Discussion: Psychoanalytic and Trait Theory

JISE VOL. 6, NO. 2 32

ISSN 2166-2681 |Volume 6, Number 2 | (2018) © Journal of Interdisciplinary Studies in Education

MINDSET, GRIT, OPTIMISM, PESSIMISM and LIFE SATISFACTION IN

UNIVERSITY STUDENTS with and without ANXIETY and/or

DEPRESSION

Beth Tuckwiller

William R. Dardick

George Washington University

Abstract

We explored mindset, grit, optimism, and pessimism as predictors of university students' life

satisfaction. In consideration of the dual-factor model of mental health, we examined the strength

of these predictors on life satisfaction in college students with self-reported anxiety and/or

depression and those without. Optimism and pessimism predicted life satisfaction overall, but the

relative contribution of each was significantly different based on self-reported anxiety/depression.

College students with anxiety and/or depression also demonstrated significantly more fixed

mindsets, lower levels of grit, optimism and life satisfaction, and higher levels of pessimism. We

discuss the implications of these findings for university wellness programming and initiatives,

and future research directions investigating these constructs for university students.

Keywords: mindset, grit, optimism, college students, depression, anxiety

Introduction

There are a number of important reasons for college/university communities to be proactive regarding the mental health of their students. In addition to obvious practical and

ethical reasons to promote mental health among members of the university community, it is als o

notable that mental health status in young adulthood has significant implications for alcohol and

substance abuse (Eisenberg, Gollust, Golberstein, & Hefner, 2007), academic

performance/college GPA (Andrews & Wilding, 2004; Antaramian, 2015; Renshaw & Cohen,

2014), physical health (Renshaw & Cohen, 2014), and overall engagement in the college

experience (Antaramian, 2015). Thus, the promotion of mental health during this developmental

period can help guide young adults toward a developmental trajectory with fewer risky

experiences (e.g., substance abuse) and more positive outcomes (Hunt & Eisenberg, 2009).

Furthermore, the young adult brain is quite plastic well into the 20s (Martel & Fuchs, 2017), and

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much can be accomplished during the emerging adult developmental period to promote mental

health, healthy behaviors, and positive habits to promote long-term health, wellness, and success

(Arnett, 2007).

However, recent data suggest that increasing numbers of university students are

experiencing poor mental health. This trend has been referred to as the college “mental health

crisis” (Pinder-Amaker & Bell, 2012; Schwartz & Kay, 2009). Although it is possible that the

observed increased rates of mental health conditions among university students indicate

increased willingness to seek help from formal university services, it is also likely that it is

partially accounted for by a moderate increase in the actual rate of mental disorders among

university students (Hunt & Eisenberg, 2009). By any metric, mental health disorders are

considered highly prevalent among university students, and significant numbers of students

report struggling with stress, anxiety, and depression (American College Health Association,

2014; Hunt & Eisenberg, 2009; Pinder-Amaker & Bell, 2012). Statistics fluctuate based on

assessment of psychological distress (which may include reports of sub-clinical mental health

symptoms) vs. assessment of DSM-5 criteria for a mental disorder. However, in one high quality

study of mental illness prevalence among college students, the estimated prevalence of any

anxiety or depressive disorder was 15.6% of undergraduates (Eisenberg et al., 2007). Awareness

and promotion of mental health should be an important target for university communities.

Positive Psychology and Mental Health

Within the field of psychology in the last two decades, a rapidly burgeoning interest in

positive psychological development and outcomes has emerged. This shift from a primarily

deficit-oriented focus is evident in the dramatic increase in empirical positive psychology

research, which explores, broadly, that which “goes right in life” (Peterson, 2006; p. 4) and the

correlates of human flourishing and thriving (Seligman, 2002). More traditional approaches to

psychology, which have historically highlighted pathology and treatment, are now integrating

frameworks from positive psychology to facilitate a more holistic approach to both the empirical

study of and applied approaches to positive human functioning (Slade, 2010). This integration

can be seen in recent, progressive approaches to clinical psychology and the related practice of

mental health counseling, in which there is an increasing focus on both the treatment of

pathology and the promotion of well-being (Antaramian, 2015; Slade, 2010).

Dual factor model of mental health. The dual factor or two continua model of mental

health has emerged to suggest that mental health is comprised of not merely the absence of

psychopathology, but also the presence of optimal psychological functioning and well-being

(Renshaw & Cohen, 2014; Westerhof & Keyes, 2010). This comprehensive model of mental

health takes into account positive indicators of wellness, such as subjective well-being, in

conjunction with indicators of psychological distress (Suldo & Shaffer, 2008). Rather than

conceptualizing mental health and illness as residing on one spectrum, this model suggests that

psychological health / well-being is a complementary yet distinct construct from

psychopathology (Eklund, Dowdy, Jones, & Furlong, 2011).

