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Personal Growth Initiative in the Therapeutic Process: An Exploratory Study

Ingrid K. Weigold1, Rebecca A. Boyle1, Arne Weigold2, Stephen Z. Antonucci1, Heike B. Mitchell1, and Caitlin A. Martin-Wagar1

AbstractPersonal growth initiative (PGI), an individual’s active and intentional engagement in the growth process, was originally developed as a potentially useful construct in therapy. Although it has repeatedly been related to psychological well-being and distress, few studies have examined PGI in clinical samples. The current study investigated the role of PGI in a sample of 295 clients at a community-serving training clinic. Data were collected at two time points. Confirmatory factor analyses supported a second-order model with four first-order PGI factors: Readiness for Change, Planfulness, Using Resources, and Intentional Behavior. Using cross-lagged panel analysis, PGI at Time 1 was found to predict psychological distress endorsed at Time 2 after accounting for distress at Time 1.

Keywordspersonal growth initiative, Personal Growth Initiative Scale-II, therapy, counseling, clients

1The University of Akron, Akron, OH, USA2Notre Dame College, South Euclid, OH, USA

Corresponding Author:Ingrid K. Weigold, Department of Psychology, The University of Akron, 290 East Buchtel Avenue, Akron, OH 44325-4301, USA. Email: weigold@uakron.edu

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Across various therapeutic techniques and theoretical orientations, the client is considered to be a strongly influential factor in the therapeutic process (Bohart, 2000; Prochaska & DiClemente, 2005). Two of the most important client char-acteristics are the clients’ willingness and ability to be involved in the change process and their use of the opportunity to learn how to manage their problems in general (Bohart, 2000). Therefore, clients’ readiness and ability to change have clear implications for the counseling process and therapeutic outcomes (Prochaska & DiClemente, 2005). Consequently, it would be advantageous for therapists to understand their clients’ general tendency towards active involvement in the change process and to be able to assist them in developing their abilities in this area (Robitschek, 1998; Robitschek et al., 2012).

One construct that is strongly tied to an individual’s change process is personal growth initiative (PGI). PGI refers to the intentional and active engagement in the growth process in domains that are salient to the individ-ual. Personal growth occurs with the individual’s full awareness and active participation in the process (Robitschek, 1998). PGI is multidimensional, encapsulating both cognitive (e.g., the ability to plan the change process) and behavioral components (e.g., the ability to engage in the previously devel-oped plan; Robitschek et al., 2012). PGI is an aspect of human agency and, similar to other agentic characteristics such as self-efficacy, is a skill that can change and develop (Robitschek et al., 2012; Weigold & Robitschek, 2011). Given PGI’s conceptual and empirical ties to both the change process and mental health, in the current study we sought to examine the construct in a sample of therapy clients.

PGI Conceptualization

There are two main tenets of PGI: intentionality and transferability (Robitschek et al., 2012; Weigold, Porfeli, & Weigold, 2013). Intentionality refers to the active and conscious process of personal growth (Robitschek, 1998, 1999; Robitschek et al., 2012). This type of self-change is distinct from other, broader conceptualizations of personal growth that occur outside of intention or awareness (Robitschek, 1999), such as the personal growth dimension in Ryff and Keyes’ (1995) theoretical conceptualization of psy-chological well-being. However, consistent with such broader conceptualiza-tions, PGI is considered to facilitate positive growth (Robitschek, 1999). Such growth includes both modifying those personal aspects individuals choose to change and retaining those personal aspects that they intentionally decide not to change (Robitschek et al., 2012).

The second tenet of PGI is transferability. PGI is not specific to certain life domains; rather, it is evidenced in the areas of life that are important for a

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particular person (Robitschek, 1998; Robitschek et al., 2012). The same skills can be used for growth in different domains (e.g., vocational, social) and across different life stages (e.g., emerging adulthood, middle adulthood). Consequently, individuals with high levels of PGI are thought to use these skills in any area in which they desire to grow (Robitschek, 1999; Robitschek et al., 2012).

PGI is related to theories of change. It is partially drawn from the prepara-tion stage of Prochaska and DiClemente’s (2005) transtheoretical model of change (Robitschek, 1998). During this stage, clients intentionally desire to change in a specific life domain and are simultaneously ready to engage in actions to facilitate such change (e.g., Prochaska & DiClemente, 2005). PGI likewise focuses on cognitive and behavioral skills to facilitate the growth process; however, rather than focusing on specific behaviors and growth areas, PGI’s transferability represents an orientation toward change in a more general sense (Robitschek, 1998).

PGI was developed as an aspect of human agency; consequently, it shares similarities with other agentic characteristics, such as self-efficacy and hope, and yet is also conceptually distinct from these other constructs. PGI draws from self-efficacy theory such that it is expected that those who intentionally engage in the growth process will also have self-efficacy for doing so; how-ever, PGI encompasses specific actions as well as cognitive beliefs (Robitschek, 1998). PGI and hope are both future-oriented and positive, but hope is more focused on general goal-seeking rather than on personal growth (Shorey, Little, Snyder, Kluck, & Robitschek, 2007).

Although the intentional process of personal growth can occur in many environments, PGI scholars have highlighted its potential usefulness in the therapeutic process. PGI was originally developed as a reflection of “one of the basic tenets of the field [of counseling]: teaching clients the skills needed to have a productive and fulfilling life” (Robitschek, 1998, p. 197; Gelso & Fretz, 1992). Regarding the two tenets of PGI, high levels of intentionality are thought to lead to high levels of psychological well-being and low levels of psychologi-cal distress in clients, whereas transferability allows clients to successfully engage in the change process amidst various life challenges. Consequently, PGI may offer therapists and clients a framework for evaluating clients’ abili-ties to intentionally be involved in the growth process. In addition, for clients with low levels of PGI, therapists can teach their clients the skills necessary to develop and engage in plans for growth (Robitschek et al., 2012).

