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The Counseling Psychologist2018, Vol. 46(5) 608 –631

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Character Strengths and First-Year College Students’ Academic Persistence Attitudes: An Integrative Model

Brandon R. Browning1, Ryon C. McDermott1, Marjorie E. Scaffa1, Nathan R. Booth1, and Nicole T. Carr1

AbstractHigher education scholars produce the majority of research on student persistence. However, counseling psychologists may be uniquely situated to help students persist toward graduation by enhancing strengths. The present study integrated counseling and higher education models to examine college students’ character strengths (i.e., hope and gratitude) as predictors of student persistence variables (i.e., academic integration and institutional commitment). Drawing on higher education theories of persistence, we examined the mediating effects of academic integration on the associations between character strengths and institutional commitment among first-year undergraduate students (N = 653). Controlling for social support, greater academic integration mediated the associations between character strengths and institutional commitment in a structural equation model. Consistent with higher education theories emphasizing academic integration as a precursor to institutional commitment, character strengths may be important for understanding academic integration and persistence. Implications for prevention and the integration of counseling psychology and higher education perspectives are discussed.

1University of South Alabama, Mobile, AL, USA

Corresponding Author:Brandon R. Browning, Counseling and Instructional Sciences, University of South Alabama, 307 N. University Blvd. #130, Mobile, AL 36608, USA. Email: browning@southalabama.edu

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Keywordspositive psychology, hope, gratitude, student persistence, student attrition

According to the National Student Clearinghouse Research Center (2016), only 61% of college students enrolled in 2014 re-enrolled with their institu-tion the next academic year. More than half of all student attrition takes place in the first year of college (Deberard, Julka, & Deana, 2004). Students who drop out of college are often limited economically and struggle to pay back expensive loans (Nguyen, 2012). Thus, identifying variables related to first-year students’ academic persistence (i.e., whether they re-enroll each year) is an important area of inquiry. Although higher education researchers are the predominant contributors to the literature regarding student persistence and student retention (Seidman, 2005), counseling psychologists and college counselors may be uniquely positioned to develop and implement interdisci-plinary programs addressing student persistence, given their work as front-line providers in student counseling centers. Indeed, considering that mental health concerns exacerbate and contribute to college student adjustment problems (e.g., National Alliance on Mental Illness, 2012), counseling psy-chologists could have an especially important role helping students adjust to their first year at an institution of higher education.

Counseling psychologists’ historical focus on assets and strengths (Gelso, Nutt-Williams, & Fretz, 2014), position them well to address the need for more research regarding student strengths in higher education. For example, researchers have identified a variety of academic and contextual variables related to student persistence; however, most inquiry has focused on vari-ables that decrease the likelihood of persisting in college. In response, schol-ars in higher education fields have called for more research concerning students’ personal and relational strengths to understand how these may increase persistence (e.g., Demetriou & Schmitz-Sciborski, 2011). Considering that a recent review of positive psychology suggested that it was underutilized in practice (Magyar-Moe, Owens, & Scheel, 2015), more scholarship is needed to explore positive psychological theories in relation to real-world problems. The present study addressed this need by examining character strengths in relation to the concern of student persistence.

Although there are numerous positive psychological traits (i.e., character strengths) available in the extant literature, two dispositional variables fre-quently used by counseling psychologists, hope and gratitude, may hold promise as malleable constructs that could be increased to produce academic benefits (Feldman & Dreher, 2012; Mofidi, El-Alayli, & Brown, 2014). Hope (c.f., Snyder, 2002) consists of goal-oriented cognitions signifying students’

610 The Counseling Psychologist 46(5)

positive perceptions of their ability to achieve their goals, as well as their ability to develop routes around obstacles. By contrast, gratitude (McCullough, Emmons, & Tsang, 2002) consists of a tendency to recognize and appreciate interpersonal and intrapersonal experiences.

Because hope and gratitude may have important academic implications (Gallagher, Marques, & Lopez, 2016; Mofidi et al., 2014), they might con-tribute to classic and contemporary models of student persistence in higher education. To date, however, researchers have yet to examine hope or grati-tude in relation to widely studied persistence variables in higher education such as students’ perceptions of being academically integrated and commit-ted to persisting at their institution (e.g., Davidson, Beck, & Grisaffe, 2015). Accordingly, in the present study we tested an integrative model (counseling psychology and higher education) specifying the combined contributions of first-year college students’ character strengths (i.e., hope and gratitude) as predictors of academic integration and institutional commitment. In the next sections, theory and research supporting the model are briefly reviewed.