Subjective well-being and life satisfaction. Subjective well-being (SWB) is a positive

psychology construct comprised of affective and cognitive components (Diener, Emmons,

Larsen, & Griffin, 1985). The “subjective” aspect of SWB is essential to the construct, in that an

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individual holds an understanding of his or her own degree of well-being based on a personal

evaluation of it, without external evaluative criteria being imposed upon them (Diener et al.,

1985). One component of subjective well-being is life satisfaction, which can be thought of as an

individual’s own cognitive appraisal of SWB, comprised of a subjective judgment of one’s own

quality of life (Diener et al., 1985). Life satisfaction is an important indicator of overall well-

being and positive mental health in university students.

Recent research has suggested that the presence of positive mental health indicators may

act as a buffer in the presence of negative mental health indicators. For example, Suldo and

Shaffer (2008) found that adolescents with symptoms of psychopathology but also high degrees

of subjective well-being perceived better social functioning and physical health when compared

to their peers with similar degrees of psychopathology but lower levels of subjective well-being.

Similarly, Renshaw and Cohen (2014) found that life satisfaction (a component of subjective

well-being) was negatively correlated with college students’ overall psychological distress and

positively predictive of interpersonal connectedness, physical health and academic achievement.

Broadly, subjective well-being has been linked to higher GPAs and higher levels of engagement

in the college experience (Antaramian, 2015).

Positive Psychological Factors and College Students

Life satisfaction. College student life satisfaction has been investigated across the globe

and associated with higher GPAs (Rode et al., 2005), self-discipline and intrinsic motivation

(Khramtsova, Saarnio, Gordeeva, & Williams, 2007), self-esteem (Zhang, Zhao, Lester, & Zhou,

2014), and self-efficacy (Coffman & Gilligan, 2002), as well as lower levels of stress (Kaya,

Tansey, Melekoğlu, & Çakiroğlu, 2015) and depression (Pilcher, 1998). Research investigating

the role of life satisfaction in university students from a dual factor model of mental health

perspective revealed that when life satisfaction is considered alongside indicators of

psychological distress (e.g., anxiety, depression, and/or somatization symptoms), it provides

additional, unique predictive value relative to interpersonal functioning, academic achievement,

and physical health (Renshaw & Cohen, 2014). In other words, life satisfaction is an important

component of university students’ experiences and outcomes in general, and should be explicitly

considered a construct of interest for university students who are experiencing psychological

distress.

Optimism. Upon reviewing the college student positive psychology and outcomes

literature, optimism emerged as a construct of interest because it has significant predictive value

on the life satisfaction levels of college students (Bailey, Eng, Frisch, & Snyder, 2007; Yalçin,

2011). Furthermore, optimism is malleable. Seligman (2006) popularized the notion that

optimism can be learned, and research suggests that optimism interventions can result in

increased happiness and decreased depression (Shapira & Mongrain, 2010), and elicit increased

optimistic thinking (Peters, Flink, Boersman, & Linton, 2010). Thus, optimism can be shaped

through intervention, and should be a construct of particular interest in terms of college student

life satisfaction.

Mindset and Grit. Two additional constructs – mindset and grit – emerged in our review

of the college student positive outcomes literature, because they relate to both psychological

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processes (e.g., approach/avoidance, attribution, willpower, persistence) as well as educational

ones (e.g., academic achievement, GPA, advanced course completion). The construct of mindset

was popularized by the work of Carol Dweck (e.g., 2006) and can be defined in terms of two

orientations: fixed or growth. Students with a fixed mindset view intelligence as a primarily

inherent, stable trait that can’t be changed, while those with a growth mindset view it as

changeable in response to experiences and effort (Dweck, 2006). College students’ growth or

fixed mindset orientations toward intelligence and academic achievement are malleable and have

been correlated to academic achievement scores (Aditomo, 2015; Aronson, Fried, & Good, 2002;

Paunesku, Yeager, Romero, & Walton as cited in Yeager & Dweck, 2012; Shively &Ryan,

2012) and increased college retention of students from underrepresented groups (PERTS, 2017).

