the development and initial validation of the work volition scale-student version

AUTHOR QUERY FORM

Journal title: TCP

Article Number: 417147

Dear Author/Editor,

Greetings, and thank you for publishing with SAGE. Your article has been copyedited, and we have a few queries for you. Please respond to these queries when you submit your changes to the Production Editor.

Thank you for your time and effort.

Please assist us by clarifying the following queries:

No Query

1 Please check that all authors are listed in the proper order; clarify which part of each author’s name is his or her surname; and verify that all author names are correctly spelled/punctuated and are presented in a manner consistent with any prior publications.

2 Please provide a note for Table 1 explaining what the bold numbers represent.

3 In the paragraphs that follow the Confirmatory Factor Analysis heading, should 90% CI be expressed as a range or is it correct to be in brackets?

4 Should “90% CI =” be inserted here?

5 Should “90% CI =” be inserted here?

6 Please confirm that the Declaration of Conflicting Interests statement is accurate and correct.

7 Please confirm that the Funding statement is accurate and correct.

8 Please provide a brief biography for each author.

417147 TCP

The Counseling PsychologistXX(X) 1 -28

© The Author(s) 2011Reprints and permission:

sagepub.com/journalsPermissions.navDOI: 10.1177/0011000011417147

http://tcp.sagepub.com

XXX10.1177/0011000011417147Duffy et al.The Counseling Psychologist© The Author(s) 2011

Reprints and permission:sagepub.com/journalsPermissions.nav

1University of Florida, Gainesville, FL, USA2Michigan State University, East Lansing, MI, USA

Corresponding Author:Ryan D. Duffy, University of Florida, Department of Psychology, P.O. Box 112250, Gainesville, FL 32611 Email: [email protected]

The Development and Initial Validation of the Work Volition Scale–Student Version

Ryan D. Duffy, PhD,1 Matthew A. Diemer,2 and Alex Jadidian1[AQ: 1]

Abstract

The present study sought to develop and validate an instrument to measure work volition, defined as the perceived capacity to make occupational choices despite constraints, among college students. In Study 1, an exploratory factor analysis was conducted with a large and diverse sample of college students, finding a reliable scale with two factors, volition and constraints. In Study 2, with a new sample of college students, a confirmatory factor analysis was completed finding a final 16-item scale with strong model fit and internal con-sistency. In addition, the Work Volition Scale–Student Version (WVS-SV) was found to correlate in expected directions with career decision self-efficacy, core self-evaluations, career locus of control, career barriers, and the Big 5 personality traits. Finally, in Study 3, the WVS-SV was found to have strong test–retest reliability. Implications for practice and further research are discussed.

Keywords

work volition, college students, career development

2 The Counseling Psychologist XX(X)

Scholars within vocational psychology have taken a leading role in studying the mechanisms by which individuals make career decisions. Several major theories have emerged to explain choice behavior and these theories have emphasized a variety of components, including values, interests, skills, self-efficacy, outcome expectations, and gender norms (Brown & Lent, 2005; Fouad, 2007). Implicit among most of these theories is an assumption of volition, that individuals have the power or capacity to choose their career path. Recent scholarship has suggested that this assumption may not be true for all individuals, especially those from marginalized and/or stigmatized backgrounds (Blustein, 2006; Blustein, McWhirter, & Perry, 2005; Duffy & Dik, 2009). Because most career development theories presuppose what we label “work volition,” it is vital to understand what role work volition may play in the career development process. The first step in this process is clearly conceptualizing and developing a measure of the work volition construct. In the present study, we detail the development of an instrument assessing work volition among college students and hope this instrument will complement and extend vocational research with this population.Theoretical Background. Studying the lives of individuals with high degrees of barriers and/or restricted personal choice has been of increasing impor-tance in vocational psychology in recent years (Blustein, 2006, 2008). Within this niche, the Psychology of Working Framework (PWF) proposed by Blustein and colleagues has most strongly emphasized the role of volition in the career development process (Blustein, 2006; Blustein, Kenna, Gill, & DeVoy, 2008). The PWF theorizes that work provides a primary avenue for individuals to meet their needs for survival, relatedness, and self-determination (Blustein, 2008). The PWF recognizes, however, that the capability of many non-privileged individuals to meet these needs through work is stunted due to a lack of work volition. In this sense, volition might be viewed on a con-tinuum, with almost all individuals having some volitional constraints to their desired career choice.

Although work volition is a centerpiece of the PWF, it is never explicitly defined. According to the Merriam Webster Dictionary, volition is defined as “the power of choosing or determining.” Within the PWF, Blustein (2006, 2008) focused on both a general sense of volition as well as the broad swath of constraints that can limit volition. Using this definition as a starting point, and incorporating the main emphases of the PWF, we herein define work volition as the perceived capacity to make occupational choices despite con-straints. An individual with high work volition would likely perceive an expanse of potential job opportunities and few familial, financial, or structural barriers to pursue these opportunities. Conversely, an individual with low work

Duffy et al. 3

volition would likely perceive very limited job options, perhaps ultimately resulting in pressure to take jobs antithetical to one’s personal preferences. The PWF has pushed the construct of work volition to the scholarly forefront over the past decade, but several related constructs have been present in many vocational theories prior to the PWF. Like most new constructs, work volition is built on decades of previous vocational research. We briefly review two related constructs (career barriers and career locus of control) as they have been studied with college student populations and discuss how work volition may be related and distinct.

Related Constructs Within Vocational PsychologyCareer barriers. Perhaps the most closely aligned construct within the voca-

tional literature to work volition is career barriers, and empirically or theoreti-cally this construct exists in most major theories. Career barriers are defined as factors that may negatively affect one’s career path, and a robust literature exists on this construct as it pertains to college student career development. Notable barriers studied include those related to socioeconomic status (SES), race/ethnicity, gender, disability, and sexual orientation. Among student pop-ulations, these barriers have been shown to limit job opportunities by way of discrimination, lead to reduced access to resources and fewer occupational role models, increase the perception of heightened family demands, and limit the expanse of possible career options (Adams, Cahill, & Ackerlind, 2005; Blustein et al., 2002; Ladany, Melincoff, Constantine, & Love, 1997; Luzzo, Hitchings, Retish, & Shoemaker, 1999; McWhirter, 1997; Perrone, Sedlacek, & Alexander, 2001; Schmidt & Nilsson, 2006).

Career-related barriers and work volition likely occupy a similar scholarly space as they share a common emphasis on vocational constraints. However, we hypothesize that barriers and volition are distinct constructs. Work volition pertains to an individual’s capacity to make career decisions in the face of constraints, whereas barriers pertain to potential factors that may limit career progress. Specifically, work volition is more tied to a sense of agency in spite of barriers, instead of just the barriers themselves. For example, the widely used Career Barriers Inventory (CBI; Swanson, Daniels, & Tokar, 1996) gives participants a series of barrier-related statements and asks them to what degree they might hinder their career progress (e.g., Not wanting to relocate for my job/career; Conflict between marriage/family plans and my career plans). The true distinction between these two constructs can be discovered only empirically, and in the current study we will examine the relation of work volition to career barriers using the CBI.


Career locus of control. Career locus of control pertains to the extent to which individuals view outcomes as dependent on their own actions (internal) or dependent on the control of the difficulty of the task, powerful others, or chance factors (external) (Trice, Haire, & Elliott, 1989). Research has found that students endorsing a more internal locus of control tend to be more efficacious in their career decision making and report stronger career adaptability (e.g., Brown, Glastetter-Fender, & Shelton, 2000; Duffy, 2010). Although we expect work volition and career locus of control to be related, career locus of control pertains to the perception of luck and chance in the career process (external locus of control) or the perception of viewing career outcomes as dependent on one’s own action (internal locus of control), whereas work voli-tion pertains to power over choosing or determining one’s work life. Once again, the relation of these constructs will be empirically tested.

