Free
Research Article
Issue Date: November 09, 2015
Published Online: November 09, 2015
Updated: January 01, 2020
Development and Psychometric Evaluation of the Vocational Fit Assessment (VFA)
Author Affiliations
  • Andrew C. Persch, PhD, OTR/L, is Assistant Professor, Division of Occupational Therapy, The Ohio State University, Columbus; andrew.persch@osumc.edu
  • P. Cristian Gugiu, PhD, is Assistant Professor, Quantitative Research, Evaluation, and Measurement, The Ohio State University, Columbus
  • James A. Onate, PhD, AT, ATC, FNATA, is Associate Professor, Division of Athletic Training, The Ohio State University, Columbus
  • Dennis S. Cleary, MS, OTD, OTR/L, is Assistant Professor, Division of Occupational Therapy, The Ohio State University, Columbus
Article Information
Assessment Development and Testing / Rehabilitation, Participation, and Disability
Research Article   |   November 09, 2015
Development and Psychometric Evaluation of the Vocational Fit Assessment (VFA)
American Journal of Occupational Therapy, November 2015, Vol. 69, 6906180080. https://doi.org/10.5014/ajot.2015.019455
American Journal of Occupational Therapy, November 2015, Vol. 69, 6906180080. https://doi.org/10.5014/ajot.2015.019455
Abstract

OBJECTIVE. The objective of this study was to determine the psychometric properties of the Vocational Fit Assessment (VFA) by examining its factor structure and subscale reliability.

METHOD. This prospective cross-sectional study used two surveys (one for worker abilities and one for job demands) to collect the data needed for the psychometric evaluation of the VFA. Latent parallel analysis and ordinal exploratory factor analysis were used to iteratively refine VFA subscales.

RESULTS. Ten unidimensional subscales emerged from factor analysis of VFA items: (1) Cognitive Abilities, (2) Communication Skills, (3) Computer Skills, (4) Higher Task-Related Abilities, (5) Interpersonal Skills, (6) Lower Task-Related Abilities, (7) Physical Abilities, (8) Safety, (9) Self-Determination, and (10) Work Structure. Subscale internal consistency (ordinal α) was ≥.86 for VFA for worker abilities and ≥.77 for VFA for job demands.

CONCLUSION. The unidimensional structure of VFA subscales and estimates of internal consistency lend initial evidence in support of their reliability and validity.

People with disabilities experience decreased rates of employment (U.S. Bureau of Labor Statistics, 2013), socioeconomic status (Butterworth et al., 2013), health (Petrovski & Gleeson, 1997; Ross & Mirowsky, 1995), and quality of life (Kober & Eggleton, 2005) compared with those without disabilities. These decreases are especially true for the 3% of the U.S. population who live with an intellectual disability (U.S. Department of Health and Human Services, 2012), a disorder characterized by decreased independence and intelligence (Wehman, 2013). Support services for people with intellectual disabilities are available through various private, nonprofit, and public organizations. However, the societal costs of these supports are substantial. The Centers for Disease Control and Prevention (2006)  estimated that the total costs (e.g., decreased productivity, health care, support services) to support a person with an intellectual disability over his of her lifetime exceed $1 million. The unemployment and underemployment of people with intellectual disabilities contribute substantially to these costs. Unfortunately, most services for this group fail to facilitate a successful transition from school to integrated, community employment.
“Job matching is the collaborative, data-based decision making process used by transition teams to determine the best fit between an individual’s abilities and preferences and the job’s environmental and occupational demands” (Persch et al., in press). Recent research demonstrates that current job-matching practices within the U.S. special education and vocational rehabilitation systems are collaborative, multidimensional, and highly variable (Persch et al., in press). Similarly, job-matching research emphasizes the variety of practices of professionals engaged in assessment of workers’ abilities and job demands (Daston, Riehle, & Rutkowski, 2012; Kilsby & Beyer, 2002; Morgan, 2008, 2011).
