Development of the Career Indecision Profile

Abstract

Prior research using a 167-item measure of career indecision (Career Indecision Profile-167 [CIP-167]) has suggested that career choice difficulties may be associated with four major sources of career indecision: neuroticism/negative affectivity, choice/commitment anxiety, lack of readiness, and interpersonal conflicts. The purpose of this study was to develop a shorter and more efficient measure of these four major sources of indecision for future use in research and counseling. The development of the measure (CIP-65) is described and the results of a confirmatory factor analytic study of its structure are presented along with initial reliability and validity data. We conclude by discussing implications for future research on the CIP-65 and its potential use in counseling individuals with choice-making difficulties.

Keywords

career indecision neuroticism negative affectivity career choice anxiety career choice readiness interpersonal conflict confirmatory factor analysis

Research over the past 40 or so years has found that a large number of variables are related to career indecision (see Brown & Rector, 2008). Identified variables include those representing internal states (e.g., choice anxiety) and traits (e.g., trait anxiety), as well as such contextual variables as external barriers and interpersonal conflicts. Recently, meta-analytic (Brown & Rector, 2008) and factor analytic (Brown et al., 2012) research have suggested that scores on various measures of these variables covary sufficiently to form four major underlying sources of career indecision difficulty. Brown and Rector (2008), for example, conducted secondary factor analyses on 24 published correlation matrices that collectively included all variables that had been found to relate to career indecision in past research. After meta-analytically combining factor loadings, they identified four major latent variables that seemed to account for a substantial amount of covariation among 37 measured variables.

Subsequently, Brown et al. (2012) sought to explore the robustness of the Brown and Rector (2008) four-factor solution by creating a 167-item inventory (Career Indecision Profile-167 [CIP-167]) that included 4 to 6 items designed to measure each of the 37 variables that loaded on at least one factor in the Brown and Rector (2008) meta-analysis. The CIP-167 was administered to a large sample of college undergraduates and the resultant interitem correlation matrix was subjected to principal factor analyses with oblique rotations. Results revealed that a four-factor solution fit the data well that both replicated and extended the Brown and Rector (2008) solution. The four factors seemed to represent (a) neuroticism/negative affectivity (i.e., trait anxiety, depressive affect, self-consciousness, vulnerability, and a tendency to dwell on things that might go wrong), (b) choice/commitment anxiety (i.e., need for self-information and occupational information and an inability to commit to—and anxiety about—making a career decision), (c) lack of readiness (i.e., lack of goal directedness, planfulness, conscientiousness, and confidence in career decision-making abilities), and (d) interpersonal conflicts (i.e., discouragement by and disagreements with others).

Finally, Brown et al. (2012) also found that their four-factor solution could be uncovered via secondary analyses of correlation matrices used to test the factor structure of two instruments designed to measure alternative cognitive (Gati, Krausz, & Osipow, 1996) and personality/emotional (Saka, Gati, & Kelly, 2008) models of career indecision. Together the results of these studies suggest that four latent factors (neuroticism/negative affectivity, choice/commitment anxiety, lack of readiness, and interpersonal conflicts) might provide a robust and parsimonious model for understanding difficulties in career decision making in U.S. undergraduate students. Unfortunately, the length of the measure (CIP-167) used in the Brown et al. (2012) studies makes it somewhat prohibitive to be used widely in either research or practice.

Therefore, the primary purposes of the present study are to (a) develop a shorter measure of Brown and colleagues’ four-factor model of career indecision, (b) test its factor structure via confirmatory factor analyses (CFAs), and (c) provide preliminary psychometric information on the measure. Data provided by Brown et al. (2012) on the CIP-167 were used to create a 65-item version (CIP-65) that included only items that loaded most saliently on one of the four factors and that adequately covered all the major facets of each factor. We then conducted CFAs to test the factor structure of career decision-making difficulties measured by the CIP-65 versus two alternative models in an independent sample of college students. We also calculated internal consistency estimates for scores on each of the four subscales and gathered validity evidence on the scales. Validity evidence addressed two questions: (a) Do scores on the four subscales of the CIP-65 differentiate college students seeking help via enrollment in undergraduate career courses from college students not enrolled in career courses? (b) Are there relations between scale scores and self-reported levels of career indecision? We also explored for potential gender and race/ethnicity differences on scale scores.

