The Development of the CASVE-CQ: A CIP Perspective on Assessing Decision-Making Progress

Abstract

The CASVE-Cycle Questionnaire (CASVE-CQ) was developed to assess career decision-making progress and operationalizes the Cognitive Information Processing Theory’s CASVE Cycle decision-making approach. Development occurred across three unique studies. In the pilot study’s college student sample (N = 323) and initial adult sample (N = 427), two exploratory factor analyses supported a theoretically consistent six-factor solution. A confirmatory factor analysis in the second adult sample (N = 342) confirmed the factor structure, resulting in a 42-item measure with six subscales. A second-order factor analysis assessed the utility of a CASVE-CQ total score. Consistent with theory, this model did not converge, and a total score for the CASVE-CQ was not supported. Supporting the validity of the CASVE-CQ as a decision-making progress measure, greater decision-making activity in each phase/subscale was associated with lower career decision-making difficulties, stable vocational identity, and greater career commitment. Continued test development steps and theory, research, and practice implications, are discussed.

Keywords

CASVE cycle cognitive information processing theory test development integration of theory research and practice

Barbarovic and Sverko (2019) recently noted that career development theorists, practitioners, and researchers have long discussed and integrated the importance of career decision making into their work. This long-term and wide-reaching focus on career decision making highlights the critical nature of understanding career decision making to advance the field’s theory, research, and practice. Tools and theoretical conceptualizations relevant to the understanding and measurement of career decision making already exist; yet, a gap was noted during the second author’s supervision of graduate students providing career counseling. These practitioners-in-training were often uncertain or incorrect in their assessment of where clients were in their career decision-making process. For example, if a client discussed needing help with a resume, the trainee often assumed the client was ready to execute the decision to find a particular job instead of questioning the need to further explore options or consider whether the desired jobs matched their values. It became clear that a tool to better illuminate the appropriate steps of the decision-making process would be advantageous.

This article presents the development of a new self-report measure, the CASVE Cycle Questionnaire (CASVE-CQ), based on a theoretically driven decision-making process (i.e., the CASVE cycle) that allows practitioners to assess the decision-making progress of their clients. The CASVE Cycle is based in Cognitive Information Processing (CIP) Theory (Sampson et al., 2004, 2020) and is composed of phases critical to making complex decisions. The CASVE-CQ items and scales map on to the phases of the CIP CASVE Cycle, filling the gap in our field’s career decision-making assessment needs. To date, guiding clients through the CASVE Cycle to make a satisfying career decision has been largely based on a theoretical conceptualization of effective decision-making components or considerations. The CASVE-CQ provides an empirical measure of how people engage these phases in their career decision-making process that is consistent with the original theoretical statements. This article presents the CASVE-CQ’s extensive development process, as well as the research, theory, and practice implications of this new assessment tool. The initial development process spanned three unique samples from both college students and working adults. These data are presented across three discrete studies that involve exploratory and confirmatory factor analysis, as well as construct validity evidence.

Career counseling has long been informed by decision-making assessments. Many existing assessments can tell us about a test-taker’s decision-making style, approach, and difficulties encountered (e.g., Gati et al., 1996; Osipow et al., 1976; Super et al., 1981; Swanson & Tokar, 1991). Other important measures inform us of a test-taker’s confidence or thoughts associated with making a career decision (Sampson et al., 1996; Taylor & Betz, 1983). Additionally, several researchers and theorists have addressed the importance of assuring clients are provided the skills and knowledge necessary to engage in the complex process of making a career decision (Gati et al., 2001; Lent & Brown, 2020; Sampson et al., 2020). Yet, none of the existing measures tell us where a client is in their decision-making process, what additional factors they may need to consider, or illuminate next steps that might help them make a more satisfying decision. Additionally, the theories that highlight the importance of these client needs have not yet developed a psychometrically sound tool to guide this process.

Many important observations and advances in the area of career decision making have been made in recent years. Recent studies have addressed the importance of the context within which people make decisions, as well as the challenges they may face in the decision-making process (Blustein et al., 2019; Lent & Brown, 2020). Lent and Brown also noted that career decisions are unlikely to be one-time events, as a series of expected and unexpected changes are likely to be encountered in any person’s career path. Gati and colleagues have highlighted the importance of career decision making for decades (Gati, 1986) and have developed informative taxonomies (Gati & Asher, 2001) and assessments to guide our understanding of career decision making (Gati et al., 1996) that have not yet been articulated within a guiding theory. By operationalizing the CIP theory-based CASVE Cycle (Sampson et al., 2020), the CASVE-CQ seeks to address the gaps in decision-making assistance in the literature. Having an assessment of a client’s progress within the CASVE Cycle will enhance professionals’ ability to teach their clients about the re-occurring career decision-making task while taking into account personal factors and context.

Again, theoretical guidance was critical in the CASVE-CQ development. Cognitive information processing (CIP) theory was developed as a systematic, consumable, and inclusive conceptualization of career development (Sampson et al., 2004, 2020). Comprehensive accounts of CIP theory can be found in several sources (Osborn et al., 2019; Sampson et al., 2004, 2020). The CIP theory’s main constructs are depicted in a pyramid with three main segments that influence one another to provide a comprehensive picture of the content of career choice (Figure 1). The pyramid’s base is composed of self and options knowledge. These domains are often assessed and operationalized by pairing with concepts and tools from Holland’s RIASEC Theory (Holland, 1997; Reardon & Lenz, 2015). The pyramid’s mid sector is the decision-making domain operationalized via the CASVE cycle. The apex of the pyramid involves metacognitions or career-related thinking. This domain is measured using the Career Thoughts Inventory (CTI; Sampson et al., 1996). CTI scores indicate a client’s level of negative career-related thinking, as well as readiness to engage in the career decision-making process (Sampson et al., 1998).

