Hadi Alnasir Research Proposa

Hadi Alnasir
Research Proposal
Independent variable 1: Sex
Independent variable 2: anxiety
Dependent variable: Stress
Question #1
My first independent variable (sex) and my dependent variable
(stress) are related. Men and
women tend to experience stress differently. Similarly, men and
women react differently to
stress.
I expect women to score higher than men on the dependent
variable. Women suffer more stress

compared to men. A 2010 study discovered that women are
more likely to experience an
increase in stress levels as compared to men. Women are also
more likely to report emotional
and physical symptoms of stress compared to men (APA, 2012).
The stress gap between men
and women is because their stress response is different. Women
have a different hormonal
system that usually causes them to react more emotionally and
become more fatigued.
Similarly, women are exposed to more stress-related factors
since they assume several roles in
their daily life.
Question #2
My second independent variable (anxiety) is related to my
dependent variable (stress). Anxiety
and stress can both cause severe physical and mental health
issues, such as depression, muscle
tension, substance abuse, personality disorders, and insomia
(Powell & Enright, 2015). Both are
emotions and normal responses that can become disruptive and
overwhelming to day-to-day

life. They can interfere with important aspects of life, such as
work, relationships,
responsibilities, and school.
An increase in anxiety can increase stress levels. Research
indicates that excessive anxiety can
lead to stress-related symptoms such as difficulty concentrating,
insomnia, irritability, muscle
tension, and fatigue. Individuals can manage their anxiety and
stress with relaxation techniques.
This includes breathing exercises, yoga, physical activity, art
therapy, meditation, and massage.
References
APA. (2012). 2010 Stress in America: Gender and Stress.
Retrieved from:
https://www.apa.org/news/press/releases/stress/2010/gender-
stress
Powell, T., & Enright, S. (2015). Anxiety and stress
management. Routledge.
Running Head: GENDER AND STRESS AS PREDICTORS OF
DEPRESSION

Gender and Stress as Predictors of Depression
Zae’Cari Nelson
California Baptist University
1
More than 17 million adults in the United States experience the
ill effects of depression,
making it perhaps the most well-known mental illness in the
U.S.A. Depression influences an
expected one out of 15 adults. What's more, one out of six
individuals will encounter depression
in their life (What is Depression?). There are a mind-boggling
number of elements that can
prompt depressive symptoms in male and female individuals,
one of which is held to be a rise in
stress hormone disturbances. Male and female genders rely on
different coping strategies to
handle stressors adequately and effectively. The purpose of this
study is to explore gender as a
predictor of depression controlling for stress.

Depression is a medical condition that is both common and
serious. It influences how an
individual think, act, and feel. Depression also results in a loss
of interest in things that were
once enjoyable. An assortment of emotional and physical
problems as well as a decrease in one’s
ability to function at home and/or in a public setting can be
prompted by the onset of depression
(What is Depression?). Physicians and researchers have studied
depression for several years and
have led research on numerous individual symptoms along with
clustered symptoms, and an
excess of factors that might be the reason for developing
depressive symptoms. Some research
studies identify participants by gender and age. Depressive
symptoms based on life stressors of
medical students ages twenty-six to thirty were examined in a
recent study. The study showed
that depressive symptoms based on life stressors of medical
students ages twenty-six to thirty
expanded by 71% over a 3-month, 6-month, 9-month, and 12-
month time span (Fried, 2015).
This specific study comprised of an aggregate of 3,021 people:
52.6% female and 48.4% male.

Additionally, depressive symptoms when explained by both
males and females, are almost
1
2
identical when life stress is a contributory factor of the major
depressive disorder. This suggests
that gender may not be a significant predictor of stress and
depression.
Literature Review
Stress. Stress is characterized as how much you feel
overpowered or incapable to adapt
because of pressing factors that are unmanageable (Stress MHF,
2021). At any rate, stress is our
body's reaction to life occurrences as well as circumstances
(Sha, 2006). Social conditions,
financial conditions, environment, and hereditary qualities are
overall contributors of an
individual's degree of stress. Numerous contributors of stress
can shift vastly from one individual
to another, male and female, and so does how reactions are
processed. Given the way that good

and regrettable life changes can both be a stressor for one
individual, it would not be implausible
if stress factors like separation, work layoff, death, or monetary
troubles prompt sentiments of
depression. It has been determined that men and women manage
stress factors differently.
Though the two sexes regularly report comparable feelings of
anxiety, yet report fluctuating
physical and emotional symptoms (Shih, 2004). Many
depression symptoms carry somewhat of a
comparative result as having life altering stressors. In this way,
to appropriately identify the
aspect of gender in predicting depression, it is important to
govern for the possible effect of
stressors.
Gender. According to The World Health Organization, gender is
the qualities of men,
women, boys, and girls that are communally developed also
including standards, practices and
functions related with being a man, woman, boy or girl, just as
correlations with one another. In a
socially productive way, gender will differ from one
environment to another and can change after
some time. Gender is estimated to affect depression since an

individual’s stress hormones and an
individual’s genetics proposes predominant controlling factors
that add to a mental illness like
2
3
depressive disorder. Ongoing studies have discovered that
women are more probable than men to
encounter depression (Zwicker and DeLongis, 2010). A few
examinations show that 33% of
women will encounter a substantial depressive episode during
their life. (Hyde, 2020). There is
around a 40% chance of heritability when first-degree family
members such as parents, children,
and siblings, have depression (Sowa and Lustman, 1984).
Existing studies that concentrate on the
correlation between gender and depression has been led
primarily centered around age groups,
the current study will expand the literature by researching if
depression is predicted by gender.
To state the hypothesis, depression controlling for the impact of
stressors gender will largely be

predicted by depression.
Method
Participants
101 adult Online and Professional Studies undergrads at
(CBU) were the participants. California Baptist University is a
private university of the Christian
faith located in the Southern region of California; Riverside,
CA and has an enlistment of around
11,317 students. The participants ages extended from 18 to 54
years; 12 males and 88 females. In
addition to providing spiritual and social expansion
opportunities, the Online and Professional
Studies division at CBU offers over 40 fully accredited online
degree programs to their students.
Measures
Sex/Gender. Respondents were queried to specify their gender.
Feedback choices
included male (12%) and female (87%).
Stress. A four item self-report measure of stress levels in adult
college students was used
to measure stress levels of the participants. This subscale i s
called the DASS-21 Stress Subscale,

3
4
it’s responses consist of stress states such as "I feel that I
utilize a ton of anxious energy") on a
7-point Likert-type scale going from 1 (doesn't concern me by
any stretch of the imagination) to
7 (more often than not). Participants recorded their responses
using the applicable corresponding
stress states. In past studies, proof of moderate to strong
internal reliability was found, with a
range of .59 to .81 for Chronbach's alphas (Osman et al. 2012).
The validity of measure is
supported by positively correlating scores from the measure of
mixed depression and
anxiety/stress and DASS-21 Stress subscale scores.
Factor analysis. To inspect the factor structure of the four -item
scale, an exploratory
factor analysis (EFA) was executed.
According to Kaiser’s K-1 rule, there is a one-factor
solution due to only one factor

having an eigenvalue greater than 1. The principal factor
clarified 57.57% of the variety in
scores. To establish if the scale acted for a
unidimensional construct, a scree test was
administered. The point of inflection is plainly indicated
by the scree plot at the second
component. This suggests a one-factor solution. Afterward,
to establish if all items loaded
heavily on the first factor, the component matrix had to be
inspected. As the result, each of the
four items were determined to have loadings with absolute
values more than .40 on the first
factor.
4
5
Reliability analysis. To establish the stress scales’ internal
reliability, a reliability analysis
was administered. As the result of the reliability analysis, there
was a Chronbach's alpha score
of .75. This was acceptable for psychome tric purposes.
Distribution of composite scores. At last, the four-item scale

was used to generate a
composite score, which produced a homogenized (M=0.00,
SD=1.00) stress level score for all
respondents in the sample, going from - 1.52 to 2.71. The
distribution was slightly leptokurtic
with a moderate positive skew as the result. Scores were on the
low to mid-range with very few
showing significant degrees of stress for respondents.
5
6
Depression. The DASS-21 Depression Subscale was used to
measure the levels of
depression for respondents. This subscale is a four item self-
report measure of depression levels
in adult college pupils. Respondents provided responses to
items showing a depressed state (e.g.,
"I feel that I have nothing to anticipate") on a 7-point Likert-
type scale going from 1 (doesn't
concern me by any means) to 7 (more often than not). In earlier
studies, indications of moderate

to strong internal reliability was found, with Chronbach's alphas
going from .63-.70 (Osman et
al. 2012). It was found that scores on a measure of depression
and anxiety/stress combined
correspond positively with scores from the DASS-21 depression
subscale. Therefore, the validity
of the measure is supported.
Factor analysis. To inspect the four-item scale factor structure,
an Exploratory Factor
Analysis (EFA) was ran.
6
7
An eigenvalue greater than 1, was reported for only one factor
and 71.74% of the
variation in scores was explained by the first factor. Thus, in
accordance with Kaiser's K-1 rule,
the data offer that there is a one-factor solution. Similarly, to
establish the unidimensional
construct within the scale, a scree test was ran. Since the point
of inflection is shown plainly at
the second component on the scree plot, a one-factor solution is

implied. Moreover, to establish
if all the items loaded heavily onto the first factor, the
component matrix was inspected. As result
of the inspection, it was determined that loadings with absolute
values over .40 on the first factor
was apparent for all four of the items.
7
8
Reliability analysis. To establish the internal reliability of the
depression level scale, a
reliability analysis was ran. As result of the reliability analysis,
the Chronbach's alpha score was .
86, which was satisfactory for psychometric purposes.
Distribution of composite scores. Using the four-item scale, a
composite score was
generated. Resulting in a homogenous (M=0.00, SD=1.00)
depression level score for all
respondents in the sample going from - .81 to 3.48.
Subsequently, the distribution result was
leptokurtic with a positive skew. Participants scored mostly on

the low end of the depression
level scale with only a few signifying high levels of depression.
8
9
Procedure
Questionnaires were anonymously administered to the adult
student participants that were
enrolled in the Online and Professional Studies division of CBU
for the psychology major.
Because the online questionnaire was completed outside of class
hours, students were given
additional credit.
Results
Descriptive Statistics
Before visually inspecting the correlation between independent
and dependent variables,
the figures had to be established first. Next, an examination was
completed to establish the
correlation between sex and depression. Since sex is a

categorical predictor, a boxplot was used
to execute the examination.
Median depression level for both genders was established
through a visual examination
of the boxplot. The visual examination holds that the median
depression level for females is like
that of males. For the male scores there is more variability as
some males scored comparatively
9
10
high whereas the females scored slightly low with some
potential outliers. A slight positive
correlation amongst stress levels and depression was apparent in
a visual assessment of the
scatterplot. Generally, as stress levels increased so did
depression levels. Though, some
respondents scored low in both. To inspect the correlation
amongst each variable, both
independent and dependent, a correlation test was run.
The results of the correlation test concluded that stress and

depression had a significant
moderate positive correlation, r (96) = .49, p<.01. However,
gender and depression presented a
very small positive relationship r (96) = .06, p=.59. An issue
with collinearity was not presented
in this model as the two independent variables do not
significantly correlate with one another.
Checking Assumptions
Homogeneity of variance. The analysis was comprised of both
independent variables
held in the model to test the assumptions. Predicted values and
unstandardized residuals were
kept as new factors in the dataset. The predicted values were
placed on the x-axis while the
residuals were placed on the y-axis of the scatterplot. This was
done to test for homogeneity of
variance.
10
11
The distribution of residuals was revealed through a visual
examination of the scatterplot.