The brief review of these two related constructs is meant to underscore how the construct of work volition is not completely new to vocational psychology. However, we also believe that work volition is distinct from these previous constructs, captures a broad vocational construct related to the perceived capacity to make occupational choices despite constraints, and accordingly advances the literature in this area.Gender and Race/Ethnicity. Similar to career barriers and career locus of control, work volition is concerned with the perception of choice, or lack thereof, in the career development process. Researchers in vocational psychol-ogy have taken a leading role in studying how gender and race/ethnicity may ultimately affect one’s ability to freely choose the careers one wants to pursue. For example, several studies have shown that women perceive more barriers to choice than men (Luzzo, 1995; Luzzo & McWhirter, 2001; Swanson et al., 1996). In addition, Fouad and Byars-Winston (2005) conducted a meta-analysis of research investigating the relation between culture and vocational choice variables and found significant differences in the perceptions of barriers, with ethnic minorities perceiving more barriers to choice than those in the ethnic majority. As such, in the current study we will also examine if, and to what extent, our newly developed instrument of work volition differs for men and women and for majority and minority racial/ethnic groups.

The Present Study Although instruments exist to assess specific career barriers (i.e., Swanson et al., 1996) and to assess work volition in adult populations (Duffy, Diemer, Perry, Laurenzi, & Torrey, in press), no instruments exist to assess this construct in college student populations. The adult version of the Work Volition Scale (WVS) has items tailored to individuals currently in the working world and

Duffy et al. 5

has been shown to be reliable and valid, correlating with related constructs such as work locus of control and predicting job satisfaction (Duffy et al., in press). However, work volition may manifest itself differently for students anticipating their future careers and, as such, the adult WVS is not develop-mentally appropriate for student populations. The current study seeks to address this limitation by developing an instrument of college student work volition, using large samples of diverse students.

In Study 1, we describe the development of the instrument and explore its reliability and factor structure. Duffy et al. (in press) found the adult WVS to have three subscales: one pertaining to general volition, another to structural constraints on volition (e.g., the current state of the economy), and another on financial constraints to volition (e.g., due to one’s financial situation needing to take any available job). As such, we hypothesized that the factor structure of the student version would mirror the adult version, with subscales assessing general volition, structural constraints, and financial constraints.

In Study 2, we conduct a confirmatory factor analysis to determine good-ness of fit and scale structure as well as compare work volition to potentially overlapping constructs (career decision self-efficacy, career locus of control, personality, core self-evaluations, and career barriers) to determine its unique-ness and convergent and divergent validity. Career barriers and career locus of control were assessed given their potential overlap with work volition. Career decision self-efficacy (CDSE) was chosen to ensure that work volition was not simply measuring a confidence in one’s ability to make career decisions. Personality traits were measured to ensure that work volition was not simply measuring an underlying personality type, and core self-evaluations were measured to ensure that work volition was not simply measuring a positive view of oneself. Based on results from the adult WVS (Duffy et al., in press), we hypothesized that work volition will weakly correlate with dimensions of personality (thus will not be an underlying personality trait), will weakly cor-relate with career barriers, will moderately correlate with career locus of control, and will strongly correlate with core self-evaluations. No research to date has examined the work volition–CDSE link. However, because each of these vari-ables assesses vocational constructs related to positive career development, we hypothesized that these will be related, but distinct, variables.

In addition, based in previous research that has found significant differences in career barriers for women and people of color (e.g., Fouad & Byars-Winston, 2005; McWhirter, 1997), in Study 2 we test differences between men and women and between White students and students of color on overall Work Volition Scale scores as well as differences on individual items. These differ-ences will be tested using MIMIC (multiple indicators and multiple causes)


models, which are powerful ways to test for group differences in subscale means and/or how groups respond to individual items (Kline, 2005). Given previous research on increased work-related barriers for women versus men and minority racial/ethnic groups versus majority racial/ethnic groups (Fouad & Byars-Winston, 2005; Lopez & Ann-Yi, 2006; Luzzo & McWhirter, 2001; Swanson et al., 1996), we hypothesize that work volition scores will be higher for men and higher for those identifying as White. Finally, in Study 3, we assess the test–retest reliability of the final instrument.

Study 1Method

Procedure. Participants were recruited through the psychology participant pool. Students who were enrolled in psychology courses are required to gain research participation credits, of which completing this study was one option. To complete the survey, students logged onto the departmental research web-site and had the ability to choose from a number of different studies to partici-pate in, this study being one of those. If participants selected the present study, they were directed to the survey, which was housed on an external data collection website to maintain confidentiality. Participants received no pres-sure to participate in the present study and were notified via an informed con-sent form that they could withdraw without penalty at any time.

Participants completed the survey for this study online during the fall 2009 semester, and after their participation was complete, their appropriate class instructors received notice in order to grant class credit. To ensure a diverse sample in terms of gender, race/ethnicity, and major, we tracked the demo-graphic information of participants as surveys were being completed. At one time point, we limited the survey to only male participants, allowing us to get a more balanced sample in this domain (the sample was diverse across race/ethnicity and major without needing to restrict participation).Participants. Participants consisted of 379 undergraduate students at a large southeastern university. The sample was 42.7% male (N = 162) and 56.2% female (N = 213), with 1.1% not reporting their gender. The mean age of the sample was 18.47 years old (SD = 0.88). The racial/ethnic distribution in our sample was 57.3% White (N = 217), 9.8% African American (N = 37), 7.7% Asian (N = 29), 5.3% South American (N = 20), 4.5% Cuban (N = 17), 4.5% Caribbean (N = 17), 2.7% Puerto Rican (N = 10), 2.4% other (N = 9), 1.6% Central American (N = 6), 1.3% Middle Eastern (N = 5), 0.8% Mexican (N = 3), 0.8% Pacific Islander (N = 3), 0.3% Cape Verdean (N = 1), and 0.3% American

Duffy et al. 7

Indian (N = 1). The six most represented majors were 10.3% biology (N = 39), 10.3% health science (N = 39), 9.2% psychology (N = 35), 7.7% nursing (N = 29), 5.8% exploratory (N = 22), and 3.4% applied physiology and kine-siology (N = 13).Instrument. The development of the present instrument occurred concur-rently with the development of a scale to measure work volition among adults (Duffy et al., in press). For the college student version of this scale, item stems were developed to reflect the perceived capacity to make future occupational choices despite constraints. Following the guidelines proposed by Worthington and Whittaker (2006), a series of steps was taken to construct the initial Work Volition Scale. First, we conducted a thorough review of the literature for arti-cles highlighting the Psychology of Working Framework and/or work volition. This review both informed our definition of the construct as well as informed our item construction. Based on this review, in tandem with research team collaboration, we constructed 45 items to assess work volition. These items were split into two groups: items that assessed general work volition (i.e., I will be able to choose jobs that I want) and items that assessed constraints to work volition (i.e., Due to my financial situation, I will need to take any job I can find). The latter half of these items each contained a general work volition stem, while also including a potential volitional constraint. It was decided to capture both the general feeling of volition and volition in the face of constraints given the focus of each of these components within the PWF.

Second, these items were sent to a panel of five expert reviewers who were asked to rate the clarity of each item, rate how well each item adhered to the work volition definition, and provide any additional item suggestions. Based on this feedback, a final pool of 27 items was retained. Of this final pool, any items that required rewording to be future oriented were adapted. For example, the item, “I feel total control over my job choices,” was changed to, “I feel total control over my future job choices.” Other example items include, “I will have little choice in the jobs that I will have,” “The only thing that matters in choosing a job is to make ends meet,” and “Due to my life circumstances, I will feel forced to choose a certain job.” Participants were asked to answer each of these 27 items on a 7-point Likert-type scale from strongly disagree to strongly agree.