The Self-Directed Search (SDS; Holland, 1997), Job Match Pattern (Swenson, 2000), and Your Employment Selections (Morgan, 2008, 2011) assessments all purport to facilitate and inform the job-matching process. The SDS is a test of career interests in which results are presented in a format that enables cross-referencing with occupations that match a person’s personality type. The Job Match Pattern assesses mental abilities, interests, and 24 personality factors with ratings that are used informally to create a pattern for each person or job assessed. The hiring authority can then use this information to evaluate how well a person matches a given job. Your Employment Selections is an Internet-based, career preference assessment designed for people with limited reading capacity. Users watch videos that demonstrate and describe up to 120 different jobs. Preferred jobs are selected and compared with the results of a skills assessment conducted by a jobs professional. Despite the benefits of these tools, none of them provide a method for informing job-matching decisions based on the abilities of workers and demands of jobs.
Systematic procedures that increase the reliability, effectiveness, and efficiency of job-matching practices are needed to improve postsecondary employment outcomes for people with intellectual disabilities. Toward this end, the Transition, Employment, and Technology (TET) Lab at The Ohio State University developed the Vocational Fit Assessment (VFA). The VFA is the result of a 5-yr process of iterative instrument development and refinement. A secondary analysis of the National Longitudinal Transition Study–2 (NLTS2; U.S. Department of Education, 2009) conducted by the TET Lab indicated that nearly 70% of the work done by youth and young adults with disabilities occurs in clerical, custodial, food service, retail, trade, and manufacturing sectors. These data were coded using the Standard Occupational Classification (SOC) system, enabling cross-referencing with the Occupational Information Network (ONET). Using SOC codes to access ONET data, TET Lab staff identified the task demands for 153 different jobs. Transformation of task demands to instrument items resulted in a preliminary item pool of 2,970 items. These items were piloted, refined, and subjected to expert review over a period of 24 mo, a process that resulted in winnowing of the item pool to 126 items.
In consideration of the factors that influence vocational performance, the VFA was designed to assess individual worker abilities, assess job demands, identify the pros and cons of each potential job match, and identify areas of need that are suitable for intervention. It was therefore necessary to tailor item presentation for each of two VFA applications (i.e., the VFA for worker abilities [VFA–W] and the VFA for job demands [VFA–J]). Each VFA item is composed of a unique combination of a tailored instructional prompt, common item stem, and customized rating scale (Supplemental Figure 1; available online at http://otjournal.net; navigate to this article, and click on “Supplemental”). For the VFA–W, the prompt reads, “To what degree does the worker demonstrate the ability to,” and for the VFA–J, it reads, “To what degree does the job demand the ability to.” These prompts are followed by a common item stem. VFA–W uses a 3-point ordinal scale of ability (2 = high ability, 1 = some ability, 0 = low ability), and the VFA–J uses a 3-point ordinal scale of demand (2 = high demand, 1 = some demand, 0 = low demand).
Procedurally, the results of a single VFA–W are intended to be compared with data from multiple VFA–Js. This comparison is based on a validated clinical reasoning algorithm (Persch, 2014). The outputs of this comparative algorithm are the pros and cons of each potential job match and the gap between worker abilities and job demands (Figure 1). Pros reflect alignment of job demands and worker abilities; cons reflect misalignment of job demands and worker abilities; and the gap is the area for intervention, which may target skill development or environmental modification.
Figure 1.
Example of a vocational fit chart for a food service job.
The chart depicts the pros, cons, and areas for intervention for each potential job match that are the results of comparing one worker’s abilities, measured using the VFA–W, with the demands of one job, measured using the VFA–J. The numbers within each wedge of the chart reflect the number of common item stems modeled as pros, cons, or areas for intervention.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities. Actual vocational fit charts present pros in green, cons in red, and areas for intervention in yellow.
Figure 1.
Example of a vocational fit chart for a food service job.
The chart depicts the pros, cons, and areas for intervention for each potential job match that are the results of comparing one worker’s abilities, measured using the VFA–W, with the demands of one job, measured using the VFA–J. The numbers within each wedge of the chart reflect the number of common item stems modeled as pros, cons, or areas for intervention.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities. Actual vocational fit charts present pros in green, cons in red, and areas for intervention in yellow.
