait Anger Scale (TAS)
The TAS (London & Spielberger, 1983) consists of fifteen items
that that assess anger as a relatively stable personality trait. Trait
anger is defined in terms of how frequently a respondent feels state
anger over time. The trait-anger items are rated on four-point scales
from Almost Never (One) to Almost Always (Four). Scores are the
sum of the item ratings. The TAS has very good internal consistency.
78
S.S. Wong / Learning and Individual Differences 22 (2012) 76–82
The TAS demonstrated convergent and discriminant validity
(Corcoran & Fischer, 2000).
2.2.6. Perceived stress scale-four
The PSS-Four (Cohen, Kamarck, & Mermelstein, 1983) is the shortversion of the PSS-Fourteen, which is designed to measure the degree
to which situations in one's life are appraised as stressful. The PSSFour is composed of four items (numbers two, six, seven, fourteen)
that were correlated most highly with the fourteen-item PSS- Fourteen. Respondents are instructed to circle the number representing
one of the four alternatives, which ranges from Never (Zero) to
Very Often (Four). Total PSS-Four score can range from zero to sixteen. The PSS- Fourteen showed adequate internal and testreliability and is correlated in the expected manner with a range of
measures (e.g., depressive symptoms, frequency of physical illness,
health behaviors, life-events, health behaviors, physical illness, social
anxiety). The coefficient alpha reliability estimate for the PSS-Four
was .72. The test-retest reliability of the PSS-Four over a two month
interval was .55.
2.2.7. Oxford Happiness Questionnaire-Short (OHQ-Short)
The OHQ-Short (Hill & Argyle, 2002) is designed for measuring
personal happiness. It is strongly correlated with the full OHQ. The
OHQ-Short is a six-point Likert scale ranging from Agree Strongly
(One) to Disagree Strongly (Six). Three items are reverse scored.
Scores range from eight to forty-eight. Internal consistency and
short-term test-retest reliability were found to be satisfactory. Both
versions of OHQ have demonstrated validity with measures of happiness, personality, self-esteem, satisfaction, life orientation, and life
regard.
2.2.8. Satisfaction with Life Scale (SWLS)
The SWLS (Diener, Emmons, Larsen, & Griffin, 1985) is a concise
five-item measure of global life satisfaction and is suitable for all
ages, from adolescents to adults. Respondents indicated their extent
of agreement with each of the item (for example, “In most ways my
life is close to my ideal”) on a seven-point Likert scale ranging from
One (strongly disagree) to Seven (strongly agree). The reliability and
validity of the SWLS has been considered adequate (Diener et al.,
1985; Neto, 1993; Pavot, Diener, Colvin, & Sandvik, 1991).
3. Results
Besides descriptive statistics and correlational analyses, they were
two sets of six hierarchical multiple regression analyses for each of
the six dependent variables – depression, anxiety, anger, stress, life
satisfaction, and happiness using the same four predictors of age,
sex, positive thinking, (positive automatic thoughts) and negative
thinking (positive automatic thoughts) in each of the analyses. In
set 1 (Equation 1) six hierarchical multiple regression analyses, age
and sex were entered at the first step (Model 1) for predicting each
of the six criterion variables, followed by negative thinking at second
step (Model 2), and finally positive thinking at third step (Model 3).
Model 3 also shows the results of each of the six standard simultaneous regression analyses when all four same predictors were entered together for each of the criterion variables. In set 2 (Equation
2), the sequence of entry is reversed at steps 2 and 3, after controlling
for age and sex at first step (Model 1). In contrast to set 1, positive
thinking is entered at second step and negative thinking is entered
at third step. However, Model 3 for both set 1 and set 2 do present
the same results for both sets of hierarchical multiple regression analyses since all same four predictors are entered together.