College Persistence Attitudes

The present integrative model requires a basic understanding of key con-structs within the higher education literature. Specifically, the study of col-lege persistence (i.e., enrolling each year until completion) has a long history dating back to the early 1930s, with the earliest models of student retention (i.e., whether a student leaves the institution) drawing from Durkheim’s (1951) suicide theory. Several decades later, researchers developed retention theories that, although still focused on interpersonal factors (e.g., belonging-ness) from suicide mortality research, began to target involvement in college. Several contemporary theories were developed regarding the importance of engagement and investment as contributors to student retention (e.g., Astin, 1984; Bean & Eaton, 2000; Tinto, 1987). Most notably, Tinto (1987, 1993) created a theory and a testable model addressing multiple personal and envi-ronmental factors as predictors of student retention. Although Tinto’s model covers a variety of influences, a key aspect is that individual differences and experiences ultimately impact how an individual becomes integrated into the academic and social aspects of college which, in turn, contribute to a sense of institutional commitment over time (Tinto, 1993). The basic principles of Tinto’s model have been supported in numerous longitudinal and cross-sec-tional studies across different decades of investigation (e.g., see Aljohani [2016] for a review).

Drawing largely from Tinto’s (1993) overall model, investigators have identified two variables that are important predictors of student persistence

Browning et al. 611

(e.g., Davidson, Beck, & Milligan, 2009): academic integration (i.e., being engaged in, and satisfied by, the academic aspects of college) and institu-tional commitment (i.e., wanting to persist at the same institution). Researchers have found positive associations between academic integration and students’ institutional commitment (Davidson et al., 2015), and some evidence sug-gests that academic integration may mediate the associations between per-sonal and environment factors and institutional commitment (Davidson et al., 2015). Moreover, self-reported levels of academic integration and institu-tional commitment have been shown to be viable predictors of first-year per-sistence, even when measured as early as 3 weeks into a student’s first semester (Woosley & Miller, 2009). Accordingly, identifying variables that may predict academic integration and institutional commitment may have important implications for addressing student persistence.

Hope

Decisions to ultimately leave or stay at a university are driven by a combina-tion of individual difference factors and institutional characteristics, accord-ing to most theories of student retention in higher education (Astin, 1984; Bean & Eaton, 2000; Tinto, 1993). Historically, researchers have focused on individual vulnerabilities or weaknesses that may increase the risk of non-completion (Demetriou & Schmitz-Sciborski, 2011). As a result, many stu-dent deficits or situational challenges have been identified as contributing to a lack of student persistence (e.g., Mamiseishvil & Deggs, 2013). Although remediation or early intervention addressing these factors may be useful, positive psychology’s focus on strengths in relation to a person’s environ-ment has the potential to bring together the organizational efforts of the insti-tution with the science of psychology (Shushok & Hulme, 2006). Therefore, positive psychology is needed to understand the adaptive characteristics of students and how they contribute to college persistence.

The construct of hope (Snyder, 1994, 2000, 2002) may be particularly helpful for understanding college student persistence. Snyder (2002) defined hope as a positive motivational state based on one’s perceived capability to achieve one’s goals. Hope consists of the ability to generate strategies or pathways (pathways cognitions) toward one’s goals and the motivation and self-efficacy (agency cognitions) to carry out those strategies (Snyder, 2000). The relationship between pathways and agency thinking appears to be both additive and iterative (Rand & Cheavens, 2009), such that hope represents the latent combination of agency and pathways thinking (Gomez et al., 2015).

Researchers have found positive associations between hope and a number of positive attributes, including academic and athletic performance (c.f., Rand

612 The Counseling Psychologist 46(5)

& Cheavens, 2009). Regarding college student performance in particular, two longitudinal studies indicated that greater levels of hope predicted higher grade point averages, higher graduation rates, and lower attrition rates, when controlling for college entrance exam scores and intelligence (Snyder et al., 2002) or academic self-efficacy and academic engagement (Gallagher et al., 2016). Despite the established connections between higher hope and greater retention/persistence, researchers have yet to test potential mediators that may explain the positive effects of hope on these variables. Scholars have argued that hopeful college students are more likely to persist to graduation, in part, because their goal is to be academically successful (Rand & Cheavens, 2009), and hopeful individuals generally find ways to meet their goals (Snyder, 2000). Drawing on higher education models, therefore, one possibility is that students with high levels of hope may become committed to persisting at their university, in part, because they are likely to become academically integrated and invested in their education.

Gratitude

Gratitude is another positive psychological trait that may hold promise with respect to college student persistence. Trait gratitude has been defined by McCullough et al. (2002) as a “generalized tendency to recognize and respond with grateful emotion to…the positive experiences and outcomes that one obtains” (p. 112). Gratitude has been positively related to mental, physical, and social indicators of health and well-being (Bartlett, Condon, Cruz, Baumann, & Desteno, 2012).

Compared to hope, gratitude has received less attention in the literature as a potential correlate of student persistence. However, in one of the few studies of gratitude and college persistence variables, Mofidi et al. (2014) found moderate to strong positive relationships between trait gratitude and a measure of academic integration and degree commitment in a small sam-ple (N = 54) of undergraduate students. Additionally, a semester-long grati-tude journaling intervention demonstrated significant increases in students’ reports of meaning and engagement compared to control conditions (Flinchbaugh, Moore, Chang, & May, 2012). Researchers have speculated that higher levels of gratitude may contribute to academic integration and student retention by increasing the likelihood that students will appreciate and find meaning in their educational experiences (Mofidi et al., 2014). Thus, consistent with higher education models of student persistence, grati-tude could constitute an individual difference variable that potentially facil-itates greater academic engagement and commitment to persisting toward earning a degree.