Figure 1.

Cognitive information processing theory’s pyramid of information processing.

CIP theory has been researched extensively and integrated into career practice in a variety of settings (Osborn et al., 2020; Sampson et al., in press). Osborn and colleagues (2020) have provided evidence for the validity of the pyramid. The Career Thoughts Inventory (CTI; Sampson et al., 1996) has generated the most research relevant to the theory, demonstrating the relationship of negative career thoughts to implications in the career decision-making process (e.g., Galles et al., 2019; Saunders et al., 2000). Others have shown the effectiveness of comprehensive CIP-based career interventions (Leuty et al., 2015; Osborn et al., 2020; Reed et al., 2001). Given the effectiveness of CIP theory integrated into assessment and practice, continued efforts to support the empirical basis and psychometrically sound assessment theory components, such as the CASVE Cycle, is warranted.

This research focuses on the decision-making, mid sector of the pyramid, the CASVE cycle (Figure 2). The CASVE Cycle concerns the process of career choice and has not yet been operationalized via psychometrically sound assessment. Sampson and colleagues (2020) said that CIP theory involves “the examination of individual cognition, affect, and action, which combine with family, social, economic, and organizational factors, to influence the occupational, educational, training, employment, and leisure choices of individuals over a lifetime” (p. 6). Additionally, “The number and sequencing of these choices vary with some persons making numerous choices and others making fewer choices, with persons sequencing their choices from different starting and ending points” (Sampson et al., in press, Types of Career Choices section). The CASVE cycle is meant to be an accommodating yet structured phase-based model of career decision making that can address these needs over a lifetime and allows a person to take the particulars of their situation and context into account. CIP theory presents an organized approach to the process of career decision making, the influencing factors, and the impact from other CIP Pyramid components such as negative thinking, self-knowledge, and options knowledge. In CIP theory, a career problem is often stated as a gap between where the client is and where they want to be. The CASVE cycle provides the process to determine where one wants to be (Sampson et al., 2004).

Figure 2.

The CASVE cycle.

The CASVE-CQ offers a tool to assess clients’ progress in closing the gap and identifying next steps helpful in moving forward. The acronym CASVE summarizes the five decision-making phases which are conceptualized as fluid and interconnected yet systematic (Sampson et al., 2004). Individuals can return to previous phases or restart the cycle when dissatisfaction occurs in the decision-making process, but completion of phases in the CASVE order is considered the ideal path toward a quality decision that coincides with one’s desired career path. The first phase, Communication, begins when one is triggered, by either internal or external awareness, to acknowledge the need to make a career decision, determine what type of decision needs to be made, and define where one would like to be in the future. The Analysis phase is when individuals clarify the knowledge they possess against their exploration of options, self-knowledge, and decision-making skills. The Synthesis phase focuses on expanding and narrowing possible options through elaboration and crystallization to identify three to five viable options. The Valuing phase consists of examining the costs and benefits of each of the remaining options and subsequently rank-ordering the top options. Decision-making is typically conceptualized as an independent process; however, factors such as family, significant others, community, and work environment, with respect to culture, should also be considered (Arthur, 2000; Rounds, 1990; Sampson et al., 2004) and are integrated aspects in the Valuing phase. Execution, the fifth phase, involves acting upon the steps necessary, as outlined in a time-based task list, to obtain one’s top career option. At the end of the CASVE cycle, individuals loop back to the Communication phase a second time to evaluate their decision-making process and the choice they executed. Considerations in the second visit to the Communication phase may involve identification of a new decision to be made (e.g., I’ve decided to go to graduate school, but I am not sure how to select a program.) or dissatisfaction with one’s original choice executed. Although fluidity of the CASVE cycle is acknowledged, the structured approach allows individuals to continue to return to specific tasks while moving forward in their personal decision-making experience (Sampson et al., 2004).

The Present Studies

The ultimate goal of the CASVE-CQ development is to provide clients and practitioners with a decision-making progress guide that allows practitioners to target career interventions at missed or next phases in the decision-making process to better assure an ideal approach toward a satisfying decision. An additional goal is to add a research tool to the continued study of career decision making, as well as the progression of CIP theory and research to practice.

The development of the CASVE-CQ was approached through three discrete studies. Study 1 involved initial item development with a focus on generating items consistent with the theoretical tasks associated with each phase of the CASVE cycle. The task-based items could be endorsed if the test-taker had engaged in or fully considered the activity or task. Study 1 involved an expert review and some pilot studies with various undergraduate student samples culminating in an EFA with a college student sample. The Study 1 pilot work allowed the researchers to engage in a working adult sample with more confidence in the content of the new measure. Based on the results of Study 1, Study 2 presented a revised version of the CASVE-CQ to a working adult sample and conducted a second EFA. The resulting CASVE-CQ scales represent the five phases of the CASVE cycle, with the sixth scale representing the return to the Communication phase to evaluate the decision. To confirm the six scales identified in Study 2, Study 3 involved a CFA with a new working adult sample. Study 3’s results confirmed the six-factor structure of the measure and indicated that there is not a meaningful total score. Study 3 also presents evidence of convergent and divergent validity through comparisons with existing instruments.

Study 1: Initial Item Development and College Student Pilot Data

Method

Instrument

CASVE-CQ items were initially created based on the most current conceptualization of the CASVE cycle after a thorough review of related measures and current career-decision making literature in and outside of CIP theory (Sampson et al., 2004). This was in conjunction with brainstorming by a CIP theory subject matter expert and a group of graduate students trained in CIP theory with experience utilizing the CASVE cycle decision-making process with clients. Items were written to address specific phases of the CASVE cycle and to address typical client activities or considerations associated with that CASVE phase.