3.1. Preliminary statistics
The means, standard deviations, and standardized coefficient alphas of the measures used in this study are shown in Table 1. The
measures used in this study were also found to have internal consistency reliability estimates mostly of acceptable values, with standardized coefficient alphas ranging from .68 to .98. One item (item
number 7) from the Perceived Scale Scale-4 was deleted because
the item was found to have negative correlation coefficients with
the remaining three items, and the standardized coefficient alpha
was found to be .15. After deleting the item number 7, the PSS-3
was found to have a higher standardized coefficient alpha value of
.68.
The various correlation coefficients of the measures used in the
study are reported in Table 2. Age was significantly correlated with
sex only. Males (M = 27.286, SD = 3.226) tended to be older than females in the current sample (t = −7.462, p b .001). Sex was significantly correlated with depression, anxiety, and stress. Females
tended to score higher than males on the BDI-II (Females:
M = 12.303, SD = 8.278; Males: M = 10.293, SD = 9.136; t = 2.192,
p b .05), BAI (Females: M = 31.615, SD = 8.466; Males: M = 29.007,
SD = 9.523; t = −2.787, p b .01), and PSS-3 (Females: M = 5.613,
SD = 2.278; Males: M = 4.600, SD = 2.541; t = 4.073, p b .001). Negative thoughts were inversely correlated with positive thoughts.
3.2. Thinking and depression
Depression was significantly correlated with negative thinking,
positive thinking, and sex in descending order of magnitude. Hierarchical multiple regression analyses showed that negative thinking
significantly accounted for 29% of the variance in depression scores
after controlling for age, sex, and positive thoughts; in comparison
to positive thinking, which significantly accounted for only 1% of the
variance in depression scores after controlling for age, sex, and negative thoughts (see Table 3).
3.3. Thinking and anxiety
Anxiety was significantly correlated with negative thinking, positive thinking, and sex in descending order of magnitude. Hierarchical
multiple regression analyses showed that negative thinking significantly accounted for 20% of the variance in anxiety scores after controlling for age, sex, and positive thoughts; in comparison to
positive thinking, which significantly accounted for only 1% of the
variance in anxiety scores after controlling for age, sex, and negative
thoughts (see Table 4).
3.4. Thinking and anger
Anger was significantly correlated with negative thinking and positive thinking in descending order of magnitude. Hierarchical multiple regression analyses showed that negative thinking significantly
accounted for 11% of the variance in anger scores after controlling
for age, sex, and positive thoughts; in comparison to positive
Table 1
Means, standard deviations, and standardized coefficient alphas.
Variable
n
M
SD
Std. Coefficient Alpha
Negative Thinking
Positive Thinking
Depression
Anxiety
Anger
Stress
Life Satisfaction
Happiness
395
393
382
396
397
398
397
396
61.34
95.20
11.53
30.70
29.34
5.26
20.66
32.34
23.36
20.71
8.63
8.90
6.73
2.41
6.70
6.73
.98
.97
.91
.92
.87
.68
.88
.73
S.S. Wong / Learning and Individual Differences 22 (2012) 76–82
79
Table 2
Bivariate correlations among variables.
Measure
1
2
3
4
5
6
7
8
9
10
1. Age
2. Sex
3. Negative Thinking
4. Positive Thinking
5. Depression
6. Anxiety
7. Anger
8. Stress
9. Life Satisfaction
10. Happiness
–
.35**
–
− .08
− .04
–
.07
.09
− .60**
–
− .06
− .11*
.75**
− 54.**
–
− .05
− .14**
.54**
− .31**
.63**
–
.0
.02
.47**
− .35**
.46**
.41**
–
− .09
− .20**
.66**
− .50**
.67**
.56**
.36**
–
.04
.02
− .58**
.56**
− .56**
− .40**
− .38**
− 46**
–
.04
.02
− .66**
.62**
− .68**
− .43**
− .36**
− .60**
.63**
–
Note. ** p b .01 (2-tailed); * p b .05 (2-tailed).
thinking, which significantly accounted for only 1% of the variance in
anger scores after controlling for age, sex, and negative thoughts (see
Table 5).
thinking, which significantly accounted for only 7% of the variance in
life satisfaction scores after controlling for age, sex, and negative
thoughts (see Table 7).