As the result of the visual examination, it was determined that
the residuals are not evenly
distributed. Clusters and slight fanning appear on the scatterplot
also. Since equal predictive
power for persons at the high end of the line and the low end of
the depression level scale is not
present in the model, the assumption of homogeneity of
variance is not supported.
Normally distributed residuals. A histogram of the distribution
was constructed to find
out how residuals were distributed. The goal was to determine if
residuals were normally
distributed. To do so, a normal curve was superimposed on top.
As the result, the assumption of
normally distributed residuals was not supported because they
are not normally distributed.
11
12
Multiple Regression Analysis
Lastly, to establish if the hypothesis was supported, an
interpretation for each of the

output tables in the multiple regression analysis was conducted.
12
13
The model resulted in significantly improved predictions than
predictions based solely on
the mean because the F-statistic was statistically significant,
F(2, 93) = 16.28, p < .001.
According to the table, gender is not a statistically significant
predictor of the
respondent’s depression levels. However, stress level is.
Controlling for stress, my hypotheses is
not supported.
The regression equation is:
The expected depression level score for a student that has an
average level of stress is .03.
Controlling for stress, there is a .10 standard deviation decrease
in gender which does not
correlate to a .03 standard deviation in depression levels.
13

14
Discussion
In view of the outcome, my hypothesis was rejected. With the
reason being because when
controlling for stressors, depression is not significantly
predicted by sex. However, when using
sex as an independent variable, it makes for an unbalanced
participant pool since the number of
participants total to 101, but with only 12 males and 89 females.
A more equally balanced
number of participants would have essentially prompted an
improved and more precise outcome.
In spite of the fact that review discoveries are positively
precise, there are a few limitations. To
begin, this study included a wide array of participants from ages
18 years of age to 54 years of
age. I view such large age fluctuations as a substantial
limitation. As an individual’s level of
maturity has much contingency on the way they process a
stressor. Regardless of sex, adults that
have more life experiences then to process stressors more
maturely than adults that have less

experiences in life.
The results of this investigation liken results from a study that
was conducted in February
2015 by Eiko I Fried, an Associate Professor in Clinical
Psychology at Leiden University. In the
study led by Fried, male and female participants described their
depressive symptoms similarly
to how they were described in this study. Which suggests that
when correlating stress and
depression, sex may not be a significant factor.
This study, specifically, can undoubtedly be summed up to the
college populace by just
interpreting the results from an sample populace and applying it
to the populace at large: Making
the investigation more equivalent as it relates to sex and
increasing the sample size to be greater
than 101 participants. Hypotheses formed by many speculators
that theorize that males have less
life stressors than females and therefore suffer from symptoms
of depression less than females
are only stating a hypothesis not back by evidence that any
statistical significance is present.
14

15
In light of the results of this study, future scientists ought to
follow an alternate
methodology to acquire more accurate and exact discoveries.
Future scientists would benefit
from choosing an equivalent measure of male and female
participants just as dividing the
participants by age. This set out as a better procedure for future
studies and establishes a
clearer path for impending research to trail.
My experimental results conclusion rejected my hypothesis on
sex assuming a huge part
in stress and depression. Truth be told, this study completely
annulled my underlying speculation
though I firmly held males would have less stressors and thusly
lower levels of depression than
females. My general impression, which is not in accordance
with my referenced study or the
present study, is that the exploration study was very amazing,
and I would be keen on proceeding
with it with a more unambiguous scope of participants to decide

whether there is any chance of
truth to my original investigation and research.
15
16
References
Stress. Mental Health Foundation.
https://www.mentalhealth.org.uk/a-to-
z/s/stress#:~:text=Stress%20can%20be%20defined%20as,of%2 0
pressures%20that
%20are%20unmanageable.
Shih, J. H.-F. (2004). Sociotropy/autonomy and depression:
Gender differences and the
mediating role of stressful life events ProQuest Information &
Learning]. APA PsycInfo.
http://libproxy.calbaptist.edu/lo gin?
url=https://search.ebscohost.com/login.aspx?
direct=true&db=psyh&AN=2004-99010- 099&site=ehost-
live&scope=site
Zwicker, A., & DeLongis, A. (2010). Gender, stress, and
coping. In J. C. Chrisler & D. R.
McCreary (Eds.), Handbook of gender research in psychology,
Vol 2: Gender research in
social and applied psychology. (pp. 495-515). Springer Science
+ Business Media.
https://doi.org/10.1007/978-1-4419-1467-5_21

Sowa, C. J., & Lustman, P. J. (1984). Gender differences in
rating stressful events, depression,
and depressive cognition. Journal of Clinical Psychology, 40(6),
1334- 1337.
https://doi.org/10.1002/1097-4679(198411)40:63.0.CO;2-8
What is Depression? American Psychiatric Association.
https://www.psychiatry.org/patientsfamilies/depression/what-is-
depression
Sha, T. (2006). Optimism, Pessimism and Depression; The
Relations and Differences by Stress
Level and Gender. Acta Psychologica Sinica, 38(6), 886-901.
Fried, E. I., Nesse, R. M., Guille, C., & Sen, S. (2015). The
differential influence of life stress on
individual symptoms of depression. Acta Psychiatrica
Scandinavica, 131(6), 465- 471.
https://doi.org/10.1111/acps.12395
Hyde, J. S., & Mezulis, A. H. (2020). Gender differences in
depression: Biological, affective,
cognitive, and sociocultural factors. Harvard Review of
Psychiatry, 28(1), 4- 13.
https://doi.org/10.1097/HRP.0000000000000230
16
https://doi.org/10.1002/1097-4679(198411)40:63.0.CO;2-8
https://doi.org/10.1007/978-1-4419-1467-5_21
**See attachment labeled >> Research Proposal<< for my
Variables (remember you helped me complete that assignment).
NOW: that you have finalized your research question, you will

begin to write the introduction to your final paper. The
introduction must be between 2 and 3 full pages (not counting
the title page). Begin by introducing the problem and proceed to
walk the reader through the relevant literature. Using
PsychINFO, locate at least five empirical articles related to
your topic. These must be correctly and meaningfully cited
within your introduction.Your introduction should adhere to the
following format:
· APA title page (title should allude to all of your key variables)
· Introduction (identify the problem area, explain the
importance of the problem area, define the dependent variable
in light of prior literature)
· Lit Review (define each independent variable in light of prior
literature, explain why and how each independent variable
should influence the dependent variable, cite at least one study
in which the theorized relationship [or a similar relationship]
occurred)
· Explain how your study extends the literature
· Clearly state your hypothesis / research question
For additional guidance in the completion of this assignment:
· See the attachment for an example of an introduction section.
NEXT: You will complete the method section of your final
capstone project. The Method section must include the
following subsections: Participants, Measures, and Procedure.
Under the Measures subsection, you need to provide detailed
information about all of the variables in your study. For each
instrument you use in your study, include a sample item and list
the response options. Also, cite at least one prior study which
used that same instrument. Provide the internal reliability of
that measure, as reported by the previous author, as well as
some validity evidence for the measure. I have provided some
useful articles in a separate folder (“Instrumentation Articles”)
to help you with this part of the assignment. Using the attached
dataset, you will then examine and report the factor structure of
the items, as well as their internal reliability.
—>See the attachment for an Example of an exemplary method

section
If you are having trouble finding appropriate articles for the
Exemplary method section, feel free to look At the attachment
labeled below:
*Depression, Anxiety, Osman
*Self -Esteem
*Parental Attachment
*Private Religious practices
*Optimism
-----> Also I attached a copy of a students completed work in
order for you to see a completed example **Attachment is
labeled Student Example. Please don't copy the students' work.
You can use the numbers, calculations, charts. So that will make
the process easier. But don't use the words/writings.
Running head: GENDER AND STRESS AS PREDICTORS OF
DEPRESSION 1
Ana Marie Agee Booth
Gender and Stress as Predictors of Depression 2
Over 17 million adults in the United States suffer from some

sort of depression, making it
one of the most common mental illnesses in America.
“Depression affects an estimated one in 15
adults (6.7%) in any given year. And one in six people (16.6%)
will experience depression at
some time in their life.” (What is Depression?). There are an
overwhelming number of factors
that can lead to depressive symptoms in both men and women,
one of which is said to be an
elevation in stress hormone disturbances. Both genders rely on
different coping methods to
process stressors effectively and successfully in a manageable
method. Thus, the purpose of this
study is to investigate gender as a predictor of depression
controlling for stress.
Depression is defined as a common and serious medical illness
that negatively affects how you
feel, the way you think and how you act and causes feelings of
sadness and/or a loss of interest in
activities you once enjoyed. It can lead to a variety of emotional
and physical problems and can
decrease your ability to function at work and at home (What is
Depression?). Doctors and
scientists have studied depression for many years. They have