×
The lineage of items in NLTS2, SOC, and ONET data lends substantial credibility to the content validity of the VFA. In addition, a panel of experts found that VFA–W and VFA–J items have high content relevance and appropriate language (Persch, 2014). These experts also noted when items were good, bad, easy, hard, superfluous, or missing something. These data were used to further refine the VFA pool of common item stems. Despite these findings, psychometric evaluation of reliability and validity is necessary before the VFA is used to make job-matching decisions. Accordingly, the purpose of this study was to further develop the VFA by determining the factor structure of its items and obtaining estimates of subscale reliability.
Method
Participants
Participants were Project SEARCH professionals (e.g., teachers, job coaches, employers) involved in the job-matching process. Project SEARCH prepares young adults with disabilities for competitive, integrated employment in community settings (Daston et al., 2012). Students enrolled in Project SEARCH programs across the country rotate through three internships during their final year of high school. Project SEARCH professionals supplement these internships with intensive training in work and functional skills. This level of service is made possible by the unique consolidation of resources (i.e., fiscal, social) from local school districts, employers, departments of developmental disabilities, and state vocational rehabilitation agencies. This best-practice, collaborative, business-led internship program is attractive to students, families, funders, and other stakeholders because its graduates consistently achieve competitive employment at above-average rates (Wehman et al., 2013, 2014). Project SEARCH professionals are representative of the target population (i.e., stakeholders involved in job matching) and were recruited to participate in this study.
Study Design and Data Collection
This prospective cross-sectional study used two electronically administered surveys to collect data for psychometric evaluation of the VFA–W and VFA–J. Surveys were administered online using SurveyMonkey (Palo Alto, CA) between February and April 2014. SurveyMonkey is a secure, encrypted, online tool for collecting survey data that is approved by the institutional review board. Each survey was designed to address the research questions and included two parts: demographic questions and either VFA–W or VFA–J items.
Data Analysis
Data from the surveys were analyzed using multiple, complementary psychometric techniques. This iterative analytical plan was designed to answer the research questions by revealing the factor structure and reliability of VFA subscales and included the following procedures (Figure 2). First, the principal investigator (Persch) evaluated VFA common item stem factor structure by reviewing all 126 common item stems and categorizing similar item stems into hypothetical subscales that could be tested using statistical techniques (McDonald, 1999). Categorization decisions were based on the job-matching process, worker abilities, and job demands.
Figure 2.
Data analysis plan using latent parallel analysis and ordinal exploratory factor analysis.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Figure 2.
Data analysis plan using latent parallel analysis and ordinal exploratory factor analysis.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
×
Second, the dimensionality of hypothesized VFA common item subscales was evaluated and iteratively refined using latent parallel analysis (LPA) and ordinal exploratory factor analysis (OEFA). The purpose of LPA and OEFA was to develop unidimensional VFA–W and VFA–J subscales that contain the same common item stems.
LPA was conducted on all hypothesized VFA–W and VFA–J subscales to inform factor retention decisions (Hayton, Allen, & Scarpello, 2004). This analysis is appropriate when polytomous data arise from normally distributed continuous latent variables (Gugiu, Coryn, & Applegate, 2010), as is the case for worker abilities and job demands. The accuracy of LPA is superior to other methods (e.g., K1, scree plot) of factor extraction (Hayton et al., 2004). The polychoric correlation (Jöreskog, 1994) matrix generated during LPA was then used as input for OEFA (Muthen & Muthen, 2012). The results of LPA were also used to define OEFA parameters such as the number of factors to retain (Hayton et al., 2004) and method of rotation (Costello & Osborne, 2005). The Promax method (Hendrickson & White, 1964) of oblique rotation was used when LPA indicated a multifactor solution. Items that failed to load onto a single-factor solution were removed from the analyses and returned to the developmental item bank (Tabachnick & Fidell, 2000). Factor analytical procedures were discontinued once a consistent unidimensional structure was identified for each of the hypothesized subscales.
Third, post hoc tests of reliability were conducted on the VFA–W and VFA–J using ordinal α, a nonparametric alternative to Cronbach’s α (Gugiu et al., 2010). Ordinal α, like Cronbach’s α, provides a psychometrically sound estimate of internal consistency (Nunnally & Bernstein, 1994). The minimum reliability of each subscale was estimated by calculating the lower bound of a one-sided, 90% confidence interval for ordinal α.