3.5. Thinking and stress
3.7. Thinking and happiness
Stress was significantly correlated with negative thinking, positive
thinking, and sex in descending order of magnitude. Hierarchical
multiple regression analyses showed that negative thinking significantly accounted for 21% of the variance in stress scores after controlling for age, sex, and positive thoughts; in comparison to positive
thinking, which significantly accounted for only 1% of the variance
in stress scores after controlling for age, sex, and negative thoughts
(see Table 6).
Happiness was significantly correlated with negative thinking and
positive thinking in descending order of magnitude. Hierarchical multiple regression analyses showed that negative thinking significantly
accounted for 14% of the variance in life satisfaction scores after controlling for age, sex, and positive thoughts; in comparison to positive
thinking, which significantly accounted for only 7% of the variance in
life satisfaction scores after controlling for age, sex, and negative
thoughts (see Table 8).
3.6. Thinking and life satisfaction
4. Discussion
Life satisfaction was significantly correlated with negative thinking
and positive thinking in descending order of magnitude. Hierarchical
multiple regression analyses showed that negative thinking significantly accounted for 10% of the variance in life satisfaction scores after controlling for age, sex, and positive thoughts; in comparison to positive
The main objective of the present study to test the “vulnerability
hypothesis” versus “buffer hypothesis” pertaining to the relative incremental contributions of negative thinking versus positive thinking
in predicting depression, anxiety, anger, stress, life satisfaction, and
happiness in an Asian sample. While the current study is unique in
Table 3
Summary of hierarchical multiple regression analyses for variables predicting depression.
Table 4
Summary of hierarchical multiple regression analyses for variables predicting anxiety.
Variable
Equation 1
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
NAT
Step 3 (Model
Age
Sex
NAT
PAT
Equation 2
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
PAT
Step 3 (Model
Age
Sex
PAT
NAT
B
SE B
Beta
t
R2 Change
p
Depression
1)
.01
− .08
− 1.87
.13
.99
− .03
− .10
− .61
− 1.89
.08
− 1.56
.28
.09
.65
.01
.04
− .09
.75
.98
− 2.40
22.08
.09
− 1.41
.25
− .06
.09
.64
.02
.02
.04
− .08
.68
− .13
Depression
1.03
− 2.19
16.00
− 3.10
.01
− .08
− 1.87
.07
.13
.99
− .03
− .10
− .61
− 1.89
.00
− 1.11
− .22
.11
.84
.02
.00
− .06
− .54
.03
− 1.32
− 12.21
.09
− 1.41
− .06
.25
.09
.64
.02
.02
.04
− .08
− .13
.68
1.03
− 2.19
− 3.10
16.00
2)
.56
3)
1)
.01
2)
.28
3)
.29
.07
.54
.06
.00**
.33
.02*
.00**
.00**
.31
.03*
.00**
.00**
.54
.06
.00**
.98
.20
.00**
.00**
.31
.03*
.00**
.00**
Note. ** p b .01; * p b .05; NAT = Negative Automatic Thinking; PAT = Positive Automatic
Thinking; p = p value.
Variable
Equation 1
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
NAT
Step 3 (Model
Age
Sex
NAT
PAT
Equation 2
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
PAT
Step 3 (Model
Age
Sex
PAT
NAT
B
SE B
Beta
t
R2 Change
p
.02
.02*
.94
.01*
.00**
.29
.00**
.00**
.00**
.29
.00**
.00**
.55
Anxiety
1)
.01
− 2.71
.13
1.01
.00
− .15
.07
− 2.69
.12
− 2.59
.20
.11
.85
.02
.05
− .14
.54
1.06
− 3.04
12.47
.12
− 2.63
.21
.01
.11
.86
.02
.02
.05
− .14
.55
.03
Anxiety
1.05
− 3.08
10.34
.60
.01
− 2.74
.13
1.01
.00
− .15
.07
− 2.69
.05
− 2.32
− .13
.13
.97
.02
.02
− .13
− .30
.42
− 2.41
− 6.17
.12
− 2.63
.01
.21
.11
.86
.02
.02
.05
− .14
.03
.55
1.05
− 3.08
.60
10.34
2)
.28
3)
.01
1)
.02
2)
.09
3)
.20
.02*
.94
.01*
.00**
.67
.02*
.00**
.00**
.30
.00**
.55
.00**
Note. ** p b .01; * p b .05; NAT = Negative Automatic Thinking; PAT = Positive Automatic
Thinking; p = p value.