Development of the CIP-65

Items for the CIP-65 were chosen from the longer CIP-167 on the basis of the strength of their factor loadings in the Brown et al. (2012) factor analysis (all loaded above .43 on their respective factors and cross-loaded less than .20 on the other three factors). They were also chosen so that they would collectively represent the underlying facets of each of the four factors with minimal within-factor item redundancy. Factor I (neuroticism/negative affectivity) items were chosen so that they would represent trait anxiety and worry, including dwelling on what might go wrong with a decision (k = 4), depressive affect (k = 5), self-consciousness (k = 4), and vulnerability and dependency, including dependent decision-making style (k = 8). The primary facets of Factor II (choice/commitment anxiety) included need for occupational information (k = 5) and self-information (k = 4), choice anxiety and discouragement (k = 3), approach–approach conflict (k = 4), and an inability to commit (k = 6). Items included on the Lack of Readiness scale (Factor III) were career decision-making self-efficacy beliefs (k = 5), planfulness and conscientiousness (k = 4), and rational decision-making style (k = 4). One self-esteem item loaded saliently on this factor in the Brown et al. (2012) solution and was retained for this version of the CIP. All 5 interpersonal conflict items that loaded saliently on Factor IV in the Brown et al. (2012) factor analysis were retained.

Using the data from the Brown et al. (2012) sample, we extracted four factors from a reduced 65-item correlation matrix via principal factor analysis and rotated the solution obliquely via the direct-oblimin method. All chosen items loaded saliently on their intended factor (loadings ranged from .44 to .88) and displayed minimal cross-loadings (cross-loadings ranged from .01 to .28). A wide range of scores was obtained on each scale and the Cronbach’s αs were all high, ranging from .88 (interpersonal conflicts) to .96 (choice/commitment anxiety). Inter-scale correlations ranged from .23 (lack of readiness and interpersonal conflicts) to .52 (neuroticism/negative affectivity and choice/commitment anxiety). A table showing loadings of each item on each factor, reliability estimates, and inter-scale correlations is available from the corresponding author upon request. We next confirmed the factor structure of the CIP-65, reestimated the reliabilities of the scores obtained on each scale, calculated inter-scale correlations, and collected validity data in an independent sample of college students.

Method

Participants and Procedure

Undergraduate participants were recruited from two Midwestern universities, both of which provided Institutional Review Board (IRB) approval for the study. As in Brown et al. (2012), one institution was located in an urban environment while the other was located in a suburban area. Students were recruited from a variety of education, psychology, research methodology, and statistics courses. In addition, participants were recruited from an elective course designed to assist students in making a choice regarding academic major or career. The CIP-65 was administered in class at the beginning of the academic semester to all participants who gave written informed consent to participate in the study, including those enrolled in the career development elective. The CIP-65 also included a page to collect demographic information (e.g., age, year in school, gender, and race/ethnicity) as well as a question on each participant’s current level of career decidedness (1 = very undecided, 6 = very decided). The CIP-65 is also available from the corresponding author upon request.

Data were initially collected from 495 students. However, questionnaires with 5% or more missing items (k = 4) were removed from the study (n = 7). Thus, the final sample size for this study was 488. Nearly 93% (n = 453) of participants had no missing data. Twenty-five individuals (5.1%) had 1 item missing; the remaining participants had 2 (n = 6) or 3 (n = 4) items missing. Values for missing items were imputed using factor means.