Browning et al. 613

The Present Study

Positive psychological traits (e.g., hope, gratitude) may contribute to the pre-diction of student persistence variables (e.g., academic integration, institu-tional commitment). However, researchers have yet to integrate hope and gratitude into contemporary models of student persistence (e.g., Tinto, 1993). Such integration could potentially address gaps in the literature in both coun-seling psychology and higher education. Drawing on the sociological per-spective of Tinto’s student departure model as well as the psychology of hope and gratitude, we tested several paths directly or indirectly derived from prior research. Specifically, our integrated model suggests that character strengths are directly related to academic integration, and academic integration, in turn, is related to institutional commitment.

Additionally, research and theory suggest that the quality of existing familial and peer relationships are confounded with hope (Fruiht, 2015), grat-itude (Kong, Ding, & Zhao, 2015), and academic persistence variables (Dennis, Phinney, & Chuateco, 2005). Therefore, controlling for the unique contributions of perceived social support in the present model should provide a more precise picture of how character strengths uniquely predict student persistence attitudes. Likewise, given that college students are a diverse group of individuals representing many different cultural perspectives, pre-liminary race and/or ethnicity and gender explorations of the present model are necessary. Indeed, although researchers have not found any notable dif-ferences in positive psychology scores between men and women, at least one study suggested that the positive benefits of hope may not always occur in samples of Black college students (Banks, Singleton, & Kohn-Wood, 2008).

We hypothesized that, after controlling for perceived social support, (a) character strengths (i.e., hope and gratitude) would be positively related to academic integration and institutional commitment, (b) academic integration would be positively associated with institutional commitment, and (c) aca-demic integration would mediate the associations between character strengths and institutional commitment. Given the relative lack of research on gender and racial differences and the novel integration of variables in the present model, no hypotheses were advanced regarding exploratory moderation effects of race, ethnicity, or gender.

Method

Procedures and Participants

Data were collected in the fall of 2015 as part of an annual institutional assessment of students enrolled in first-year experience courses at a midsized

614 The Counseling Psychologist 46(5)

university in the South. Participants were provided 30 min of class time to voluntarily complete paper and pencil assessments approximately one month into their first semester as part of an institutional assessment containing the variables of interest. In addition, they were asked to complete a series of ad hoc questions about college student engagement and social experiences. Each classroom’s student teaching aide distributed the survey, and each professor delivered responses to the Office of Institutional Research, where the data were entered into the university research database. Institutional Review Board approval for the use of archival data (i.e., the present assessment) was obtained from the institution before analyzing and processing data for the present study.

Response rates within each class varied (85–100%); however, due to some professors opting to not participate in the survey, approximately 35% (N = 653) of all students enrolled in a first-year experience course participated. Consistent with the institution’s overall population of first-year students, the present sample was diverse with respect to race and ethnicity: African American (31.1%), White non-Latino (50.4%), Latino (2.6%), Asian (3.4%), multiracial (3.5%), Native Hawaiian or Pacific Islander (0.2%), American Indian or Alaskan Native (0.8%), unknown (3.1%), and missing (4.9%). The sample primarily consisted of women (63%) and mean age was 18.03 (SD = 0.79). Although no indicators of socioeconomic status were collected directly, the university population consisted of 45% Pell Grant recipients at the time of data collection.

Measures

Dispositional Hope. We used the Adult Trait Hope Scale (ATHS; Snyder et al., 1991) to measure dispositional hope. The ATHS is an 8-item self-report instrument that assesses hope in two domains: agency (e.g., “I ener-getically pursue my goals”) and pathways (e.g., “There are lots of ways around my problem”). Participants respond to each item on an 8-point Lik-ert-type scale that ranges from 1 (definitely false) to 8 (definitely true). The four items from each subscale are averaged, and higher scores indicate greater agency and pathways thinking, respectively. Hellman, Pittman, and Munoz’s (2013) meta-analysis of the ATHS’s total scale reliability found acceptable mean estimates of internal consistency (α = .82) and test-retest reliability (r = .80) over 16 studies. The ATHS has indicated acceptable test-retest reliabilities over 3 weeks (r = .85), 8 weeks (r = .73), and 10 weeks (r = .76; Hellman et al., 2013). In the original validation study, the ATHS positively correlated with expectations of positive outcomes, con-trol, perceived problem-solving abilities, and self-esteem, and negatively

Browning et al. 615

correlated with hopelessness and depression (Snyder et al., 1991). Internal consistency coefficient alphas were acceptable for agency (.83) and path-ways (.80) subscale scores in the present study.