Results An exploratory factor analysis (EFA) was first carried out with the Mplus program (Muthén & Muthén, 2006). The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was .92 and Bartlett’s test of sphericity was significant


(p < .001), indicating that the relation between WVS items was strong enough to conduct factor analyses (Tabachnick & Fidell, 1996). Factors were extracted using maximum likelihood methods with an oblique rotational method (promax), because factors were hypothesized to be related. Data were analyzed under FIML (full information maximum likelihood) conditions, which uses all existing data points instead of deleting essential information by removing cases pairwise or listwise (Graham, 2003).

A factor solution was obtained by considering Kaiser’s criterion, the scree plot, the interpretability of obtained factor solutions, and model fit indices provided by Mplus, which initially suggested two- and three-factor models (Preacher & MacCallum, 2003). The EFA was also used to inform the retention and removal of items making up the WVS measure (Worthington & Whittaker, 2006). Items were removed if they did not load on distinct factors of at least three items (Kahn, 2006). Items that exceeded our a priori criteria—loading at .40 and above and without cross-loading (difference of loadings across factors must not exceed .10)—were retained (Tabachnick & Fidell, 1996). Because the fit of the two- and three-factor models was comparable, the interpretability of each model was examined to determine the final EFA model (Worthington & Whittaker, 2006). The three-factor solution had several items that cross-loaded and the meaning of this third factor was somewhat “muddled.” Thus, the two-factor solution was selected because it yielded a more interpretable solution with comparable model fit to the three-factor solution.

Consideration of these criteria resulted in a final scale made up of two factors and 17 items. Model fit indices reported by the Mplus program for this type of analysis suggested that the two-factor solution was a very good fit to the data. The standardized root mean square residual (SRMR) value (.05) was well below the suggested .10 cutoff and root mean square error of approxima-tion (RMSEA) value (.05, 90% CI = .05–.06) was below the recommended .06 cutoff (Hu & Bentler, 1999; Martens, 2005). This solution was composed of two conceptually meaningful factors reflective of the underlying work volition construct. The first factor, volition, consisted of 7 items that mea-sured an individual’s perceived capacity to make future occupational choices. The second factor, constraints, consisted of 10 items (reverse coded) that measured constraints to volition.

The total Work Volition Scale–Student Version (WVS-SV) had an internal consistency of .92, and the two subscales had Cronbach’s estimates of .78 (volition) and .89 (constraints). As hypothesized, the two factors were moder-ately correlated—constraints correlated with volition (r = .53)—supporting the use of an oblique rotation in the EFA. Table 1 depicts the loading of these items onto these two factors.

Duffy et al. 9

Table 1. Exploratory Factor Analysis: Study 1 Factor Loadings for the Work Volition Scale–Student Version (N = 379)

Loading

Factor Name and Item 1 2

Factor 1: Volition, α = .78 1. I will be able to change jobs if I want to. .54** −.07 2. Discrimination will not affect my ability to

choose a job..40** −.01

3. Once I enter the work world, I will easily find a new job if I want to.

.64** −.20

4. I will be able to choose jobs that I want. .81** .01 5. I will learn how to find my own way in the world

of work..62** −.02

6. I feel total control over my future job choices. .66** −.01 7. I will be able to do the kind of work I want to,

despite external barriers..67** .01

Factor 2: Constraints, α = .89 8. What I want has little impact on my future job

choice..20 .46**

9. In order to provide for my family, I will have to take jobs I do not enjoy.

.21 .57**

10. Due to discrimination, I do not feel I have complete control over my career choices.

.27 .43**

11. Due to my financial situation, once I get a job I couldn’t change jobs even if I wanted to.

.24 .48**

12. I feel that my family situation limits the types of jobs I might pursue.

.22 .59**

13. I worry that my life circumstances will prevent me from achieving my long term career goals.

.30 .47**

14. Due to my financial situation, I will need to take any job I can find.

−.12 .83**

15. The only thing that matters in choosing a job is to make ends meet.

−.13 .76**

16. I know I won’t like my future job, but it will be impossible for me to find a new one.

.17 .65**

17. When looking for work, I’ll take whatever I can get.

−.13 .66**

**p < .01.[AQ: 2]


Discussion

Study 1 suggests that the WVS-SV consists of two factors. The scale was reli-able, with the total scale and two subscales evidencing very good-to-strong internal consistency. The subscales were also found to overlap but were distinct enough to be considered separate scales. Using the adult WVS as a foundation, we hypothesized that three factors would emerge relating to general volition and structural and financial constraints. Partially supporting our hypotheses, one factor did emerge relating to general volition, but in the case of the WVS-SV only one constraint subscale emerged. These two subscales speak to two key components of volition for students: the general, perceived capacity to make future occupational choices and the capacity to make future choices despite constraints. Differences between the adult WVS and the WVS-SV factor structure may speak to adult populations hav-ing a more nuanced view of volitional constraints. For student populations who likely have yet to enter the working world full-time, the perceived con-straints on one’s ability to make future career decisions may be more diffuse. In sum, the results of this study offer preliminary support for a reliable, stu-dent-focused work volition instrument.

Study 2Method

Procedure. Similar to Study 1, a new group of participants was recruited via the psychology participant pool, received class credit for taking part in this study, and was informed that they could withdraw from the study at any time without penalty. As in Study 1, we restricted recruitment to ensure a sample that was diverse according to gender, race/ethnicity, and major. At one time point, we restricted the survey to participants who identified their race/ethnicity as a group other than White, thus ensuring diversity according to race/ethnicity. Participants completed this survey during the spring 2010 academic semester.Participants. Participants were a new sample of 312 undergraduate students at a large southeastern university. The sample was 32.1% male (N = 100) and 67.9% female (N = 212). The mean age of the sample was 19.47 years old (SD = 1.56). The racial/ethnic distribution of the sample was 61.6% White (N = 189), 11.4% African American (N = 35), 7.2% Asian (N = 22), 4.9% South American (N = 15), 3.9% Cuban (N = 12), 3.6% Caribbean (N = 11), 3.3% Puerto Rican (N = 10), 1.3% Central American (N = 4), 1.3% American Indian (N = 4), 1.0% Middle Eastern (N = 3), and 0.7% other (N = 2). The six

Duffy et al. 11

most represented majors were psychology with 29.8% (N = 93), biology with 6.7% (N = 21), nursing with 4.5% (N = 14), criminology with 3.5% (N = 11), English with 3.5% (N = 11), and sports management with 2.6% (N = 8).

InstrumentsWork volition. To recreate the same conditions for replicating the Study 1

factor structure obtained via EFA in Study 1 with confirmatory factor analyses (CFAs) in Study 2, the same 27-item version of the Work Volition Scale–Student Version was administered, rather than only the 17 items that loaded onto the two factors. Administering the 17-item version suggested by the EFA would make it more likely to obtain the same factor structure in CFAs, as only those items suggested by the EFA would be considered in the CFAs. In essence, this would bias CFAs toward recovering the same factor structure identified in the EFA. It is a more stringent test of the WVS-SV factor struc-ture to give the same 27-item version in Study 2 and determine if the same factor solution is obtained in CFAs. Worthington and Whittaker (2006) simi-larly argued that no changes be made to an instrument being administered to participants across the EFA and CFA stages of the instrument validation pro-cess. Participants responded to these 27 items on a 7-point Likert-type scale from strongly disagree to strongly agree.