Test-takers could answer “yes” to an item if they considered themselves to have completed the activity or considered the issue in the item content. Test-takers could answer “no” if they had not engaged in the associated activity or issue, or if the item content did not apply to their situation. While the test developers acknowledge the potentially limiting nature of a two-point answer option (Preston & Colman, 2000), the yes/no answer option was intentionally chosen to aid in the practical use of the CASVE-CQ. Additionally, there is precedent in psychometrically sound career assessment for the use of two-point answer options (Holland & Messer, 2013) and evidence that additional answer options do not substantially change the results profile of career assessments (Bullock-Yowell et al., 2017). From a theoretical perspective, partial completion or some considerations of the tasks or ideas encompassed by the CASVE-CQ items would not constitute what is necessary to have fully addressed that phase of the decision-making cycle. It was critical that the items allowed test-takers to fully endorse (or not) any one task to best assess their engagement with that aspect of the decision-making process. Therefore, the yes/no answer options were easily justified from a practical and theoretical perspective.

The items were then reviewed by a group of graduate students new to the theory, a group of graduate students with thorough training and experience with the theory, and an established expert in CIP theory. The graduate students new to the theory were asked to individually and then as a group brainstorm and report any CASVE phase content they did not think was addressed by the items presented. They were also asked to comment on the clarity of the items. This feedback was taken into consideration by the graduate students more thoroughly trained in the theory and its application. These graduate students used this information to clarify item content, as well as add and eliminate items. The established CIP theory expert reviewed this list of items to be included in the pilot research process for theory consistency and full coverage of the breadth of the theory as it applied to each phase of the CASVE cycle. Item wording was edited, and some repetitive items were eliminated.

Participants and Procedure

After obtaining IRB approval, data collection took place in four phases. Phase 1 involved an initial in-person focus group with seven undergraduate students recruited through the online subject pool used by the School of Psychology. They provided written and oral feedback on the content, meaning, clarity, and wording of the 79 items that were initially developed. In Phase 2, three CIP content experts who are engaged in CIP-based research and/or practice and operating in three different professional settings reviewed the 79 items. The experts responded to a scale assessing the clarity and the theory-based essential nature of the item following an expert review process suggested by Lawshe (1975). Experts could also provide open comments on each item. Input from the focus group and expert review resulted in the reduction of the item pool to 60 items. In Phase 3, the 60 items were administered online to 54 undergraduate students to evaluate the reliability of the items. Phase 3 results indicated an acceptable alpha coefficient (α = .85) for all 60 items. Reliability was also considered for the items written to address each phase of the CASVE cycle, but lower than desired reliability was found for CASVE phase-based item groupings (αs = .42–.64). The researchers proceeded to Phase 4 of Study 1 with the understanding that some items from each subscale would be eliminated based on the exploratory factor analysis, likely boosting the reliability of individual subscales. In Phase 4 of Study 1, 323 undergraduate students (Female: 215, Male: 106, Sex not reported: 2; Age: 18–25, M = 20; .6% American Indian, 4% Asian, 29.7% African American, 3.4% Hispanic, 61.3% White, .9% Other) recruited through the online subject pool and student listservs completed an online survey that included a demographic questionnaire and the 60-item CASVE-CQ. Participants received research credit or a chance to earn a $25 gift card. As recommended by Meade and Craig (2012) careless responding was assessed by blending two directed response items (e.g., “Answer ‘disagree’ to this question”) into two of the measures. Participants who answered either item incorrectly were removed.

Results

In Study 1, an exploratory factor analysis using principal axis factoring with a direct Oblimin oblique rotation was conducted in the Statistical Package for the Social Sciences (SPSS; Version 22.0). In order to determine the number of factors to extract, eigenvalues (Kaiser, 1958), Cattell’s scree test (Cattell, 1966), parallel analysis (Horn, 1965), minimum average partial (Velicer, 1976), and established CIP theoretical knowledge were utilized. It was predicted that because the CASVE cycle has five phases, five factors would be extracted. However, because the CASVE cycle phases are cyclical in nature, the communication phase involves tasks that are completed at the beginning and end of the CASVE cycle. Therefore, it was predicted that six factors may emerge and remain consistent with the theory.

A six-factor structure was determined to best represent the data and incorporate the CIP theoretical foundation. Minimum average partial suggested six factors were appropriate (Velicer, 1976), the scree test suggested seven factors (Cattell, 1966), parallel analysis suggested up to 10 factors (Horn, 1965), and theory suggested five or six factors. The six-factor structure accounted for a cumulative 42.96% of the variance and represents each phase of the CASVE cycle including a second portion of the communication phase, which allows a person to reassess their career decision. Tabachnick and Fidell’s (2001) recommend that each item have a factor loading of at least .32. One item was deleted that had a loading below .25. Items with loadings between .25 and .30 were evaluated for potential deletion, and three items were deleted that met this criterion. Six items with loading between .25 and .30 were retained due to their theoretical-consistency and essential nature with defining their associated CASVE phase. One item was deleted that was determined to be negatively affecting the reliability of factor 6. In Phase 4, the final CASVE-CQ resulted in 55 items with the number of items loading on each subscale and alpha levels as follows: Total CASVE-CQ-55 items (α = .93) Communication 1–4 items (α = .76), Analysis—10 items (α = .84), Synthesis—14 items (α = .85), Valuing—5 items (α = .59), Execution—14 items (α = .86), and Communication 2–8 items (α = .83).