Dispositional Gratitude. The Gratitude Questionnaire-6 (McCullough et al., 2002) is a 6-item self-report measure that assesses an individual’s disposition to experience gratitude. Four items include statements reflecting gratitude that are positively scored (e.g., “I have much in life for which to be grateful”), and two items are reverse scored (e.g., “When I look at the world, I don’t see much for which to be grateful”). Items are scored on a 7-point Likert-type scale that ranges from 1 (strongly disagree) to 7 (strongly agree), and higher average scores indicate greater dispositional gratitude. The measure dis-played good internal consistency for the validation sample (α = .82) and was positively correlated with life satisfaction, vitality, subjective happiness, and positive affect, and negatively correlated with negative affect, anxiety, and depression (McCullough et al., 2002). In the present study, scores demon-strated acceptable internal consistency (α = .79).

Social Support. The Social Support Scale (Hamby, Grych, & Banyard, 2015) is an 11-item instrument measuring perceived social support from family (e.g., “My family really tries to help me”), friends (e.g., “I can count on my friends when things go wrong”), and adults other than family (e.g., “In my life right now, there are adults other than my parents who would give me good suggestions and advice”). The instrument was adapted from the Multi-dimensional Measure of Perceived Social Support (Zimet, Dahlem, Zimet, & Farley, 1988). Participants respond to each item on a 4-point Likert-type scale that ranges from 1 (not true about me) to 4 (mostly true about me). In a rural, low-income sample, the total score showed good internal consistency (α = .90) and correlated moderately with other interpersonal resource mea-surements in the expected direction. In the present study, the internal consis-tency for the Social Support Scale total scale score was acceptable (α = .88).

Student Persistence Variables. To measure these constructs, we utilized two scales from the College Persistence Questionnaire-Version 2 (CPQ-V2; Davidson et al., 2015): the institutional commitment scale and the academic integration scale. The CPQ-V2 expanded upon the CPQ-V1 (Davidson et al., 2009), increasing the number of items from 53 to 68 and moving from a 6-factor model to a 10-factor model. The institutional commitment scale con-sists of four items (e.g., “How confident are you that this is the right college or university for you?”; Davidson et al., 2015). The 7-item academic integra-tion scale includes items such as “How well do you understand the thinking of your instructors when they lecture or ask students to answer questions in

616 The Counseling Psychologist 46(5)

class?” Participants respond to each item on a 5-point Likert-type scale that provides a range of appropriate responses tailored for that item (e.g., 1 = very unconfident and 5 = very confident for the item “How confident are you that this is the right college or university for you?”). Higher scores indicate greater academic integration and institutional commitment. The CPQ-V2 includes an additional option for “not applicable.” Scales of the CPQ-V2 produced sub-stantially improved predictions of student retention over and above typical variables in student databases (Gore, 2010). In the present study, the internal consistency estimates were acceptable for academic integration (α = .83) and institutional commitment (α = .81).

Primary Analysis Plan

We used structural equation modeling (SEM) to examine the direct and indi-rect effects in the hypothesized model. Several steps were followed to evalu-ate the model following best-practice recommendations for SEM (e.g., Kline, 2016). First, a measurement model was examined to assess whether the latent variables had been adequately represented by their observed item parcels. Next, a structural model was used to estimate the regression weights of the hypothesized prediction paths. Following the examination of a structural model, bootstrap tests were then performed to determine the significance lev-els of the indirect effects hypothesized in the mediated structural model.

To determine whether the associations between positive psychology vari-ables, academic integration, and institutional commitment differed by gender (male or female) or race and/or ethnicity (Black students or non-Latino White students), we tested for measurement and structural invariance via multi-group SEM (Cheung & Lau, 2012; Cheung & Rensvold, 2002; Kline, 2016; Vandenberg, 2002). Specifically, Kline (2016) recommended three forms of invariance to test moderation in SEM: configural invariance, factorial invari-ance, and direct-effect invariance. Configural invariance (i.e., ensuring that the same basic pattern of factor loadings is appropriate for each group) is an important requirement for examining factorial invariance (i.e., ensuring that each latent variable is measuring the same construct across groups) which, in turn, is a precondition for examining direct-effect invariance (i.e., invariance of direct effects between latent variables to determine moderation). Direct-effect invariance is the final step of the moderation analysis, and if significant differences in the strength of the relationships between latent variables are found, then moderation is evident (Kline, 2016). Together, these analyses provide a test of moderation not otherwise available through traditional regression analyses, given that the latter do not account for the confounds of measurement error or potential measurement differences across different groups (Kline, 2016).

Browning et al. 617

We used Kline’s (2016) recommended four indices to evaluate the fit of each model: the comparative fit index (CFI; .95 or greater suggests a good fit), the Tucker-Lewis Index (TLI; .95 or greater indicates a good fit), the root-mean-square error of approximation (RMSEA; .05 or less denotes a good fit) with a 90% confidence interval (CI), and the standardized root-mean-square residual (SRMR; .08 or less suggests a good fit). We also report the chi-square test statistic (a nonsignificant value indicates a good fit), although it was interpreted with caution given the large sample size.