conducted research on many
individual symptoms as well as clustered symptoms, in addition
to the plethora of factors that
may be the cause of developing depressive symptoms. Many of
the research studies identify
participants according to their gender and then their ages. For
example, a recent study indicated
that depressive symptoms based on life stressors of medical
interns ages twenty-six to thirty,
increased by 71% over a 3-, 6-, 9-, and 12-month time period
(Fried, 2015). This particular study
consisted of a total of three thousand and twenty-one
individuals, with 48.4% being male and
52.6% being female. Furthermore, both males and females
explain their depressive symptoms
almost equally in terms of life stress as a causal factor of major
depressive disorder, suggesting
that gender may not be a significant factor when correlating
stress and depression.
Literature Review
Stress. Stress is defined as the degree to which you feel

overwhelmed or unable to cope
as a result of pressures that are unmanageable (Stress MHF,
2021). At the very least, stress is our
body’s response to specific life events and/or situations (Sha,
2006). Social circumstances,
economic circumstances, environment, and genetics are all
contributors to a person’s level of
stress. As the many contributions of stress can vary immensely
from person to person, male and
female, it also varies in how responses are processed. Given the
fact that positive and negative
life changes both have the ability to cause one to experience
some form of stress, it would not be
surprising if stress factors such as divorce, job loss, death, or
financial difficulties led to feelings
of depression. Stress factors are managed very differently
among men and women. Both genders
typically report similar stress levels, yet report varying physical
and emotional symptoms (Shih,
2004). Many of the symptoms of depression have a rather
similar look as having life changing
stressors. Therefore, in order to properly understand the role of
gender in predicting depression,
it is necessary to control for the likely influence of stressors.

Gender. The World Health Organization defines gender as the
characteristics of women,
men, girls and boys that are socially constructed including
norms, behaviors and roles associated
with being a woman, man, girl or boy, as well as relationships
with each other. In a socially
constructive manner, gender will vary from environment to
environment and can change over
time. Gender is theorized to impact depression because one’s
stress hormones and one’s genetics
offer largely influential factors that contribute to a mental
illness such as depressive disorder.
Recent research has found that women are more likely than men
to experience depression
(Zwicker & DeLongis, 2010). “Some studies show that one-
third of women will experience a
major depressive episode in their lifetime.” (Hyde, 2020). There
is a high degree of heritability
(approximately 40%) when first-degree relatives
(parents/children/siblings) have depression
(Sowa & Lustman, 1984).

As existing research study on the relationship between gender
and depression has been
conducted primarily focused on age groupings, the current study
will extend the literature by
investigating whether gender predicts depression. The
hypothesis is that gender will significantly
predict depression controlling for the influence of stressors.
Method
Participants
Participants were 101 adult college students enrolled in the
Online and Professional
Studies division at California Baptist University (CBU). CBU is
a private Christian university
located in Riverside, California with an enrollment of
approximately 11,317 students on campus
and online. The age of the adult college student participants
ranged from 18 to 54 years
(M=28.29, SD=7.41). Of the 101 participants, 88 were female
(87%) and 12 were male (12%).
The Online and Professional Studies division at California
Baptist University offers more than
40 fully online and fully accredited degree programs while also
providing spiritual and social

development opportunities.
Measures
Sex/Gender. Respondents were asked to indicate their gender.
Response options
included female (87%) and male (12%).
Stress. Adult college student’s levels of stress were measures
using the DASS-21 Stress
Subscale, a four item self-report measure of stress levels in
adult college students. Participants
responded to items indicating a stressed state (e.g., “I feel that I
use a lot of nervous energy”) on
a 7-point Likert-type scale ranging from 1 (does not apply to me
at all) to 7 (most of the time).
Evidence of moderate to strong internal reliability has been
found in previous studies, with
Chronbach’s alphas ranging from .59 to .81 (Osman et al. 2012).
DASS-21 Stress subscale scores
are positively correlated with scores on a measure of mixed
depression and anxiety/stress, thus
supporting the validity of the measure.

Factor analysis. An exploratory factor analysis (EFA) was
conducted in order to
examine the factor structure of the four-item scale.
Only one factor had an eigenvalue greater than 1, thus
suggesting a one-factor solution
according to Kaiser’s K-1 rule. The first factor explained
57.57% of the variation in scores. A
scree test was also conducted in order to determine whether the
scale represented a
unidimensional construct. The scree plot shows the point of
inflection clearly at the second
component, thus suggesting a one-factor solution. Next, the
component matrix was examined to
see whether all of the items loaded heavily onto the first factor.
All four items had loadings with
absolute values over .40 on the first factor.
Reliability analysis. Next, a reliability analysis was conducted
in order to determine the
internal reliability of the stress level scale. This analysis
produced a Chronbach’s alpha score of .

75, which was acceptable for psychometric purposes.
Distribution of composite scores. Finally, a composite score
was created from the four-
item scale, generating a standardized (M=0.00, SD=1.00) stress
level score for each adult college
student in the sample ranging from -1.52 to 2.71. The resulting
distribution was slightly
leptokurtic with a moderate positive skew. Most of the
respondents scored on the low to middle
end of the stress level scale with only a few indicating high
levels of stress.
Depression. Adult college student’s levels of depression were
measured using the DASS-
21 Depression Subscale, a four item self-report measure of
depression levels in adult college
students. Participants responded to items indicating a depressed
state (e.g., “I feel that I have
nothing to look forward to”) on a 7-point Likert-type scale
ranging from 1 (does not apply to me
at all) to 7 (most of the time). Evidence of moderate to strong
internal reliability has been found

in previous studies, with Chronbach’s alphas ranging from .63-
.70 (Osman et al. 2012). DASS-
21 depression subscale scores are positively correlated with
scores on a measure of mixed
depression and anxiety/stress, thus supporting the validity of
the measure.
Factor analysis. An exploratory factor analysis (EFA) was
conducted in order to
examine the factor structure of the four-item scale.
Only one factor had an eigenvalue greater than 1, thus
suggesting a one-factor solution
according to Kaiser’s K-1 rule. The first factor explained
71.74% of the variation in scores. A
scree test was also conducted in order to determine whether the
scale represented a
unidimensional construct. The scree plot shows the point of
inflection clearly at the second
component, thus suggesting a one-factor solution. Next, the
component matrix was examined to
see whether all of the items loaded heavily onto the first factor.
All four items had loadings with

absolute values over .40 on the first factor.
Reliability analysis. Next, a reliability analysis was conducted
in order to determine the
internal reliability of the depression level scal e. This analysis
produced a Chronbach’s alpha
score of .86, which was good for psychometric purposes.
Distribution of composite scores. Finally, a composite score
was created from the four-
item scale, generating a standardized (M=0.00, SD=1.00)
depression level score for each adult
college student in the sample ranging from -.81 to 3.48. The
resulting distribution was
leptokurtic with a positive skew. Most of the respondents scored
on the low end of the depression
level scale with only a few indicating high levels of depression.
.

Procedure
The adult college students were administered the questionaires
anonymously. All of the
students, majoring in psychology, were currently enrolled in the
Online and Professional Studies
division of California Baptist University. The surveys were
completed online, outside of
classroom hours and each student was given extra credit for
completion of the online
questionaire.
Results
Descriptive Statistics
First, figures were constructed in order to visually examine the
relationship between each
independent variable and the dependent variable. A boxplot
was used to examine the
relationship between sex and depression because sex is a
categorical predictor. A scatterplot was
used to examine the relationship between stress and depression
because stress is a continuous
predictor.

A visual inspection of the boxplot revealed that the median
depression level for females
appears to be similar to that of the males. There is more
variability among the male scores than
the female scores. Some males scored relatively high, while the
females scored somewhat low
with several potential outliers and otherwise unusual responses.
A visual inspection of the
scatterplot revealed that there appeared to be a slight positive
relationship between stress levels
and depression. In most cases, as stress levels increased,
depression also increased. However,
some college students scored low on stress levels and also low
on depression levels.
Next, a correlation test was conducted to examine the
relationships between each
independent variable and the dependent variable. There was a
significant moderate positive
correlation between stress and depression, r(96) = .49, p<.01.
There was also a very small
positive correlation between gender and depression r(96) = .06,
p=.59. The two independent

variables were not significantly correlated; thus, collinearity
was not an issue in this model.
Checking Assumptions
Homogeneity of variance. In order to test the assumptions, the
analysis was run with
both independent variables included in the model. Predicted
values and unstandardized residuals
were saved as new variables in the dataset. To test for
homogeneity of variance, a scatterplot
was created with the predicted values on the x-axis and the
residuals on the y-axis.
A visual inspection of the scatterplot revealed that the residuals
are not evenly distributed
and there appears to be clusters and some slight fanning. This
means that the model does not
have equally predictive power for individuals at both the high
end and the low end of the
depression level scale. Thus, the assumption of homogeneity of
variance is not supported.

Normally distributed residuals. In order to determine whether
residuals were normally
distributed, a histogram of the distribution was created with a
normal curve superimposed on top.
A visual inspection of the histogram revealed that the residuals
are not normally distributed.
Thus, the assumption of normally distributed residuals is not
supported.
Multiple Regression Analysis
Finally, each of the output tables in the multiple regression
analysis was interpreted in
order to determine whether my hypothesis was supported.
The R2 coefficient was .26. This means that 26% of the
variation in adult college
student’s depression levels are predicted by the model, while
74% of the variation was not
predicted by the model. The amount of predictive ability was
determined to be substantively
significant.