Study 1 Discussion

Study 1 established a viable factor structure consistent with CIP theory in a sample of college students and highlighted some areas for improvement when transitioning to the working adult sample. For example, the Valuing subscale demonstrated unacceptably low internal consistency. In this paper’s subsequent studies, the continued development of the CASVE-CQ addresses more reliable item content for the Valuing subscale by item expansion that better attends to a variety of value perspectives, as values can be very person specific. While it is important to avoid items that could only apply to a small subgroup of individuals, item content should be flexible enough to allow most values to be expressed and considered in the decision-making process. In the final Study 1 version of the CASVE-CQ, the number of items across subscales varied widely. Continued development focused on translating the understandings from this pilot work to a working adult sample, developing new items for scales with fewer items, and better balancing items across subscales.

Study 2: EFA with Working Adults

Method

Instrument

Because the results of Study 1 highlighted areas of improvement for the CASVE-CQ, Study 2 involved the development of new items and the modification of existing items in an attempt to increase the stability of factor structure, balance item numbers across scales, and reliability. Twenty-eight original items from the Study 1 version of the CASVE-CQ were combined with 19 Study 1 items that were edited to improve clarity and 41 newly generated items. The editing of items focused on wording clarity and simplifying statements. The new items were designed to fit the Communication 1 and Valuing phases, both of which had fewer Study 1 items than the other phases. As noted, the Valuing phase has some complexities and possible individual variation to consider to fully address the issues potentially faced in this phase of a decision-making process. Thus, many items were written to address the wide variety of potential valuing considerations. This resulted in a total of 88 CASVE-CQ items utilized in Study 2 analysis.

Participants and Procedure

After obtaining IRB approval, participants were recruited via Amazon’s Mechanical Turk (MTurk), an online system used to recruit working or employment-seeking adult research participants who complete questionnaires for payment (Mason & Suri, 2012). Extending the understanding of the CASVE-CQ to a working adult sample allowed for the potential to use this measure with a wider variety of career counseling populations in the future. The 427 participants identified as female (n = 295), male (n = 129), or transgendered (n = 3). Ages ranged from 18 to 70 (M = 37.4, SD = 11.2), and participants identified as 0.3% American Indian, 7.7% Asian, 8.7% African American, 4.7% Hispanic, 77% White, and 1.2% other. Participants resided in multiple regions of the United States (15.2% West, 24.8% Midwest, 23.9% Northeast, 36.1% South) and 54.9% had a bachelor’s degree or higher. Only 5.4% indicated they were unemployed, while the others were employed across a variety of job categories with the highest percentages in education (17.8%), heath science (9.4%), business (6.1%), and human services (6.1%). The majority (68.9%) indicated they were employed in these roles full-time with incomes ranging from under $20 K (24.1%) to over 100 K (5.2%) per year. Participants completed a demographic form and the 88-item CASVE-CQ.

To identify careless responding in the online survey, two directed response items were included (e.g., “Select ‘strongly disagree’ for this question”), and measure completion time was recorded (Meade & Craig, 2012). Participants were excluded if they were under 18, lived outside the United States, or failed the directed response items. Because the nature of the CASVE-CQ items assume one is making or contemplating a career decision, the survey included items to assure the participants were actively considering a career decision or change (e.g., “Do you ever contemplate an employment change (i.e., changing careers, positions, employers)?”). Participants who indicated no active career decision contemplation or that they were not employed and not seeking employment or education were routed out of the survey.

Results

In Study 2, an exploratory factor analysis (EFA) using principal axis factoring with an Oblimin oblique rotation was conducted in SPSS (Version 22.0). Parallel analysis (Horn, 1965), minimum average partial (Velicer, 1976), and eigenvalues were used to extract factors. Additionally, information from the Study 1 exploratory factor analysis was used to inform the present analysis (e.g., six factor rotation). Therefore, an exploratory factor analysis (EFA) was conducted due to the continued early developmental work (i.e., new item development, item modification) on the CASVE-CQ. Although instability in exploratory factor analysis is typically high, Osborne and Fitzpatrick (2012) recommended comparing more than one exploratory factor analysis to create a more stable factor structure, particularly with factor loadings of .32 or above (Tabachnick & Fidell, 2001).

Minimum average partial (Velicer, 1976), which indicated 10 factors, and parallel analysis (Horn, 1965), which indicated eight factors, were used to inform factor selection. Cattel’s scree test indicated five factors (Cattell, 1966). However, the five, seven, and eight factor solutions were unable able to produce a theoretically meaningful foundation or factors with four or more items that had factor loadings of .32 or above. Six factors were found in Study 1, therefore, this was used to derive a meaningful factor solution.

Because reducing items and balancing the number of items on each scale was a goal of this EFA, Kahn’s (2006) best practices for factor analysis were used as a guide in item elimination. Items were systematically eliminated one at a time in each factor solution based on low loadings below .3 (19 items) and double loadings (11 items). In addition, items were then eliminated from factors that did not have predominate items from that phase of the CASVE cycle or loaded onto the wrong scale (nine items). Next, items were eliminated that were similar and had lower factor loadings (below .4) to create factors with a realistic number of items (seven items). Forty-two items remained after this process of elimination. The six-factor CASVE Cycle Questionnaire accounted for a cumulative 50.72% of the variance. Item-factor loadings can be seen in Table 1. Retained items did not load above .3 on any other factor than the factor indicated. Reliability for the factors and total score were acceptable: CASVE-CQ total score (α = .73), Communication 1 (α = .84), Analysis (α = .85), Synthesis (α = .85), Valuing (α = .81), Execution (α = .85), and Communication 2 (α = .78).

Table 1.