The final sample size did not meet the minimum rule of thumb for structural equation models, which suggests from 5 to 10 observations for each estimated parameter (Kline, 2010). However, a post hoc power analysis on the ability to detect a minimum root mean square error of approximation (RMSEA) value of .05 was completed. Based on an α level of .05 and a sample size of 488 with 2009 degrees of freedom, the power to detect an RMSEA as low as .05 was 1.00 (Preacher & Coffman, 2006), indicating that the sample size was sufficient to confirm the factor structure of the CIP-65.

The majority of the participants were female (76.6%) and Caucasian (68.4%). Other ethnic groups represented in the study were African American (7.8%), Latino/Latina (9.3%), Asian American (7.2%), and multiracial (5.1%). The remainder of participants selected “other” (n = 6) or did not respond to the ethnicity item (n = 6). Participants ranged in age from 18 to 58 years (M = 21.83, SD = 6.02). Their average level of career decidedness was 4.61(SD = 1.38, range 1–6). The current sample was comparable to the sample employed by Brown et al. (2012) except that there were fewer African Americans (7.8% vs. 18%) and more participants from other ethnic minority groups in this sample (Mexican American = 9.3% vs. 5.5%; mixed race = 5.1% vs. 3.8%; Asian American = 7.2% vs. 1.6%).

Data Analyses

A CFA comparing alternative models was completed with LISREL 8.80. Each model was tested using maximum likelihood estimation on the covariance matrix. An assessment of the skewness on items revealed a range of −.40 to 2.26. Two items had skew values greater than 2.00 but were included in the analyses to remain consistent with prior work on the CIP-65. The range of kurtosis values on item responses was from −1.33 to 5.51, but since CFA solutions tend to be robust in the face of kurtosis deviations (Jackson, Gillaspy, & Purc-Stephenson, 2009) the items were considered sufficiently univariate normal to proceed with the CFAs.

The primary model tested was a four-factor oblique model used to develop the CIP-65 (Brown et al., 2012). The four factors in this model were neuroticism/negative affectivity, choice/commitment anxiety, lack of readiness, and interpersonal conflicts. Items were allowed to load only on the factor in which they loaded saliently in the exploratory factor analysis (EFA). The model allowed the four factors to correlate as suggested by the study completed by Brown and his colleagues (2012) and by the EFA results. The first alternative model tested was a single-factor model. In this model, all 65 items were allowed to load on a single latent variable. The second alternative model was based on the structure proposed by Brown and Rector’s (2008) meta-analysis before it was modified by Brown et al. (2012). The difference between this and the primary model is that items assessing difficulty committing to a choice were assigned to the lack of readiness rather than the choice/commitment anxiety factor (see Brown & Rector, 2008). This model was also oblique, allowing the factors to correlate.

The overall pattern of fit for the three models was assessed by examining both absolute and relative fit indices including the root mean square error of approximation (RMSEA), standardized root mean square residual (SRMSR), nonnormed fit index (NNFI), and comparative fit index (CFI). Following standard conventions (Browne & Cudeck, 1989; Hu & Bentler, 1998), models were judged as having acceptable fit with RMSEA and SRMSR values less than .08, and as having good fit if their values were .05 or less. For NNFI and CFI, the standard for acceptable fit is .90 and good fit is .95 (Bentler, 1990; Bentler & Bonett, 1980). In addition, the two nested models (the hypothesized model and the one-factor model) were subjected to a chi-square difference test to determine their comparative fit.

Results

The one-factor model and the rational four-factor model did not adequately fit the data (see Table 1). For the one-factor model, two of the fit indices displayed borderline fit. However, there was not a clear consensus across the other fit indices. A closer inspection of this model revealed that many of the items failed to load saliently (loading >.40) on the single factor. The rational four-factor oblique model based on Brown and Rector’s (2008) meta-analysis also did not fit the data. While two of the four fit indices were acceptable, there was not a clear consensus of fit, suggesting that the model should not be retained as acceptable. Further analysis of this model showed a very high correlation between Factors II and III (r = .92), indicating that they might constitute a single construct rather than two separate factors. This finding provided additional evidence that the model was not acceptable. It should be noted that in this model commitment items that loaded on the second factor in Brown et al. (2012) were rationally assigned to the third factor on the basis of the Brown and Rector (2008) meta-analytic results. This likely contributed to the large correlation between Factor II and Factor III in the four-factor alternative model.