Measurement and Evaluation of PGI

PGI was originally measured using the unidimensional nine-item Personal Growth Initiative Scale (Robitschek, 1998). However, Robitschek et al.

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(2012) refined this measure to better represent the multidimensional con-struct of PGI. The current measure of PGI, the Personal Growth Initiative Scale-II (PGIS-II), consists of four interrelated subscales measuring the cog-nitive and behavioral components of PGI. The two subscales assessing the cognitive components are Readiness for Change, which is an individual’s knowledge of when to begin the change process (e.g., “I can tell when I am ready to make specific changes in myself”), and Planfulness, which is an individual’s development of a plan to change (e.g., “I set realistic goals for what I want to change about myself”). The two behavioral subscales are Using Resources, which is an individual’s use of external resources during the change process (e.g., “I ask for help when I try to change myself”), and Intentional Behavior, which is an individual’s behavioral engagement in the change process (e.g., “I take every opportunity to grow as it comes up”; Robitschek, 2012, p. 287). Use of the PGIS-II has been promising, as research employing this measure has shown adequate to strong reliability and validity in community, student, and international populations (e.g., Robitschek et al., 2012; Weigold, Weigold, Russell, & Drakeford, 2014; Yakunina, Weigold, & Weigold, 2013; Yang & Chang, 2014). The PGIS-II’s factor structure has been confirmed across several samples, with a four-factor correlated solution typically being selected over a single-factor or second-order solution (e.g., Robitschek et al., 2012; Weigold et al., 2014; Yakunina et al., 2013).

There has been some empirical support for the tenets of intentionality and transferability. For example, Robitschek (1999) showed that PGI was posi-tively related to growth that was both intentional and in awareness, as well as negatively related to growth that was unintentional or both unintentional and not in awareness, in a sample of college students. Additionally, Robitschek and Cook (1999) and Weigold et al. (2013) found that PGI predicted voca-tional identity development in college students. Finally, PGI has been shown to increase in adults as a consequence of participating in wilderness renewal activities (Robitschek, 1997).

A link between PGI and psychological health has also been established. Primarily cross-sectional empirical research has repeatedly shown PGI to be positively related to facets of psychological well-being and negatively related to aspects of psychological distress (e.g., Robitschek & Kashubeck, 1999; Robitschek & Keyes, 2009; Shigemoto, Low, Borowa, & Robitschek, 2016; Vartanian, Smyth, Zawadzki, Heron, & Coleman, 2014; Weigold et al., 2013). For example, PGI has been found to predict psychological, emotional, and social well-being in college students, accounting for between 18% (emo-tional) and 51% (psychological) of the variance in one study (Robitschek & Keyes, 2009). Additionally, PGI has negatively predicted depression and positively predicted posttraumatic growth in a sample of college students

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(Shigemoto et al., 2016). PGI has also been used in combination with related constructs to predict psychological symptoms. For example, both PGI and self-esteem loaded on a latent factor of intrapersonal resources, which, in turn, negatively predicted body dissatisfaction in college women (Vartanian et al., 2014).

Research in various parts of the world has corroborated the findings from U.S. samples regarding the link between PGI and psychological well-being and distress. For instance, PGI has been shown to be a protective factor against functional impairment in a sample of those affected by genocide in Rwanda (Blackie, Jayawickreme, Forgeard, & Jayawickreme, 2015). In addi-tion, PGI has been positively related to hope and goal-setting, and negatively related to neuroticism, in a sample of adults in Australia (Klockner & Hicks, 2008). Finally, PGI has been negatively related to aspects of distress (e.g., depression, Internet addiction) and positively related to well-being (e.g., resilience) in Chinese college students (Yang & Chang, 2014).

Despite the available literature supporting PGI’s relationship to psycho-logical well-being and distress, few studies have examined PGI in clinical samples, and the available studies have provided limited information regard-ing its use in the therapeutic process (Klockner & Hicks, 2008; Robitschek & Hershberger, 2005). For example, Klockner and Hicks (2008) found that community members who had previously sought psychosocial interventions had higher levels of PGI than those who had not sought treatment. Robitschek and Hershberger (2005), who assessed therapy clients at their intake appoint-ments, found that PGI was significantly and positively related to several expectations about the counseling process, as well as to being in the action stage of change as defined by Prochaska and DiClemente (2005).

In summary, PGI is well established in the literature. Although it was developed in part for use in therapy settings, few studies have examined PGI’s role in therapy (see Robitschek et al., 2012), and existing studies used the original measure of PGI (Klockner & Hicks, 2008; Robitschek & Hershberger, 2005). There is preliminary evidence that PGI has a role in the counseling process, yet no research to date has systematically examined the relation of PGI to clients’ psychological distress.

Current Study

The current study sought to examine the role of PGI in the therapeutic pro-cess. We assessed current therapy clients at two different time points: at intake (Time 1) and upon last completion of the PGIS-II (Time 2). In addition to assessing PGI’s relationships to demographic factors and psychological distress, we investigated two hypotheses. First, we hypothesized that a

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four-factor correlated solution, corresponding to the four factors of the PGIS-II, would provide a good fit for the data using confirmatory factor anal-ysis (CFA). Similar to the results of past research (e.g., Robitschek et al., 2012; Weigold et al., 2014; Yakunina et al., 2013), we expected this solution to be selected as a better fit than either a single-factor model (in which all items loaded onto a single PGI factor) or a second-order model (in which all items loaded onto the four first-order factors, and the four first-order factors loaded onto a higher-order factor). Second, we examined both PGI and psy-chological distress across time by using a cross-lagged panel analysis. A tech-nique based in structural equation modeling (SEM), cross-lagged panel analyses allow researchers to determine how variables collected at different points in time might relate to each other. This typically includes paths from each variable at Time 1 to each variable at Time 2 (Martens & Haase, 2006). We expected there to be a significant and positive path from PGI at Time 1 to PGI at Time 2, and a significant and positive path from psychological distress at Time 1 to psychological distress at Time 2. We also expected PGI at Time 1 to negatively predict psychological distress at Time 2; this would be consis-tent with the conceptual relationship of high levels of intentionality leading to low levels of psychological distress (Robitschek et al., 2012), as well as the results of cross-sectional research indicating that PGI negatively predicts psychological distress (e.g., Shigemoto et al., 2016). Given that research has not examined if psychological distress predicts PGI, we made no hypothesis regarding the impact of psychological distress at Time 1 to PGI at Time 2. However, we examined this path to test whether the relationship between psychological distress and PGI in cross-sectional research might be due to distress predicting PGI levels.