A related construct, grit, is conceptualized as persistence and passion for a goal that may

have no immediate payoff (Duckworth, Peterson, Matthews, & Kelly, 2007). Duckworth and

colleagues have investigated the outcome correlates of grit and have found that an individual’s

self-reported level of grit is more highly correlated with positive life outcomes than intelligence

or academic achievement (Duckworth & Eskreis-Winkler, 2013; Duckworth et al., 2007;

Duckworth, Kirby, Tsukayama, Berstein, & Ericcson, 2010; Robertson-Kraft & Duckworth,

2014). For example, grit has reliably predicted the retention rate among West Point cadets in

challenging classes, and overall college G.P.A in Ivy League undergraduates, above and beyond

I.Q. (Duckworth, et al., 2007). Duckworth and Eskreis-Winkler (2013) report that there are

empirically-observed moderate positive correlations between grit and growth mindset. Grit has

also been investigated in regard to life satisfaction, and at least one prior study found that grit

significantly predicted college student life satisfaction (Singh & Jha, 2008).

Theoretical links and conceptual framework. Optimism – a stable expectancy that “good

things” will happen – is integral to students’ abilities to cultivate and maintain growth-oriented

mindsets. A growth mindset is inherently optimistic in that the individual holds an expectancy

that after a period of prolonged experience or effort, a “good thing” will happen (e.g.,

intelligence will increase). Similarly, high levels of grit suggest an optimistic approach in that it

is unlikely that most individuals would persist in challenging tasks without expectation of an

eventual positive result. Thus, we were surprised at the lack of studies exploring the relative

contributions of each of the constructs of optimism, mindset, and grit to college student life

satisfaction. While studies have examined relationships among more psychologically-oriented

constructs, such as hope and optimism, with life satisfaction (e.g., Bailey et al., 2007), we could

not find a study that directly examined the relationship between the more educationally-oriented

construct of mindset to life satisfaction in college students, and only one study that investigated

the educationally-oriented construct of grit relative to life satisfaction in college students (Singh

& Jha, 2008). We were surprised by this gap in the literature in light of the reported correlations

among optimism and life satisfaction (e.g., Bailey et al., 2007), optimism, grit, and mindset (see

Duckworth & Eskreis-Winkler, 2013; Tuckwiller, Dardick, & Kutscher, 2017), and grit and life

satisfaction (Singh & Jha, 2008), Furthermore, in light of the dual factor model of mental health

in which increased positive functioning is paramount, we wanted to understand the relationships

among these positive variables for university students.

Constructs in the context of mental health. Furthermore, in one meta-analytic review,

researchers observed a correlation between an individual’s mindset and affective state; that is,

JISE VOL. 6, NO. 2 36

those with more fixed mindsets were observed to report more negative emotions (Gal &

Szamoskozi, 2016). This prompted us to think about university students with mental health

issues, particularly those who report experiencing depression and anxiety, and we wanted to

explore the relationships among optimism, mindset, grit, and life satisfaction in university

students who self-report mental health conditions. Again, in light of the dual factor model of

mental health, we believed it was especially important to explicitly describe the relationships

among these positive variables and life satisfaction for this subset of the university student

population.

The Present Investigation

To investigate these relationships, we conducted an exploratory study of mindset, grit and

optimism and student life satisfaction and examined differences between two groups of college

students: those who self-report anxiety and/or depression, and those who self-report no mental

health conditions. We hypothesized that optimism, mindset, and grit were correlated to and

predictive of life satisfaction in college students. We hypothesized that both mindset and grit

would provide additional unique predictive value of overall college student life satisfaction

above and beyond optimism. However, we suspected that there would be significantly lower

levels of optimism, mindset, grit and life satisfaction in college students who self-report mental

health conditions, and that optimism, mindset, and grit may not demonstrate the same predictive

patterns of life satisfaction for students with mental health conditions.

Method

Participants

Participants were selected through a stratified random sample of 2,000 undergraduate and

graduate students at a mid-Atlantic university who were invited to participate via email. Two

hundred and forty-five undergraduate and graduate students completed surveys. The responding

sample identified as more female (69.8%) and Caucasian (63.67%) when compared to the

initially invited participants who were 58.5% female and 51.9% Caucasian. Average age of the

participants was M = 26.3 (SD = 8.99) years.

Procedure

Students were asked to complete a survey delivered via the Qualtrics© platform with an

option to enter a raffle for a new iPad© mini to incentivize participation. The survey included

items from several psychological measures (see Measures section below) as well as 17

demographic items including for example, gender, ethnicity, GPA, and mental health conditions.

Directions indicated that responses would remain anonymous and that there were no right or

wrong answers.