Table 1.

Summary of Fit Statistics for CIP-65 Models Tested.*

Model	Chi-square	df	RMSEA	SRMR	NNFI	CFI
One factor	11,872.33	2,015	.16	.12	.90	.90
Brown and Rector (2008)	8,417.86	2,009	.10	.11	.93	.94
Brown et al. (2012)	6,495.63	2,009	.07	.08	.95	.95

Note. RMSEA = root mean square error of approximation; SRMSR = standardized root mean square residual; NNFI = nonnormed fit index; CFI = comparative fit index.

*All chi-square are statistically significant (p < .0001).

The four-factor oblique model suggested by Brown et al. (2012) fit the data well. All fit indices were within the acceptable range. In addition, all items loaded significantly (p ≤ .05) on their respective factors. The chi-square difference test between the four-factor model and the one-factor model revealed that the four-factor model fit the data significantly better than did the one-factor model (delta chi-square = 5,376.7, df = 6, p < .0001). Taken together, these results suggested that the CIP-65 is assessing a multidimensional construct with four distinct factors.

Next, we inspected the individual loadings of items on each of the four factors in the accepted model (see Table 2). The first factor accounted for a substantial amount of the variance in each of the items ranging from 19.4% to 56.5%. All intended items displayed significant loadings on Factor I ranging from .44 to .75. Factor II accounted for between 38.2% and 73.2% of the variance in the items. The individual item loadings on the second factor were larger than on the first factor, with a range of .62–.86. Factor III accounted for between 13.7% and 64.2% of the variance in the items. The loadings on the third factor ranged from .36 to .80. Finally, the fourth factor accounted for between 52.0% and 79.4% of the variance in the items. The factor loadings for these five items were between .72 and .89.

Table 2.

CIP-65 Factor Loadings.*

Item	Item description	Standardized loading	R ²
NNA14	Often feel fearful and anxious	0.75	.57
NNA9	Hard time forgetting when things go wrong	0.75	.56
NNA17	Often feel insecure	0.74	.55
NNA1	Takes a long time to feel good after setbacks	0.70	.50
NNA4	Often feel overwhelmed	0.70	.49
NNA18	Stress makes me ill	0.68	.46
NNA6	Take setbacks harder than others	0.66	.43
NNA19	Often feel ashamed	0.65	.42
NNA12	I’m a worrier	0.64	.41
NNA21	No confidence in decisions unless friends support them	0.64	.41
NNA11	So sad it’s hard to go on	0.64	.41
NNA13	Focus on what will go wrong in deciding	0.63	.40
NNA8	Worry what others think of me	0.62	.38
NNA5	Easily embarrassed	0.61	.37
NNA2	Often feel like crying	0.60	.36
NNA3	Often feel tired and worn out	0.58	.34
NNA20	Need encouragement from others when deciding	0.58	.33
NNA15	Think about alternatives after deciding	0.57	.32
NNA7	Hard to make decisions without help	0.54	.30
NNA10	Hope my problems will just go away	0.54	.29
NNA16	Sleeping more or less	0.44	.19
CC21	Need information on careers might like	0.86	.73
CC23	Hard time narrowing down	0.85	.71
CC22	Feel nervous when deciding	0.84	.71
CC12	Not enough occupational information	0.84	.71
CC16	Can’t commit, don’t know other options	0.83	.69
CC19	Don’t know enough about occupations	0.83	.69
CC18	Need more information on successful options	0.82	.67
CC4	Often feel discouraged about deciding	0.79	.63
CC9	Need to learn more about myself	0.77	.59
CC20	Need to learn decision-making	0.76	.58
CC2	Need to learn more about what I want	0.75	.55
CC14	Need to learn more about my interests	0.74	.54
CC17	Concerned goals might change	0.73	.53
CC10	Difficult because like so many things	0.72	.52
CC15	Concerned interests might change	0.72	.51
CC7	Need a better idea of my talents	0.72	.51
CC8	Conflict = number or careers are appealing	0.71	.50
CC13	Know options but not ready to commit	0.70	.49
CC3	Interests might change too much	0.70	.49
CC6	Can’t decide between two good options	0.68	.46
CC24	Don’t know much about occupations	0.68	.46
CC11	Rather keep myself open than committing	0.67	.45
CC5	Sometimes feel directionless	0.62	.39
CC1	Uncomfortable committing now	0.62	.38
LR8	Confident I’ll achieve goals R	0.80	.64
LR10	Confident can overcome obstacles R	0.75	.56
LR14	Confident find career perform well in R	0.72	.52
LR12	Will be able to find a career R	0.70	.49
LR9	Given effort, I can solve most problems R	0.59	.35
LR13	Always work productively R	0.57	.33
LR11	Try to excel at everything R	0.54	.29
LR5	Keep going when bad things happen R	0.51	.26
LR1	Strive hard to achieve goals R	0.50	.25
LR7	I’m a worthwhile person R	0.49	.24
LR3	Always think carefully before deciding R	0.41	.17
LR6	Usually able to carry out my plans R	0.39	.15
LR4	Thoroughly consider consequences R	0.38	.15
LR15	Verify information when deciding R	0.37	.14
LR2	Plan ahead for important decisions R	0.36	.13
IC4	Important people disagree with plans	0.89	.79
IC5	Important people discourage plans	0.84	.70
IC3	Important people don’t support plans	0.80	.64
IC1	Going against wishes of others	0.74	.54
IC2	Contradictory information from others	0.72	.52