Method

Participants

Participants consisted of adult clients seeking counseling services between May and December 2015 at an outpatient community mental health clinic operated within a large, urban, Midwestern university. Counseling services were provided by master’s- and doctoral-level trainees in counseling psy-chology, marriage and family therapy, school counseling, and clinical mental health counseling. Therapists in training worked under the supervision of licensed professionals in their fields. Trainees were required to participate in weekly individual and group supervision to ensure ethical and effective prac-tice. There were 68 trainees seeing clients in the clinic during the timespan of the study. These trainees saw an average of 8.49 clients each (SD = 4.72;

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range: 1–30). They were each supervised by one of seven supervisors licensed in the trainee’s specific field of study.

A total of 295 clients provided usable data for analysis. Please note that per-centages for the participant descriptives do not always add up to 100% due to rounding. The sample was roughly evenly split between men (n = 145, 49%) and women (n = 149, 51%), with one individual (<1%) who identified as trans-gender. Ages ranged from 18 to 82 (M = 34.37, SD = 12.30). Participants reported identifying primarily as White/European American (n = 206, 70%), followed by Black/African American (n = 67, 23%), Biracial/Multiracial (n = 6, 2%), Other (n = 6, 2%), and Arab American, Asian American, and Hispanic/Latin American (n = 1 each, <1% each); seven participants (2%) did not answer this question. Participants primarily identified as heterosexual (n = 241, 82%), followed by bisexual or lesbian (n = 7, 3% each), gay (n = 5, 2%), and question-ing (n = 1, <1%); 14 individuals (5%) indicated that they preferred not to respond, and 20 participants (7%) did not answer this question. Approximately one third of clients were currently married (n = 98, 33%), with the remainder reporting they were currently single (n = 56, 19%), cohabiting (n = 52, 18%), dating (n = 30, 10%), partnered (n = 16, 5%), separated (n = 8, 3%), divorced (n = 7, 2%), remarried (n = 2, 1%), or widowed (n = 1, <1%); 25 participants (8%) did not answer this question. The psychological services were provided primarily to individuals of a lower socioeconomic strata, with approximately one third of clients reporting an annual household income of ≤$20,000 (n = 95, 32%) and only 12% (n = 35) reporting an annual household income of >$50,000.

Regarding the type of therapeutic services, participants engaged in indi-vidual therapy (n = 116, 39%), couples’ counseling (n = 136, 46%), anger management (n = 27, 9%), and family therapy (n = 16, 5%). Twenty-nine clients (10%) were involved with the court system such that they were court-ordered to receive psychological services as a condition of probation and/or parole for a criminal offense, recommended to participate in treatment by a legal entity, or both. The number of sessions each client attended between Time 1 and Time 2 ranged from 1 to 14 (M = 4.69, SD = 2.98), and the num-ber of sessions clients missed between Time 1 and Time 2 ranged from 0 to 8 (M = 2.10, SD = 1.65). Treatment was not time-limited for most clients; only those who participated in anger management had a session limit. These 27 clients attended between 1 and 11 sessions (M = 3.37, SD = 2.34).

Measures

Demographic information. All clients completed a demographic questionnaire during the first session. Information gathered included sex, age, racial and/or eth-nic identity, sexual orientation, relationship status, and annual household income.

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PGI. PGI was assessed through the PGI Scale-II (PGIS-II; Robitschek et al., 2012). The measure consists of 16 items corresponding to four subscales reflecting the interrelated cognitive and behavioral components of personal growth initiative: (a) Readiness for Change (RC; four items) describes an ability to recognize readiness to engage in a specific growth process; (b) Planfulness (PL; five items) describes a capacity to develop a realistic scheme to enact change; (c) Using Resources (UR; three items) identifies an ability to mobilize support for growth; and (d) Intentional Behavior (IB; four items) describes an engagement in behavior to enact change. The PGIS-II item response format utilizes a 6-point Likert-type scale ranging from 0 (disagree strongly) to 5 (agree strongly). Items for each subscale were averaged; higher scores indicated higher levels of the respective aspect of PGI. The four-factor structure of the PGIS-II has been examined and confirmed in diverse samples (e.g., Robitschek et al., 2012; Weigold et al., 2014; Yakunina et al., 2013). Paired-sample t-tests found no significant differences for the PGIS-II factor and total scores given across 1-, 2-, 4-, and 6-week intervals, suggesting evi-dence of test-retest reliability (Robitschek et al., 2012). The PGIS-II’s results have shown convergent, discriminant, and cultural evidence of validity. For instance, the PGIS-II factor and total scores were all positively and primarily significantly correlated with measures of conceptually related constructs, including assertiveness, instrumentality, expressiveness, and general self-efficacy, as well as the original measure of personal growth initiative. The PGIS-II factor and total scores also had small and generally nonsignificant correlations with social desirability (Robitschek et al., 2012; Weigold et al., 2014). Additionally, canonical correlation indicated that three of the four PGIS-II factors were positively associated with several aspects of Black racial identity in a sample of African American college students (Weigold et al., 2014).

Psychopathology and global functioning. We assessed global experiences of dis-tress using the Brief Symptom Inventory (BSI; Derogatis & Melisaratos, 1983). Developed as a brief version of the Symptom Checklist-90-Revised (Derogatis, 1977), this widely used self-report inventory consists of 53 items that each load onto one or more of nine clinical symptom dimensions and three indices of global functioning. To avoid issues of multicollinearity, we chose to use only one of the three global indices in the current study, the global severity index (GSI). The GSI is the average of all 53 items and reflects psychological distress; it is recommended by the BSI’s authors above the other two indices for measuring distress levels (Derogatis & Melisaratos, 1983). Items on the BSI are answered using a 5-point Likert-type scale rang-ing from 0 (not at all) to 4 (extremely).