The F-statistic was statistically significant, F(2, 93) = 16.28, p
< .001, indicating that the
model results in significantly better predictions than those
based solely on the mean.
This table reveals that stress level is a statistically significant
predictor of adult college
student’s depression levels. Gender is not a statistically
significant predictor of adult college
student’s depression levels, controlling for stress. Thus, my
hypotheses is not supported.
The regression equation for this model is:
ŷ = -.03 - .51x1 + .41x2
Where .03 is the expected depression level score for a student
who has an average level of stress.
Controlling for stress, there is a .10 standard deviation decrease
in gender which does not
correspond to a .03 standard deviation in depression levels.
Discussion
Based on the results, gender was not a significant predictor of
depression, controlling for

stressors and my hypothesis was rejected thus being
disconfirmed. This study consisted of 101
participants, 89 being female and 12 being male. This makes for
an unbalanced participant pool,
especially when using gender as an independent variable. A
more equally balanced selection of
participants would have led to significantly better and more
accurate results. Although study
findings are certainly accurate, there are some limitations. First,
this study used a wide range of
adults ages 18-54. I consider this a very strong limitation as
young adults, whether male or
female, process stressors more immaturely than adults who have
more life experience.
The results of this study correspond with the results in the 2015
study conducted by
Fried, in which males and female explained their depressive
symptoms almost equally and
suggesting that gender may not be a significant factor when
correlating stress and depression.
This study, in particular, can easily be generalized to the
college and university
population by simply inferring the results from a sample
population and applying it to the

population at large: Increasing the sample size to a population
significantly larger than 101
college students and making the study more equal as far as
gender.
Many theorize that females have more life stressors and
therefore suffer from depression
more than men, but these theories are just that and have not
been shown to possess any statistical
significance.
Based on the results of this study, future researchers shoul d
follow a different
methodology to obtain more accurate and precise findings.
Future researchers would benefit
from selecting an equal amount of male and female participants
as well as separating the
participants into age groups. This will better serve additional
future studies and set a more
specific path for future research to follow.
My data-based conclusion disputed my speculation that gender
would play a large role in
stress and depression. In fact, this study somewhat drastically

negated my initial hypothesis. I
strongly felt that women would definitely have more stressors
and therefore higher levels of
depression. This was not in line with my referenced study or the
present study. My overall
impression of this research study was actually quite surprising
and I would be interested in
continuing it with a more specific range of participants to
determine if there is any possibility of
truth to my initial investigation and research.
Works Cited
Fried, E. I., Nesse, R. M., Guille, C., & Sen, S. (2015). The
differential influence of life stress
on individual symptoms of depression. Acta Psychiatrica
Scandinavica, 131(6), 465-
471. https://doi.org/10.1111/acps.12395
Hyde, J. S., & Mezulis, A. H. (2020). Gender differences in
depression: Biological, affective,
cognitive, and sociocultural factors. Harvard Review of
Psychiatry, 28(1), 4-
13. https://doi.org/10.1097/HRP.0000000000000230
Sha, T. (2006). Optimism, Pessimism and Depression; The
Relations and Differences by Stress

Level and Gender. Acta Psychologica Sinica, 38(6), 886-901.
Shih, J. H.-F. (2004). Sociotropy/autonomy and depression:
Gender differences and the
mediating role of stressful life events ProQuest Information &
Learning]. APA
PsycInfo. http://libproxy.calbaptist.edu/login?
url=https://search.ebscohost.com/login.aspx?direct=true&db=ps
yh&AN=2004-99010-
099&site=ehost-live&scope=site
Sowa, C. J., & Lustman, P. J. (1984). Gender differences in
rating stressful events, depression,
and depressive cognition. Journal of Clinical Psychology, 40(6),
1334-
1337. https://doi.org/10.1002/1097-
4679(198411)40:6<1334::AID-
JCLP2270400609>3.0.CO;2-8
Stress. Mental Health Foundation.
z/s/stress#:~:text=Stress%20can%20be%20defined%20as,of%20
pressures%20that
%20are%20unmanageable.
What is Depression? American Psychiatric Association.
https://www.psychiatry.org/patients-
families/depression/what-is-depression
Zwicker, A., & DeLongis, A. (2010). Gender, stress, and
coping. In J. C. Chrisler & D. R.
McCreary (Eds.), Handbook of gender research in psychology,
Vol 2: Gender research
in social and applied psychology. (pp. 495-515). Springer
Science + Business
Media. https://doi.org/10.1007/978-1-4419-1467-5_21

https://doi.org/10.1007/978-1-4419-1467-5_21
https://www.psychiatry.org/patients-families/depression/what-
is-depression
https://www.psychiatry.org/patients-families/depression/what-
is-depression
pressures%20that%20are%20unmanageable.
https://doi.org/10.1002/1097-4679(198411)40:6
http://libproxy.calbaptist.edu/login?url=https://search.ebscohost
.com/login.aspx?direct=true&db=psyh&AN=2004-99010-
http://libproxy.calbaptist.edu/login?url=https://search.ebscohost
http://libproxy.calbaptist.edu/login?url =https://search.ebscohost
https://doi.org/10.1097/HRP.0000000000000230
Journal of Personality and Social Psychology
1994, Vol. 67, No. 6, 1063-1078
Copyright 1994 by the American Psychological Association,
Inc.
0022-3514/94/S3.00

Distinguishing Optimism From Neuroticism (and Trait Anxiety,
Self-Mastery, and Self-Esteem): A Reevaluation of the
Life Orientation Test
Michael F. Scheier, Charles S. Carver, and Michael W. Bridges
Research on dispositional optimism as assessed by the Life
Orientation Test (Scheier & Carver, 1985)
has been challenged on the grounds that effects attributed to
optimism are indistinguishable from
those of unmeasured third variables, most notably, neuroticism.
Data from 4,309 subjects show that
associations between optimism and both depression and aspects
of coping remain significant even
when the effects of neuroticism, as well as the effects of trait
anxiety, self-mastery, and self-esteem,
are statistically controlled. Thus, the Life Orientation Test does
appear to possess adequate predictive
and discriminant validity. Examination of the scale on
somewhat different grounds, however, does
suggest that future applications can benefit from its revision.
Thus, we also describe a minor modi-
fication to the Life Orientation Test, along with data bearing on
the revised scale's psychometric
properties.
Accumulating evidence from a variety of sources suggests
that dispositional optimism is beneficial for physical and psy-
chological well-being. For example, Aspinwall and Taylor
(1992) have recently shown that optimistic persons adjust more
favorably to important life transitions than do persons who are
more pessimistic in outlook. In a similar vein, Litt, Tennen,
Affleck, and Klock (1992) have reported that optimistic women
who are unsuccessful at in vitro fertilization respond better to

the failure than do women who are more pessimistic. Concep-
tually similar results have also been reported by Scheier et al .
(1989). Their study tracked a group of men undergoing coro-
nary artery bypass surgery. Optimistic men evidenced a more
rapid physical recovery after their surgery and reported a higher
quality of life 6 months postoperatively than did the more pes -
simistic men in the sample. Nor are these the only beneficial
effects for dispositional optimism that have been reported in
the literature (for a more comprehensive review, see Scheier &
Carver, 1992).
Related research suggests that these differences in outcomes
derive partly from differences between optimists and pessimists
in the manner in which they cope with the challenges in their
lives. Optimists differ from pessimists in their stable coping
Michael F. Scheier and Michael W. Bridges, Department of
Psychol-
ogy, Carnegie Mellon University; Charles S. Carver,
Department of Psy-
chology, University of Miami.
Data collection for this article was supported by National
Science
Foundation (NSF) Grants BNS87-17783 and BNS87-06271.
Prepara-
tion of the article was facilitated by NSF Grants BNS90-11653
and
BNS90-10425, American Cancer Society Grant PBR-56, and
National
Heart, Lung, and Blood Institute grant HL44436-01A1.
We are indebted to Joel Greenhouse for consulting on those
aspects
of the data analysis pertaining to factor analysis, and to Jeffrey
Thomas

for assisting in the preparation of data tables.
Correspondence concerning this article should be addressed to
Mi-
chael F. Scheier, Department of Psychology, Carnegie Mellon
Univer-
sity, Pittsburgh, Pennsylvania 15213.
tendencies (Carver, Scheier, & Weintraub, 1989) and in the
kinds of coping responses that they spontaneously generate
when given hypothetical coping situations (Scheier, Weintraub,
& Carver, 1986). Optimists also differ from pessimists in the
manner in which they cope with serious disease (Friedman et
al., 1992) and with concerns about specific health threats (e.g.,
Carver etal., 1993; Stanton& Snider, 1993; Taylor etal., 1992).
A general characterization of the findings of this research is
that
optimists tend to use more problem-focused coping strategies
than do pessimists. When problem-focused coping is not a pos-
sibility, optimists turn to more adaptive emotion-focused cop-
ing strategies such as acceptance, use of humor, and positive
reframing of the situation. Pessimists tend to cope through overt
denial and by mentally and behaviorally disengaging from the
goals with which the stressor is interfering, regardless of
whether something can be done to solve the problem or not.
These findings regarding optimism are consistent with the
model of behavioral self-regulation from which our own work
on optimism grew (e.g., Carver & Scheier, 1981, 1990a; Scheier
& Carver, 1988). This is a model that has roots in the long tra-
dition of expectancy-value theories in psychology. In this
model, people are seen as remaining engaged in efforts to over -
come adversity to reach goals as long as their expectancies of
eventual success are sufficiently favorable. When their doubts
are too severe, people are more likely to give up on the threat-
ened goals. These differences in expectancies are also assumed

to be paralleled by variations in affective experience (for
details,
see Carver & Scheier, 1990b). With enough movement toward
desired goals, affect is positive. If movement toward desired
goals is sufficiently impeded, affect is negative.
Although this viewpoint on behavior and affect can be ap-
plied in terms of situational variations in expectancies across
time or events, it can also be applied in terms of individual
differences. Optimists are people who tend to hold positive ex-
pectancies for their future; pessimists are people who tend to
hold more negative expectations for the future. Thus, our anal -
1063
1064 M. SCHEIER, C. CARVER, AND M. BRIDGES
ysis of how optimism versus pessimism leads to different re-
sponses to adversity is one application of a more general model
of the processes that underlie behavior, a model that is applica -
ble to a wide range of motivational issues and contexts.
Critique and Challenge
Much of the research on optimism and pessimism (although
certainly not all of it; e.g., Beck, Steer, Kovacs, & Garrison,
1985; Reker & Wong, 1983) has made use of the Life Orienta-
tion Test (LOT; Scheier & Carver, 1985) to assess individual
differences on this dimension. This scale has recently been crit-
icized by others. By implication, this criticism also undermines
the integrity of the optimism construct. The primary purpose
of this article is to address these issues.
Most of the criticism aimed at the scale involves the third