Results

Preliminary Analyses

Prior to our primary analyses, we examined data for missing values, univari-ate and multivariate outliers, and normality violations. First, of the 653 par-ticipants who completed the survey, only a few (0.3%) had missing values. Thus, we used full information maximum likelihood estimation to manage missing data in our primary analysis. Second, the number of participants with univariate outliers (1.2%) and multivariate outliers (2.0%) was minimal, sug-gesting that outliers could be ignored (Meyers, Gamst, & Guarino, 2013).

Finally, we examined scores for violations of normality. All variables of interest evidenced a slight to moderate negative skew, suggesting the need for a maximum likelihood estimator with robust standard errors in our primary analyses. In addition to exploring data for missing values, outliers, and nor-mality, we examined zero-order correlations between each variable in the total sample. Table 1 displays the zero-order correlations, as well as the means and standard deviations of each variable of interest.

Primary Analysis

After our preliminary analyses, we tested the specified SEM measurement and structural models using a maximum likelihood estimator with robust standard errors. All analyses used Mplus version 7.31 (Muthén & Muthén, 1998–2015). We used full information maximum likelihood estimation to address the modest number of missing values in the sample.

Measurement model. Because SEM generally requires at least three manifest (i.e., observed) indicators for each latent variable (Kline, 2016), item parcels were generated as observed indicators to form latent variables of social sup-port, gratitude, and academic integration. We used the parceling procedures recommended by Russell, Kahn, Spoth, and Altmaier (1998) by performing

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an exploratory factor analysis for items in each respective instrument with a one-factor solution. Individual items in a given measure were then assigned to three parcels in an iterative fashion to ensure that loadings were as bal-anced as possible across parcels (Russell et al., 1998).

As stipulated by hope theory (Snyder et al., 1991), we formed the hope latent variable by using the agency and pathways subscales as indicators. In addition, because the Institutional Commitment subscale consisted of less than six items, we used item indicators instead of parcels to form an institu-tional commitment latent variable.

The measurement model provided an acceptable fit, χ2 (80) = 219.62, p < .001, CFI = .96, TLI = .95, RMSEA = .05, 90% CI [.04, .06], and SRMR = .05. Moreover, all of the factor loadings were large in magnitude and statistically significant, indicating that each latent variable was appropriately capturing the variance and covariance among its respective item parcels, items, or subscale indicators (see Table 2). Table 3 summarizes the intercorrelations among latent variables in the model. Of note, all latent variables were modestly to moderately positively correlated.

Structural model. In the structural model, hope and gratitude predicted academic integration which, in turn, predicted institutional commitment; the model pro-vided acceptable fit identical to the measurement model, χ2 (80) = 219.62, p < .001, CFI = .96, TLI = .95, RMSEA = .05, 90% CI [.04, .06], and SRMR = .05. Standardized path coefficients are depicted in Figure 1, controlling for social support. The covariate of social support explained 20% of variation in hope and

Table 1. Means, Standard Deviations, and Zero-Order Correlations Among the Research Variables

Variable 1 2 3 4 5 6 7 M SD

1. Social support — .38*** .34*** .40*** .40*** .30*** .16*** 3.52 0.512. Agency — .62*** .90*** .44*** .38*** .24*** 6.82 0.943. Pathways — .90*** .30*** .28*** .12*** 6.68 1.684. Hope — .41*** .37*** .20*** 6.69 0.925. Gratitude — .32*** .26*** 6.10 0.846. Academic

integration— .35*** 3.83 0.70

7. Institutional commitment

— 4.32 0.81

Note. N = 653.***p < .001.

Browning et al. 619

Table 3. Correlations Among the Latent Variables in the Measurement Model

Variable 1 2 3 4 5

1. Social support — .44*** .47*** .36*** .21***2. Hope — .53*** .48*** .27***3. Gratitude — .39*** .30***4. Academic integration — .44***5. Institutional commitment —

Note. N = 653.***p < .001.

22% of variation in gratitude. Combined, hope, gratitude, and the covariate explained 27% of the variation in academic integration and 21% of the variation in institutional commitment.

Table 2. Factor Loading of the Measured Indicators for the Latent Variables in the Measurement Model

Latent variableUnstandardizedfactor loading SE

Standardizedfactor loading

Social support Parcel 1 1.00 — .87*** Parcel 2 1.09 .04 .94*** Parcel 3 1.21 .06 .81***Hope Agency 1.00 — .91*** Pathways 0.74 .07 .68***Gratitude Parcel 1 1.00 — .83*** Parcel 2 0.95 .05 .71*** Parcel 3 0.69 .04 .82***Academic integration Parcel 1 1.00 — .80*** Parcel 2 1.07 .06 .79*** Parcel 3 0.88 .07 .66***Institutional commitment Item 1 1.00 — .73*** Item 2 1.06 .08 .55*** Item 3 0.82 .11 .76*** Item 4 1.18 .09 .89***

Note. N = 653.***p < .001.