Results From Exploratory Factor Analysis of the CASVE-CQ—Study 2 (N = 427).

Item	Factor Loading
Item	1	2	3	4	5	6
1. Communication 1
1. I am overwhelmed by making a career decision.	−.53
2. I have difficulty thinking about my future career goals.	−.47
3. There is pressure in my life to make a career decision.	−.43
4. I have a problem concerning my career path.	−.48
5. I worry about needing to make a career decision.	−.61
6. I feel sad or worried when I think about my need to make a career choice.	−.75
7. I find ways to avoid making a career decision.	−.47
8. The amount of effort it takes to make a career decision is overwhelming.	−.67
2. Analysis
1. I am unsure where to begin to solve my career problem.		−.45
2. I need help identifying my career options.		−.67
3. I need more information about my career options.		−.76
4. I need more information about myself to make the best career choice.		−.75
5. I do not have enough information to compare my career or job options accurately.		−.66
3. Synthesis
1. I can identify many career or job options that match my values.			.66
2. I can identify multiple jobs that match my career interests.			.82
3. I can identify multiple career options that match my career-related skills.			.79
4. I have explored a large amount of career or job options and then narrowed those down to a few I feel good about.			.44
5. I can narrow my career or job options to a few that I am seriously considering.			.40
6. I can compare my career or job options based on information I have gathered about them.			.48
7. I know the strengths and weaknesses of each of my career options based on my own career values.			.39
8. I have compared the advantages and disadvantages and benefits associated with each of my career options.			.40
4. Valuing
1. The career options I am considering satisfy my career values.				.36
2. I have considered the costs and benefits of my career options.				.45
3. The options I am considering match my values, interests, skills, and preferences.				.58
4. Who I am (e.g. culture, place in the community and society) fits with the options I am considering.				.56
5. My career options match my aspirations.				.58
6. My career choice fits well with my lifestyle.				.60
7. My career choice is a good match with my personality.				.69
8. My career choice will enable me to live life in the way I want/prefer.				.55
5. Execution
1. I am ready to take the necessary steps to reach my career goal.					.52
2. I am taking the necessary steps to reach my career goal.					.73
3. I am in the process of achieving my career goals.					.72
4. I know what I will need to be doing in six months from now to reach my career goal.					.57
5. I have a plan of action to achieve my career goal.					.70
6. I will try out my top career choice.					.45
7. I have applied for my top career choice.					.52
8. I have begun the training and/or education necessary for my top career choice.					.55
6. Communication 2
1. Even though I have told others I have made my career decision, I do not feel very good about it.						−.60
2. I made a career choice but think I may need to make a new choice.						−.60
3. My career choice has not improved my situation.						−.67
4. The career choice I made has not made me feel any better.						−.78
5. I continue to experience anxiety even though I have made my career choice.						−.49

Note. Loadings below .3 have been suppressed in the table.

Study 2 Discussion

Study 2 improved upon the version of the CASVE-CQ presented in Study 1 by developing new items and revising existing item content. In a sample of mostly employed adults contemplating a career decision or change, the CASVE-CQ’s theory consistent six-factor structure based on strong and unique item loadings was supported. This resulted in a 42-item six-subscale version of the CASVE-CQ with acceptable internal consistency for all for all subscales. Subsequent development steps involved confirming this factor structure in a new sample.

Study 3: Initial CFA With Working Adults and Validity Evidence

Study 3 had two main goals, to confirm the factor structure found in the Study 2 EFA and provide support for the validity of CASVE-CQ scores. Three commonly used career development measures were used to assess the validity of the CASVE-CQ. Under the assumption that completion of a substantial number of decision-making tasks would indicate less difficulty with the decision-making process, three predictions about the relationship between the CASVE-CQ and career decision-making difficulties were made. First, greater completion of tasks across all CASVE-CQ subscales (as indicated by a higher subscale score) would be negatively correlated with career decision-making difficulties. Second, because goals typically stabilize after one narrows their respective career options to a few, vocational identity will be positively correlated with greater completion of Synthesis, Valuing, Execution, and Communication 2 subscales. Thirdly, we expect that commitment to a career increases once action to execute that career plan has been implemented. Therefore, the CASVE-CQ Execution and Communication 2 subscales will be positively correlated with career commitment.

Method

Instruments

The 42-item CASVE-CQ developed in Study 2 was utilized for the Study 3 CFA and validity analysis.

The Career Decision-Making Difficulties Questionnaire (CDDQ; Gati et al., 1996) was used to assess participants’ career decision-making difficulties. The CDDQ assesses difficulties in the areas of readiness, lack of information, and inconsistent information. There are 34 items that each utilize a 1 (does not describe me) to 9 (describes me well) scale. Items include content such as, “work is not the most important thing in one’s life and therefore the issue of choosing a career doesn’t worry me much” (Gati et al., 1996). The CDDQ total score is the result of an average of 10 subcategory scores, with higher scores reflecting higher levels of career decision-making difficulties. An internal consistency of .95 was found in a sample of college students (Gati et al., 1996). When college students completed the CDDQ, the Career Decision-Making Self-Efficacy Scale (Taylor & Betz, 1983), and Career Decision Scale (Osipow et al., 1976) correlations of −.50 and .77 were found, respectively. These studies support the CDDQ as a valid tool for assessing difficulties in career decision making. The CDDQ was expected to be negatively correlated with the CASVE-CQ, supporting discriminant validity among career decision-making difficulties and higher levels of career decision-making task completion.