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The BSI was normed across multiple levels of psychiatric care, including inpatient, outpatient, and community samples. Reliability estimates provided evidence for the temporal stability of the BSI clinical and global functioning scales, with test-retest correlations over a 2-week period ranging from .68 to .91; all test-retest correlations for the global indices were above .80. The BSI’s symptom dimensions indicated convergent validity such that the BSI subscales correlated as expected (Derogatis & Melisaratos, 1983) with simi-lar subscales on the Minnesota Multiphasic Personality Inventory (Hathaway & McKinley, 1942); the subscale scores have also been shown to decrease during treatment (e.g., Piersma, Reaume, & Boes, 1994). Research has shown support for the measurement invariance of the BSI’s factor structure across African American, European American, and Latin American participants with severe mental illness (Hoe & Brekke, 2009), as well as confirmed the BSI’s nine-factor structure in a sample of individuals with intellectual dis-abilities (Wieland, Wardenaar, Fontein, & Zitman, 2012).

Engagement indicators. Data regarding referral, participation, and termination were gathered through a therapy records review. Information obtained through this review included the total number of sessions attended between Time 1 and Time 2, the referral source, and the reason for ending treatment. Premature termination was operationalized as the unexpected discontinua-tion of the therapeutic relationship prior to the acquisition of treatment goals as mutually agreed upon by the client and provider. This definition is consis-tent with other research examining client disengagement from psychotherapy (Swift & Greenberg, 2012).

Procedure

The community mental health clinic where the study was completed allowed research to be conducted as part of its routine procedures. Clients were informed of this at intake and provided consent for their responses to questionnaires gathered for therapeutic purposes to also be used in research. At this clinic, the BSI was only completed prior to clients’ appointments at intake, the end of each semester (fall, spring, and summer), and termination. For this study, the PGIS-II was added when clients completed the BSI (i.e., intake, the end of the semester, and termination). Two time points were used. Time 1 was at the intake appointment (prior to meeting with a therapist), and Time 2 was the final time at which clients completed the PGIS-II during the course of the study. All participants completed the PGIS-II at Time 1 (n = 295), and slightly over half (n = 159, 54%) completed it at Time 2. Thus, most of the data provided was captured, as few participants completed the PGIS-II at three (n = 30, 10%) or

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Table 1. Means, Standard Deviations, Internal Consistencies, and Mean Changes for the PGI Factors and the GSI

Time 1 Time 2 Δ Time 1–Time 2

Variable n α M (SD) n α M (SD) df t p

1. PGI_RC 295 .87 3.39 (1.07) 159 .89 3.49 (1.04) 158 −2.75 .007*

2. PGI_PL 295 .91 3.21 (1.17) 159 .93 3.44 (1.05) 158 −4.16 <.001*

3. PGI_UR 295 .83 2.73 (1.32) 159 .87 3.16 (1.22) 158 −4.92 <.001*

4. PGI_IB 295 .88 3.50 (1.08) 159 .90 3.69 (1.01) 158 −3.58 <.001*

5. GSI 282 .97 0.95 (0.71) 142 .96 0.69 (0.58) 139 3.49 .001*

Note. PGI = personal growth initiative; GSI = Global Severity Index; PGI_RC = Readiness for Change; PGI_PL = Planfulness; PGI_UR = Using Resources; PGI_IB = Intentional Behavior.*p is significant at the Bonferroni-corrected alpha level of .01.

more (n = 17, 6%) time points. The data also include participants who prema-turely terminated and yet completed at least two time points (n = 23, 8%). At both Time 1 and Time 2, a small number of participants who completed the PGIS-II failed to also complete the BSI (n = 13 [4%] and n = 17 [6%], respectively).

Results

Preliminary Analyses

Preliminary analyses were conducted using SPSS v. 23. Missing data were accounted for using available item analysis (Parent, 2013) for participants missing less than 20% of items per scale. Means, standard deviations, inter-nal consistencies, and mean changes across time for the four PGI factors and the BSI’s GSI collected at Time 1 and Time 2 are shown in Table 1. Correlations examining the relations of PGI and the GSI at Time 1 and Time 2 are shown in Table 2. Intercorrelations among the PGI factors within each time point were strong and positive. Both the magnitudes and direction are consistent with past literature on PGI (e.g., Robitschek et al., 2012; Weigold et al., 2014; Yakunina et al., 2013). Correlations between the PGI’s four fac-tors and the GSI were all significant and negative.

Due to participant attrition from Time 1 to Time 2, we compared participant responses to the PGIS-II and GSI administered at Time 1 for those who pro-vided complete data at both time points (ns = 159 and 142, respectively) with those who only provided complete data at Time 1 (ns = 136 and 140, respec-tively). A MANOVA indicated no significant differences between groups for

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the four Time 1 PGI factor scores, F(4, 290) = 1.31, p = .27, η2P = .02. However,

an ANOVA suggested that the two groups differed significantly on the GSI, F(1, 280) = 9.03, p = .003, η2

P = .031, such that those who did not complete data at both time points reported significantly higher distress at Time 1. Using the suggested η2

P effect sizes of .0099, .0588, and .1379 to indicate small, medium, and large effects, respectively (Cohen, 1969; Richardson, 2011), the effect size for the GSI was small.