Measures

Mindset: General and self-theory. The Implicit Theories of Intelligence Scale (ITI-

General) (Dweck, 2000) contains eight items, four measuring a “fixed” mindset factor and four

measuring a “growth” mindset factor. The scale is designed to measure an individual’s attitudes

about general intelligence and its malleability. De Castella and Byrne (2015) developed the

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Revised Implicit Theories of Intelligence (Self-Theory) Scale (ITI-S) from the original ITI Scale

(Dweck, 2000), with “all eight items reworded so that each statement reflected a first-person

claim” (p. 250). The scale measures individuals’ beliefs about the malleability of their own

intelligence. Both scales demonstrated good internal consistency in past research (α = .87 for

ITI-General and α = .90 for ITI-Self). For our sample, the ITI-General Cronbach’s α = .943 and

for the ITI-Self, Cronbach’s α = .960.

Optimism. The Life Orientation Test – Revised (LOT-R) (Scheier et al., 1994) is a 10-

item instrument designed to measure dispositional optimism. Three items measure optimism,

three items measure pessimism, with four unscored filler items. A prior psychometric evaluation

of the instrument (n = 2,055), yielded an α = .78 (Scheier et al., 1994).

There is an ongoing discussion in the field of positive psychology regarding the structure

of the LOT-R as either a unidimensional or two-factor model. That is, does it measure optimism

as one factor, along a unidimensional spectrum (e.g., low to high optimism) or does it measure

two factors – optimism and pessimism – as distinct constructs (e.g. pessimism is not merely “low

optimism”) (e.g., Bailey et al., 2007; Cano-García et al., 2015; Creed, Patton, & Bartrum, 2002)?

In light of this discussion, we conducted a confirmatory factor analysis of our LOT-R data. We

tested two models, the first comprised of the LOT-R optimism items and pessimism items as one

factor (n=6), and the second model with the LOT-R optimism items as one factor (LOT-RO)

(n=3) and the LOT-R pessimism items as a second factor (LOT-RP) (n=3). The two-factor model

solution was the best fit for our data with a change in χ2 (1, n = 245) = 31.24, so the two-factor

model was retained. Table 1 contains the fit statistics for the two models, confirming the two-

factor model. Reliabilities for these subscales was good; LOT-RO  = .794 and LOT-RP  =

.856. Because the two-factor solution was the best fit for our data, we used both variables of

optimism and pessimism in our analysis and discussion of results.

Table 1

Fit Indices for One and Two Factor Models of LOT-R

Model χ2 df AIC GFI CFI RMSEA SRMR

One-Factor 48.302 9 72.302 0.931 0.947 0.134

(0.098, 0.172) 0.047

Two-Factor 17.066 8 43.066 0.977 0.988 0.073

(0.021 0.113) 0.027

Note: χ2 = chi-square, df = degrees of freedom, AIC = Akaike Information Criteria, GFI = Goodness of fit index,

CFI = comparative fit index, RMSEA = root mean square error of approximation with 90% confidence intervals,

SRMR = standardized root mean square residual

Grit. The eight-item Short Grit Scale (Grit-S) (Duckworth & Quinn, 2009) measures two

factors of grit: consistency of interest and perseverance of effort. In eight separate samples across

two studies, Cronbach’s α ranged from .77 to .85 (Duckworth et al., 2007; Eskreis-Winkler,

Duckworth, Shulman, & Beal, 2014). For our sample, Cronbach’s α = .839.

Life satisfaction scale. The Satisfaction with Life Scale (SWLS) (Diener et al., 1985) is

a five-item scale that measures an individual’s overall satisfaction with life and well-being. The

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scale has demonstrated good internal consistency across diverse samples, typically in the α =.80

to .89 range. For our sample, Cronbach’s α = .899.

Scale scores. Participants selected their amount of endorsement of items using a 6-point

Likert response scale ranging from ‘strongly disagree’ to ‘strongly agree.’ On every measure, we

used a 6-point scale without a neutral response, so that participants were required to select

responses that indicated endorsement of an item in one direction or other (e.g., toward agree or

disagree). Each aggregate score was created by summing the scores on the scale or subscale.

Demographic items. Participants responded to 17 demographic items including typical

demographic variables (e.g., gender, ethnicity) and additional items including GPA, SAT/ACT

score, disability status, and current mental health conditions.

Mental health condition. Fifty students in our sample self-identified as having anxiety

and/or depression. They indicated this self-perception of mental illness by responding to a survey

question: “Are you currently experiencing any mental health conditions?” If students responded

“Yes,” they were auto-directed to a drop-down menu of mental illness diagnoses including

Anxiety, Bipolar Disorder, Depression, Eating Disorder, Schizophrenia, Substance Abuse, and

Other. Fifty participants indicated that they were currently experiencing depression, anxiety, or

both: thirteen indicated Anxiety, seven indicated Depression, and thirty indicated both Anxiety

and Depression. We did not query participants regarding official psychiatric diagnoses from a

medical professional, as our interest was in college students who perceived themselves to be

experiencing a mental health condition and self-reported it. In the same way that subjective

wellbeing is dependent upon the individual’s understanding or personal evaluation of it, we

believe that an individual’s subjective evaluation of his or her mental health, without external

evaluative criteria imposed upon that judgment, is an important and useful metric.