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Significance of indirect effects. Lastly, we used bootstrap procedures rec-ommended by Shrout and Bolger (2002) to determine the significance of indirect (i.e., mediation) effects. Specifically, the procedure involved instructing Mplus to generate 1,000 bootstrap samples to estimate bias-corrected confidence intervals for each of the proposed mediation rela-tionships. If the 95% confidence intervals did not include zero, the indirect effects were considered statistically significant at the .05 level (Shrout & Bolger, 2002). Table 4 displays the direct and indirect effects as well as the bootstrapping results. Each of the indirect effects was statistically sig-nificant, indicating that academic integration partially mediated the asso-ciations between character strengths (i.e., hope, gratitude) and institutional commitment.

Race and Gender Exploratory Moderation Analyses Using Multigroup SEM

Invariance for gender. Tests of a configural invariant (i.e., same basic pattern of factor loadings with no cross-group equality constraints) measurement model in each group indicated acceptable fit for men and women, χ2 (160) = 326.27, p < .001, CFI = .95, TLI = .93, RMSEA = .06, 90% CI [.05, .07], and SRMR = .06. Thus, we proceeded with tests of metric invariance by testing a model in which the factor loadings of each observed variable on its respective

Figure 1. Final structural model displaying standardized regression coefficients. Indicators of latent variables, social support as a latent covariate variable, disturbance terms, and error terms are not displayed for readability.*p < .05. **p < .01. ***p < .001.

621

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01.

622 The Counseling Psychologist 46(5)

latent variable were constrained to be equal across men and women. Despite the large sample size, the scaled chi-square difference test was nonsignifi-cant, albeit on the cusp, ∆χ2 (10) = 18.23, p = .05, indicating that the imposi-tion of cross-group equality constraints was not a significantly worse fit than the configural model. Furthermore, the change in CFI was negligible (∆CFI = -.001), and zero was present in all but one of the 99% confidence intervals of the differences in the unstandardized factor loadings across groups. Together, these findings indicated that metric invariance was generally sup-ported, meaning that each of the instruments in the present study appeared to be measuring the same constructs across men and women.

Because metric invariance was supported, we explored structural invariance by testing a model in which the regression paths between latent variables were constrained to be equal between men and women compared to a model with no cross-group equality constraints. The configural invariant structural model with no cross-group equality constraints generally evidenced an acceptable fit to the data, χ2 (160) = 326.63, p < .001, CFI = .95, TLI = .93, RMSEA = .06, 90% CI [.05, .06], and SRMR = .06. The imposition of cross-group equality constraints on the direct effects in the model did not significantly impact the chi-square. Indeed, these cross-group equality constraints resulted in a smaller chi-square, χ2 (160) = 322.26, p < .001, than that for the configural invariance structural model. Moreover, the CFI (.95) also increased, and 0 fell within the 99% confidence interval for each difference between men and women in the associations between hope and help-seeking. These findings indicated that gen-der did not moderate the direct associations in the model.

Invariance for race. The configural invariance model, in which Black and non-Latino White students’ parameters were freely estimated without any cross-group equality constraints, yielded an acceptable fit to the data, χ2 (160) = 318.75, p < .001, CFI = .95, TLI = .94, RMSEA = .06, 90% CI [.05, .06], and SRMR = .05. The scaled chi-square difference test was nonsignificant after the imposition of cross-group equality constraints on all factor loadings, ∆χ2 (10) = 7.47, p = .68. The CFI change was also neg-ligible, ∆CFI = .002, and 0 was only absent in one of the 99% confidence intervals for each factor loading. Thus, a preponderance of evidence sup-ported the presence of metric invariance.

Next, the structural model with no cross-group equality constraints on the direct effects evidenced an acceptable fit to the data, χ2 (160) = 316.74, p < .001, CFI = .95, TLI = .93, RMSEA = .06, 90% CI [.05, 07], and SRMR = .06. Moreover, the imposition of cross-group equality constraints on the direct effects did not significantly change the chi-square, ∆χ2 (5) = 7.31, p = .20), or the CFI (∆CFI = -.001), and 0 fell within the 99% confidence interval for each

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direct effect. These findings indicated that racial–ethnic minority status (i.e., identifying as Black) did not moderate the associations between positive psy-chology and academic persistence in the structural model.

In sum, results of multigroup SEM procedures revealed that there were no differences among the basic patterns of factor loadings for men and women, or for Black and non-Latino White students across the predictor, mediator, and dependent variables, supporting configural invariance. Likewise, each of the constructs in the measurement model appeared to have the same meaning across gender, race, and ethnicity, supporting metric invariance. Tests of direct-effects invariance further indicated that there was no moderation pres-ent in the direct effects of the model for race, ethnicity, or gender.

