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Mil Psychol. 2020; 32(1): 111–126.
Published online 2020 Feb 4. doi:10.1080/08995605.2019.1652483
PMCID: PMC10013511
PMID: 38536353
James F. Johnson,a Sophie Romay,a and Laura G. Barronb
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ABSTRACT
This article describes development and validation of a web-based vocational interest tool designed to help recruits and re-trainees identify enlisted career fields that match their preferences for work contexts, activities, and functional roles in support of the Air Force mission. The tool has recently been implemented for use by members considering re-training, and is undergoing pilot testing for potential use in the recruiting process. We first describe how the AF-WIN was developed, based on adaptation of the taxonomy from a Navy vocational interest tool (Navy’s Job Opportunities in the Navy [JOIN]), followed by surveys of subject matter experts (SMEs) in 132 Air Force career fields on relevant job markers. We then describe a validation study in which job incumbents completed the AF-WIN and reported their level of job satisfaction within their current career field; results show that incumbents the AF-WIN algorithm identified as a good match for their career field reported substantially higher levels of job satisfaction than incumbents identified as a relatively poor match based on the tool. Finally, we provide results from initial beta-testing of the tool in a sample of recent enlisted trainees on perceived accuracy, utility, and functionality of the tool for use in the initial job assignment process.
KEYWORDS: Vocational interest, work interest, job satisfaction, person-job fit, military
What is the public significance of this article?---An organization’s effectiveness is based on the people who comprise it. Thus, selection and placement of personnel directly impact that effectiveness. Historically, cognitive testing has been the dominant tool for these purposes. However, cognitive tests have been shown to be limited in predicting elements of success beyond technical proficiency. They do not predict well those aspects of performance which depend on the individual’s motivation to perform well over time, or to remain with the organization over time. For these outcomes, noncognitive attributes such as personality and vocational interests provide critical predictive information. This special issue demonstrates the effectiveness of personality and interest measures in a military context, and how these tools are transforming the military selection and classification process. The effort reported in this issue marks major changes in the selection and classification process, changes that can help both military and civilian organizations be more productive and successful.
Despite nearly a century of vocational interest research and wide use of interest measures in educational planning and occupational counseling, relatively little attention has been paid to vocational interests in the personnel selection literature (Nye, Su, Rounds, & Drasgow, 2012; Van Iddekinge, Putka, & Campbell, 2011). Despite this neglect, meta-analyses of the vocational interest literature demonstrate interest is a valid predictor of task and training performance, Organizational Citizenship Behaviors (OCBs), and Counterproductive Work Behaviors (CWBs) (Nye et al., 2012; Nye, Su, Rounds, & Drasgow, 2017; Van Iddekinge, Roth, Putka, & Lanivich, 2011). Further research shows vocational interest has incremental validity beyond measures of cognitive ability and personality for predicting desirable performance criteria (Van Iddekinge et al., 2011) and job satisfaction (Earl, 2015; Rottinghaus, Hees, & Conrath, 2009). The current study describes the development, validation, and evaluation of perceived accuracy, utility, and usability of an Air Force-specific vocational interest tool, the Air Force Work Interest Navigator (AF-WIN), designed to improve person-job match of recruits to enlisted Air Force careers.
Unlike civilian workforce applicants who voluntarily apply for specific jobs/careers, enlisted U.S. Air Force recruit job assignments are heavily dependent on meeting career-specific minimum cognitive, physical, and medical requirements. As a result, while the Air Force has 130+ enlisted career fields available to recruits, assignment opportunities are constrained by career field requirements, needs of the Air Force, and technical training class availability. The Air Force recruited more than 31,000 enlisted Airmen in FY 2016 (Losey, 2016), many of which are recent high school graduates with negligible job experience and little knowledge of available Air Force careers. Therefore, the Air Force may benefit significantly from systematically assessing and incorporating recruit vocational interest in the enlisted classification process via the AF-WIN.
The major objective of this paper is to present the AF-WIN as a potentially beneficial tool for the enlisted Air Force selection and classification process. We begin by first briefly reviewing the vocational interest and psychology literature, focusing on the contributions made to vocational interest theory by the United States Armed Forces since World War II to date. In Study 1 we detail the development of the AF-WIN taxonomy from initial Navy influence, adaptation, and refinement for Air Force use, and describe how Air Force-specific job profiles were developed for 132 enlisted career fields. In Study 2, we validate whether the AF-WIN survey is predictive of Air Force incumbent job satisfaction using a concurrent validation study approach. Finally, in Study 3, we field test the validated AF-WIN survey tool using a sample of Air Force Basic Military Training (BMT) trainees – individuals currently in the middle of the selection/classification process – and present findings on their perceptions of survey utility, accuracy, and usability for Air Force use.1
Vocational interest
Ingerick and Rumsey (2014) summarized work interests as “preferences for performing selected work activities or working in certain environments (or contexts) that purposefully influence work-related choices and behavior” (p. 166). The authors noted work interests are relatively stable individual differences with a dispositional component and are reflective of one’s work-related identity and values. Work interests, therefore, impact vocational and organizational choices as well as work-related behavior and performance (Ingerick & Rumsey, 2014).