variable problem. Smith, Pope, Rhodewalt, and Poulton (1989)
were the first to raise this issue, doing so with respect to trait
anxiety. That is, they questioned whether effects attributable to
optimism might really be due to variance that optimism shared
with trait anxiety. Consistent with this view, Smith et al. (1989)
reported relatively high correlations between optimism and
trait anxiety across two independent samples. Smith et al. also
showed that it was possible to eliminate the significant negative
association that they found between optimism and reports of
physical symptoms by controlling for the effects of trait
anxiety.
In contrast, the association between trait anxiety and reports of
physical symptoms remained significant even after the effects
of dispositional optimism were removed. Similarly, significant
zero-order correlations between optimism and different varie-
ties of coping were largely eliminated when the effects of trait
anxiety were controlled, whereas many of the associations be-
tween anxiety and coping remained strong after removing the
effects of dispositional optimism. In a similar vein, Marshall
and Lang (1990) have also raised the third variable problem,
but with respect to self-mastery rather than trait anxiety (see
also Robbins, Spence, & Clark, 1991).
We have several observations to make with respect to this
work. Our first point concerns the nature of the outcome vari -
ables that have been examined across studies. Both Smith et
al. (1989) and Robbins et al. (1991) examined the relationship
between optimism and health complaints, and neither found an
independent effect for optimism when variables such as trait
anxiety were controlled. Smith et al. also examined coping
strategies, however, and found that optimism was an indepen-
dent predictor of certain coping responses. Similarly, Robbins
et al. (1991) also examined health maintenance behaviors and
found optimism to be an independent predictor of these. The
point here is that optimism may be a stronger independent pre-
dictor of some outcomes than of others. Shared variance may

explain the association between optimism and symptom report-
ing, for example, but may not fully explain the link between
optimism and other outcomes of interest.
There is a second (though related) issue here, as well. Previous
researchers have used outcome measures that were somewhat
limited in scope. For example, Smith et al. (1989) relied on only
five coping categories to explore associations between disposi -
tional optimism and coping tendencies, controlling for trait anx-
iety. Yet a far greater number of coping responses can be identi -
fied and measured reliably (Carver et al., 1989). The possibility
thus remains that dispositional optimism may be uniquely re-
lated in important ways to outcome variables that went un-
measured in these earlier studies. In this regard, it is interesting
to note that dispositional optimism was associated with eight
different coping qualities in Carver et al.'s (1993) study of a d-
justment to breast cancer surgery. Of these eight coping re-
sponses, only half were measured in the Smith et al. (1989)
study.
Our third observation has to do with the nature of the pre-
dictors with which optimism has been compared. Most of the
concern to date has centered around the overlap between opti -
mism and neuroticism or negative affectivity, as indexed by one
or another measure of chronic anxiety. Note, however, that neu-
roticism is conventionally viewed as a multifaceted construct
that consists partly of the absence of optimism (i.e.,
pessimism).
Thus, there is a distinct conceptual link between constructs. On
the other hand, neuroticism also incorporates a host of other
factors, such as self-doubt, emotional lability, and worry. Com-
bining qualities in this way can create problems of interpreta-
tion (Carver, 1989) because it becomes very difficult to identify
which components of neuroticism underlie a given effect. As a
hypothetical example, it might be the pessimism facet of neu-

roticism that relates to such variables as active coping, plan-
ning, giving up, and positive reinterpretation. The emotional
lability component may not be as good a predictor of these vari -
ables, but may relate well to other variables such as the experi -
ence of physical symptoms.
The other constructs under consideration here also have con-
ceptual overlap with optimism, though in a different way. Self-
mastery is the perception that one exerts control over the events
in one's life (Pearlin & Schooler, 1978). This construct thus in-
corporates a strong sense of positive expectancy for the future,
but weds to it a sense of personal responsibility for that expec -
tancy. Self-esteem shares ground with optimism in a more
diffuse way. Self-esteem represents a sense of self-worth, which
carries the implication that one will be accepted rather than
rejected by others, and that one is not a failure in one's life.
These consequences, of course, involve positive versus negative
outcomes, thus linking self-esteem conceptually to optimism.
As with self-mastery, what seems to differentiate this concept
from optimism involves (at least in part) a kind of ascription to
the self. The ascription in this case, however, is not one of con-
trol but rather of an intrinsic tie between feelings of worth or
the self's value and positive outcomes.
In sum, the alternative constructs being examined here all
have conceptual as well as empirical overlap with optimism.
Each, however, incorporates at least one additional quality that
takes it beyond optimism. In the case of neuroticism, there may
be several such qualities.
Our fourth and final observation is that results pertaining to
the discriminant validity of the LOT have not all been com-
pletely negative. As noted earlier, Smith et al. (1989) found that
the associations between self-blame and optimism (in both of
their studies) and seeking of social support and optimism (in
one of their studies) remained significant even when trait anxi -

ety was controlled. Robbins et al. (1991) found that the associ-
ation between optimism and health maintenance behaviors re-
mained intact, even when the effects of manifest anxiety, instru-
REEVALUATING THE LIFE ORIENTATION TEST 1065
mentality, anger, and alienation were simultaneously controlled.
Indeed, of all the variables studied, optimism was one of only
two that made significant independent contributions to the pre-
diction of health maintenance behaviors. More recently, Aspin-
wall and Taylor (1992) have reported that optimism predicts
adjustment to the first semester of college, independent of self-
esteem, locus of control, and desire for control. Finally, Mroc -
zek, Spiro, Aldwin, Ozer, and Bosse (1993) have found that op-
timism continues to predict psychological distress among mid-
dle-aged men, even after the distress measure is adjusted for
differences in neuroticism. Given these various considerations,
it seems premature to conclude that the LOT has no predictive
validity independent of other measures. In the same way, it
seems premature to close the book on optimism-pessimism as
an independent construct.
In this article we address this set of questions further. For the
past several years, we have been collecting information from
large groups of respondents on a variety of personality
variables,
coping styles, and other outcome measures. The personality bat-
tery includes a measure of optimism-pessimism (the LOT) as
well as measures of self-esteem, trait anxiety, self-mastery, and
neuroticism. The measure of coping is broad in scope and cov-
ers a wide range of diverse coping tendencies. The other
outcome
measures include both physical symptoms (number and inten-
sity) and a measure of depression.

The primary purpose of this article is to use the data we have
assembled to reexamine the predictive validity of the LOT by
using it and the other personality factors to predict variations in
coping, symptoms, and depression. Two general sets of predic-
tions are advanced. First, on the basis of findings reported by
Smith et al. (1989), Robbins et al. (1991), and Mroczek et al.
(1993), we expect that zero-order correlations between opti-
mism and physical symptoms will be substantially reduced
when the data are adjusted for trait anxiety and neuroticism.
Second, we expect that other associations involving optimism,
coping, and depression will remain strong, even after the data
are adjusted for the various personality factors that have been
measured.
Study 1: Reevaluating the Life Orientation Test
Method
Subjects and Procedure
A total of 4,309 undergraduates from Carnegie Mellon
University and
the University of Miami participated in the research (1,846
women,
2,417 men, and 46 participants who did not indicate their
gender). Par-
ticipation was in partial fulfillment of a psychology research
require-
ment. AH subjects completed a number of scales as part of a
larger pre-
testing packet. Packets were administered in large group testing
sessions
across successive semesters from 1988 to 1990. Because of time
con-
straints, not all groups received all scales. As a result, sample

sizes for
analyses vary from analysis to analysis.
Measures
Optimism. Optimism was measured by using the LOT (Scheier
&
Carver, 1985). The LOT is an eight-item self-report measure
(plus four
filler items) assessing generalized expectancies for positive
versus nega-
tive outcomes. Respondents were asked to indicate their degree
of
agreement with statements such as "In uncertain times, I usually
expect
the best," and "I hardly ever expect things to go my way," using
a 5-
point response scale ranging from 0 (strongly disagree) to 4
(strongly
agree). Of the 8 scored items, 4 are worded in a positive
direction and 4
are worded in a negative direction. After reversing the scoring
for the
negatively worded items, item scores were totaled to yield an
overall
optimism score with high scores representing greater optimism.
In our
sample, scores ranged from 0 to 32. Cronbach's alpha was .82.
Neuroticism. The Emotional Stability subscale of the Guilford-
Zimmerman Temperament Survey (GZTS; Guilford,
Zimmerman, &
Guilford, 1976) was used to assess neuroticism. Participants
were asked
to indicate if a series of 30 statements were true for them, using

a 3-
point response scale (1 = yes, 2 = no,i = uncertain). Two sample
items
are "You are sometimes bubbling over with energy and
sometimes very
sluggish," and "Your mood often changes from happiness to
sadness, or
vice versa, without your knowing why." Item responses are first
recoded
as needed so that higher scores indicate higher levels of
neuroticism. To
recode, responses receiving a score of 1 or 2 are reversed,
responses
receiving a score of 3 are left the same. An overall neuroticism
score is
then computed by totaling the number of responses receiving a
score of
1 (responses receiving a 2 or 3 do not contribute to the overall
score).
The Emotional Stability subscale of the GZTS has been
recommended
as a good measure of neuroticism by others (e.g., Costa &
McCrae,
1985). Cronbach's alpha for the present sample was .85.
Self-mastery. Self-mastery was assessed by using Pearlin and
Schooler's (1978) Self-Mastery Scale (SMS). This seven-item
instru-
ment assesses the extent to which a person generally feels as
though
he or she manifests personal mastery over life outcomes (e.g.,
"What
happens to me in the future mostly depends on me," and
"Sometimes
I feel that I am being pushed around in life"). Its basic
psychometric

properties are well established (Pearlin & Schooler, 1978), and
it has
been used successfully in the past with several different
community-
based populations (e.g., Pearlin & Schooler, 1978). It was also
the scale
that Marshall and Lang (1990) used in their study exploring the
predic-
tive power of optimism and self-mastery with respect to
depression.
Cronbach's alpha for the SMS in the present sample was .75.
Self-esteem. Rosenberg's (1965)10-item Self-Esteem Scale (or
SES)
was used to assess self-esteem. The scale, which provides a
convenient
measure of global attitudes about the self, has five negatively
worded
items and five positively worded items. Participants were asked
to indi-
cate their agreement on a scale of 1 (strongly disagree) to 4
(strongly
agree) with statements such as "I feel I have a number of good
qualities,"
and "At times, I think I am no good at all." This scale is one of
the most
widely used measures of self-esteem and has displayed good
reliability
and validity (Crandall, 1973; Rosenberg, 1965). In our sample,
the scale
had an internal reliability of .88.
Trait anxiety. The trait form of the State-Trait Anxiety
Inventory
(STAI; Spielberger, Gorsuch, & Lushene, 1974) was used to
measure