Note. NNA items loaded onto Factor I (neuroticism/negative affectivity), CC items loaded onto Factor II (choice/commitment anxiety), LR items loaded onto Factor III (lack of readiness), IC items loaded onto Factor IV (interpersonal conflicts), R indicates items are reverse scored.

*All factor loadings are statistically significant (p < .05).

In sum, the four-factor model uncovered by Brown and colleagues (2012) and represented by the CIP-65 was replicated in our independent sample. Specifically, the model fit the data adequately (and better than two alternative models), all items loaded significantly on their assigned factors, and the factors were shown to be both unique and interrelated. Correlations between factors were similar to those attained in the EFA reported earlier and ranged from .19 (Factors III and IV) to .45 (Factors I and II).

Psychometric Data

Cronbach’s α estimates on the scores of all four scales were high: Factor I α = .93, Factor II α = .97, Factor III α = .88, and Factor IV α = .89. These reliability data are promising and are comparable to the Cronbach’s αs obtained from the EFA results that were reported earlier in this article.

In order to gather preliminary construct validity information on the CIP-65 scales we tested to see whether students enrolled in career planning courses (CC, n = 129) would display significantly higher means on each of the CIP-65 scales than students in other undergraduate courses (UG, n = 359); we also explored the correlations between scale scores and self-reported levels of career decidedness. Results showed that the CC students scored significantly higher than the UG students on three of the four CIP-65 scales: Neuroticism/Negative Affectivity, t(486) = 2.32, p = .02, d = .24; Choice/Commitment Anxiety, t(486) = 5.36, p < .001, d = .55; and Lack of Readiness, t(486) = 2.53, p = .01, d = .25. No significant differences were revealed between CC and UG students on the Interpersonal Conflicts scale, t(486) = −.75, p = .46, d = .08. CC students also displayed significantly lower levels of decidedness when compared to the UG students, t(486) = −4.99, p < .001, d = −.48.