80
S.S. Wong / Learning and Individual Differences 22 (2012) 76–82
Table 5
Summary of hierarchical multiple regression analyses for variables predicting anger.
Variable
Equation 1
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
NAT
Step 3 (Model
Age
Sex
NAT
PAT
Equation 2
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
PAT
Step 3 (Model
Age
Sex
PAT
NAT
B
SE B
Beta
t
R2 Change
p
Anger
1)
.00
− .03
.25
.10
.76
− .02
.02
− .33
.33
.04
.35
.14
.09
.67
.01
.02
.03
.47
.39
.53
10.45
.04
.45
.12
−.04
.09
.67
.02
.02
.02
.03
.41
− .11
Anger
.41
.67
7.20
− 1.99
− .03
.25
.10
.76
− .02
.02
− .33
− .33
.00
.61
− .12
.09
.72
.02
.00
.04
− .36
.03
.39
.00
.04
.45
− .04
.12
.09
.67
.02
.02
.02
.03
− .11
.41
.41
.67
− 1.99
7.20
2)
.22
3)
.01
1)
.00
2)
.13
3)
.11
.92
.74
.74
.00**
.70
.60
.00**
.05
.68
.50
.00**
.05
.92
.74
.74
.00**
.98
.39
.00**
.00**
.68
.50
.05
.00**
Table 7
Summary of hierarchical multiple regression analyses for var. predicting life satisfaction.
Variable
Equation 1
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
NAT
Step 3 (Model
Age
Sex
NAT
PAT
Equation 2
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
PAT
Step 3 (Model
Age
Sex
PAT
NAT
B
SE B
Beta
t
R2 Change
p
.00
.60
.35
.99
.00**
.87
.83
.00**
.00**
.89
.46
.00**
.00**
Life Satisfaction
1)
.10
.10
.11
.80
.05
.00
.95
.01
.02
− .14
− .18
.09
.65
.01
.01
− .03
− .58
.16
− .21
− 14.03
.01
− .46
− .12
.11
.08
.62
.02
.02
.01
− .03
− .39
.33
Life Satisfaction
.14
− .74
− 7.84
6.65
.10
.01
.11
.80
.05
.00
.95
.01
.05
− .62
.19
.09
.67
.01
.02
− .04
.56
.53
− .94
13.20
.01
− .46
.11
− .12
.08
.62
.02
.02
.01
− .03
.33
− .39
.14
− .74
6.65
− 7.84
2)
.34
3)
.07
1)
.00
2)
.31
3)
.10
.60
.34
.99
.00**
.60
.35
.00**
.00**
.89
.46
.00**
.00**
Note. ** p b .01; * p b .05; NAT = Negative Automatic Thinking; PAT = Positive Automatic
Thinking; p = p value.
Note. ** p b .01; * p b .05; NAT = Negative Automatic Thinking; PAT = Positive Automatic
Thinking; p = p value.
its sample composition and comparative approach to studying negative and positive thinking together across multiple negative and positive health outcomes, some limitations of the current study should be
noted before discussing the findings. First, the findings may not generalize to clinical samples. Further studies using clinical samples are
needed to replicate the results. Second, the cross-sectional design of
the study limits the causal interpretation of the results. Future studies
using experimental or prospective research designs may be helpful to
illuminate this issue.
Before discussing the study main findings, an examination of the
demographic variables showed that they played a relatively minor
explanatory role compared to negative thinking and positive thinking. Age did not significantly predict any of the criterion variables,
even though the males in the current sample are much older than
Table 6
Summary of hierarchical multiple regression analyses for variables predicting stress.