trait anxiety. This scale is composed of 20 Likert items
evaluating the
extent to which the respondents experience a variety of feelings
such
as happiness, self-confidence, tension, and disappointment (e.g.,
"I feel
content," and "I worry too much over something that really
doesn't
matter"). The scale has been used extensively in prior
psychosocial re-
search and its psychometric properties have been well
documented (see,
e.g., Watson & Clark, 1984). Cronbach's alpha for the current
sample
was .89.
Depression. The Beck Depression Inventory (BDI) short form
(Beck, Rial, & Rickels, 1974) was used to assess depression.
The BDI
assesses attitudes and symptoms derived from clinical
observations that
are typically observed in depressed psychiatric patients but not
in non-
depressed psychiatric patients (Beck, Ward, Mendelson, Mock,
& Er-
baugh, 1961). The scale has been widely used and has well -
established
psychometric properties (Beck, Steer, & Garbin, 1988). The
short form,
13-item version used in our study assessed attitudes and
symptoms

across several different domains, including (but not limited to)
mood,
sense of failure, lack of satisfaction, social withdrawal, and
indecisive-
ness. For each item, respondents were asked to choose from a
group of
four statements (rated 0 to 3 in depressive symptomatology) the
state-
ment that best described the way they were feeling that day.
Scores for
the present sample ranged from 0 to 39 (with higher scores
indicating
greater depression). Cronbach's alpha was .87.
Physical symptoms. Self-reports of physical symptoms were as-
sessed with the Cohen-Hoberman Inventory of Physical
Symptoms
(CHIPS; Cohen & Hoberman, 1983). The CHIPS comprises a
list of 38
commonly experienced physical symptoms (e.g., back pain,
headache,
and stuffy head or nose). Symptoms more psychological in
nature (e.g.,
feeling depressed or anxious) are explicitly excluded from the
list. Sub-
jects were asked to indicate how much they had been bothered
by each
of the symptoms in the past 4 weeks, using a 5-point scale (1 =
not at all
to 5 = extremely). In past research (Cohen & Hoberman, 1983),
the
CHIPS was found to predict use of student health facilities
among two
separate samples of college students (Cohen & Hoberman,
1983),
thereby supporting the construct validity of the scale.

Responses to the CHIPS were scored in two ways in our
research.
First, a measure of symptom intensity was computed by
summing the
degree to which subjects reported being bothered by each
symptom
across all 38 symptoms. Scores for symptom intensity ranged
from 38
to 162. Second, a simple tally was made of the number of
symptoms for
which subjects indicated they were bothered to some degree,
ignoring
the extent to which they were bothered (i.e., a score of 1 was
given for
each item that received a response of 2 or more). Scores for
number of
symptoms ranged from 0 to 35 in the present sample.
Coping. Coping was measured by the COPE (Carver et al.,
1989).
The COPE is a 60-item, multidimension coping instrument
designed to
assess 15 conceptually distinct methods of coping. The 60 items
repre-
sent a large range of coping responses including (but not limited
to)
active coping, positive reinterpretation and growth, seeking of
social
support for emotional reasons, denial, mental and behavioral
disengage-
ment, and focusing on and venting of emotions (e.g., "I do what
has to
be done, one step at a time," "I turn to work or other substitute
activities
to take my mind off things," and "I talk to someone about how I

feel").
Participants were instructed to indicate how much they usually
did each
of the things that the items reflected when they encountered
difficulties
or problems in their lives, using a 4-point Likert scale ( 1 = 7
usually
don't do this at all to 4 = I usually do this a lot). Cronbach's
alpha for
the 15 scales in the current study ranged from .37 (mental
disengage-
ment) to .93 (use of religion). With the exception of mental
disengage-
ment, the remainder of the alphas were all above .59, with the
majority
of the scales above .70. The average alpha across the 15 scales
was .73.
Results
Correlations Among Predictors
The zero-order correlations among the five predictor vari-
ables are shown in Table I.1 As can be seen, all of the intercor -
relations among the predictors were significant. It is also note -
worthy, however, that the magnitudes of the correlations be-
tween the LOT and the other predictors were only in the
moderate range (the average correlation between the LOT and
the other predictors was .54, which was the lowest average cor -
relation that was obtained). This generally suggests that the
LOT had less in common with the other predictors than the
other predictors had in common with each other, with the pos-
sible exception of self-mastery, which also had a relatively low
average correlation (.56).2
Table 1

Correlations Among Predictor Variables
Variable 1
1. Optimism
r —
N
2. Self-mastery
r
N
3. Trait anxiety
r
N
4. Neuroticism
r
N
5. Self-esteem
r
N
2
.55
1,883
—
3
- . 5 9
1,420

- . 6 9
572
—
4
- . 5 0
1,692
- . 4 8
569
.74
1,423
5
.54
595
.58
624
- . 7 2
181
- . 6 6
181
Note. All correlations specified here reached significance a t p <
.001,
two-tailed.
Is Optimism a Predictor of Outcomes?

Zero-order correlations between predictors and outcomes are
shown in Table 2. As can be seen, all of the predictors were
moderately correlated with depression to about the same de-
gree. Depression correlated highest with self-esteem and lowest
with neuroticism. In addition, all of the predictors were signifi -
cantly correlated with the two symptom measures. For symp-
tom intensity, the highest correlations involved neuroticism
and trait anxiety. The lowest correlation involved optimism. A
similar ordering occurred for number of symptoms.
As a group, the predictor variables were also substantially
correlated with different aspects of coping. All of the predictors
correlated significantly with active coping, planning, positive
re-
interpretation and growth, denial, mental disengagement, and
behavioral disengagement. Correlations between predictors and
outcomes tended to be higher for the more negative coping ten-
dencies (e.g., mental and behavioral disengagement) than for
the more positive coping tendencies (e.g., active coping and
planning).
With the exception of dispositional optimism, associations
between the predictors and the remaining coping tendencies
were more sporadic. For example, whereas four of the five pre-
1 Portions of the analyses from Study 1 have been briefly
described
elsewhere (in Scheier et al., 1989, and in Scheier and Carver,
1992).
2 Subsidiary analyses conducted separately by gender suggested
that
the associations between the predictors tended to be somewhat
higher
for women than for men, often significantly so, given the size of
the

samples. This pattern corresponds to the gender differences
reported by
Scheier and Carver (1985) on a similar set of data. Subsidiary
analyses
were always conducted on the data to assess the effects of
gender. Very
few other gender differences emerged, however, in either Study
1 or
Study 2. For example, of the 72 partial correlations reported in
Table 2,
only one involved a significant difference due to gender. The
few gender
differences that did emerge seemed random in nature and were
basically
uninterpretable. As a result, gender is discussed only when there
are
meaningful differences to report.
Table 2
Correlations Between Predictor Variables and Outcomes
Outcomes
Depression
r
N
Number of symptoms
r
N
Intensity of symptoms

r
N
Active coping
r
N
Planning
r
N
Suppression of competing
activities
r
N
Restraint
r
N
Positive reinterpretation
and growth
r
N
Use of humor
r
N
Seeking instrumental social
support
r
N

Seeking emotional social
support
r
N
Turning to religion
r
N
Acceptance
r
N
Denial
r
N
Focusing on and venting of
emotions
r
N
Mental disengagement
r
N
Behavioral disengagement
r
N
Use of drugs or alcohol
r
N

Optimism
-.42***
1,900
- . 2 1 * * *
1,015
-.25***
1,015
.30***
813
.30***
813
.14***
815
.12***
814
.47***
815
.10**
815
.16***
814
.12***
815

.22***
816
.10**
816
-.17***
815
-.10**
815
-.18***
816
.33***
816
- . 1 1 * *
816
Self-mastery
-.43***
1,306
-.27***
443
-.28***
443
.32***
375
27***

375
.09
375
.08
375
.34***
375
.10
375
.10
375
.11*
375
.08
375
.11*
375
-.17***
375
- . 0 9
375
-.23***
375
- . 4 1 * * *

375
.16**
375
Trait anxiety
-.49***
547
.47***
315
4 7 *»*
315
-.28***
394
-.17***
394
.00
396
-.19***
395
-.23***
395
- . 1 3 * *
396
-.02
396

.02
396
- . 0 3
397
-.15**
397
.32***
397
.32***
396
.34***
397
.45***
397
.27***
397
Self-esteem
-.54***
604
-.26***
443
-.27***
443

.25***
375
.20***
375
.07
375
.19*
375
.33***
375
.12*
375
.08
375
.07
375
.06
375
.04
375
-.20***
375
- . 1 0
375

-.17***
375
-.38***
375
-.08
375
Neuroticism
.41***
545
.51***
591
.54***
591
-.20***
393
-.10*
393
.02
395
-.17*
394
-.20***
394
- . 1 0

395
.05
395
.11*
395
-.05
396
-.10*
396
.26***
396
.42***
395
.42***
396
37*. *
396
.19***
396
*p< .05, two-tailed. * * p < .01, two-tailed. ***p< .001, two-
tailed.