The correlations presented in Table 3 show that all four scale scores correlated significantly with self-reported levels of decidedness, with the magnitude of correlations ranging from −.24 for Neuroticism/Negative Affectivity to −.71 for Choice/Commitment Anxiety. The bivariate correlations also showed a strong relationship among level of decidedness, scale scores, and age. Thus, partial correlations (controlling for age) between the scale scores and the level of decidedness are also included in Table 3 and ranged from −.20 to −.69.

Table 3.

Correlations Between Level of Decidedness and Scale Scores.*

	Neuroticism/negative affectivity	Choice/commitment anxiety	Lack of readiness	Interpersonal conflicts
Bivariate correlation	−.24	−.71	−.38	−.25
Correlation controlling for age	−.20	−.69	−.36	−.23

*All correlations are statistically significant (p < .001).

Finally, we also explored whether there were any gender or race/ethnicity differences on scale scores. These analyses revealed that women scored significantly higher than men on the Neuroticism/Negative Affectivity scale: M = 67.65 (SD = 19.24) and 61.73 (SD = 20.15) for women and men, respectively; t(485) = 2.84, p = .005. The other scales showed no gender differences, with the female versus male mean being 74.63 (SD = 29.19) versus 71.10 (SD = 29.36), 29.23 (SD = 8.70) versus 30.38 (SD = 10.04), and 9.71 (SD = 5.83) versus 9.61 (SD = 5.06) for the Choice/Commitment Anxiety, Lack of Readiness, and Interpersonal Conflicts scales, respectively. Because of the small numbers of participants from specific non-White racial/ethnic groups, we compared the mean of Whites to those of a combined race/ethnicity sample. These analysis revealed no significant racial/ethnic differences except on the Interpersonal Conflicts scale, t(481) = 2.56, p = .012. Members of racial/ethnic minority groups scored higher (M = 12.23, SD = 6.41) than Caucasian participants (M = 9.28, SD = 5.25). Inspection of the means obtained on this scale by specific racial/ethnic minority groups suggested that this difference may largely be attributed to the scores obtained by Asian American participants who scored higher on this scale than any other racial/ethnic group (M = 14.60, SD = 8.09 for Asian Americans versus M = 9.21, SD = 1.87 for the other racial/ethnic groups combined).

Discussion

The primary purpose of this study was to develop a measure of career indecision (CIP-65) that would be long enough to adequately represent the four primary factors found in prior research but short enough to be useful for future research and practice. The items comprising the CIP-65 were chosen to ensure that all major facets of neuroticism/negative affectivity, choice/commitment anxiety, lack of readiness, and interpersonal conflicts were covered with multiple items, yet displayed minimal overlap in content. The results of our CFA suggested that the four-factor model used to develop the CIP-65 fit the data well and that all items loaded significantly and saliently on their assigned factor. Internal consistency estimates of the scores on each of the four scales were all quite high, and, for the most part, scores behaved as expected in validity analyses. The lone exception was that scores on the interpersonal conflicts scale did not significantly differentiate students enrolled in career classes from those not enrolled in such classes, and the effect size estimate obtained in this analysis was quite small. Scores on this scale did, however, correlate significantly (and moderately) with the self-reported levels of career decidedness. The reason for this apparent discrepancy is not immediately obvious but could be due to the fact that substantially fewer students from collectivist cultures (e.g., Asian Americans) were enrolled in the career class than in the sample as a whole. Past research has shown that interpersonal (i.e., family) conflicts represent a major source of career decision-making difficulty among Asian Americans, especially among those who are more acculturated into the mainstream U.S. culture (Ma & Yeh, 2005; Mau, 2004). Thus, interpersonal conflict concerns might not have been as prevalent in the career class as they were in the sample as a whole.

Comparison of mean scores by gender and race/ethnicity also revealed differences that would be expected on the basis of prior theory and research. Specifically, the higher average scores obtained by women than by men on the Neuroticism/Negative Affectivity scale is consistent with prior literature on trait neuroticism, especially for the trait anxiety, depression, self-consciousness, and vulnerability facets of neuroticism covered by the CIP-65 Neuroticism/Trait Negative Affectivity scale (e.g., Costa & McCrae, 1992). The higher scores obtained by the combined racial/ethnic minority sample than by the White participants could also be expected on a measure of interpersonal conflict, especially since the scores obtained by Asian Americans in this study seemed to account for most of the differences obtained in this comparison.