Analyses

We used prior empirical findings and theory to guide the development of our data

analytic plan. We conceptualized life satisfaction as an outcome variable and the other variables

as potential predictors of life satisfaction. We analyzed descriptives for outliers and examined

correlations among variables of interest. We compared the mean group scores, using independent

samples t-tests and effect sizes, of optimism, pessimism, mindset, grit and life satisfaction scores

of university students with anxiety and/or depression and those without any reported mental

health conditions. As discussed, we conducted comparative confirmatory factor analysis of the

LOT-R data. We then conducted a system of regression models to examine the predictive values

of general mindset, self-mindset, optimism, pessimism, and grit with life satisfaction.

Results

Descriptives for the Scales

Internal consistency for all of the scales and subscales was adequate with a range of α =

.794 to α = .960, as reported in the Measures section. Correlations among all six of the

scales/subscales (general mindset, self-theory mindset, optimism, pessimism, grit, and life

satisfaction) were statistically significant. See Table 2 for correlations.

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Table 2

Correlations Among Constructs

LS GM SM LO LP GR

LS 1 0.162* 0.226** 0.606** 0.548** 0.265**

GM 1 0.881* 0.229* 0.285** 0.145*

SM 1 0.297** 0.322** 0.166**

LO 1 0.696** 0.345**

LP 1 0.207**

GR 1 Note: * significant at the .05 level. ** significant at the .001 level. LS = life satisfaction; GM =

general mindset; SM = self-mindset; LO = optimism; LP = pessimism; GR = grit.

Mean group differences on scales by mental health condition. An independent

samples t-test was conducted for each of the scale and subscale values to determine if the means

of those who self-reported anxiety and/or depression were different from those who self-reported

no mental health conditions. See Table 3 for mean differences and standard deviations of each of

the six subscales. Means for each of the scale/subscale scores were statistically significantly

different for those with and without self-reported mental health conditions, with the exception of

general mindset. The effect size d ranged from small to medium effect, d = -0.308 for general

mindset to a large effect, d = -1.066 for optimism on life satisfaction. Participants who did not

report mental health conditions consistently reported higher scaled scores on general mindset,

self-mindset, optimism, grit, and life satisfaction and lower levels of pessimism than did those

with anxiety and/or depression.

Table 6

Mean Group Differences: With and Without Self-Reported Mental Health Condition

Mean

difference

95% CL Mean

difference

σ2 t sign d

SWLS -4.568 -6.001 -3.135 4.580 -6.280 <.0001 -0.997

ITI-G -2.429 -4.896 0.039 7.884 -1.940 0.054 -0.308

ITI-S -2.659 -5.199 -0.119 8.117 -2.060 0.040 -0.328

LOTR-O -2.926 -3.785 -2.068 2.744 -6.710 <.0001 -1.066

LOTR-P -2.406 -3.289 -1.524 2.820 -5.370 <.0001 -0.853

Grit -3.037 -4.998 -1.076 6.268 -3.050 0.003 -0.485

Note: the df for all Independent sample t tests was 239; SWLS=Satisfaction with Life Scale; ITI-G =

Implicit Theories of Intelligence Scale - General Mindset’ ITI-S = Implicit Theories of Intelligence

Scale – Self-Theory; LOTR- O = Life Orientation Test – Revised, Optimism Items; LOTR-P = Life

Orientation Test – Revised, Pessimism Items.

Regression Analyses

We ran a system of six multiple regression models. The first two models were nested and

tested whether life satisfaction could be predicted from general mindset, self-mindset, optimism

and grit, later adding mental health condition on the data set as a whole to determine if the

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change in R2 was significant. We further conducted two sets of additional regressions: two on the

sub-sample of participants who had identified as having anxiety and/or depression (n=50) and

the others on the remaining participants who had not reported any mental health conditions

(n=191). The set of regressions on the sub-samples focused on optimism and pessimism as

predictors as a baseline model, and determined if either could be removed with the use of

backward elimination, which starts with all effects and deletes effects that do not meet the

criteria to be left in the model (p < .10). Models 3 and 4 were reported as baseline models prior

to backward elimination of variables and models 5 and 6 were examined to see if optimism or

pessimism alone in the model functioned as a significant predictor for our model. Four

participants had missing data for the question asking if they were currently experiencing a mental

health condition and were not included in the model. The multicollinearity for all regressions

with multiple predictors was adequate with tolerance above .10 and variance inflation factor less

than 10 for all predictors.