As this is one of the first studies to examine PGI in a therapeutic setting, we conducted a series of MANOVAs to determine if there were demographic differences among the four PGI factors (see Table 3). Assessing Time 1 and Time 2 separately, we examined differences among type of session attended (individual, couples, family, and anger management), court involvement (i.e., court ordered or referred; yes and no), premature termination (yes and no), gender (men and women), and racial or ethnic identity (African American and European American). We also examined the correlations between the PGI factors and age, number of sessions attended, and number of sessions missed. It should be noted that we did not have enough individuals identify-ing as transgender to include them in the gender analysis, nor did we have enough people identifying as Arab American, Asian American, Biracial/Multiracial, Latin American, or Other to include them in the racial and ethnic identity analysis.

None of the MANOVAs were significant using an alpha level modified for the number of analyses conducted (p ≤ .005). Additionally, correlations between the PGI factors and age, number of sessions attended, and number of sessions missed were all small and nonsignificant (r range: .01–.15).

Table 2. Correlations of PGI Factors and GSI at Time 1 and Time 2

Variable 1 2 3 4 5 6 7 8 9 10

1. PGI_RC Time 1 — 2. PGI_PL Time 1 .79*** — 3. PGI_UR Time 1 .52*** .50*** — 4. PGI_IB Time 1 .76*** .76*** .54*** — 5. GSI Time 1 −.23*** −.39*** −.22*** −.31*** — 6. PGI_RC Time 2 .64*** .61*** .34*** .60*** −.25*** — 7. PGI_PL Time 2 .54*** .67*** .33*** .53*** −.30*** .85*** — 8. PGI_UR Time 2 .45*** .45*** .63*** .50*** −.18* .62*** .64*** — 9. PGI_IB Time 2 .54*** .62*** .31*** .71*** −.28*** .78*** .76*** .64*** — 10. GSI Time 2 −.27*** −.45*** −.18* −.26** .69*** −.33*** −.44*** −.31*** −.29*** —

Note. PGI = personal growth initiative; GSI = Global Severity Index; PGI_RC = Readiness for Change; PGI_PL = Planfulness; PGI_UR = Using Resources; PGI_IB = Intentional Behavior.*p ≤ .05. **p ≤ .01. ***p ≤ .001. Some correlations with the same magnitude have different p-levels due to rounding or n-size differences.

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Table 3. Results of MANOVAs Examining Group Differences on Levels of PGI

Variable F df p η2P n

Type of Session on PGI: T1 1.27 12, 870 .23 .02 295Type of Session on PGI: T2 2.13 12, 462 .01 .05 159Court Involvement on PGI: T1 0.90 4, 290 .46 .01 295Court Involvement on PGI: T2 2.27 4, 154 .06 .06 159Premature Termination on PGI: T1 1.55 4, 290 .19 .02 295Premature Termination on PGI: T2 1.25 4, 154 .29 .03 159Gender on PGI: T1 2.35 4, 289 .06 .03 294Gender on PGI: T2 0.77 4, 153 .55 .02 158Race/Ethnicity on PGI: T1 2.86 4, 268 .02 .04 273Race/Ethnicity on PGI: T2 1.78 4, 143 .14 .05 148

Note. PGI = personal growth initiative; T1 = Time 1; T2 = Time 2. None of the MANOVAs were significant at the Bonferroni-corrected alpha level of p ≤ .005.

Confirmatory Factor Analyses

To assess the factor structure of the PGIS-II at Time 1 (N = 295), we con-ducted a CFA run in Mplus v. 7.4 (Muthén & Muthén, 1998–2015). As our data showed evidence of multivariate non-normality, χ2(816) = 2133.69, p < .001, all analyses used robust maximum likelihood estimation. The full infor-mation maximum likelihood method was used to handle missing data (Enders & Bandalos, 2001). Consistent with past research (e.g., Robitschek et al., 2012; Weigold et al., 2014), we examined three potential solutions. The first was a single-factor model in which all PGIS-II items loaded onto one global PGI factor. The second was a four-factor model consisting of the four PGIS-II subscales, which were allowed to covary with each of the subscale items loading on the appropriate factor. The final model was a second-order solu-tion in which each subscale item loaded onto its respective factor, and the four PGIS-II subscales (factors) loaded onto one global PGI factor.

To assess relative model fit, we examined several goodness-of-fit indices: the robust model χ2, the Comparative Fit Index (CFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). The χ2 is expected to be nonsignificant in well-fitting models, although this seldom occurs (Byrne, 2010). A CFI ≥ .90 indicates adequate fit for the data, with ≥ .95 denoting good fit. The RMSEA should ideally be ≤ .05, with values ≥ .10 indicating poor fit (see Byrne, 2010, for a review). An SRMR ≤ .08 indicates good fit (Hu & Bentler, 1999). We also reported the Akaike Information Criterion (AIC) for each model. The AIC does not provide information about individual models; rather, it is used in

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comparisons to select the best-fitting model. It is suitable for comparing both nested and non-nested models (Burnham & Anderson, 2002).

Fit indices for all three models are shown in Table 4. The single-factor solution’s fit indices suggested poor model fit, with only the SRMR of .074 indicating good fit. The four-factor and second-order models’ fit indices pri-marily suggested adequate fit, with the SRMRs of .062 and .064, respec-tively, indicating good fit.

To determine the best-fitting model of the ones examined, we followed the recommendations of Weston and Gore (2006) to “test significant improvement in model fit with a chi-square difference test and improve-ment in other fit indices” (p. 746). Consequently, we used both the scaled chi-square difference test (for single-factor compared to four-factor) and AIC (for all comparisons). When comparing a series of models, the one with the smallest AIC is compared to the other models, such that a differ-ence in AIC of more than 10 indicates no support for the fit of the larger AIC comparison model, a change of 4 to 7 suggests marginal support, and a change of 0 to 2 indicates strong support (Burnham & Anderson, 2002). Based on recommendations to also consider model parsimony when using the AIC to determine the better-fitting model (e.g., Burnham, Anderson, & Huyvaert, 2011; Weston & Gore, 2006), we selected the model with the lower AIC as the better-fitting model, unless there was strong support for both models (i.e., a change of 0–2), in which case we selected the more parsimonious model.