Results
Participant Demographics
A total of 166 participants completed the VFA–W survey. Participants were predominantly White (88.0%) women (81.9%) of working age (99.4%). Primary occupations included teachers (38.8%), job coaches (23.7%), and job developers (10.6%). Participants found employment in high schools (40.7%), in postsecondary education institutions (21.7%), and with community rehabilitation providers (10.1%). Participants reported an average of 8.21 (σ = 7.63) yr of experience supporting people with disabilities in transition to adulthood.
A total of 105 participants completed the VFA–J survey. Like those who completed the VFA–W, participants were predominantly White (90.6%) women (84.4%) of working age (99.0%). Primary occupations included teachers (39.3%), job coaches (23.6%), and job developers (10.7%). Participants found employment in high schools (42.5%), in postsecondary education institutions (19.5%), and with community rehabilitation providers (13.3%). Participants reported an average of 8.56 (σ = 7.66) yr of experience supporting people with disabilities in transition to adulthood. See Table 1 for all demographic information.
Table 1.
Participant Demographics
Participant Demographics×
Group, %
CharacteristicVFA–W (n = 166)VFA–J (n = 105)
Age
 18–2415.76.3
 25–3419.328.1
 35–4418.119.8
 45–5423.525.0
 55–6422.919.8
 65–740.60.01
Gender
 Female81.984.4
 Male18.115.6
Race
 American Indian or Alaska Native1.20.0
 Black or African-American6.06.3
 Hispanic4.21.0
 Native Hawaiian or other Pacific Islander0.62.1
 White88.090.6
Primary occupationa
 Student11.43.6
 Caregiver or parent0.40.0
 Teacher38.839.3
 Related services professional1.21.4
 Job developer10.610.7
 Vocational rehabilitation professional1.62.1
 Rehabilitation professional1.20.0
 Employer0.41.4
 Advocate4.17.1
 Special education assistant0.80.7
 Special education administrator1.22.1
 Job coach23.723.6
Place of employmentb
 Middle school0.50.9
 High school40.742.5
 Postsecondary education institution21.719.5
 Vocational rehabilitation agency4.24.4
 Community rehabilitation provider10.113.3
 Private business22.219.5
Table Footer NoteNote. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.×
Table Footer NoteaSome nominal response categories (e.g., academic) have been omitted.
Some nominal response categories (e.g., academic) have been omitted.×
Table Footer NotebSome nominal response categories (e.g., county board of developmental disabilities) have been omitted.
Some nominal response categories (e.g., county board of developmental disabilities) have been omitted.×
Table 1.
Participant Demographics
Participant Demographics×
Group, %
CharacteristicVFA–W (n = 166)VFA–J (n = 105)
Age
 18–2415.76.3
 25–3419.328.1
 35–4418.119.8
 45–5423.525.0
 55–6422.919.8
 65–740.60.01
Gender
 Female81.984.4
 Male18.115.6
Race
 American Indian or Alaska Native1.20.0
 Black or African-American6.06.3
 Hispanic4.21.0
 Native Hawaiian or other Pacific Islander0.62.1
 White88.090.6
Primary occupationa
 Student11.43.6
 Caregiver or parent0.40.0
 Teacher38.839.3
 Related services professional1.21.4
 Job developer10.610.7
 Vocational rehabilitation professional1.62.1
 Rehabilitation professional1.20.0
 Employer0.41.4
 Advocate4.17.1
 Special education assistant0.80.7
 Special education administrator1.22.1
 Job coach23.723.6
Place of employmentb
 Middle school0.50.9
 High school40.742.5
 Postsecondary education institution21.719.5
 Vocational rehabilitation agency4.24.4
 Community rehabilitation provider10.113.3
 Private business22.219.5
Table Footer NoteNote. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.×
Table Footer NoteaSome nominal response categories (e.g., academic) have been omitted.
Some nominal response categories (e.g., academic) have been omitted.×
Table Footer NotebSome nominal response categories (e.g., county board of developmental disabilities) have been omitted.
Some nominal response categories (e.g., county board of developmental disabilities) have been omitted.×
×
Theoretical Categorization
In accordance with the concept of content validity (McDonald, 1999), VFA analysis began with grouping of common item stems into hypothesized subscales. Categorical decision making was based on a thorough review of the literature; content area expertise; and knowledge of worker abilities, job demands, and the job-matching process. This procedure resulted in the development of eight hypothesized subscales, each representing a portion of the global content domain, the work of people with disabilities: (1) Physical Abilities, (2) Self-Determination, (3) Physical Environment, (4) Interpersonal Relationships, (5) Task-Related Abilities, (6) Work Structure, (7) Cognitive Abilities, and (8) Computer Skills.