Table 8
Summary of hierarchical multiple regression analyses for var. predicting happiness.
Variable
Equation 1
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
NAT
Step 3 (Model
Age
Sex
NAT
PAT
Equation 2
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
PAT
Step 3 (Model
Age
Sex
PAT
NAT
B
SE B
Beta
t
R2 Change
p
Stress
1)
.04
− .02
− .98
.04
.27
− .03
− .20
− .51
− 3.64
.02
− .93
.07
.03
.20
.00
.02
.03
.65
.56
− 4.59
17.45
.02
− .88
.06
− .02
.99
.03
.01
.01
.02
− .17
.57
− .14
Stress
.58
− 4.37
12.25
− 3.10
− .02
− .98
.04
.27
− .03
− .20
− .51
− 3.64
− .00
− .80
− .06
.03
.24
.01
.00
− .16
− .49
− .07
− 3.37
11.09
.02
− .88
− .02
.06
.03
.20
.01
.01
.02
− .17
− .14
.57
.58
− 4.37
− 3.10
12.25
2)
.43
3)
.01
1)
.04
2)
.23
3)
.21
.00**
.61
.00**
.00**
.57
.00**
.00**
.05
.56
.00**
.00**
.00**
.00**
.61
.00**
.00**
.94
.39
.00**
.00**
.56
.00**
.00**
.00**
Note. ** p b .01; * p b .05; NAT = Negative Automatic Thinking; PAT = Positive Automatic
Thinking; p = p value.
Variable
Equation 1
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
NAT
Step 3 (Model
Age
Sex
NAT
PAT
Equation 2
Step 1 (Model
Age
Sex
Step 2 (Model
Age
Sex
PAT
Step 3 (Model
Age
Sex
PAT
NAT
B
SE B
Beta
t
R2 Change
p
.00
.54
.32
.91
.00**
.96
.92
.00**
.00**
.99
.50
.00**
.00**
Happiness
1)
.10
.09
.11
.77
.05
.01
.99
.11
.00
− .06
− .19
.08
.58
.01
.00
− .00
− .67
.06
− .10
− 17.33
.00
− .36
− .13
.11
.07
.54
.01
.02
.00
− .03
− .46
.34
Happiness
− .01
− .67
− 10.29
7.62
.10
.09
.10
.77
.05
.01
.99
.11
.04
− .54
.20
.08
.61
.01
.02
− .04
.62
.47
− .89
15.27
.00
− .36
.11
− .13
.07
.54
.02
.01
.00
− .03
.34
− .46
− .01
− .67
7.62
− 10.29
2)
.44
3)
.07
1)
.00
2)
.38
3)
.14
.54
.32
.91
.00**
.64
.37
.00**
.00**
.99
.50
.00**
.00**
Note. ** p b .01; * p b .05; NAT = Negative Automatic Thinking; PAT = Positive Automatic
Thinking; p = p value.
S.S. Wong / Learning and Individual Differences 22 (2012) 76–82
the females, due to compulsory military service for males before entering University. A person's sex was also found not to be related to
either type of thinking. This means that males are as likely as females
to think both negatively as well as positively, contrary to common
gender stereotype.
Similar to previous research findings in the West (Zahn-Waxler,
Crick, Shirtcliff, & Woods, 2006), sex was significantly correlated
with depression, anxiety, and stress. Hierarchical multiple regression
results showed that females were more likely than males to be
stressed and anxious. However, the gender effect sizes were weaker
when compared to both types of thinking after thinking is entered
into the equations. These results implied the importance of examining factors beyond sex when explaining individual differences in psychological maladjustment as well as psychological well-being.