Table 4. Fit Indices for Confirmatory Factor Analysis Models and Cross-Lagged Panel Analysis Structural Equation Models

Model Robust χ2 df CFI RMSEA [90% CI] SRMR AIC

CFA Models 1. Single-factor 576.04 104 .81 .124 [.114, .134] .074 13,364.39 2. Four-factor 317.15 98 .91 .087 [.076, .098] .062 12,995.05 3. Second-order 318.59 100 .91 .086 [.076, .097] .064 12,996.07Structural Equation Models 1. Autoregressive 73.26 32 .96 .066 [.046, .086] .058 4,947.19 2. PGI T1 to GSI T2 68.32 31 .97 .064 [.043, .084] .046 4,943.79 3. GSI T1 to PGI T2 72.42 31 .96 .067 [.047, .088] .056 4,948.93 4. All paths in 1, 2, and 3 67.62 30 .97 .065 [.045, .086] .046 4,945.72

Note. N = 295. df = degrees of freedom; RMSEA = root-mean-square error of approximation; CI = confidence interval; SRMR = standardized root mean square residual; AIC = Akaike Information Criterion; T1 = Time 1; T2 = Time 2. GSI refers to the Brief Symptom Inventory’s Global Severity Index. PGI refers to the Personal Growth Initiative Scale-II’s four factors—Readiness for Change, Planfulness, Using Resources, and Intentional Behavior—which were represented as measured variables of one latent factor. All χ2 were significant at p ≤ .001.

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There was a substantial difference in the AIC when comparing the single-factor model to both the four-factor model (∆AIC = 369.34) and the second-order model (∆AIC = 368.32), with the single-factor model having the larger AIC in both cases. Additionally, the scaled chi-square difference test comparing the sin-gle-factor and four-factor models was significant, χ2(6) = 142.25, p < .001. The single-factor solution also had worse fit indices for the CFI, RMSEA, and SRMR than the other models. When comparing the four-factor and second-order mod-els, there was a small difference in AIC, ∆AIC = 1.02. There were minimal dif-ferences in the CFI, RMSEA, and SRMR between the models. Consequently, the more parsimonious second-order model was selected as the best fit. Parameter estimates for this model are shown in Figure 1.

PGI and Psychological Distress Across Time

To assess the potential relationships of PGI and psychological distress at Time 1 to PGI and psychological distress at Time 2, we conducted a cross-lagged panel analysis. This is an SEM-based technique that is suitable when there are two variables measured across two time points and the direction of causality is unclear (Martens & Haase, 2006). Although causation cannot be determined by correlational designs, cross-lagged panel analyses allow for the assessment of the relative relationships variables collected at earlier points in time have to

Readiness for Change

Planfulness

Using Resources

Intenonal Behavior

I 2

I 4

I 7

I 9

I 15

I 6

I 12

I 14

I 1

I 3

I 5

I 10

I 13

I 8

I 11

I 16

.68

.80

.85

.84

.68

.86

.85

.92

.80

.76

.79

.83

.72

.80

.82

.85

Personal Growth

.94

.90

.67

.92

Figure 1. Second-order model of the Personal Growth Initiative Scale-II. All loadings are significant at p ≤ .001.

Weigold et al. 495

those gathered at later points in time; this is accomplished by examining the paths from each Time 1 variable to each Time 2 variable using different con-figurations (Martens & Haase, 2006). Our variables of interest were PGI and psychological distress at Time 1 and Time 2. Consistent with the results of our factor analysis, each of the four PGI factors loaded onto a general PGI factor at both Time 1 and Time 2. Psychological distress at Time 1 and Time 2 was measured using the GSI. Given that the number of sessions clients attended between Time 1 and Time 2 varied, we added it as a potential covariate to determine its relationship to the Time 2 variables.

Prior to conducting the cross-lagged panel analysis, we assessed the mea-surement model. This consisted of the PGI latent factors at Time 1 and Time 2, the GSI at Time 1 and Time 2, and the number of sessions attended; these were all allowed to covary. The PGI factor loadings were constrained to be equal at Time 1 and Time 2, and the PGI factors’ error terms were allowed to covary. As the variables showed evidence of multivariate nonnormality, χ2(286) = 619.13, p < .001, we again used robust maximum likelihood esti-mation in Mplus. We also used the full information maximum likelihood method for missing data. The measurement model generally showed good fit for the data: robust χ2(36) = 72.77, p < .001, CFI = .970, RMSEA = .059, 90% CI [.039, .078], SRMR = .046. All parameter estimates were significant at p < .01, except for the paths associated with the number of sessions. Although the number of sessions significantly related to psychological dis-tress at Time 1, β = -0.12, p = .04, it did not significantly relate to PGI at Time 1, β = -0.10, p = .11, PGI at Time 2, β = -0.06, p = .44, or psychological dis-tress at Time 2, β = -0.02, p = .76. Because the number of sessions did not significantly relate to either of the Time 2 variables, it was removed from the model. The more parsimonious measurement model also generally suggested good fit for the data: robust χ2

(30) = 67.62, p < .001, CFI = .968, RMSEA = .065, 90% CI [.045, .086], SRMR = .046.

Next, we conducted the cross-lagged panel analysis. We assessed four models, following the recommendations for conducting such analyses (see Martens & Haase, 2006). The first model tested the autoregressive effects, or the paths from Time 1 to Time 2, for each variable (i.e., PGI at Time 1 to PGI at Time 2 and the GSI at Time 1 to the GSI at Time 2). The second model was the same as the first, except that we added a path from one variable at Time 1 to the other variable at Time 2 (i.e., from PGI at Time 1 to the GSI at Time 2). The third model was the same as the first, except it added the opposite path from that of the second model (i.e., from the GSI at Time 1 to PGI at Time 2). The final model was the full model with all paths included. As is common for cross-lagged panel analyses, the two Time 1 variables were allowed to covary, as were the disturbances between the Time 2 variables and the error terms

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between the PGI factors at Time 1 and Time 2 (see Martens & Haase, 2006). Additionally, we constrained the PGI factor loadings to be invariant between Time 1 and Time 2.