Discussion

The present study tested an integrative model examining the combined con-tributions of first-year college students’ character strengths (i.e., hope and gratitude) as predictors of persistence variables (i.e., academic integration and institutional commitment).

In support of our first hypothesis that character strengths (i.e., hope and gratitude) would be positively related to academic integration and institu-tional commitment, both hope and gratitude evidenced significant positive associations with academic integration in the measurement and structural models. These findings are consistent with several studies indicating that hope is positively related to academic achievement and engagement in col-lege students (e.g., Gallagher et al., 2016). Our findings also extend the litera-ture on the associations between gratitude and academic achievement (e.g., Mofidi et al., 2014) and suggest that the relationships between these variables are evident even in a larger sample. Together, these findings are consistent with Tinto’s (1993) model of student departure, which broadly states that academic integration is facilitated by a combination of individual differences and institutional experiences. Based on the conceptual definitions of hope and gratitude, the present results suggest that individual differences oriented toward positive goal accomplishments and a general tendency to feel grateful may help promote students’ perceptions that they are thriving academically (i.e., truly enjoying the academic experience) in college.

In support of our second and third hypotheses, (that academic integra-tion would be positively associated with institutional commitment, and that academic integration would mediate the associations between character strengths and institutional commitment) academic integration was posi-tively associated with institutional commitment, and hope and gratitude evidenced significant positive indirect effects on institutional commitment

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through academic integration. Thus, consistent with higher education mod-els of persistence, academic integration partially mediated the associations between character strengths and a desire to persist at the university among first-year students in our sample. However, our results suggest that aca-demic integration may function somewhat differently as a mediator with respect to hope versus gratitude. Specifically, hope was not associated with institutional commitment in the final structural model, although it was moderately related to institutional commitment in the measurement model. A key difference between these two models is that the structural model included academic integration as a mediating variable. Thus, the associa-tion between hope and institutional commitment was statistically explained by academic integration in this sample. In other words, academic integra-tion may be integral to translating higher levels of hope into greater institu-tional commitment, although future longitudinal research is needed to determine this. By contrast, gratitude evidenced a significant direct effect on institutional commitment in the structural model, despite the mediating effect of academic integration.

One potential explanation for the lack of association between hope and insti-tutional commitment after accounting for the mediating role of academic inte-gration, is that hopeful students, possibly because they tend to be goal oriented (Rand & Cheavens, 2009), may commit to persisting at a particular institution largely based on their classroom experiences and satisfaction with their course-work. For example, one item on the academic integration measure used in the present study explicitly asked respondents how much of a connection they saw between their current schooling and their eventual career goals. Therefore, one direction for future research may be to explore whether first-year students with high or low levels of hope are more or less likely to determine their commitment to the institution based on how well they perceive that institution can help them meet their goals. However, grateful students’ institutional commitment appears to be less dependent on levels of academic integration, potentially because trait gratitude represents a dispositional tendency to be more satisfied with—and thankful for—one’s situation and resources overall (McCullough et al., 2002).

In addition to clarifying the relative contributions of hope and gratitude as predictors of student persistence variables, it is important to note that the confounding effects of social support were partitioned out in the direct and indirect effects in the final structural model. Social support has been identi-fied as a foundational and critical variable within several higher education models of student persistence (Astin, 1984; Bean & Eaton, 2000; Tinto, 1993), and thus the present findings indicate that hope and gratitude are capa-ble of making unique contributions to a model of student persistence above and beyond the contributions of social support.

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Although perceived social support was treated as a covariate in the present study, it is noteworthy that it evidenced some of the most robust relationships with character strengths, accounting for approximately 20% of the variation in hope and in gratitude. These findings are consistent with those of numer-ous studies suggesting a connection between hope or gratitude and social support (e.g., Fruiht, 2015; Kong et al., 2015). Moreover, considering that a supportive social network is a critical resource that may help students suc-ceed academically (Dennis et al., 2005), the present findings suggest that hope and gratitude may constitute personal, internal strengths for students that help them become academically integrated beyond the external contribu-tions of social support.

Lastly, our exploratory moderation analyses indicated that the present measurement and structural models were valid across men and women, as well as across non-Latino White and Black students. Consistent with theo-retical assertions (e.g., Lopez et al., 2000), these findings indicate that the positive benefits of hope and gratitude to academic persistence variables appeared to be generalizable across different cultural groups in the present study. The lack of measurement or structural noninvariance (i.e., moderation) in the present model, however, only suggests that the measurement of each construct and its correlates was similar for different cultural groups. Thus, it does not preclude the possibility that such measurements are missing impor-tant culturally relevant constructs or components. Additional research is still needed, particularly qualitatively driven analyses, of how hope and gratitude influence academic persistence across different gender or racial and ethnic identities and experiences.