The CIP-65 obviously requires further research before its utility can be established. Brown et al. (2012) developed a beginning nomological network for future construct validity research on each scale. For instance, one of their recommendations was to ensure that negative affectivity is adequately represented on the Neuroticism/Negative Affectivity scale by correlating scores on these scales with separate measures of negative affectivity and neuroticism. They also suggested that students scoring high on this scale may prematurely foreclose on less than adequate options as an avoidance strategy and that they may develop less ambitious career and educational goals than lower scorers. The hypothesized tendency for premature foreclosure may also explain the relatively modest (though statistically significant) correlation obtained in this study between neuroticism/negative affectivity scores and self-reported levels of decidedness. That is, high scorers on a scale measuring neuroticism/negative affectivity may have already foreclosed on an (albeit less than satisfying) option and thus report themselves to be somewhat decided. Future research might, therefore, explore whether neuroticism/negative affectivity scores might account for more variance in choice satisfaction than in decidedness (i.e., correlate more negatively with measures of choice satisfaction than with measures of decidedness).

In relation to the Choice Anxiety/Commitment scale, Brown et al. (2012) suggested that scores on this scale could relate to maximizing versus satisfying decision styles. Maximizers (see Schwartz et al., 2002) tend to seek out the best possible option and experience more anxiety in the process than satisfiers (who search for a good enough option). Thus, maximizers might score higher on the Choice/Commitment Anxiety scale than satisfiers. Brown et al. (2012) also suggested that scores on this scale may correlate highly with the level of concern that students have with the current economy.

Finally, Brown et al. (2012) suggested that scores on the Lack of Readiness scale may reflect the degree to which students have achieved conflictual and functional independence from their parents and that scores on the Interpersonal Conflicts scale may relate to the degree to which students from collectivist cultures have acculturated into U.S. society. In the former case, lack of readiness scores should relate negatively to scores on measures of conflictual and functional independence. In the latter case, levels of acculturation would be hypothesized to relate positively to interpersonal conflict scores.

The major limitation of the present study is that the sample was fairly homogeneous. Females and Caucasians were overrepresented and the sample was entirely composed of college students (albeit some were of nontraditional age for college students; ages range from 18 to 58). Therefore, important next steps in research on the CIP-65 are to determine whether the structure of the measure is the same across different groups, especially across gender, ethnicity, socioeconomic status, and age. Tests of measurement invariance of the CIP-65 internationally are also warranted before it can be used to explore cross-country differences in career decision-making difficulties or to assess decision-making difficulties of persons outside of the United States.

However, should future research continue to support the structure and validity of the CIP-65, counselors could use it in two ways. It could function as a diagnostic tool to tailor interventions to the primary sources of client difficulties (see Brown & Rector, 2008; Rounds & Tinsley, 1984; Whiston & James, 2012). Additionally, it might be a useful measure of counseling outcome for interventions focused on choice difficulties (see Björnsdóttir, Einrsdóttir, & Vilhjálmsdóttir, 2011).

Footnotes

Acknowledgments

The authors thank Anne Siena Ballard, Theresa Chan, Anneliese Kranz, Colleen Martin, and Meaghan Rowe-Johnson for their assistance with this research and for reading and commenting on earlier drafts of this article. We also thank Meghan Roche, Jessica Stegmaier, James Wade, and Patricia Wisneski for reading and commenting on earlier drafts.

Declaration of Conflicting Interests

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

Funding

The authors disclosed the following financial support for the research, authorship, and/or publication of this article: Jason Hacker, Matthew Abrams, and Andrea Carr were supported by Graduate Assistantships provided by the Graduate School and School of Education of Loyola University Chicago.

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