dictors (self-mastery, trait anxiety, neuroticism, and optimism)
were all significantly associated with drinking and substance
abuse, only optimism correlated significantly with suppression
of competing activities, seeking instrumental social support,
and turning to religion. Indeed, of the five predictors, only opti -
mism related significantly to every coping tendency that was
assessed. Undoubtedly, this was due in part to the greater num-
ber of subjects who completed the LOT, but it was also due in
part to the greater magnitude of the correlations.
Is Optimism a Unique Predictor of Outcomes?
The first four columns of Table 3 present partial correlations
between optimism and the outcome variables, controlling for
each of the other predictors in turn. As can be seen, many of the
zero-order correlations involving optimism remain significant
even after the other predictors are statistically controlled, albeit
the correlations are usually reduced in magnitude. This charac-
terization holds for associations involving depression and many
aspects of coping, including active coping, planning, suppres -
sion of competing activities, positive reinterpretation and
growth, seeking social support for instrumental reasons, and
turning to religion. Taken in isolation, none of the other predic-
tors was able to render the correlations between optimism and
these various outcomes nonsignificant.
Associations involving number of symptoms and symptom
intensity fared less well, particularly the associations involving
symptom number. That is, with respect to symptom number,
the correlation with optimism was reduced to nonsignificance
if any of the other predictors was statistically controlled. With
respect to symptom intensity, inclusion of either trait anxiety or
neuroticism reduced the correlation with optimism to near
zero. It is interesting that the correlation between optimism and
symptom intensity remained significant even when the data
were first adjusted individually for self-mastery and self-

esteem.
The last column of Table 3 presents partial correlations be-
tween optimism and the outcomes, controlling for all four of
the predictors simultaneously. This obviously is a much more
stringent test of the unique predictive power of dispositional
op-
timism, for the simple reason that as covariates are added there
is less and less residual variance to predict. As can be seen,
how-
ever, significant partial correlations were obtained between op-
timism and planning, positive reinterpretation and growth,
seeking support for instrumental reasons, and turning to reli-
gion, even when the associations were adjusted for all four of
the
other predictors at once. Associations between optimism and
active coping and between optimism and suppression of com-
peting activities remained close to significance as well. We find
these results particularly striking and quite inconsistent with
the notion that dispositional optimism as measured by the LOT
is nothing more than trait anxiety, self-esteem, self-mastery, or
neuroticism under a different label.
Does Optimism Emerge as a Distinct Factor Among the
Items in This Item Set, and Is This Factor a Predictor of
Outcomes?
The partial correlations just reported begin to address the
issue of the independence of optimism from the other concepts
under consideration. In the case of partial correlations, the sole
concern is whether or not optimism as assessed by the LOT is a
unique and independent predictor of outcomes. There is an-
other way to address the contribution being made by optimism,
however, and that is through the use of factor analysis. That is,
recall our suggestion that characteristics such as neuroticism

are multifaceted, and that optimism comprises only one of
many different facets. The critical question, then, is to deter-
mine the importance of the optimism-pessimism component
in producing the associations that are observed. One way of get-
ting information relevant to this point is to factor analyze the
items on the LOT along with the items contained on the other
predictors. If the optimism component is important, the opti-
mism items ought to emerge as distinct, factor analytically,
from
the items comprising the other scales. Moreover, factor scores
computed on the basis of such an optimism factor should ac-
count for a significant amount of variance in the outcomes.
To explore these possibilities, several sets of factor analyses
were conducted on the data set. One set of analyses (conducted
on a sample of 1,312) grouped together items from the LOT,
GZTS, and STAI. The second set of analyses (conducted on a
sample of 845) grouped together items from the LOT, SES, and
SMS. Separate analyses were run on these two subsets of items
because an insufficient number of subjects had been adminis-
tered all five predictors. Thus, a combined factor analysis would
not have been meaningful. The predictors were grouped as they
were because self-esteem and self-mastery tend to measure
more positive aspects of behavior, whereas trait anxiety and
emotional instability tend to measure aspects of behavior that
are more negative in nature (cf. Marshall et al., 1992).
Initial analyses were conducted, using a principal compo-
nents extraction technique, followed by a Varimax rotation to
achieve a final solution. The number of factors retained for final
rotation in these analyses was determined by setting the eigen-
value at 1.0. The item set containing the LOT, GZTS, and STAI
yielded 13 factors. Five factors emerged from the item set con-
taining the LOT, SES, and SMS. The findings from the tw o data
sets were highly similar, however, with respect to the items
com-

posing the LOT. In each case, two factors emerged, the first of
which was defined by the positively worded LOT items (i.e., the
optimism items), the second of which was composed of the neg-
atively worded LOT items (i.e., the pessimism items).34 In each
case, the absolute loading for each LOT item was above .58 on
its relevant factor (the average loading for the LOT items across
the two factors and across the two data sets was .69).
Although these initial analyses yielded interpretable results
with respect to the items from the LOT, they were not totally
satisfactory for other reasons. Most important, there seemed to
be an over-extraction of factors. That is, for each data set,
factors
emerged that were defined by only one or two items each. As a
result, follow-up analyses were performed, in which only three
factors were retained for final rotation. The decision to retain
3 It is not uncommon for the positively and negatively worded
Life
Orientation Test items to load on separate factors. We have
obtained
such solutions in the past (Scheier & Carver, 1985), as have
others (e.g.,
Marshall, Wortman, Kusulas, Hervig, & Vickers, 1992).
4 An item was said to define a factor if it loaded higher on that
factor
than on any other factor in the analysis.
Table 3
Partial Correlations Between Optimism and Outcomes

Outcome
Depression
r
N
Number of symptoms
r
N
r
N
Active coping
r
N
Planning
r
N
Suppression of competing
activities
r
N
Restraint
r
N
Positive reinterpretation
and growth
r

N
Use of humor
r
N
Seeking instrumental social
support
r
N
Seeking emotional social
support
r
N
Turning to religion
r
N
Acceptance
r
N
Denial
r
N
Focusing on and venting of
emotions
r
N

r
N
r
N
Use of drugs and alcohol
r
N
Self-mastery
-.24***
1,303
-.08
440
-.12**
440
.16**
372
.19***
372
.11*
372
.09
372
.36***

372
.06
372
.13*
372
.08
372
.22***
372
.05
372
- . 1 0
372
- . 0 6
372
-.07
372
-.14**
372
- . 0 3
372
Variable controlled for
Trait anxiety

-.19***
544
.09
312
.04
312
.17***
391
.25***
391
.17***
393
.01
392
.43***
392
.03
393
.19***
393
.17***
393
.25***
394

.01
394
.02
394
.12*
393
.02
394
- . 1 0
395
.07
394
Self-esteem
-.18***
592
-.09
312
-.12**
440
.20***
372
.23***
372
.13*

372
.07
372
.37***
372
.04
372
.14**
372
.11*
372
.23***
372
.10
372
-.08
372
-.06
372
- . 1 1 *
372
-.17***
372
-.08

372
Neuroticism
.28***
542
.06
588
.03
588
24***
390
.29***
390
.18***
392
.07
391
.43**
391
.06
392
.21***
392
.21***
392

.23***
393
.06
393
- . 0 6
393
.14**
392
.03
393
-.19***
393
- . 0 2
393
All variables
-.11
175
.04
175
.09
175
.13
175

.20**
175
.14
175
.03
175
.36***
175
.02
175
.16*
175
.13
175
.24***
175
.03
175
.01
175
.09
175
.03
175

-.04
175
.04
175
*p< .05, two-tailed. **p< .01, two-tailed. ***p< .001, two-
tailed.
three factors was prompted by two considerations. First, the
items on which each data set was based came from three sepa-
rate scales. Second, scree tests (Cattell, 1966) on the analyses
just reported suggested the viability of a three-factor solution in
each case.
The results of these three-factor analyses yielded simpler and
more interpretable solutions. With respect to the analysis in-
volving the LOT, SES, and SMS, the first factor was denned by
nine items from the SES and one item from the SMS (which
loaded lowest of the nine items). This Self-esteem factor ac-
counted for 31.9% of the variance. The second factor was
totally
denned by the eight items from the LOT. Factor loadings for
the LOT items on this Optimism factor ranged from .49 to .74
(averaging .60). The Optimism factor accounted for an addi -
tional 8.1% of the variance. The final factor was denned by six
items from the SMS and one item from the SES (which loaded
second lowest of the seven items). This Self-mastery factor ac-
counted for an additional 6.2% of variance.
In the analysis involving the LOT, GZTS, and STAI, the first
factor was defined by 23 items (8 items from the LOT, 9 items

from the STAI, and 6 items from the GZTS). GZTS and STAI
items loading high on this factor tended either to reflect explic-
itly an optimistic orientation (e.g., "You have usually been opti -
mistic about your future") or reflect a positive mood state (e.g.,
"I am happy"). The eight LOT items were among the 11 highest
loading items on this factor (factor loadings for the LOT items
ranged from .51 to .71, averaging .61). The three highest
loading
items on this factor all came from the LOT. This Optimism fac-
tor accounted for 23.1% of the variance. The second factor was
defined by 20 items (16 from the GZTS and 4 from the STAI).
Given the high number of GZTS items defining the factor, it is
not surprising that the factor tended to reflect emotional labil -
ity. The two highest loading items were "Your mood often
changes from happiness to sadness, or vice versa, without your
knowing why," and "You have frequent ups and downs in mood,
sometimes with and sometimes without apparent cause." This
Emotional Instability factor accounted for an additional 5.2%
of the variance. The final factor to emerge from this analysis
involved feelings of worry, dread, and excessive rumination. It
was defined by eight items from the GZTS and seven items from
the STAI. Illustrative items loading high on the Worry factor
include "Disappointments affect you so little that you seldom
think about them twice" (negatively weighted), "You give little
thought to your failures after they are just passed" (negatively
weighted), and "I worry too much over something that doesn't
matter." The Worry factor accounted for an additional 3.5% of
the variance among the items in this item set.
To determine whether the Optimism factors in these data sets
contributed significantly to the prediction of outcomes, we gen-
erated factor scores reflecting the Optimism factor for each set
of data. Because the Varimax final rotations were based on ini -
tial principal components analyses, the factor scores defining
the Optimism factor in each data set should have been statisti -

cally independent from the factor scores defining the remaining
two factors. Consistent with this expectation, intercorrelations
among the three sets of factor scores from each set of data were
all found to be zero. The first column of Table 4 presents corre -
lations between Optimism factor scores and outcomes for the
data set involving the LOT, SMS, and SES (correlations
between
outcomes and scores on the Self-esteem and Self-mastery fac-
tors are also presented in Table 4). As can be seen, the Opti -
mism factor scores fared fairly well in this data set, corr elating
significantly with 12 of the 18 outcomes that were assessed. Ta-
ble 5 presents correlations between Optimism factor scores and
outcomes for the data set involving the LOT, STAI, and GZTS
(correlations between outcomes and scores on the Emotional
Lability and Worry factors are also presented in Table 5). Opti -
mism factor scores fared even better in this data set, correlating
Table 4
Correlations Between (Positive Scale) Factor Scores and
Outcomes
Outcome
Depression
Number of symptoms
Active coping
Planning
Suppression of competing activities
Restraint coping
Positive reinterpretation and growth
Use of humor
Seeking instrumental social support
Seeking emotional social support
Turning to religion
Acceptance