The vocational identity (VI) subscale of the My Vocational Situation (MVS; Holland et al., 1980) assesses one’s identity or vocational standing and goal stability. It consists of 18 true-false items and is scored by summing the items answered false so that higher scores indicate more stable career goals. One item from the VI subscale of the MVS is, “No single occupation appeals to me strongly.” It has been used in more than 50 studies, and test-retest reliability was found to be moderate while evidence of construct validity was high (Holland et al., 1993). The reliability for the VI subscale ranges from .76 to .86 (Diemer & Blustein, 2007; Holland et al., 1980). Scores on the MVS are correlated with negative career thoughts (Saunders et al., 2000), vocational commitment (Thorbecke & Grotevant, 1982), rational career decision-making styles (Leong & Morris, 1989), and other constructs relevant to the purposes of the present study that link the validity of the MVS in measuring perceived goal stability. The VI subscale of the MVS was expected to be positively correlated with the CASVE-CQ, supporting convergent validity among vocational identity and higher levels of career decision-making task completion.

The Career Commitment Inventory (CCM; Carson & Bedeian, 1994) identifies an individual’s level of career commitment, or meaningful investment, in his or her vocation. Three subscales—career identity, career planning, and career resilience—make up the 12-item measure which can be answered from 1 (strongly disagree) to 5 (strongly agree) with items such as, “My line of work/career field is an important part of who I am.” A maximum total score of 60, which was utilized in this study, indicates higher satisfactory commitment to one’s chosen career. Carson and Bedeian (1994) found internal consistencies of .79–.85 for the CCM. When correlated with another measure of career commitment, years of education, and organization commitment; validity was supported with correlations of .75, .18, and −.05, respectively (Blau, 1985; Carson & Bedeian, 1994). The CCM was expected to be positively correlated with the CASVE-CQ and each phase of the CASVE cycle, supporting convergent validity among career commitment and higher levels of career decision-making task completion.

Participants and Procedure

While the sample was unique, the same procedures were utilized that were described for Study 2. In Study 3, a new sample of 342 participants recruited through MTurk responded to the full survey. These participants identified as female (n = 216), male (n = 123), transgendered (n = 2), genderqueer (n = 1), ranged in age from 18 to 70 (M = 34.6, SD = 10.2), and identified as 1.2% American Indian, 7.3% Asian, 6.1% African American, 3.8% Hispanic, 81% White, or 0.6% other. These participants also indicated that they resided in multiple regions throughout the U.S. (12.6% West, 23.7% Midwest, 24% Northeast, 39.8% South) and 52.7% had a bachelor’s degree or higher. Only 8.2% indicated they were unemployed while the others were employed across a variety of job categories, with the highest percentages in education (11.4%), heath science (7.3%), and finance (6.1%). The majority (64.6%) indicated they were employed in these roles full-time with incomes ranging from under $20 K (22.2%) to over 100 K (6.4%) per year. Participants completed a demographic form and a 42-item version of the CASVE-CQ.

Results

The first goal of Study 3 was to confirm the factor structure found in Study 2 using Mplus version 8.3 (Muthén & Muthén, 2017). The fit of the model data was assessed through multiple fit-indices including Root Mean Square Error of Approximation (RMSEA) at or below .06, comparative fit index (CFI) above .90, goodness of fit index (GFI) above .90, and Tucker-Lewis index (TLI) above .90, which are suggestive of a good fitting model (Bentler, 1990; Hu & Bentler, 1999; Valero & Topa, 2015). The confirmatory factor analysis provided strong evidence for a six-factor structure with good model fit, χ² (804) = 1187.43, p < .001, CFI = .96, TLI = .95, RMSEA = 0.037, with 90% CI: .033 to .042, high items loadings on the factors (see Table 2), and Cronbach alphas at .79 or higher (see Table 3). Factor correlations are also presented in Table 3.

Table 2.

Results from the Confirmatory Factor Analysis of the CASVE-CQ—Study 3 (N = 324).

Item	Factor Loading
Item	1	2	3	4	5	6
1. Communication 1
1. I am overwhelmed by making a career decision.	0.90
2. I have difficulty thinking about my future career goals.	0.90
3. There is pressure in my life to make a career decision.	0.48
4. I have a problem concerning my career path.	0.80
5. I worry about needing to make a career decision.	0.84
6. I feel sad or worried when I think about my need to make a career choice.	0.87
7. I find ways to avoid making a career decision.	0.78
8. The amount of effort it takes to make a career decision is overwhelming.	0.80
2. Analysis
1. I am unsure where to begin to solve my career problem.		0.92
2. I need help identifying my career options.		0.94
3. I need more information about my career options.		0.91
4. I need more information about myself to make the best career choice.		0.67
5. I do not have enough information to compare my career or job options accurately.		0.81
3. Synthesis
1. I can identify many career or job options that match my values.			0.73
2. I can identify multiple jobs that match my career interests.			0.83
3. I can identify multiple career options that match my career-related skills.			0.83
4. I have explored a large amount of career or job options and then narrowed those down to afew I feel good about.			0.78
5. I can narrow my career or job options to a few that I am seriously considering.			0.86
6. I can compare my career or job options based on information I have gathered about them.			0.85
7. I know the strengths and weaknesses of each of my career options based on my own career values.			0.85
8. I have compared the advantages and disadvantages and benefits associated with each of my career options.			0.81
4. Valuing
1. The career options I am considering satisfy my career values.				0.91
2. I have considered the costs and benefits of my career options.				0.70
3. The options I am considering match my values, interests, skills, and preferences.				0.81
4. Who I am (e.g. culture, place in the community and society) fits with the options I am considering.				0.71
5. My career options match my aspirations.				0.82
6. My career choice fits well with my lifestyle.				0.74
7. My career choice is a good match with my personality.				0.75
8. My career choice will enable me to live life in the way I want/prefer.				0.65
5. Execution
1. I am ready to take the necessary steps to reach my career goal.					0.65
2. I am taking the necessary steps to reach my career goal.					0.91
3. I am in the process of achieving my career goals.					0.85
4. I know what I will need to be doing in 6 months from now to reach my career goal.					0.79
5. I have a plan of action to achieve my career goal.					0.88
6. I will try out my top career choice.					0.78
7. I have applied for my top career choice.					0.62
8. I have begun the training and/or education necessary for my top career choice.					0.70
6. Communication 2
1. Even though I have told others I have made my career decision, I do not feel very good about it.						0.69
2. I made a career choice but think I may need to make a new choice.						0.74
3. My career choice has not improved my situation.						0.81
4. The career choice I made has not made me feel any better.						0.96
5. I continue to experience anxiety even though I have made my career choice.						0.86