Model 1. After confirming the two-factor model of optimism, we conducted the

regression with five predictor variables (general mindset, self-theory mindset, optimism,

pessimism, and grit) to predict life satisfaction. The results of model 1 indicated that the five

predictors explained a total of 40.6% of the total variance in life satisfaction (R2 =.406,

F=(5,239) 32.60, p<.001). Optimism (t=5.44, df=1, p<.001) and pessimism (t=3.55, df=1,

p<.001) were statistically significant predictors with unstandardized slopes (.662, .414) and

standardized partial slopes (.397, .251) respectively. The other predictors in the model were not

significant after accounting for optimism and pessimism.

Model 2. Next, we examined mental health status as an additional sixth predictor variable

of life satisfaction. The results of model 2 indicated that the predictors explained a total of 42%

of the total variance in life satisfaction (R2 =.42, F=(6,234), 28.23, p<.001). The change in R2

between model 1 and model 2 was significant (R2 =.02 F=(1,234), 7.20, p=.008). Optimism

(t=4.80, df=1, p<.001) and pessimism (t=3.28, df=1, p=.001) remained significant predictors

with unstandardized slopes (.589, .381) and standardized partial slopes (.356, .230). In addition,

self-reported mental health condition was also significant (t=2.68, df=1, p=.008) with an

unstandardized slope (1.779) and standardized partial slope (.147), indicating that participants

who self-reported no mental health conditions reported significantly higher levels of life

satisfaction. These results indicated that even after accounting for the effects of general mindset,

self-mindset, optimism, pessimism, and grit, self-reported mental health status was still a

significant predictor of life satisfaction.

Models 3 and 4. The results of Model 3, predicting life satisfaction of college students

with self-reported depression and/or anxiety, indicated that the overall model was significant (R2

=.255, F=(2,47) 8.03, p=.001) but that no individual predictor was significant. Model 4,

predicting life satisfaction of college students without depression and/or anxiety, indicated that

the overall model was significant (R2 =.347, F=(2,188) 49.95, p<.001). In Model 4, optimism

(t=5.89, df=1, p<.001) and pessimism (t=2.85, df=1, p=.005) were statistically significant

predictors with unstandardized slopes (0.749, 0.345) and standardized partial slopes (.437, .211),

respectively.

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Models 5 and 6. Considering Model 3 for college students with self-reported depression

and/or anxiety (in which the overall model was significant and the two predictors – optimism and

pessimism – which were significant in Models 1 and 2, were not significant), we chose to

conduct a backward selection to determine if optimism or pessimism alone could explain

significant effects from the previous model. Model 5 was a backward elimination method of

baseline Model 3 and removed optimism, predicting life satisfaction of college students with

depression and/or anxiety, and indicated that the overall model was significant (R2 =.239,

F=(2,48) 15.08, p<.001). Pessimism (t=3.88, df=1, p<.001) was a statistically significant

predictor with an unstandardized slope (0.783) and standardized partial slope (.489). Model 6

retained both predictors and is the same as Model 4 for college students without self-reported

depression and/or anxiety.

Discussion

Findings and Implications

The present study offers several broad insights regarding the relationships among and

measurement of the selected variables of mindset, grit, optimism, pessimism, and life satisfaction

in university students. It also provides important information about levels and predictors of life

satisfaction of university students who self-identify as having anxiety and/or depression.

Optimism and pessimism predict life satisfaction. Our LOT-R data measuring

optimism in university students indicated that a two-factor model of optimism and pessimism

was the best-fitting model for our data. While we considered the perspective that optimism may

in fact be a unidimensional construct (e.g., Vautier, Raufaste, & Carious, 2003), our data

suggested that pessimism was a distinct and important construct to consider for our sample of

college students, particularly those with anxiety and depression. We observed that pessimism

was the strongest predictor of life satisfaction for those self-reporting anxiety and depression,

and we discuss these findings more fully below.

Using this two-factor model of optimism, we found that in our total sample of 245

college students, approximately 40.6% of the variance in life satisfaction could be predicted by

general and self-theory mindset, optimism, pessimism, and grit. While the overall model was

significant, only optimism and pessimism were significant predictors, indicating that after

accounting for optimism and pessimism, mindset (both general and self-theory) and grit did not

add any significant predictive value to the model. These significant findings relative to optimism

and pessimism on college student life satisfaction are similar to a prior study (Bailey et al.,

2007), and when considered in the context of the dual factor model of mental health, have

important implications for university wellness services and developmental programming. That is,

optimism is malleable and can be learned, and targeted interventions to increase levels of

optimism may be an important component of university-wide initiatives to support increased

wellbeing among university students. Programs targeting the development of optimism may be

an important part of first-year experiences, residence hall advisor trainings, and college-wide

messaging and mission statements, and the presence of these programs would signal university

cultures that value and promote wellness.