All models were assessed using the same goodness-of-fit indices that were used for the CFAs: the robust model χ2, the CFI, the RMSEA, the SRMR, and the AIC. To compare models, both the AIC and the change in chi-square were examined, as the latter is typically used for this analysis. The second and third models were compared to the first model (autoregressive effects only) to determine if the addition of either path provided a better fit. The best-fitting of these first three models was compared to the final model (Martens & Haase, 2006).

The fit indices for the four models are shown in Table 4. All showed good fit for the data. The second model, which added the path from PGI at Time 1 to the GSI at Time 2, had a lower AIC than the first autoregressive model (ΔAIC = 3.40). Similarly, the scaled chi-square difference test was signifi-cant, ∆χ2(1) = 4.94, p = .026. These both suggested that the addition of the path improved model fit. The third model, which added the path from the GSI at Time 1 to PGI at Time 2, had a similar AIC to the first autoregressive model (ΔAIC = 1.74), and the scaled chi-square difference test was not sig-nificant, ∆χ2(1) = 0.33, p = .57. These suggested that the more parsimonious autoregressive model should be selected.

The second model (with the path from PGI at Time 1 to the GSI at Time 2) was the best-fitting of the first three models; thus, it was compared to the final model to determine which was the better fit. This final model included all the paths in the first, second, and third models. The AIC for this model was similar to that of the second model (ΔAIC = 1.93), and the scaled chi-square difference test was not significant, ∆χ2(1) = 0.11, p = .74, providing support for the selection of the more parsimonious second model.

Together, these results indicate that the second model is the best fit. This model with parameter estimates is shown in Figure 2. The model accounted for significant variance in both PGI at Time 2 (56%, p < .001) and the GSI at Time 2 (58%, p < .001).

Discussion

The current study sought to examine the role of PGI in the therapeutic pro-cess by analyzing the factor structure of the PGIS-II and assessing its rela-tionship to psychological distress across time. The results of our CFAs provided support for the second-order model in which each subscale item of the PGIS-II loaded onto its respective factor (Readiness for Change, Planfulness, Using Resources, or Intentional Behavior); in turn, these factors

Weigold et al. 497

loaded onto one global PGI factor. Previous research on the factor structure of the PGIS-II in student and community samples typically selected a four-factor correlated model, rather than a second-order model, as the best-fitting solution (e.g., Robitschek et al., 2012; Weigold et al., 2014; Yakunina et al., 2013; Yang & Chang, 2014). However, in each of these studies, the four-factor and second-order models had similar goodness-of-fit indices, indicat-ing only small differences between the solutions, which is consistent with the findings of the current study. Also, the authors of the PGIS-II supported cal-culating both separate factor scores and a total scale score (Robitschek et al., 2012). Consequently, although the best-fitting model in the current study dif-fers from the model selected in previous studies, it aligns with the develop-ment of the PGIS-II.

In addition to the factor structure, we also assessed how PGI changed across time and how it related to changes in psychological distress. First, all the PGIS-II factors significantly increased from Time 1 to Time 2. Previous results assessing the temporal stability of PGI did not find significant differ-ences at an alpha level of .05 for any of the factors across 1-, 2-, 4-, and 6-week time intervals (Robitschek et al., 2012). Consequently, our findings

PGI Time 1

PGI Time 2

GSI Time 1

GSI Time 2

.75***

.69***

–.15* –.35***

RC1 PL1 UR1 IB1 RC2 PL2 UR2 IB2

.88*** .88*** .62*** .85*** .91*** .93*** .69*** .86***

.38†***

.39†**

.61†***

.57†***

–.27††

***

Figure 2. Best-fitting model for the cross-lagged panel analysis. RC1 = Readiness for Change at Time 1; PL1 = Planfulness at Time 1; UR1 = Using Resources at Time 1; IB1 = Intentional Behavior at Time 1; RC2 = Readiness for Change at Time 2; PL2 = Planfulness at Time 2; UR2 = Using Resources at Time 2; IB2 = Intentional Behavior at Time 2; PGI = personal growth initiative; GSI = Global Severity Index. Unstandardized parameter estimates for RC, PL, UR, and IB on PGI at Time 1 were constrained to be equal to their counterparts at Time 2. †Error terms allowed to covary; ††disturbances allowed to covary.*p ≤ .05. **p ≤ .01. ***p ≤ .001.

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indicate that the increase in PGI factor scores did not occur simply due to the passage of time. Relatedly, the four PGI factors at Time 1 were significantly and negatively correlated with the GSI at Time 2.

Finally, we used a cross-lagged panel analysis to examine the relative impact of PGI and psychological distress at Time 1 on PGI and psychological distress at Time 2. The final model indicated strong, positive paths from PGI at Time 1 to PGI at Time 2 and from the GSI at Time 1 to the GSI at Time 2. The error covariances between the PGI factors at Time 1 and Time 2 were positive and significant for all four factors. Additionally, PGI at Time 1 was found to significantly and negatively predict distress at Time 2. The opposite path from distress at Time 1 to PGI at Time 2 was not significant and did not improve model fit. Although causation cannot be determined from this study, these findings indicate that PGI is more likely to predict distress levels across time than that distress levels predict PGI over time (Martens & Haase, 2006).

Overall, the results of the cross-lagged panel analysis indicate that PGI increases across the therapeutic process and has a positive effect on later distress, even when earlier levels of distress are taken into account. It should be noted that, although the path from PGI at Time 1 to distress at Time 2 was significant, the parameter estimate was relatively small (-.15). This may be due to the large parameter estimates shown by the autoregressive paths, as each variable at Time 2 was, unsurprisingly, significantly impacted by its counterpart at Time 1. Additionally, there was attrition of participants with more severe distress. Although the attrition effect size was small for the GSI, this could still have affected the parameter estimates. Consequently, future research is needed to examine these relationships.