Dimensionality and Factor Structure
Iterative psychometric evaluation of the VFA–W and VFA–J surveys began after completion of the theoretical and descriptive analyses. The multistep process of LPA was conducted on each hypothesized VFA–W (n = 166) subscale using SAS software (Version 9.3; SAS Institute, Cary, NC). First, a polychoric correlation matrix was created based on VFA–W data for selected items. Second, the polychoric correlation matrix was used as the input for LPA. Third, using Monte Carlo simulations, we created 250 random datasets to match the number of respondents (n = 166) and frequency distribution of hypothesized subscale items (Gugiu, Coryn, Clark, & Kuehn, 2009). Finally, the LPA program computed, extracted, and compared eigenvalues for the real and random datasets. Real factors whose eigenvalues exceeded random values were retained.
The results of LPA (i.e., number of factors to retain) were used to specify OEFA of hypothesized subscales for VFA–W data. According to common factor analytical conventions, subscales were named using key words from the highest loading items for each factor. Items with factor loadings less than .30 were removed (Costello & Osborne, 2005), and analyses were repeated. No scale required more than one revision. These analyses were replicated in tandem using VFA–J data. This iterative, analytical procedure resulted in the development of 10 subscales (Table 2) with parallel factor structures; that is, final VFA–W subscales contained the same common item stems as final VFA–J subscales (see Supplemental Figures 2–11 and Supplemental Tables 1–10; available online at http://otjournal.net).
Table 2.
Reliability Determination for VFA Subscales
Reliability Determination for VFA Subscales×
VFA–WVFA–J
SubscalekOrdinal αMink*Ordinal αMink*
Cognitive Abilities6.89.778.78.5311
Communication Skills6.91.807.85.678
Computer Skills16.99.9814.91.8516
Higher Task-Related Abilities8.92.848.85.7010
Interpersonal Skills6.86.697.82.609
Lower Task-Related Abilities8.93.868.88.769
Physical Abilities10.91.8310.86.7411
Safety7.86.708.86.708
Self-Determination11.95.9110.92.8511
Work Structure6.88.757.77.5111
Table Footer NoteNote. k = number of items per subscale; k* = number of items required to achieve at least .80 reliability, computed using the Spearman–Brown prophecy formula; Min = minimum reliability for each subscale; VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Note. k = number of items per subscale; k* = number of items required to achieve at least .80 reliability, computed using the Spearman–Brown prophecy formula; Min = minimum reliability for each subscale; VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.×
Table 2.
Reliability Determination for VFA Subscales
Reliability Determination for VFA Subscales×
VFA–WVFA–J
SubscalekOrdinal αMink*Ordinal αMink*
Cognitive Abilities6.89.778.78.5311
Communication Skills6.91.807.85.678
Computer Skills16.99.9814.91.8516
Higher Task-Related Abilities8.92.848.85.7010
Interpersonal Skills6.86.697.82.609
Lower Task-Related Abilities8.93.868.88.769
Physical Abilities10.91.8310.86.7411
Safety7.86.708.86.708
Self-Determination11.95.9110.92.8511
Work Structure6.88.757.77.5111
Table Footer NoteNote. k = number of items per subscale; k* = number of items required to achieve at least .80 reliability, computed using the Spearman–Brown prophecy formula; Min = minimum reliability for each subscale; VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Note. k = number of items per subscale; k* = number of items required to achieve at least .80 reliability, computed using the Spearman–Brown prophecy formula; Min = minimum reliability for each subscale; VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.×
×
Nonparametric estimates of internal consistency obtained by calculating ordinal α ranged from .86 to .99 and from .77 to .92 for the VFA–W and VFA–J subscales, respectively. The lower bound of the 90% confidence interval for ordinal α revealed that the minimum reliability was ≥.69 for the VFA–W and ≥.51 for the VFA–J. Based on these statistics, the Spearman–Brown prophecy formula (Brown, 1910; Spearman, 1910) was used to determine the number of items to add or delete from each subscale to achieve a minimum of .80 reliability. The results of these analyses are reported in Table 2.