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Optimism and pessimism are distinct predictors / intervention targets. Furthermore, we

found evidence that had we analyzed optimism as a unidimensional construct, we would have

masked important information about the nuanced relationship of optimism and pessimism to life

satisfaction for university students. We found that pessimism, above and beyond optimism, was

an important and significant predictor of life satisfaction in college students overall. This finding

is similar to those of Bailey et al. (2007) in which pessimism predicted life satisfaction.

However, we took our analysis one step further and compared the role of pessimism as a

predictor of life satisfaction between university students with and without self-reported anxiety

and/or depression. We were surprised to find that in university students with anxiety and

depression, pessimism had a much stronger effect on life satisfaction than optimism. The

opposite was true for students without anxiety and depression, for whom optimism was the

stronger predictor of life satisfaction.

These intriguing findings suggest a distinct effect of pessimism specifically on the life

satisfaction of university students with anxiety and/or depression, indicating the need for further

exploration of the role of pessimism for these students. As Chang, Maydeu-Olivares, and

D’Zurilla (1997) stated relative to the two-factor model of the LOT-R, “the value of a two-

dimensional model rests in its ability to specify the relative contributions of the two variables

(i.e., optimism and pessimism) to the prediction of adaptational outcomes…” (p. 439). Our

findings indicate that there are clearly different relative contributions of optimism and pessimism

on the life satisfaction of college students with and without anxiety and depression, indicating

the value of applying this two-factor model for this population. Furthermore, from a dual factor

model of mental health perspective, these findings suggest that addressing optimism alone for

university students with anxiety and/or depression may not be enough. Perhaps targeted

interventions aimed to explicitly decrease pessimistic cognitions may enhance wellness and

mental health programming, especially for students with anxiety and/or depression. These

findings and their implications require further investigation.

The relationships between anxiety and depression on selected constructs. We found

that university students with self-reported anxiety and/or depression had statistically significantly

more fixed self-theory mindsets; lower levels of grit, optimism and life satisfaction; and higher

levels of pessimism when compared to their peers without mental health conditions. It is

important to note that even when we accounted for all of the other variables in the model, self-

reported anxiety and/or depression was still significantly predictive of decreased life satisfaction

in college students. As universities move toward more holistic models of student mental health

and wellness programming, it is important to recognize that university students with anxiety

and/or depression have significantly more fixed mindsets and lower levels of grit, which may

place them at risk for substandard academic and nonacademic college outcomes. This is an

important consideration in light of evidence indicating that growth mindset is associated with

college persistence, especially for students from underrepresented groups (PERTS, 2017). If

university officials know that students who self-report anxiety and depression also tend to have

more fixed mindsets, they can provide mindset interventions and potentially increase college

wellness and retention rates among these students. Furthermore, these students also experience

lower levels of optimism and life satisfaction and higher levels of pessimism, all of which may

contribute to decreased levels of subjective well-being and negative college experiences.

University communities should be aware of the significant differences in these orientations for

JISE VOL. 6, NO. 2 43

students who self-report anxiety and/or depression and consider these variables as potential

developmental and wellness programming themes and intervention targets. Measuring and

documenting the effects of programs to explicitly increase mindset, grit, and optimism and

decrease pessimism, including investigation of any requisite changes in university student life

satisfaction, will be an important area of future research.

GPA may not be an effective risk indicator. We also found, contrary to prior studies

(e.g. Antamarian, 2015; Rode et al., 2005), that life satisfaction did not correlate with GPA. Our

findings were similar to that of Renshaw and Cohen (2014) who found that when looking at life

functioning indicators (e.g., physical health, academic achievement, interpersonal relationships,

etc.) academic achievement seems to be the least effected by mental health status. In our sample

of students with self-reported anxiety and/or depression and decreased life satisfaction, there

were virtually no differences in GPA when compared to students without mental health

conditions and higher levels of life satisfaction. This suggests that GPA does not serve as a

reliable risk indicator for lower levels of life satisfaction and/or psychological distress. When

academic performance is used a sole or “first-line” risk indicator, these students may fly under

the radar and not come to the attention of established university services (e.g., referrals to college

counseling center) or wellbeing programs. Thus, average GPA/academic performance does not

necessarily indicate mental health and wellbeing.