Note. Loadings below .3 have been suppressed in the table.

p < .001 for all items.

Table 3.

Correlations and Descriptive Information of the CASVE-CQ Subscales and Validity Instruments.

Measures	1	2	3	4	5	6	7	8	9
1. Communication 1	—	.66**	.22**	.33**	.38**	.47**	.64**	−.59**	.39**
2. Analysis		—	.33**	.32**	.38**	.40**	.70**	−.60**	.35**
3. Synthesis			—	.65**	.50**	.13*	.35**	−.38**	.24**
4. Valuing				—	.49**	.28**	.43**	−.40**	.33**
5. Execution					—	.07	.31**	−.38**	.36**
6. Communication 2						—	.54**	−.48**	.30**
7. MVS							—	−.72**	.41**
8. CDDQ								—	−.41**
9. CCM									—
M	3.86	2.50	5.61	6.49	4.97	3.17	8.96	3.78	37.95
SD	2.83	1.89	2.55	2.00	2.54	1.74	5.18	1.34	8.47
α	.86	.82	.85	.80	.83	.79	.89	.93	.79

Note. MVS-My Vocational Situation; CDDQ-Career Decision Making Difficulties Questionnaire; CCM-Career Commitment Measure.

*p < .05. **p < .01.

Due to high factor correlations between Communication 1 and Analysis (r = .806, p < .001) and Synthesis and Valuing (r = .827, p < .001), alternative models were tested with a χ² difference test to see if each of these combinations represented one factor rather than two. The model collapsing Communication 1 and Analysis fit significantly worse, Δχ² (5) = 72.20, p < .05. Similarly, the model collapsing Synthesis and Valuing also fit significantly worse, Δχ² (5) = 75.5, p < .05, therefore the original model was retained. The final retained items and instructions for administering the CASVE-CQ are found in Appendix A.

To assess the utility of the CASVE-CQ as a total score, a second order factor analysis was assessed. In this model, a total factor latent variable was added to the model with the six factor latent variables as indicators, while also retaining the items as indicators of the six factors. The model failed to converge, thus the use of a total score was not supported.

The second goal of Study 3 was to provide evidence for the validity of CASVE-CQ scores. Nearly all CASVE-CQ subscales were positively correlated, with the exception of the Execution and Communication 2 subscales (see Table 3). The expected relationships of the CASVE-CQ subscales to other measures were examined using Pearson product-moment correlations. The proposed inverse relationship between career decision-making difficulties’ and decision making progress was supported across all CASVE-CQ subscales (r = −.38 to −.71, N = 342, p < .01). This supports the concept that greater career decision making task activity or progress is associated with fewer career decision-making difficulties. The proposed positive relationship between vocational identity and the latter decision-making phases was supported (r = .31 to .54, N = 342, p < .01). Yet, all CASVE-CQ subscales positively correlated with the MVS total score, indicating that stability to vocational identity is not solely associated with completion of latter-phase decision-making tasks. The suggested positive correlation between career commitment and decision-making phases of execution and communication 2 was supported (r = .36 and .30, N = 342, p < .01). Yet, all CASVE-CQ subscales were moderately and positively correlated with career commitment, indicating that career commitment is not solely associated with the latest phases of decision making.

Study 3 Discussion

Study 3 confirmed the factor structure of the CASVE-CQ in a sample of adults who were considering a career decision or change. The internal consistencies of all CASVE-CQ subscales were well within acceptable range, and a total factor latent variable, or CASVE-CQ total score, was not supported in this sample. While this could complicate the scoring process of the CASVE-CQ, this finding is consistent with the CIP theory on which the CASVE-CQ is based. When considering decision making as a process that involves movement through sequential phases of the CASVE cycle, a total score would carry little meaning (i.e., a total score would not differentiate between test-takers that had significant activity across many phases out of the recommended order and those that were following the CASVE process of decision making). Given that a total score was not statistically or theoretically supported, future research should address additional scoring methods that take into account both phase-level completion of decision-making tasks, as well as CASVE consistent completion across decision-making phases as clinically and theoretically relevant.

Important next steps in the development of the CASVE-CQ involve additional work on evaluating its validity. While the results reported here provide initial support for validity, the full picture is more complex. Higher levels of CASVE decision-making activity in any phase of the process were associated with lower career decision-making difficulties, more stable vocational identity or career goals, and greater career commitment. Future research could assess differences in client self-report of decision-making progress versus CASVE-CQ scores. CASVE-CQ subscale level validity evidence is also critical to establish. Perhaps the Career Thoughts Inventory could give insight into whether negative thinking occurs more in some phases of decision-making and is important to address with clients in certain stages. Measures that address career decision-making self-efficacy, or other CIP theory-consistent constructs, could provide additional validity evidence for CASVE-CQ scores. Once phase-based scoring thresholds and indicators of whether the phases were engaged in the recommended order are developed, validity of these scores should also be assessed.