Career decision self-efficacy. Career decision self-efficacy was assessed using the short form of the Career Decision-Making Self-Efficacy Scale (CDMSE-SF; Betz, Klein, & Taylor, 1996). The CDMSE-SF is a 25-item measure that assesses the degree to which an individual feels confident handling career decision-making tasks. This measure uses a 5-point Likert-type scale, ranging from no confidence at all to complete confidence. The CDMSE-SF has five subscales assessing self-appraisal, gathering of occupational information, selecting career goals, making future plans, and problem solving. Sample items include, “Choose a career that will fit your preferred lifestyle,” and “Choose a major or career that will fit your interests.” Higher scores are related to higher career decision self-efficacy. Betz et al. (1996) found evidence for construct validity of the CDMSE-SF when they found an association between the mea-sure and career indecision. Internal consistency reliability for the total scale was .95. In the present study, the estimated internal consistency for the total scale was .95.

Personality. Personality traits were assessed using the Mini-IPIP (Donnellan, Oswald, Baird, & Lucas, 2006). The Mini-IPIP is a shorter version of the 50-item International Personality Item Pool–Five Factor Model measure (IPIP-FFM; Goldberg, 1999). The scale contains 20 items and uses a 5-point Likert-type scale ranging from very inaccurate to very accurate. Example items on


the scale include, “am the life of the party,” and “have a vivid imagination.” The Mini-IPIP contains five subscales of Extraversion, Agreeableness, Con-scientiousness, Neuroticism, and Imagination/Intellect, which have shown long-term test–retest correlations of r = .88, .68, .77, .82, and .75, respectively (Donnellan et al., 2006). Donnellan et al. showed that each subscale had alpha levels above .60 in a series of five studies and found that the Mini-IPIP and the IPIP-FFM showed a similar pattern of criterion-related validity. The alphas for Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Imagination/Intellect in the present study were .83, .77, .79, .70, and .69, respectively.

Perception of barriers. Perception of barriers was assessed using the Career Barriers Inventory–Revised scale, or CBI-R (Swanson et al., 1996). The CBI-R consists of 70 items and 13 subscales. The 13 subscales are (a) sex discrimi-nation, (b) lack of confidence, (c) multiple-role conflict, (d) conflict between children and career demands, (e) racial discrimination, (f) inadequate prepa-ration, (g) disapproval by significant others, (h) decision-making difficulties, (i) dissatisfaction with career, (j) discouraged from choosing nontraditional careers, (k) disability/health concerns, (l) job market constraints, and (m) dif-ficulties with networking/socialization. The construct validity of the CBI-R was derived from its associations with demographic characteristics, namely, sex and ethnicity. Swanson et al. (1996) found the CBI-R subscales to have Cronbach’s alphas ranging from .64 to .86, with a median of .77. An alpha of .98 was reported for the total scale in later research (Rivera, Chen, Flores, Blumberg, & Ponterotto, 2007). In the current study, the estimated internal consistency reliability for the entire CBI-R was .98.

Career locus of control. Career locus of control was assessed by a modified version of the Career Locus of Control Scale for undergraduate students (Trice et al., 1989). This scale assesses the extent to which individuals view out-comes as dependent on their own actions (internal) or dependent on the con-trol of the difficulty of the task, powerful others, or chance factors (external). The original scale consisted of 18 items and had a KR-20 reliability coeffi-cient of .89 and .84 in women’s and men’s undergraduate samples, respec-tively, as well as a test–retest reliability coefficient of .93. An example item is, “I expect to get a job primarily on my record of hard work.” Participants were asked to answer all 18 of the statements as either true or false. For the current study, the original version of the scale was given. However, the internal consistency was extremely poor (.43), likely due to a combination of items that did not hold together well and a true/false format resulting in low vari-ability. As such, we chose to use only items that were clear, direct representa-tions of the career locus of control construct. These final three items were,

Duffy et al. 13

“Getting a good job is primarily a matter of being in the right place at the right time,” “One day I will just happen onto a career option that is right for me,” and “I believe that the right career will just come my way.” The estimated internal consistency of this modified, three-item scale was .63, and higher scores are indicative of an internal locus of control.

Core self-evaluations. Measurements of positive feelings about oneself in terms of locus of control, emotional stability, self-esteem, and generalized self-efficacy were measured using the Core Self-Evaluations Scale (Judge, Erez, Bono, & Thoresen, 2003). The Core Self-Evaluations Scale is a 12-item measure that uses a Likert-type scale ranging from strongly disagree to strongly agree. Sample items include, “I am confident I get the success I deserve in life,” and “I am capable of coping with most of my problems.” The measure has demonstrated good internal consistency with an average alpha of .84 across six measurements (Judge et al., 2003). In the present study, the estimated internal consistency reliability was .85.

ResultsConfirmatory Factor Analysis

Measurement model. The measurement model was established by examin-ing the loading of indicators onto their specified factor via confirmatory factor analyses. The initial CFA was not a good fit to the data (RMSEA = .08, 90% CI = [.07 .09][AQ: 3], CFI = .88, TLI = .87, SRMR = .06). RMSEA was above the suggested .06 cutoff for good fit, comparative fit index (CFI) and Tucker-Lewis index (TLI) were below the .90 and .95 cutoffs, respectively, variously proposed in the literature, and SRMR was above the .05 cutoff for good fit (Hu & Bentler, 1999; Kline, 2005; Martens, 2005).

Substantive concerns (primarily) and model modification indices (second-arily) were collectively used to modify the measurement model (MacCallum & Austin, 2000). One of the items that loaded significantly in the EFA on only the constraints factor—WVS-SV#17, “When looking for work, I’ll take what-ever I can get”—also appeared to significantly cross-load onto the second volition factor in the CFA, according to model modification indices. Because a CFA forces an item to load on only one factor (its loading on other factors is set to 0), the cross-loading of this item may partially account for poor model fit. It is also undesirable for an item to cross-load on multiple factors in scale construction (Worthington & Whittaker, 2006). This item was therefore removed from the WVS-SV and from all subsequent analyses. Model modi-fication indices also suggested that error covariances between five pairs of items should be estimated. Of the five pairs suggested, we freely estimated error covariances for only the two pairs of items that may share common


sources of error variance because of their semantic similarity—WVS-SV#2 and WVS-SV#10, which measure discrimination, as well as WVS-SV#10 and WVS-SV#1, which measure personal constraints. We estimated these error covariances because there was a substantive basis (their semantic similarity) for hypothesizing that they would share common sources of error variance (Kline, 2005).

Model fit indices indicated that hypothesized relations between observed variables and their corresponding latent construct were a relatively good fit to the data in this respecified CFA (RMSEA = .06, 90% CI = [.05 .07], CFI = .93, TLI = .92, SRMR = .05). In Table 2, each indicator significantly loaded onto its specified latent construct, which provides further construct validity evi-dence for the WVS-SV. Figure 1 depicts the loading of indicators onto these two factors in the measurement model. As depicted in Figure 1, both pairs of error covariances that were estimated demonstrated a significant degree of association. This suggests that shared error variance between these pairs of items was attenuated when estimating other relationships in the measurement model (Kline, 2005). The total WVS-SV scale was found to be very internally consistent (α = .88); the constraints subscale was also highly internally con-sistent (α = .87) and the general volition subscale was found to be adequately reliable (α = .70).