Discussion
The VFA provides the first job-matching instrument capable of collecting data on individual worker abilities that is congruent with data collected on job demands. These data, which are unique within the broader field of transition assessment, are integrated using a validated comparative algorithm (Persch, 2014). This process systematically identifies the pros, cons, and areas of intervention for each potential job match. The purpose of this prospective, cross-sectional, psychometric evaluation of the VFA was to determine the factor structure of VFA items and obtain estimates of subscale reliability.
The work of people with disabilities is a complex and dynamic domain, evident from the multiple, unidimensional subscales that emerged from the VFA data. Despite this complexity, the new VFA subscales represent several constructs that are logically related to the work of people with disabilities. Self-determination is a well-established predictor of postsecondary employment and independence (Wehman, 2013). Similarly, the Communication and Interpersonal Skills subscales are thematically linked with the construct of social competence (Carter, Moss, Hoffman, Chung, & Sisco, 2011). It is encouraging that the psychometric techniques used here showed that these subscales have high reliability (internal consistency ≥.80) and strong, positive factor loadings ≥.40. Together, these data contribute preliminary evidence of construct validity for these subscales.
It is also encouraging that the evidence of reliability and validity were strong for the Computer Skills subscale. Computer skills represent an aspect of the content domain that may be distinct from traditional types of employment (e.g., clerical, custodial, retail) mentioned previously. In response to the rapidly changing information technology environment, emphasis on developing the computer literacy and skills of people with disabilities has increased (Bruyère, Erickson, & VanLooy, 2006). Computer skills may make some people with disabilities competitive applicants for jobs that would otherwise not be available to them. For example, Aspiritech is a Chicago-based information technology company that employs people with autism as software testing engineers (Mottron, 2011). This model is based on the successes of Specialisterne, a Danish technology company with a more than 10-yr history of employing people with autism and other similar disabilities (Wareham & Sonne, 2008). The use of the Computer Skills subscale to inform job-matching decisions made for, or on behalf of, people with disabilities may help jobs professionals match these nontraditional jobs with clients. In addition, these data would identify areas in which targeted interventions could enhance computer skills, and by consequence, overall employability.
Physical abilities are also highly relevant to job matching. The Functional Capacity Evaluation (Isernhagen, 1992) is a hallmark of occupational rehabilitation and work hardening programs. Similarly, VFA physical abilities items assess individual capacity to perform several work-related movements. Integration of VFA–W data on the physical abilities of workers with VFA–J data on the physical demands of jobs is likely to yield informative data on the pros and cons of potential job matches. Task-Related Abilities and Safety subscales demonstrated moderate reliability; these constructs would benefit from the development of additional items and repeated psychometric evaluation. Work Structure and Cognitive Abilities subscales are each composed of six items. Although this number is enough to constitute a viable factor, these subscales would benefit from ongoing development.
The process of instrument development and psychometric evaluation never ends because there are always revisions to be made or new items to be tested. However, given the strong performance of the Self-Determination, Physical Abilities, Communication Skills, Interpersonal Skills, and Computer Skills subscales, employment professionals are encouraged to begin to use these items to inform job-matching decisions. The Cognitive Abilities, Safety, Work Structure, and Task-Related Abilities subscales show tremendous promise and may be used informally to help inform matching decisions.
This psychometric evaluation has both strengths and limitations. Limitations include homogeneity of the sample—survey respondents were predominantly White, female, teachers, and job coaches—which limits the ability to generalize results. Similarly, the data on Project SEARCH workers and jobs reported here represent only a portion of the abilities demonstrated by people with disabilities and of the demands of the jobs they pursue. Although Project SEARCH students have severe intellectual and developmental disabilities, the VFA is less appropriate for people with more severe or profound disabilities.
The length of the VFA as tested here was 126 items, which was in addition to the demographic and descriptive questions asked of participants. We estimate that it would take 30–60 min for participants to complete the VFA. Because no direct benefits were offered to participants, the time required for the study may have deterred the participation of others, which resulted in a sample size that was smaller than originally hoped for. Despite this limitation, the planned psychometric analyses of the VFA data were sufficiently powered; that is, we collected at least 10 VFA–W survey responses for each subscale item (Nunnally & Bernstein, 1994).