Denial
Focusing on and venting of emotions
Optimism
-.27***
- . 0 5
- . 0 9
.27***
.29***
.19***
.12*
.47***
.13*
.18***
.12*
.29***
.10
- . 0 5
-.07
-.15**
-.20***
-.04

Factor
Self-Esteem
-.38***
-.16**
-.14***
.16**
.12*
.02
.09
.18***
.08
.05
.06
- . 0 3
.00
-.17***
- . 0 3
-.06
-.29***
- . 0 2
Self-Mastery
-.38***

-.24***
-.25***
.22***
.15**
.00
.02
.17***
.00
.00
.01
.03
.07
-.18***
- . 1 1 *
-.23***
-.32***
-.18***
* p < .05, two-tailed. ** p < .01, two-tailed. *p<. 001, two-
tailed.
Table 5

Correlations Between (Negative Scale) Factor Scores and
Outcomes
Outcome
Depression
Number of symptoms
Active coping
Planning
Suppression of competing activities
Restraint coping
Positive reinterpretation and growth
Use of humor
Seeking instrumental social support
Seeking emotional social support
Turning to religion
Acceptance
Denial
Focusing on and venting of emotions
Optimism
-.48***
-.23***
-.23***
.40***
.32***
.14**

.17***
.47***
.17***
.25***
.21***
.23***
.19***
-.16**
-.07
-.15**
-.42***
- . 1 2 *
Factor
Emotional Lability
.31***
.37***
.38***
- . 0 6
- . 0 3
.08
-.01
.01

.10
.10
.12*
.04
.05
.30***
.27***
.42***
.30***
.24***
Worry
.05
.26***
.25***
- . 0 4
.05
.07
-.17***
-.02
-.19***

.15**
.20***
.09
-.15**
.11*
.40***
.17***
.13*
.10
* p < .05, two-tailed. **/>< .01,two-tailed. ***p< .001, two-
tailed.
significantly with all but one of the 18 outcomes. The sole ex-
ception was the Coping subscale assessing focusing on and
vent-
ing emotion.
It is also informative to compare the magnitude and pattern
of correlations that were obtained for the various factors that
were extracted in these analyses. With respect to the magnitude
of the correlations, it is clear that the Optimism factor in each
data set predicted about as much unique variance across out-
comes as did the remaining two factors that were extracted. In-
deed, in both sets of data the average of the absolute values of
the correlations involving the Optimism factor was higher than
the averages of the absolute values of the two remaining factors
that were extracted (sometimes almost twice as high).

The data also produced some interesting patterns of associa-
tions. Consider first the analyses involving the scales assessing
positive attributes (see Table 4). The most interesting pattern
here concerned the set of associations involving social support.
As can be seen, only the Optimism factor predicted a significant
amount of unique variance in the tendency to seek social sup-
port, and it did this with respect to both facets of support seek-
ing that were measured. Factor scores from the two remaining
factors failed to predict either facet. This suggests that the Opti -
mism component of these composite scales may be a more im-
portant correlate of these aspects of social support than either
the Self-mastery or the Self-esteem component. Of lesser inter-
est in this data set are the unique associations that emerged be-
tween the Optimism factor and restraint coping, suppression of
competing activities, use of humor, and the use of religion. Nei-
ther the Self-mastery nor the Self-esteem factor was
significantly
related to any of these other outcomes.
An interesting pattern of associations also emerged with re-
spect to the analyses involving the scales that assessed negative
characteristics (see Table 5). Within this data set, the Optimism
factor seemed the sole predictor of more active forms of coping.
That is, it correlated significantly with active coping per se, and
with two additional coping tendencies that are often associated
with active coping: planning and the suppression of competing
activities. Neither the Emotional Lability factor nor the Worry
factor were significantly related to any of these aspects of
active
coping. In addition, the Optimism factor was the only factor
that significantly predicted variations in the use of positive re-
interpretation and growth; Emotional Lability and Worry did
not.
In closing this section on factor analysis, we should note that

the absolute value of the correlation between the Optimism fac-
tor scores and raw LOT scores was .89 in each of the two sets
of data that were analyzed. This reinforces the notion that the
Optimism factors were indeed Optimism factors. It also rein-
forces the idea that the Optimism factors that emerged can be
closely approximated by using raw scores on the LOT.
Discussion
The primary purpose behind these analyses was to reevaluate
the predictive validity of dispositional optimism as assessed by
the LOT. The general conclusion we would like to draw from
the analyses is that the LOT is a viable instrument for assessing
people's generalized sense of optimism. Three sets of findings
support this conclusion. First, correlations between optimism
and the remaining predictors under consideration here were all
in the moderate range. Thus, the amount of shared variance
between optimism and the other predictors was also only mod-
erate. Although previous studies have sometimes obtained
higher correlations (e.g., Smith et al., 1989), these higher corre-
lations have tended to come from studies with smaller sample
sizes. Studies using larger samples have tended to provide
values
closer to the ones obtained here. For example, Mroczek et al.
(1993) reported a correlation of —.37 between optimism (as
measured by the LOT) and neuroticism (as measured by a short
form of the Eysenck Personality Inventory) in a community
sample of 1,192 middle-aged men. Findings such as these and
those reported here suggest that the higher correlations that
have sometimes been obtained in the past are best attributed to
perturbations in sampling.

Second, many of the zero-order correlations between dispo-
sitional optimism and the outcomes of interest remained sig-
nificant when the influence of the other predictors was partialed
out. The partial correlations were strongest for aspects of cop-
ing. These results stand in contrast to those obtained by Smith
et al. (1989). In our view, there are at least two potential
reasons
for the discrepancies. First, as just noted, the zero-order corre-
lations between optimism and trait anxiety were lower in our
sample, providing more residual variance to be predicted. Sec -
ond, the measure of coping used in our study was more compre-
hensive than that used by Smith et al. (1989). Either or both
of these differences may have contributed to the differences in
findings.
The results were weakest with respect to reporting of physical
symptoms. In the case of symptom reporting, controlling for
other predictors typically eliminated significant associations
with optimism. Reductions in the magnitude of the associations
were particularly evident and most consistent when trait anxiety
and neuroticism were controlled. This aspect of our findings
closely parallels the findings reported previously by Smith et al.
(1989), Robbins et al. (1991), and Mroczek et al. (1993). It
seems likely that most of the variance in the optimism-symp-
tom reporting relationship is due to shared variance with trait
anxiety, neuroticism, and other conceptually similar variables.
In this regard, it is interesting that trait anxiety and neuroticism
are both variables that are intimately tied to affective experi -
ence. Perhaps it was these ties to affective experience that
caused
trait anxiety and neuroticism to have the effect that they did
on symptom reporting (cf. Costa & McCrae, 1985; Watson &
Pennebaker, 1989).
A final point involves the factor analyses that were con-

ducted. Criticisms of the LOT have often ascribed its effects to
positive response bias. It is instructive in this regard that the
factor analyses did not yield a single factor reflecting
positivity-
negativity. Rather, when items comprising the various predic-
tors were subjected to factor analysis, a distinct Optimism
factor
emerged in each case, which correlated in the high .80s with
raw LOT scores. Moreover, this Optimism factor predicted a
significant amount of variance in many of the outcomes that
were measured. Because the Varimax rotation method used to
derive the factor scores was based on a principal components
analysis, the variance attributable to the Optimism factor was
wholly independent of the variance attributable to the other fac -
tors that emerged.
Taken together, these three considerations clearly suggest that
the LOT has a good deal of predictive validity, and that disposi-
tional optimism as assessed by the LOT deserves continued de-
velopment as an independent theoretical construct.
Study 2: Some Suggestions for Improvement—The
Revised Life Orientation Test
The work reported in this article was prompted primarily by
prior research questioning the predictive validity of the LOT
with respect to a set of competing predictors. Thinking about
the third variable problem, however, led us to a more general
consideration of the conceptual properties of the LOT. More
specifically, we began to reexamine the question of how well
the
items on the scale were measuring what they were intended to
be measuring: generalized expectations of good versus bad out-
comes in life.

Scheier (1988) has been critical in the past of scales whose
meaning is obscured by the presence of items that have little to
do with the constructs ostensibly being measured. For example,
it is not uncommon to find items such as "I feel blue" on anxi -
ety questionnaires, and items such as "You have usually been
optimistic about your future" on scales measuring emotional
instability. Such practices guarantee that such measures of anx-
iety will correlate with measures of depression, in the case of
the former example, and that such measures of emotional in-
stability will correlate with measures of optimism, in the case
of the latter example. It is obviously better to construct scales
in
which such built-in confounds are kept to a minimum. Only by
minimizing the overlap in item content can accurate estimates
be obtained of the degree of correlation between constructs.
Examination of the LOT with these considerations in mind
led us to identify two problematic items, both of which come
from the half of the items that are worded in a positive
direction.
The items are the following: "I always look on the bright side of
things," and "I'm a believer in the idea that 'every cloud has a
silver lining.' " What is obvious to us in retrospect is that
neither
item explicitly refers to the expectation of positive outcomes.
Rather, each refers to a particular way of reacting to problems
and stress—putting problems in the best possible light and
searching for hidden benefits and meaning when difficulties
arise.
The content of these two items is particularly bothersome, for
the following reason. When we first constructed the LOT, we
were not thinking in terms of identifying mediators of optimism
effects. We were simply trying to devise a scale that assessed
optimism as clearly as possible. Since the scale was published,
however, coping has emerged as an important mediator of opti -

Hadi Alnasir Research Proposa

Hadi Alnasir Research Proposa

Recommended

Recommended

More Related Content

Similar to Hadi Alnasir Research Proposa

Similar to Hadi Alnasir Research Proposa (18)

More from JeanmarieColbert3

More from JeanmarieColbert3 (20)

Recently uploaded

Recently uploaded (20)

Hadi Alnasir Research Proposa