MIMIC model. This heterogeneous sample of students may experience differ-ent degrees of volitional constraints and general volition. For example, young men and young women, as well as Whites and people of color, experience different constraints on their career development (Blustein, 2006) that, by extension, likely affect their work volition in different ways. MIMIC (multiple indicators and multiple causes) models are powerful ways to test for group differences in subscale means and/or how groups respond to individual items—in this case, group mean differences for the WVS-SV constraints and volition subscales and/or differences in how groups responded to WVS-SV items (Kline, 2005). MIMIC models were specified to test for gender and racial/ethnic dif-ferences in the subscale means, as well as differences in the WVS-SV items depicted in Table 2.

A MIMIC model is essentially a CFA with an exogenous covariate that is dichotomous (Gallo, Anthony, & Muthén, 1994). A gender covariate (0 = 212 young women, 67.7%; 1 = 101 young men, 32.3%) and a race/ethnicity covari-ate (0 = 118 people of color, 38.4%; 1 = 189 Whites, 61.6%) were created. Although this sample was racially and ethnically diverse, the small sample sizes for disparate racial/ethnic subgroups (e.g., N = 35 African American par-ticipants) precluded more nuanced comparisons between students of color, a limitation we address at length later in this article. In addition, although

15

Tabl

e 2.

Mea

sure

men

t M

odel

: Stu

dy 2

Fac

tor

Load

ings

for

Late

nt V

aria

bles

in t

he F

inal

Wor

k Vo

litio

n Sc

ale–

Stud

ent V

ersi

on (N

= 3

12)

Late

nt V

aria

ble

and

Indi

cato

rU

nsta

ndar

dize

d Es

timat

eSE

Estim

ate/

S.E.

Stan

dard

ized

Es

timat

e

Fact

or 1

: Vol

ition

, M =

34.

69, S

D =

5.9

0, α

= .7

0 1

. I w

ill b

e ab

le t

o ch

ange

jobs

if I

wan

t to

.0.

54.0

86.

92*

.41*

2. D

iscr

imin

atio

n w

ill n

ot a

ffect

my

abili

ty t

o ch

oose

a jo

b.0.

34.1

12.

98*

.18*

3. O

nce

I ent

er t

he w

ork

wor

ld, I

will

eas

ily fi

nd a

new

job

if I w

ant

to.

0.50

.08

5.99

*.3

6* 4

. I w

ill b

e ab

le t

o ch

oose

jobs

tha

t I w

ant.

0.97

.06

15.5

8*.8

1* 5

. I w

ill le

arn

how

to

find

my

own

way

in t

he w

orld

of w

ork.

0.75

.07

11.6

6*.6

5* 6

. I fe

el t

otal

con

trol

ove

r m

y fu

ture

job

choi

ces.

1.00

.09

11.0

1*.6

2* 7

. I w

ill b

e ab

le to

do

the

kind

of w

ork

I wan

t to,

des

pite

ext

erna

l bar

riers

.0.

76.0

611

.88*

.66*

Fact

or 2

: Con

stra

ints

, M =

50.

40, S

D =

8.8

3, α

= .8

7 8

. Wha

t I w

ant

has

little

impa

ct o

n m

y fu

ture

job

choi

ce.

0.68

.08

9.12

*.5

1* 9

. In

orde

r to

pro

vide

for

my

fam

ily, I

will

hav

e to

tak

e jo

bs I

do n

ot e

njoy

.0.

93.0

811

.30*

.61*

10. D

ue t

o di

scri

min

atio

n, I

do n

ot fe

el I

have

com

plet

e co

ntro

l ove

r m

y ab

ility

to

get

a jo

b.0.

79.0

89.

59*

.53*

11. D

ue t

o m

y fin

anci

al s

ituat

ion,

onc

e I g

et a

job

I cou

ldn’

t ch

ange

jobs

eve

n if

I wan

ted

to.

0.89

.07

12.6

8*.6

7*

12. I

feel

tha

t m

y fa

mily

situ

atio

n lim

its t

he t

ypes

of j

obs

I mig

ht p

ursu

e.1.

03.0

813

.60*

.71*

13. I

wor

ry t

hat

my

life

circ

umst

ance

s w

ill p

reve

nt m

e fr

om a

chie

ving

my

long

ter

m

care

er g

oals

.1.

05.0

813

.00*

.69*

14. D

ue t

o m

y fin

anci

al s

ituat

ion,

I w

ill n

eed

to t

ake

any

job

I can

find

.1.

07.0

813

.80*

.72*

15. T

he o

nly

thin

g th

at m

atte

rs in

cho

osin

g a

job

is t

o m

ake

ends

mee

t.0.

91.0

812

.14*

.65*

16. I

kno

w I

won

’t lik

e m

y fu

ture

job,

but

it w

ill b

e im

poss

ible

for

me

to fi

nd a

new

one

.0.

70.0

513

.18*

.69*

*p <

.05.


F2: Constraints

F1: Volition

WVS# 6

WVS# 13WVS# 12WVS# 11WVS# 10

WVS# 2 WVS# 3 WVS# 4 WVS# 5

.67*

.81*.18* .62*

.65*

.53*

eeee

e e e e e

WVS# 9

e

WVS# 8

e

WVS# 14 WVS# 15

e e

WVS# 16

e

.61*.51* .69* .72* .65* .69*

.71*

.71*

WVS# 1

e

WVS# 7

e

.41* .66*

.26*

.36*

Figure 1. Measurement model: Confirmatory factor analysisNote. N = 312. Standardized (β) coefficients are noted for factor loadings. Coefficients significant at p < .05 are indicated with an asterisk (*). Shared error covariance between WVS#3 and WVS#11 (β = .31*) is not depicted for clarity.

research has shown that there are differences in perceived choice between certain ethnic groups (Lopez & Ann-Yi, 2006), there is also a discrepancy in terms of majority and minority culture (Fouad & Byars-Winston, 2005).

Duffy et al. 17

Supporting this conceptualization, several studies outside of the United States have found that those in the ethnic minority perceive more barriers than those in the ethnic majority (Cardoso & Ferreira Marques, 2008; Stead, Els, & Fouad, 2004). Although we recognize that there are significant differences between ethnic minority groups, we chose to examine our research question in a way that would highlight important factors that affect those who do not identify with the majority ethnic group.

All latent constructs were regressed on the dichotomous gender (0 = female, 1 = male) and race/ethnicity (0 = people of color, 1 = Whites) covariates, in separate MIMIC analyses. Standard MIMIC modeling practice is to inspect modification indices to see which items exhibit differential item functioning (DIF), meaning that groups respond differently to that item (Muthén & Muthén, 2006). If a covariate significantly predicts a latent construct or observed indicator in the MIMIC model, then we have evidence of latent mean differences or differential item functioning between young men and young women or between Whites and people of color (Gallo et al., 1994).

The MIMIC model with gender as a covariate (CFI = .93, TLI = .91, RMSEA = .06, 90% CI = [.05 .07], SRMR = .05) fit very slightly worse than the baseline CFA reported above (CFI = .93, TLI = .92, RMSEA = .06, [AQ: 4][.05 .07], SRMR = .05). The gender MIMIC model failed to detect any gender differ-ences in either WVS-SV subscale or differences in the way men and women responded to any WVS-SV items. This suggests that the WVS-SV subscales and items have equivalent meanings and can be measured in the same way for young men and young women.