The analysis of VFA–W (i.e., individual abilities) and VFA–J (i.e., environmental demands) data in tandem may also be viewed as a limitation of this study. However, we believe that this feature of the analytical plan is actually an innovation in the field of transition assessment. The VFA is the first systematic assessment of both individual abilities and environmental demands to yield congruent data, a feature that enables direct, replicable comparisons of abilities and demands.
Study strengths include a strong content domain, rigorous psychometric design, and instrument implementation for and sampling of both individual abilities and environmental demands. The analytical plan is strengthened by use of LPA, OEFA, and ordinal α. These techniques acknowledge the ordinal nature of VFA data and provide a way to accurately analyze these data. Other measures with polytomous scales would benefit from the use of these psychometric tools. Further research is needed to evaluate VFA item characteristics, refine subscales, and test the clinical utility of using the VFA to inform job-matching decisions.
Implications for Occupational Therapy Practice
Occupational therapy practitioners are important members of the transition team and may benefit from keeping the following findings from this study in mind when engaged in the job-matching process:
  • The VFA is a promising new decision-support tool for informing job-matching decisions in special education and vocational rehabilitation.

  • The VFA contains factors that are known to be relevant to postsecondary success.

  • Comparison of worker abilities with job demands results in identification of the pros, cons, and areas for intervention of each potential job match.

Conclusion
The VFA was designed to assess individual abilities and job demands. The psychometric evaluation presented here provides evidence of VFA internal consistency and construct validity. Data arising from the VFA are passed through a validated clinical reasoning algorithm and result in the pros, cons, and areas for intervention of each potential job match. These job-matching reports may provide useful data for key stakeholders engaged in the job-matching process and may result in improved employment outcomes.
Acknowledgments
For Jane Case-Smith, a scholar, servant-leader, and friend of the highest order. We are forever grateful for your advising, mentoring, and friendship.
Portions of the data described in this article were presented at the 95th AOTA Annual Conference & Expo, Nashville, TN, April 2015.
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Figure 1.
Example of a vocational fit chart for a food service job.
The chart depicts the pros, cons, and areas for intervention for each potential job match that are the results of comparing one worker’s abilities, measured using the VFA–W, with the demands of one job, measured using the VFA–J. The numbers within each wedge of the chart reflect the number of common item stems modeled as pros, cons, or areas for intervention.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities. Actual vocational fit charts present pros in green, cons in red, and areas for intervention in yellow.
Figure 1.
Example of a vocational fit chart for a food service job.
The chart depicts the pros, cons, and areas for intervention for each potential job match that are the results of comparing one worker’s abilities, measured using the VFA–W, with the demands of one job, measured using the VFA–J. The numbers within each wedge of the chart reflect the number of common item stems modeled as pros, cons, or areas for intervention.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities. Actual vocational fit charts present pros in green, cons in red, and areas for intervention in yellow.
×
Figure 2.
Data analysis plan using latent parallel analysis and ordinal exploratory factor analysis.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Figure 2.
Data analysis plan using latent parallel analysis and ordinal exploratory factor analysis.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
×
Table 1.
Participant Demographics
Participant Demographics×
Group, %
CharacteristicVFA–W (n = 166)VFA–J (n = 105)
Age
 18–2415.76.3
 25–3419.328.1
 35–4418.119.8
 45–5423.525.0
 55–6422.919.8
 65–740.60.01
Gender
 Female81.984.4
 Male18.115.6
Race
 American Indian or Alaska Native1.20.0
 Black or African-American6.06.3
 Hispanic4.21.0
 Native Hawaiian or other Pacific Islander0.62.1
 White88.090.6
Primary occupationa
 Student11.43.6
 Caregiver or parent0.40.0
 Teacher38.839.3
 Related services professional1.21.4
 Job developer10.610.7
 Vocational rehabilitation professional1.62.1
 Rehabilitation professional1.20.0
 Employer0.41.4
 Advocate4.17.1
 Special education assistant0.80.7
 Special education administrator1.22.1
 Job coach23.723.6
Place of employmentb
 Middle school0.50.9
 High school40.742.5
 Postsecondary education institution21.719.5
 Vocational rehabilitation agency4.24.4
 Community rehabilitation provider10.113.3
 Private business22.219.5
Table Footer NoteNote. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.×
Table Footer NoteaSome nominal response categories (e.g., academic) have been omitted.