With regard to the hypotheses being tested, correlational results
supported Hypothesis 1A, Hypothesis 1B, Hypothesis 2A, and Hypothesis 2B. As expected, negative thinking were positively correlated
with measures of psychological maladjustment – stress, anxiety, depression, and anger (Hypothesis 1A), and negatively correlated with
measures of psychological well-being – life satisfaction and happiness
(Hypothesis 1B). As for positive thinking, the reverse pattern was
obtained as expected. The positive thinking were negatively correlated with measures of psychological maladjustment – stress, anxiety,
depression, and anger (Hypothesis 2A), and positively correlated
with measures of psychological well-being – life satisfaction and happiness (Hypothesis 2B). The correlational results obtained were congruent with the theoretical expectations as well as past research
findings. Moreover, they provided cross-cultural convergent and divergent criterion-related validity for the construct of both negative
thinking and positive thinking in an Asian sample. Similar to the
western studies (e.g., Beck, 1999, Lightsey, 1994a, 1994b; Lightsey &
Christopher, 1997; Olioff et al., 1989; Szentagotai & Freeman, 2007),
negative thinking was found to be positively related to depression,
anxiety, anger, and stress; and negatively related to happiness. In addition, negative thinking was found to be negatively related to life
satisfaction.
The hierarchical multiple regression results supported Hypothesis
3A, instead of Hypothesis 3B. The set 2 hierarchical multiple regression results showed that negative thinking accounted for more of
the incremental unique variance in depression (29% vs. 1% in set 1),
stress (21% vs. 1% in set 1), anxiety (20% vs. 1% in set 1), happiness
(14% vs. 7% in set 1), anger (11% vs. .8% in set 1), and life satisfaction
(10% vs. 7% in set 1) in order of effect size after controlling for demographic variables and positive thinking in comparison with the set 1
hierarchical multiple regression results, which controlled for demographic variables and negative thinking. These results appeared to
support the viability of the “vulnerability hypothesis” over the “buffer
hypothesis”. This is consistent with the proposal by Kendall (1984)
and Kendall and Hollon (1981) in that the presence of positive thinking may be less important in determining psychological maladjustment than the absence of negative thinking. This current finding
collaborate the ongoing area of active research into the “vulnerability
hypothesis” especially for psychological disorders, beyond maladjustments (Ingram & Price, 2010).
Nevertheless, there was also evidence to support a role of “buffer
hypothesis” for happiness and life satisfaction, since positive thinking
also accounted for significant incremental unique variance of 7% in
both measures. A smaller effect size of 1% was obtained for stress,
anxiety, depression, and anger. The results were similar to those
obtained in Western samples (e.g., Ingram, Slater, Atkinson, Scott,
1990; Lightsey, 1994b).
The current study provided a number of implications. First, the results implied that negative thinking are relatively more important
than positive thinking in explaining depression, stress, anxiety, happiness, anger, and life satisfaction. This means that evoking positive
thinking alone may not be sufficient to protect oneself from depression,
81
stress, anxiety, and anger, or increase one's happiness and life satisfaction. It is also consistent with the “power of non-negative thinking” by
a number of theorists such as Kendall (1984). Second, the results shed
further light on the idea of “exclusivity hypothesis” (Haaga, Dyck, &
Ernst, 1991, which states that “depressed people will report virtually
no positive cognitions, only negative or neutral ones” (p. 217–218).
Perhaps the severity of the depression may play a part in determining
the frequency of positive thoughts present. In the current nonclinical
sample, it was found that the presence of positive thinking do explain
about 1% (vs. 29% for negative thinking) of the incremental unique variance in depression. Third, the results were consistent with the “negativity hypothesis” (Haaga et al., 1991), which states that “depressed
persons’ thinking are more negative than are those of nondepressed
people” (p. 216). Furthermore, the “negativity hypothesis” was also
supported for other challenges such as stress, anxiety, and anger. A related area of discovery was that a logical corollary of the “negativity hypothesis” in the form of “positivity hypothesis” may be supported by
the current research. This means that satisfied and happy people thinking may be more positive than those who are not. Finally, negative and
positive thinking have directly opposite natures since they are negatively correlated, but they are not mutually exclusive. It is possible for
one individual to have both types of thinking within a specific period
of time or in a given situation/setting. However, individuals with high
PATs do have low NATs, and vice versa.
Acknowledgments
Funding for this research is facilitated by a research grant RI 11/05
WSS from the National Institute of Education.
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