Limitations

There were several limitations to this study. We relied upon the self-report of our

participants, and the requisite cautions of interpreting self-report data apply to this study. We did

not use clinical measures to assess the presence of depression and/or anxiety symptoms as

meeting the threshold for a DSM-V diagnosis; rather we identified a group of college students

who self-identified as having depression and/or anxiety. However, this group of students

measured statistically significantly differently on every variable (with the exception of general

mindset) when compared to those who did not identify as having depression and/or anxiety.

Thus, their self-report was a reliable predictor in our data. An additional limitation was our

unbalanced sample and relatively small sample size of individuals with a mental health

condition. In future work, an increased sample size will improve the validity and reliability of the

current findings.

Future Directions

Future research should continue to investigate the potential role of a pessimism factor in

the life satisfaction of college students, especially for those who self-report anxiety and/or

depression. Another important line of research will be clarifying the overlap between mindset,

grit and optimism for university students. Although one prior study found that grit predicted life

satisfaction in college students (Singh & Jha, 2008), our findings in the present study indicated

that grit had no significant predictive value on college student life satisfaction after accounting

for optimism and pessimism. This finding calls into question the reported role of grit in college

student life satisfaction. Further study is needed to clarify this relationship. Although mindset

had not been explicitly explored relative to college student life satisfaction prior to this study, our

findings indicated that it too had no predictive value above and beyond optimism and pessimism.

JISE VOL. 6, NO. 2 44

What role then does optimism play in growth mindset and the development of grit?

Perhaps, as several researchers have suggested, mindsets are domain-specific and apply narrowly

to related outcomes (e.g., a theory of intelligence predicts academic achievement; a theory of

anxiety predicts symptoms of psychological distress) (Schroder et al., 2015). However, this may

not be the case. This finding caused us to reflect on the recent article by Anderson, Turner, Heath

and Payne (2016) addressing the “parsing” of ideas and constructs of interest into narrowly

defined conceptualizations. They caution, “[f]ailure to think about potential similarities among

differently-labeled ideas makes it more difficult to appreciate the underlying power of the core

idea…” specifically in relation to educational outcomes of children who are vulnerable

(Anderson et al., 2016). We agree that it is important to focus investigations on constructs that

have meaningful outcome correlates for college student outcomes, and we suggest that those

outcomes are both academic and nonacademic. It is important to use sophisticated modeling to

assess potential overarching constructs and orientations, which may organize some of the newer

positive constructs. For example, an overarching “implicit theory of self-change” construct may

be at work in some of the overlap we observe in current constructs of interest. Thus, we believe it

will be important for future investigations to utilize structural equation modeling and

confirmatory analytic techniques that may elucidate a potential higher order factor(s) which may

organize a number of the factors currently being explored in the literature.

Finally, universities must conceptualize the university community writ large as an

ecology around each individual student and consider how positive development and mental

health and wellbeing programming can reach their students through student-, faculty-,

administrative-, and campus-level outreach. Thus, university communities must consider,

develop and implement effective approaches to increase the well-being of their students. While

students are often made aware of resources available in times of difficulty, they are rarely

directed to consider the active cultivation of increased psychological wellbeing. It is critical that

universities actively engage in the creation of college cultures of well-being, an “often neglected

dimension on the college campus” (Eichner, 2015, p.1). Selected positive activity interventions

have shown promise in relieving depressive symptoms and increasing happiness (Layous et al.,

2011) and hold the potential to inform effective university-wide wellbeing programs. The

application of the dual factor model of mental health to college orientation activities, clinic

interventions, and campus-wide developmental programming can benefit students with and

without mental health conditions. The relationships among life satisfaction, psychological

wellbeing, interpersonal connectedness, and physical health are clear and intentional action to

support positive outcomes in these domains for college students is an important step in

addressing college student wellbeing, particularly for students who self-report mental health

conditions. These efforts will fortify students as they tackle the developmental challenges of the

college years and promote cultures of mental health and wellbeing on college and university

campuses.

JISE VOL. 6, NO. 2 45

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About the Authors

Elizabeth D. Tuckwiller, Ph.D., Assistant Professor of Special Education and Disability Studies,

The George Washington University. Dr. Tuckwiller's research examines teacher wellbeing and

relationships among nonacademic variables and holistic educational, postsecondary, and

wellbeing outcomes for individuals with or at-risk for disabilities.

William R. Dardick, Ph.D., Assistant Professor of Assessment, Testing and Measurement, The

George Washington University. Dr. Dardick’s research interests include theoretical exploration

of model fit across latent/emergent models and applied methodologies to development of

psychometric instruments in education and psychology.

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