Counseling Implications

Taken together, the knowledge of when to begin the change process, the development of a plan to effect that change, and the engagement in that plan are related to lower levels of psychological distress. Our findings provide preliminary support for the assertion that understanding clients’ levels of PGI is advantageous (see Robitschek, 1998; Robitschek et al., 2012), as it appears that PGI can increase during the course of therapy, even when it is not the direct focus of the sessions. Higher levels of PGI were related to lower levels of distress at later points in time, indicating that PGI might be an effective therapeutic tool in and of itself for combating common client issues.

The current study provides a foundation for the continued assessment of PGI in therapeutic samples. Given the importance of a client’s active engage-ment to the success of therapy (Bohart, 2000), PGI may be useful in capturing the intentional, conscious use of therapy as a growth opportunity. Additionally,

Weigold et al. 499

how both therapists and clients use PGI to facilitate the therapeutic process might provide insights into the process of change. The authors of the PGIS-II have suggested that it may be beneficial for therapists to use the PGIS-II either at intake or as an outcome measure to assess clients’ skills for continu-ing to engage in the change process outside of the therapeutic relationship (Robitschek et al., 2012). Overall, further examination of the application of PGI to the therapeutic process is warranted.

Limitations

The results of the current study need to be considered in light of several limita-tions. As this is currently the only study to examine the relationship between PGI and psychological distress in a sample of therapy clients, we chose to use a naturalistic therapeutic setting without manipulating or controlling for vari-ous therapist and client demographic characteristics. This prevents us from making any causal claims related to our findings and from determining the influence of these different characteristics on the relationship between PGI and psychological distress. Second, due to collecting data at a training clinic over more than one semester, there were many therapists seeing clients during the current study. Although the influence of any one therapist on the results of the study is likely minimal, the therapists’ influence on PGI and its relation to distress remains unclear. Third, our sample consisted of clients at a commu-nity-serving training clinic, and our only requirements for participation were that clients be at least 18 years old and complete the PGI at Time 1. Therefore, our results may not generalize to other settings (e.g., student counseling cen-ters, inpatient hospitals), age groups (e.g., adolescents), or disorders (e.g., sub-stance use). Also, clients who participated at Time 1 but not at Time 2 had higher levels of distress at Time 1 compared with those who completed both time periods. Consequently, our findings involving Time 2 variables may not generalize to populations with more severe levels of psychological distress. Finally, we relied on clients’ self-reports of their psychological distress and PGI scores, potentially resulting in mono-method bias. Information from the therapists’ perspectives or standardized assessment reports might yield a more complete picture of how PGI relates to the therapeutic process.

Future Directions

Given both the results and limitations of the current study, there are different directions for future research on the role of PGI in the therapeutic process. One important area of inquiry is to what extent PGI might relate to therapeutic outcomes when other variables are considered, such as relevant agentic

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variables or client characteristics. The few studies in nonclinical samples examining PGI and other antecedent variables, such as personality traits or hope, have indicated that PGI accounts for variance above and beyond these variables in vocational and psychological outcomes (e.g., Weigold et al., 2013; Yang & Chang, 2016). However, this has yet to be examined in clinical sam-ples. Additionally, PGI might have different relationships with various disor-der diagnoses or symptoms, which has not yet been examined in the literature. Given that those with higher distress were less likely to complete both Time 1 and Time 2 in the current study, it is important that our results be replicated in populations with greater distress levels and different clinical symptoms.

A second area of study for future researchers to examine is how the rela-tionship between PGI and psychological distress across time is affected by different aspects of the therapeutic process, such as therapist characteristics (e.g., theoretical orientation) or the length of therapy (e.g., brief therapy). Relatedly, the mechanisms by which PGI increases during the course of treat-ment remain unclear. A past study examining an intervention aimed at increasing PGI has shown significant increases in PGI scores over a week (Thoen & Robitschek, 2013), which is consistent with our findings across a more variable time period and suggests that PGI might develop quickly in environments targeted toward growth. However, more research is needed to ascertain how and to what degree this might happen.

Overall, the current study is one of the first to examine PGI in a clinical sample. Given the positive impact of PGI on subsequent psychological dis-tress, PGI is a promising construct for use in therapeutic settings. The study also provides a basis for the further examination of how PGI might relate to the change process and how therapy can increase levels of PGI.

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 disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported in part by a Faculty Research Grant from The University of Akron (FRG #1805).

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Author Biographies

Ingrid K. Weigold, PhD, is a professor in the Department of Psychology at the University of Akron. Her research emphases are in positive psychology and data col-lection methodology. Her primary research interests are personal growth, interna-tional and college student well-being, and data collection methodology.

Rebecca A. Boyle, PhD, was the director of the Clinic for Individual and Family Counseling at the University of Akron at the time of the study. Her area of expertise

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is marriage and family therapy, and her primary research interests include supervi-sion, childhood trauma, and domestic violence.

Arne Weigold, PhD, is an associate professor in the Department of Psychology at Notre Dame College. His primary research interests include data collection methodol-ogy, equivalence testing procedures, scale development, and college and international student well-being.

Stephen Z. Antonucci, MA, is a counseling psychology doctoral candidate at the University of Akron and a psychology assistant at the Psycho-Diagnostic Clinic in Akron, Ohio. His primary research interests include mental health treatment, psycho-pathology, and criminal behavior.

Heike B. Mitchell, MSc, is a doctoral candidate in counseling psychology at the University of Akron who will complete her predoctoral internship at Oregon State University’s Counseling & Psychological Services. Her primary research interests include the areas of positive psychology and criminology, specifically as they relate to interpersonal trauma, restorative justice, and growth through adversity. She also has experience with career development as well as conducting research and providing clinical services for international and LGBTQ populations.

Caitlin A. Martin-Wagar, MA, is a counseling psychology doctoral student at the University of Akron. Her research and clinical interests are within the fields of eating disorders and trauma, with an emphasis on weight bias, treatment outcome, measures of well-being, and bridging there search-practice gap.