General Discussion

The CASVE-CQ is a new self-report measure of career decision-making progress based on CIP theory. It was developed across three unique studies. The samples included pilot work with college students at a mid-sized Southeastern university and adults working in the United States that were considering a career decision. A six factor, theory-consistent structure was confirmed, but a total score was not supported for the CASVE-CQ. As noted, a lack of a total score is consistent with CIP theory and the nature of the decision-making process. Moderate to high internal consistency was found across all six subscales, and correlations with other psychometrically sound career development measures provided some initial support for the convergent and discriminant validity of the new measure. The CASVE-CQ can help clients and practitioners identify the client’s career decision-making status, providing ideas for subsequent tasks to persist in effective decision making or tasks to revisit to enhance the decision-making process. Additionally, the CASVE-CQ can be implemented as a research measure to assess intervention outcomes and other research questions critical to understanding the decision-making process.

The CASVE-CQ development represents a convergence of theory, research, and practice. The CASVE-CQ was conceptualized from a Cognitive Information Processing (CIP) theoretical perspective. The six-factor factor structure that was confirmed was consistent with the CIP supported career decision-making process depicted in the CASVE cycle, adding to the empirical support of the theory. The CIP CASVE cycle is a phase-based approach to career decision making that walks the client from defining the gap between their current and desired state, through their knowledge of self and options, to expanding and narrowing options, consideration of values, execution of a plan, and evaluation of that enacted plan. The CASVE-CQ items reflect tasks or considerations consistent with the theory-based definition of these phases. Although it may complicate the scoring process to some extent, the lack of statistical support for a total CASVE-CQ score reflects the CIP concept that quality career decision making is not composed of a random set of tasks, but a fluid yet planful series of steps that allows one to consider what they know about themselves and their options before acting on a choice.

Additionally, the need for the CASVE-CQ was inspired by practice. Clinicians in training have reported difficulty identifying the decision-making progress of their clients, which effects treatment. Effectively planning interventions or identifying optimal supports for clients is critically linked to where they are in their career development process. Clinicians are likely more effective when they know if their clients still need to learn more about themselves (i.e., the Analysis phase) or if they are ready to apply for jobs (i.e., Execution phase). The CASVE-CQ allows both the client and clinician to pinpoint CASVE phases in which the client has been active. If important aspects of the decision-making process have been overlooked or not yet engaged, such as expanding options to consider (i.e., Synthesis phase), the subscale scores indicate this intervention need for the client. To summarize, research to develop the CASVE-CQ was driven by a clinician-indicated practice need, and the research was guided by an existing and established theoretical structure.

Limitations and Future Directions

There are a number of limitations in the present studies, and the results highlight several opportunities for future directions in the continued development and application of the CASVE-CQ. Continued development of the CASVE-CQ is necessary to assure best practice with the measure, and future work could address gathering additional validity evidence. The authors suggest exploring relationships between the CASVE-CQ and career decision-making self-efficacy and other measures that also are consistent with Cognitive Information Processing Theory, such as the Career Thoughts Inventory (Sampson et al., 1996). Also critical to the CASVE-CQ’s development is a better understanding of the ideal scoring method. Given that deriving a total score is not practically, theoretically, or statistically applicable, a method is needed for assessing progress or task completion within a CASVE phase, as well as progress across phases. In other words, the optimal approach for scoring and phase completion thresholds still need to be established. For instance, it is unlikely anyone will need to complete all tasks or concepts associated with every item in a phase. Yet, it is important to determine the minimum number of completed tasks to consider the phase fully addressed or complete. An indicator must be identified to evaluate the importance of client completed decision making tasks in the phase order suggested by the CASVE cycle to aid in intervention planning. Perhaps there are also response patterns that differ based on the decision being considered (e.g., change of college major vs change of paid, full-time employment). Additionally, studying the CASVE-CQ’s use in a practice setting will be another critical step in the measure’s development. Career decision-making as a skill highlights the importance of better understanding the development and utility of a measure across the lifespan from high school through retirement.

There are also some limitations with regard to our participant samples. Reservations have been expressed in the literature with the use of MTurk for participant sampling. While it is hoped much of this is mitigated by the use of validity checks (Meade & Craig, 2012), other concerns remain. While the college student sample in Study 1 represented some balance between those participants identifying as African American and White, few other minorities were represented, and women were overrepresented. Women and White identifying individuals were overrepresented in both MTurk samples. This creates questions as to whether the CASVE-CQ would operate similarly in minority populations or specific contexts where career assistance is often sought (e.g., university career centers, community adults in job skills training program). Additionally, some literature has indicated that those who respond to MTurk surveys may differ from the general population in a variety of ways (Paolacci & Chandler, 2014). Efforts to explore the structure and validity of the CASVE-CQ in minority populations, various career service programs, and non-MTurk samples is warranted.

Overall, the CASVE-CQ represents an important contribution to the career development field, as well as to decision-making science. While many measures provide important information for research and client career development, no other measure addresses how one is progressing through the career decision-making process. Monitoring, encouraging, and intervening on decision-making progress is a critical role of career practitioners. Having a psychometrically sound theory-driven means of assessing clients’ career decision-making progress will illuminate the successes and needs of clients, as well as guide the intervention strategies of practitioners. Our field regularly calls for work that integrates theory, research, and practice; the development and use of the CASVE-CQ is an exemplar of this ideal.

Footnotes

Appendix A

Authors’ Note

This article is based on the thesis and dissertation completed by Werner (2017, 2019).

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Emily Bullock-Yowell

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