Limitations and Directions for Future Research

The present findings should be interpreted in light of several key limitations. First, and most important, the cross-sectional design precludes any conclu-sions regarding causality or the temporal order of the associations within the present model. Although the present model specifications were inspired by existing longitudinal research (e.g., Gallagher et al., 2016; Snyder et al., 2002; Wood, Maltby, Gillett, Linley, & Joseph, 2008) and theory-driven rela-tionships in higher education (e.g., Tinto, 1993), longitudinal and experimen-tal research designs are still needed to ascertain the true temporal and causal connections among these variables. Second, our use of self-report measures raises the possibility that response bias and demand characteristics may have distorted the results. Third, the sample was one of convenience and only col-lected at one institution. Future investigations should gather a more represen-tative sample through multiple institutions, as well as consider alternatives to

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self-report measures such as partner reports or observational measures. Fourth, due to institutional constraints about the length of the survey, we were unable to use other existing measures pertinent to academic persistence theories, such as social integration (Tinto, 1993). Although we used a well-known and validated measure of institutional commitment, researchers have defined this construct in many different forms (Davidson et al., 2015), and the measure used in the present study did not capture the reasons behind a student’s desire to leave the university. Examining this information could help identify possible moderators of variables that were (or were not) associ-ated with institutional commitment in the structural model. Lastly, the pres-ent study did not address actual student persistence. Future investigators will need to follow students throughout their university experiences to determine how their positive character strengths influence their institutional commit-ments across all years of college.

Practice Implications

Despite the aforementioned limitations, the present findings may provide a use-ful theoretical framework for counseling psychologists who wish to develop interventions for first-year college students aimed at using their character strengths to enhance academic integration, and ultimately institutional commit-ment. Indeed, the positive associations between character strengths and persis-tence variables in the present study imply that lower levels of hope or gratitude correspond to lower levels of academic integration and institutional commit-ment. Considering that academic integration and institutional commitment measured early in the semester were salient predictors of subsequent student persistence in previous research (Woosley & Miller, 2009), early intervention may be especially beneficial. Such interventions could consist of both primary and tertiary prevention components occurring alongside the existing and ongo-ing efforts to create a positive college experience for all students.

With respect to primary prevention, focused workshops and activities aimed at increasing or harnessing hope and gratitude could be offered to first-year students. Previous researchers, for example, have identified that gratitude can be facilitated or harnessed through the use of writing grateful letters (Toepfer, Cichy, & Peters, 2012), journaling (O’Connell, O’Shea, & Gallagher, 2017), verbally expressing gratitude (Yoshimura & Berzins, 2017), or training individuals to be mindful of experiences of gratitude through psychoeducation and practice (Froh et al., 2014). Likewise, hopeful thinking can be influenced through psychoeducation and setting meaningful goals, with some evidence indicating that goal achievement can be increased by participating in a hope-focused workshop lasting as little as 90 min (Feldman & Dreher, 2012).

Browning et al. 627

Regarding tertiary prevention, the present findings suggest that students and institutions may benefit from early assessment of students’ academic integration and institutional commitment, as well as measures of their char-acter strengths. Counseling psychologists could collaborate with institutional programs and structures, such as first-year experience courses or advisors, to facilitate early intervention and help students access academic and personal resources to become more academically integrated. In consulting with advi-sors, instructors, or other institutional personnel, counseling psychologists could impart critical knowledge about positive psychology that is otherwise missing in the design of institutional programs and decision-making pro-cesses that influence first-year students. Conversations with university staff and faculty may help shift the dialogue from a problem-based perspective to a strengths-based approach based firmly in the science of psychology.

Acknowledgments

We would like to thank Ms. Cecelia Martin in the Office of Institutional Effectiveness at the University of South Alabama for her help with data collection.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interests with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publica-tion of this article.

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Author Biographies

Brandon R. Browning, PhD, is an assistant professor as well as practicum and internship coordinator in the Clinical Mental Health Counseling program at the University of South Alabama. His research interests include college student develop-ment, wellness, resilience, and spirituality.

Ryon C. McDermott, PhD, is an assistant professor and associate director of clinical training in the Combined-Integrated Clinical and Counseling Psychology doctoral program at the University of South Alabama. His research interests revolve around college student well-being, culture, and individual differences.

Marjorie E. Scaffa, PhD, is currently the Health and Wellness Counselor for the College of Medicine at the University of South Alabama. In this role, she develops wellness programming and counsels medical students. She is also the founding chair and professor emeritus of Occupational Therapy at the University of South Alabama.

Nathan R. Booth, MEd, is a PhD student in the Combined-Integrated Clinical and Counseling Psychology program at the University of South Alabama. His research interests include college student health, help-seeking attitudes and behaviors, and issues related to men and masculinities.

Nicole T. Carr, PhD, is the Associate Vice President for Student Academic Success at the University of South Alabama. In this position she provides university leadership focused on student access and success, one of the institution’s five priorities. As a sociologist, her research is most often applied in nature, and her interests include gender, inequality, and crime.