The fit of the MIMIC model with race/ethnicity as a covariate (CFI = .93, TLI = .92, RMSEA = .06, 90% CI = [.05 .07], SRMR = .05) was identical to the baseline CFA reported above (CFI = .93, TLI = .92, RMSEA = .06, [AQ: 5][.05 .07], SRMR = .05). The factor structure (significance and strength of the factor loadings) suggested by the CFA also did not change in the race/ethnicity MIMIC model. Whites and people of color did not have significantly different means for either WVS-SV subscale. However, the race/ethnicity MIMIC model did detect significant racial/ethnic differences between how Whites and people of color responded to two WVS-SV items. These two items, #2 (“Discrimination will not affect my ability to choose a job”) and #10 (“Due to discrimination, I do not feel I have complete control over my career choices,” a reverse-coded item), measured how discrimination affected par-ticipants’ perceived work volition. Standardized coefficients (β), standard errors (SE), and test statistics ([z

.])—[z

.] values larger than ±1.96 are statisti-

cally significant at the .05 level—are provided here to illustrate significant direct effects from the racial/ethnic covariate onto these items (Muthén & Muthén, 2006). Figure 2 synthesizes the MIMIC models with gender and with race/


F2: Constraints

F1: Volition

WVS# 6

WVS# 13WVS# 12WVS# 11WVS# 10

WVS# 2 WVS# 3 WVS# 4 WVS# 5

.67*

.81*.18* .62*

.65*

.53*

eeee

e e e e e

WVS# 9

e

WVS# 8

e

WVS# 14 WVS# 15

e e

WVS# 16

e

.61*.51* .69* .72* .65* .69*

.71*

.71*

WVS# 1

e

WVS# 7

e

.41* .66*

.26*

.36*

GenderCovariate

Race/EthnicityCovariate

.27*

.23*

0.09 0.09

0.03 0.06

Figure 2. MIMIC modelNote. N = 312. Standardized (β) coefficients are noted for factor loadings and paths from covariates to latent factors and observed indicators. Shared error covariance between WVS#3 and WVS#11 (β = .31*) is not depicted for clarity. Coefficients significant at p < .05 are indicated with an asterisk (*). Significant paths from covariates to observed indicators evidence differential item functioning.

ethnicity as covariates, depicting the direct effects from the covariates to the two subscales and to the observed indicators that exhibit differential item functioning, detailed below.

Racial/ethnic group was a significant predictor of WVS-SV#2 (β = .27, SE = .05, [z

.] = 5.15) and WVS-SV#10 (β = .23, SE = .05, [z

.] = 4.88). This

suggests that Whites (because people of color were coded 0 and Whites 1 in

Duffy et al. 19

the covariate) were less likely to report that discrimination constrained their ability to choose a job and make career choices. Stated another way, people of color were more likely to report discrimination constraints on their work volition. In sum, MIMIC analyses detected important racial/ethnic differences between how Whites and people of color respond to two items but failed to identify significant gender differences in WVS-SV items or subscales.Construct Validity. Correlations were computed to examine the construct validity of the WVS-SV. Given the number of correlations computed, we deter-mined significance at the p < .01 level. Namely, we sought to examine if scores on the WVS-SV correlated in the expected directions with similar career-related variables and personality traits. In addition, we sought to ensure that WVS-SV scores did not correlate so highly with related constructs as to sug-gest completing overlapping variables. As seen in Table 3, work volition strongly correlated with core self-evaluations (.60), moderately correlated with CDSE (.49), and weakly correlated with career locus of control (.21), career barriers (−.25), and all five personality traits (.16 to –.23).

Discussion The goal of Study 2 was to further validate the structure of the Work Volition Scale–Student Version and determine the degree to which it over-laps with related constructs. After removing a problematic item and control-ling for error covariances among two sets of similarly worded items, the hypothesized factor structure for the WVS-SV was shown to be a very good fit to the data. A well-fitting measurement model is an important source of construct validity, particularly in instrument development (Kline, 2005; Worthington & Whittaker, 2006). This suggests that the final 16-item scale measures the work volition construct and that the two subscales, volitional constraints and general volition, were indeed unique subfactors of the broader work volition construct.

Given the range of barriers that racial/ethnic minorities and women face in the world of work (Fouad & Byars-Winston, 2005; McWhirter, 1997), it is important to examine the factor structure of vocational assessments across racial/ethnic and gender groups. We hypothesized that scores on the WVS-SV would be lower for women compared to men and those from minority ethnic/racial groups compared to majority ethnic/racial groups. Partially disconfirm-ing our hypotheses, the MIMIC models suggest that the WVS-SV subscales are equivalent between young men and young women, as well as between Whites and students of color. This suggests that the WVS-SV instrument is appropriate for use across racial/ethnic and gender groups. Women and men

20

Tabl

e 3.

Cor

rela

tions

of t

he W

ork

Volit

ion

Scal

e W

ith C

onve

rgen

t an

d D

iver

gent

Con

stru

cts

(N =

312

)

12

34

56

78

910

1112

1. W

VS-

SV t

otal

—

2. W

VS-

SV v

oliti

on.8

4—

3

. WV

S-SV

con

stra

ints

.93

.59

—

4. C

aree

r lo

cus

of c

ontr

ol.2

1.1

4.2

2—

5

. Cor

e se

lf-ev

alua

tions

.60

.54

.53

.17

—

6. C

DSE

.49

.49

.41

.07

.46

—

7. C

aree

r ba

rrie

rs−.

25−.

23−.

23−.

14−.

26−.

12—

8

. Neu

rotic

ism

−.27

−.22

−.26

−.08

−.56

−.27

.18

—

9. O

penn

ess

.16

.18

.11

−.02

.10

.19

−.12

.00

—

10. E

xtra

vers

ion

.18

.15

.16

.02

.27

.30

−.05

−.08

.13

—

11. A

gree

able

ness

.23

.19

.22

.05

.17

.21

.09

.02

.36

.27

—

12. C

onsc

ient

ious

ness

.18

.13

.18

−.02

.25

.25

.10

−.11

.04

.15

.27

—M

85.1

535

.69

50.4

04.

7743

.25

91.9

228

0.63

11.0

115

.35

12.8

116

.42

14.8

4SD

13.2

65.

908.

831.

116.

9415

.73

17.9

33.

172.

763.

842.

743.

16

Not

e. C

orre

latio

ns g

reat

er t

han

.15

sign

ifica

nt a

t th

e p <

.01

leve

l. C

DSE

= c

aree

r de

cisi

on s

elf-e

ffica

cy.

Duffy et al. 21

also did not respond differently to any WVS-SV items. However, partially supporting our hypotheses, students of color viewed discrimination as a greater constraint to their work volition than White students in the MIMIC model. This suggests that students of color perceive discrimination-related constraints in their career decision-making process, converging with the literature (Blustein, 2006).

Practically, it suggests that practitioners using the WVS-SV could use these items to engage students in discussions of how racial/ethnic barriers affect their work volition and career development and how these barriers can be addressed and/or overcome. Because research suggests that marginalized youth with a greater consciousness of inequalities make more progress in their career development and attain higher paying, higher status occupations in adulthood (Diemer, 2009; Diemer & Blustein, 2006), practitioners could help students of color develop a greater understanding of their sociopolitical con-texts and how they might anticipate and overcome barriers they will encoun-ter to foster work volition.

The correlational analyses linking work volition to potentially related vari-ables provided an additional construct validity check. Supporting our hypoth-eses, work volition was strongly correlated with one’s general perceptions of self (core self-evaluations) and moderately correlated with one’s confidence in being able to make career decisions (CDSE). These correlations are sugges-tive of overlapping variables in the expected direction, but not to a point that may suggest non-unique constructs (i.e., work volition and core self-evaluations share only a 36% variance overlap). Additionally supporting our hypotheses, work volition was found to correlate with positive aspects of personality, but only weakly, suggesting that work volition is more prominent in those with adaptive personalities but is not an underlying personality trait. Finally, and perhaps most important, work volition was found to only weakly correlate with career locus of control and career barriers, respectively. These variables were explicitly used as validity tests given the perceived likelihood of work volition detrimentally overlapping with them. Confirming our hypotheses, work volition appears to be related to these similar constructs but also is tap-ping something more than career-related barriers or perceptions of an internal versus external career locus of control.