Some nominal response categories (e.g., academic) have been omitted.×
Table Footer NotebSome nominal response categories (e.g., county board of developmental disabilities) have been omitted.
Some nominal response categories (e.g., county board of developmental disabilities) have been omitted.×
Table 1.
Participant Demographics
Participant Demographics×
Group, %
CharacteristicVFA–W (n = 166)VFA–J (n = 105)
Age
 18–2415.76.3
 25–3419.328.1
 35–4418.119.8
 45–5423.525.0
 55–6422.919.8
 65–740.60.01
Gender
 Female81.984.4
 Male18.115.6
Race
 American Indian or Alaska Native1.20.0
 Black or African-American6.06.3
 Hispanic4.21.0
 Native Hawaiian or other Pacific Islander0.62.1
 White88.090.6
Primary occupationa
 Student11.43.6
 Caregiver or parent0.40.0
 Teacher38.839.3
 Related services professional1.21.4
 Job developer10.610.7
 Vocational rehabilitation professional1.62.1
 Rehabilitation professional1.20.0
 Employer0.41.4
 Advocate4.17.1
 Special education assistant0.80.7
 Special education administrator1.22.1
 Job coach23.723.6
Place of employmentb
 Middle school0.50.9
 High school40.742.5
 Postsecondary education institution21.719.5
 Vocational rehabilitation agency4.24.4
 Community rehabilitation provider10.113.3
 Private business22.219.5
Table Footer NoteNote. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Note. VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.×
Table Footer NoteaSome nominal response categories (e.g., academic) have been omitted.
Some nominal response categories (e.g., academic) have been omitted.×
Table Footer NotebSome nominal response categories (e.g., county board of developmental disabilities) have been omitted.
Some nominal response categories (e.g., county board of developmental disabilities) have been omitted.×
×
Table 2.
Reliability Determination for VFA Subscales
Reliability Determination for VFA Subscales×
VFA–WVFA–J
SubscalekOrdinal αMink*Ordinal αMink*
Cognitive Abilities6.89.778.78.5311
Communication Skills6.91.807.85.678
Computer Skills16.99.9814.91.8516
Higher Task-Related Abilities8.92.848.85.7010
Interpersonal Skills6.86.697.82.609
Lower Task-Related Abilities8.93.868.88.769
Physical Abilities10.91.8310.86.7411
Safety7.86.708.86.708
Self-Determination11.95.9110.92.8511
Work Structure6.88.757.77.5111
Table Footer NoteNote. k = number of items per subscale; k* = number of items required to achieve at least .80 reliability, computed using the Spearman–Brown prophecy formula; Min = minimum reliability for each subscale; VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Note. k = number of items per subscale; k* = number of items required to achieve at least .80 reliability, computed using the Spearman–Brown prophecy formula; Min = minimum reliability for each subscale; VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.×
Table 2.
Reliability Determination for VFA Subscales
Reliability Determination for VFA Subscales×
VFA–WVFA–J
SubscalekOrdinal αMink*Ordinal αMink*
Cognitive Abilities6.89.778.78.5311
Communication Skills6.91.807.85.678
Computer Skills16.99.9814.91.8516
Higher Task-Related Abilities8.92.848.85.7010
Interpersonal Skills6.86.697.82.609
Lower Task-Related Abilities8.93.868.88.769
Physical Abilities10.91.8310.86.7411
Safety7.86.708.86.708
Self-Determination11.95.9110.92.8511
Work Structure6.88.757.77.5111
Table Footer NoteNote. k = number of items per subscale; k* = number of items required to achieve at least .80 reliability, computed using the Spearman–Brown prophecy formula; Min = minimum reliability for each subscale; VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.
Note. k = number of items per subscale; k* = number of items required to achieve at least .80 reliability, computed using the Spearman–Brown prophecy formula; Min = minimum reliability for each subscale; VFA–J = Vocational Fit Assessment for job demands; VFA–W = Vocational Fit Assessment for worker abilities.×
×
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