In sum, the two-factor, 16-item WVS-SV displayed good indicators of fit and correlated in the appropriate directions and strength with personality dimensions, CDSE, core self-evaluations, career barriers, and career locus of control. These findings lend support to the use of the WVS-SV in future research studies.


Study 3Procedure and Participants Similar to Studies 1 and 2, participants were recruited via the psychology par-ticipant pool, received class credit for taking part in this study, and were informed that they could withdraw from the study at any time without penalty. Surveys were completed during the fall 2010 academic semester by a new sample of students. Participants completed the final, 16-item version of the WVS-SV and, approximately 2 weeks later, were sent an e-mail asking them to complete the same instrument. Participants were asked to develop a unique, non-identifying code that could be used as an identification marker to match up their two sets of results while also ensuring confidentiality. Fifty-seven participants completed Part 1 and Part 2 of the study, 63% of whom identi-fied as White/Caucasian and 79% identified as female. The mean age was 19.57 (SD = 4.22).

Results and Discussion Total scale scores for the WVS-SV were computed for Time 1 and Time 2 and correlated. The approximately 2-week test–retest correlation of the total scale was .73, was .59 for the volition subscale, and was .67 for the constraints subscale. These strong correlations indicate that over a period of approximately 2 weeks, the WVS-SV and associated subscales were strongly consistent in terms of individual responses. These correlations provide initial evidence that WVS-SV scores are relatively stable over a short period of time.

General Discussion The Psychology of Working Framework developed by Blustein and colleagues is unique in its application to individuals with low degrees of power in their work lives, often caused by poverty, marginalization, and stigmatization (Blustein, 2006; Blustein et al., 2008). A key component of this framework is work volition, and prior to this study no measurement tool had existed to assess this construct among student populations. Following the steps proposed by Worthington and Whittaker (2006) for scale construction, our research team developed a definition of work volition, tailored a robust list of items to assess the construct, sent this list to a panel of expert reviewers to ensure content validity, adapted items as needed to fit student populations, assessed the factor structure through an exploratory factor analysis, confirmed this factor structure via confirmatory factor analyses, examined the equivalence

Duffy et al. 23

of the constructs and items across gender and racial/ethnic groups, compared work volition to related constructs to determine construct validity, and assessed the stability of the construct with test–retest reliability. With each data col-lection, we made special efforts to ensure a large and diverse sample accord-ing to gender and race/ethnicity. All of these efforts resulted in a short, publicly accessible scale to assess work volition tailored specifically for college student populations.

Like any new measurement tool, its value is judged by its utility in future studies. First and foremost, we believe that the WVS-SV is uniquely suited for researchers studying the career development of students with high degrees of barriers. It is likely that a certain proportion of students view limits in their power to match their personal vocational preferences with majors or future jobs, and assessing work volition may give researchers added information on these students’ career decision-making process. Second, the WVS-SV taps a more global perspective of volition as opposed to specific barriers. Although certain items contain specific barriers (i.e., financial, family, discrimination), the scale as a whole was designed for participants to respond to items by con-sidering their own unique potential constraints. Accordingly, we believe the WVS-SV can be a more efficient way of tapping a student’s general sense of power in his or her future work life as opposed to attempting to measure every potential career barrier. These types of measures are long and often few of the potential barriers apply to a specific individual.

Third, the WVS-SV may be a useful tool for future researchers who attempt to understand the role of barriers or constraints within our major vocational theories. Specifically, work volition may play an important moderating role in the link of such variables as self-efficacy, interests, and values to major or career choice. It may be that only those with high levels of work volition feel able to match these personal preferences with the choice of a specific major or job after graduation. These lines of research each remain open as empirical questions that hopefully can be addressed using the WVS-SV.

Limitations The results and conclusions from this instrument development study need to be considered in light of a number of limitations. First, the sample for each study was collected through a psychology department participant pool. Although the samples were diverse in terms of gender, race/ethnicity, and major (e.g., only 9.2% psychology majors in Study 1 and 29.8% in Study 2), they remain limited to those in psychology courses within one specific uni-versity. The mere fact of being enrolled in a psychology course (regardless


of major) may have biased the results in some fashion and it is important to collect data from students via other avenues. Second, the data collected to estab-lish construct validity were purely cross-sectional and it is impossible at the current time to know how work volition relates to similar constructs over time. It is clear that this will be an important line of further research. Third, the Career Locus of Control Scale needed to be adapted to ensure greater internal consistency, which still was under a desired value. More research is needed to explore the locus of control–work volition relation, especially using a more reliable measure of locus of control.

Fourth, although these samples were relatively diverse in terms of race/ethnicity, they did not provide subsamples of racial/ethnic minority groups large enough to conduct more nuanced comparisons between racial/ethnic minority groups. The MIMIC analyses identified important racial/ethnic dif-ferences between White students and students of color, and the inability to examine within-group heterogeneity among racial/ethnic minority college students was a limitation. For example, it may be that African American col-lege students perceive greater constraints on their work volition than Asian American students—an open question that should be examined in further research. Finally, data were collected during a 1-year time span in which col-lege students in particular may have been especially attuned to a struggling economy, thereby affecting work volition scores. It will be important to assess how norms on the WVS-SV change over time.

Counseling Implications In line with the changing world of work, the tasks required of counseling those with career difficulties are evolving. Even college students, who might be considered at the top of the career privilege pyramid, are likely facing increased barriers to achieving their ultimate career goals. Career counselors are therefore tasked with assessing not only a client’s personal preferences (i.e., values, interests, and skills) but also the obstacles present in linking these pref-erences with actual job choices. We view the WVS-SV as a useful tool in this process. Namely, we believe that the WVS-SV can function as a conversation starter to help a counselor understand the limits that clients feel in their career decision making. For clients expressing low levels of volition, a counselor can then explore what factors in a client’s life are limiting this volition and the degree to which clients view these factors as malleable.

For example, a common limitation to a sense of power among college stu-dents is family expectations. A client may have the personal preferences that match an artistic career path but feel pressure from family to go into a more

Duffy et al. 25

lucrative field, such as medicine or law. A counselor could explore how impor-tant supporting one’s family is with finding the best career match. She or he could use this information to construct a more nuanced, client-driven decision-making model, factoring in a client’s own personal preferences with the voli-tional limitations of one’s current life circumstance. Similarly, students of color would be expected to perceive more discrimination-related constraints to their work volition than White students. Practitioners could explore these constraints with students of color, perhaps using this instrument as a stimulus for discussion, and develop strategies or engage in advocacy to help students overcome these racial/ethnic barriers. Because a deeper understanding of these constraints appears to help students of color overcome these barriers and foster career development and occupational attainment (Diemer, 2009; Diemer & Blustein, 2006), practitioners could help students of color similarly understand and negotiate racial/ethnic constraints to foster work volition. We hope that working with clients to fully understand these complex interrela-tions (and not judging them as right or wrong) will ultimately result in a more positive counseling experience. The WVS-SV may be a tool to facilitate these types of discussions.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interests with respect to the research, authorship, and/or publication of this article.[AQ: 6]

Funding

The author(s) received no financial support for the research, authorship, and/or publica-tion of this article.[AQ: 7]

References

Adams, E. M., Cahill, B. J., & Ackerlind, S. J. (2005). A qualitative study of Latino les-bian and gay youths’ experiences with discrimination and the career development process. Journal of Vocational Behavior, 66, 199–218. doi:10.1016/j.jvb.2004. 11.002

Betz, N. E., Klein, K. L., & Taylor, K. M. (1996). Evaluation of a short form of the Career Decision-Making Self-Efficacy Scale. Journal of Career Assessment, 4, 47–57. doi:10.1177/106907279600400103

Blustein, D. L. (2006). The psychology of working: A new perspective for career devel-opment, counseling, and public policy. Mahwah, NJ: Lawrence Erlbaum Associates.

Blustein, D. L. (2008). The role of work in psychological health and well-being: A conceptual, historical, and public policy perspective. American Psychologist, 63, 228–240. doi:10.1037/0003-066X.63.4.228


Blustein, D. L., Chaves, A. P., Diemer, M. A., Gallagher, L. A., Marshall, K. G., Sirin, S., & Bhati, K. S. (2002). Voices of the forgotten half: The role of social class in the school-to-work transition. Journal of Counseling Psychology, 49, 311–323. doi:10.1037//0022-0167.49.3.311

Blustein, D. L., Kenna, A. C., Gill, N., & DeVoy, J. E. (2008). The psychology of working: A new framework for counseling practice and public policy. Career Development Quarterly, 56, 294–308.

Blustein, D. L., McWhirter, E. H., & Perry, J. C. (2005). An emancipatory communitar-ian approach to vocational development, theory, research, and practice. The Coun-seling Psychologist, 33(2), 141–179. doi:10.1177/0011000004272268

Brown, C., Glastetter-Fender, C., & Shelton, M. (2000). Psychosocial identity and career control in college student-athletes. Journal of Vocational Behavior, 56, 53–62. doi:10.1006/jvbe.1999.1691

Brown, S. D., & Lent, R. W. (2005). Career development and counseling: Putting theory and research to work. Hoboken, NJ: John Wiley & Sons.

Cardoso, P., & Ferreira Marques, J. (2008). Perception of career barriers: The impor-tance of gender and ethnic variables. International Journal for Educational and Vocational Guidance, 8, 49–61. doi:10.1007/s10775-008-9135-y

Diemer, M. A. (2009). Pathways to occupational attainment among poor youth of color: The role of sociopolitical development. The Counseling Psychologist, 37(1), 6–35. doi:10.1177/0011000007309858

Diemer, M. A., & Blustein, D. L. (2006). Critical consciousness and career development among urban youth. Journal of Vocational Behavior, 68(2), 220–232. doi:10.1016/ j.jvb.2005.07.001

Donnellan, M. B., Oswald, F. L., Baird, B. M., & Lucas, R. E. (2006). The Mini-IPIP scales: Tiny-yet-effective measures of the Big Five factors of personality. Psycho-logical Assessment, 18, 192–203. doi:10.1037/1040-3590.18.2.192

Duffy, R. D. (2010). Sense of control and career adaptability among undergraduate stu-dents. Journal of Career Assessment, 18, 420–430. doi:10.1177/1069072710374587

Duffy, R. D., Diemer, M. A., Perry, J. C., Laurenzi, C., & Torrey, C. (in press). The con-struction and initial validation of the Work Volition Scale. Journal of Vocational Behavior. doi:10.1016/j.jvb.2011.04.002

Duffy, R. D., & Dik, B. J. (2009). Beyond the self: External influences in the career development process. The Career Development Quarterly, 58, 29–43.

Fouad, N. A. (2007). Work and vocational psychology: Theory, research, and appli-cations. Annual Review of Psychology, 58, 543–564. doi:10.1146/annurev.psych. 58.110405.085713

Fouad, N. A., & Byars-Winston, A. M. (2005). Cultural context of career choice: Meta-analysis of race/ethnicity differences. Career Development Quarterly, 53, 223–233.

Duffy et al. 27

Gallo, J. J., Anthony, J. C., & Muthén, B. O. (1994). Age differences in the symptoms of depression: A latent trait analysis. Journal of Gerontology: Psychological Sciences, 49, 251–264.

Goldberg, L. R. (1999). A broad-bandwidth, public-domain, personality inventory measuring the lower-level facets of several five-factor models. In I. Mervielde, I. J. Deary, F. De Fruyt, & F. Ostendorf (Eds.), Personality psychology in Europe (Vol. 7, pp. 7–28). Tilburg, Netherlands: Tilburg University Press.

Graham, J. W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10(1), 80–100.

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Model-ing, 6, 1–55. doi:10.1080/10705519909540118

Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J. (2003). The Core Self- Evaluations Scale: Development of a measure. Personnel Psychology, 56, 303–331. doi:10.1111/j.1744-6570.2003.tb00152.x

Kahn, J. H. (2006). Factor analysis in counseling psychology research, training, and practice: Principles, advances, and applications. The Counseling Psychologist, 34, 684–718. doi:10.1177/0011000006286347

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford.

Ladany, N., Melincoff, D. S., Constantine, M. G., & Love, R. (1997). At-risk urban high school students’ commitment to career choices. Journal of Counseling & Development, 76, 45–53.

Lopez, F. G., & Ann-Yi, S. (2006). Predictors of career indecision in three racial/ethnic groups of college women. Journal of Career Development, 33(1), 29–46. doi:10.1177/08948453062287341

Luzzo, D. A. (1995). Gender differences in college students’ career maturity and per-ceived barriers in career development. Journal of Counseling and Development, 73(3), 319–322.

Luzzo, D. A., Hitchings, W. E., Retish, P., & Shoemaker, A. (1999). Evaluating differ-ences in college students’ career decision making on the basis of disability status. Career Development Quarterly, 48, 142–150.

Luzzo, D. A., & McWhirter, E. H. (2001). Sex and ethnic differences in the perception of educational and career-related barriers and level of coping efficacy. Journal of Counseling and Development, 79, 61–67.

MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation mod-eling in psychological research. Annual Review of Psychology, 51, 201–226. doi:10.1146/annurev.psych.51.1.201

Martens, M. P. (2005). The use of structural equation modeling in counseling psychology research. The Counseling Psychologist, 33, 269–298. doi:10.1177/0011000004272260


McWhirter, E. (1997). Perceived barriers to education and career: Ethnic and gender differ-ences. Journal of Vocational Behavior, 50, 124–140. doi:10.1006/jvbe.1995.1536

Muthén, L. K., & Muthén, B. O. (2006). Mplus user’s guide (4th ed.). Los Angeles: Author.

Perrone, K. M., Sedlacek, W. E., & Alexander, C. M. (2001). Gender and ethnic differ-ences in career goal attainment. Career Development Quarterly, 50, 168–178.

Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2(1), 13–43.

Rivera, L. M., Chen, E. C., Flores, L. Y., Blumberg, F., & Ponterotto, J. G. (2007). The effects of perceived barriers, role models, and acculturation on the career self-efficacy and career consideration of Hispanic women. Career Development Quar-terly, 56, 47–61.

Schmidt, C., & Nilsson, J. E. (2006). The effects of simultaneous developmental pro-cesses: Factors relating to career development for lesbian, gay, and bisexual youth. Career Development Quarterly, 55(1), 22–37.

Stead, G. B., Els, C., & Fouad, N. A. (2004). Perceived career barriers among South African high school learners. South African Journal of Psychology, 34(2), 206–221.

Swanson, J. L., Daniels, K. K., & Tokar, D. M. (1996). Assessing perceptions of career-related barriers: The Career Barriers Inventory. Journal of Career Assess-ment, 4(2), 219–244. doi:10.1177/106907279600400207

Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.). New York: HarperCollins.

Trice, A. D., Haire, J. D., & Elliott, K. A. (1989). A career locus of control scale for undergraduate students. Perceptual and Motor Skills, 69, 555–561.

Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806–838. doi:10.1177/0011000006288127

Bios

[AQ: 8]

the development and initial validation of the work volition scale-student version

Documents