The work interest literature is well established within vocational psychology, spanning nine decades of investigation (Holland, 1959, 1997; Kuder, 1939; Strong, 1927, 1943). The most widely accepted and validated conceptual model of vocational interest is John Holland’s (1959, 1997) RIASEC model which proposes vocational interest is measured via six global dimensions – Realistic, Investigative, Artistic, Social, Enterprising, and Conventional. Holland posits increased congruence between an individual’s RIASEC profile and job is related to improved job performance and satisfaction. While not without criticism, a majority of research supports the overall structure of Holland’s RIASEC model (Tracey & Rounds, 1993).
Holland’s RIASEC-based interest inventories, including the Self-Directed Search ([SDS]; Holland, 1994) and Vocational Preference Inventory ([VPI]; Holland, 1997), are examples of construct focused scales (Van Iddekinge et al., 2011). That is, results provide feedback on vocational interests according to global, RIASEC-based constructs. In contrast, vocation focused inventories like the Strong Vocational Interest Blank (SVIB; Strong, 1943) focus on differentiating levels of fit across a number of generalized vocations (e.g., firefighter, chemist, librarian) (Van Iddekinge et al., 2011). Job focused interest scales more narrowly focus on identifying good-fit individuals for a specific job (or set of jobs) based on interests related to criteria such as job performance and turnover (Ingerick & Rumsey, 2014). Finally, basic interest inventories like the Army Vocational Interest Career Examination (AVOICE; Hanson, Paullin, Bruskiewicz, & White, 2003; Hough, Barge, & Kamp, 2001) yield values on multi-item scales that measure specific types of work (e.g., combat, computers, mechanics, food service) (Ingerick & Rumsey, 2014).
Contemporary civilian and military-specific inventories often incorporate elements from multiple inventory types. Both recent iterations of the Strong Interest Inventory (SII; Donnay, Morris, Schaubhut, & Thompson, 2004) and Holland’s Self-Directed Search (1997) incorporate construct and vocation focused scale elements. For the military, the Navy’s Job Opportunities in the Navy (JOIN; Chen & Jones, 2008) quantifies sailor interest in job cluster communities and types of work contexts and activities (e.g., analyze, maintain, operate), and identifies level of person-job fit for specific-enlisted naval Ratings (i.e., Navy jobs). Overall, a multiscale type approach to vocational interest may provide a more comprehensive, informative picture of vocational interest (Armstrong, Smith, Donnay, & Rounds, 2004; Donnay & Borgen, 1996).
History of vocational interest and the military
United States Armed Forces measurement of vocational interest began during World War II with non-cognitive questionnaires such as the Navy’s Biographical Inventory for naval aviation and Army’s Classification Inventory for infantry (Ingerick & Rumsey, 2014). Both the Air Force and Navy advanced the systematic, theoretical examination of vocational interest in the 1950s. Specifically, while initial Air Force research in vocational interest (Guilford, Christensen, Bond, & Sutton, 1953, 1954) contributed to development of Holland’s (1959, 1973, 1997) RIASEC model of vocational interest, the Navy developed the first comprehensive interest inventory, the Navy Vocational Interest Inventory (NVII), for operational selection and classification (Clark, 1955, 1961).
The Vocational Interest Career Examination (VOICE) in the 1970s, though never implemented, was the Air Force’s first major attempt to develop a comprehensive interest inventory for selection and classification (Alley & Matthews, 1982). The VOICE used a rationally developed item pool to measure constructs relevant to AF personnel and occupational titles, containing items including work tasks, leisure time activities, and desired learning experiences. Job-fit recommendations were based on the results of 20 occupational scales clustered by job similarity (Alley, 1978; Oliver, Whetzel, McCloy, & DeSimone, 2011). As part of the Department of Defense “Project A” the Army began adaptation of the VOICE in the 1980s to create the Army VOICE (AVOICE; Hanson et al., 2003; Hough et al., 2001). The AVOICE was initially heavily based on Holland’s RIASEC model, with later versions de-emphasizing broad interest constructs in favor of VOICE-like work tasks, learning experiences, and leisure activities (Ingerick & Rumsey, 2014). Like the VOICE, the AVOICE was never implemented operationally.
More recent developments in military vocational interest assessments include both the Army’s Work Preference Assessment (WPA) and the Navy’s Job Opportunities in the Navy (JOIN; Ingerick & Rumsey, 2014; Oliver et al., 2011). The WPA is based on the RIASEC model with scales for each dimension consisting of work activity, work environment, and learning opportunity items. Results are averaged at the scale and construct level; however, the WPA does not provide MOS-specific (i.e., Army job) recommendations based on person-job fit (Ingerick & Rumsey, 2014; Oliver et al., 2011). In contrast, the Navy JOIN was developed using a combination empirical and rational approach with items derived primarily from Navy Rating subject matter experts (SMEs), and is capable of identifying level of fit at the job level.
AF-WIN inventory origins: The Navy JOIN
While overall military history of vocational interest informed AF-WIN development, inventory structure, content, and processes were primarily adapted from the Navy’s JOIN interest inventory. Below we discuss development and validation of the Navy JOIN in further detail to provide context on the origins of the AF-WIN.
Initial JOIN development
Farmer et al. (2006) noted several armed service-specific requirements that were unmet with existing civilian vocational interest inventories. First, “global” RIASEC-based inventories often fail to adequately differentiate armed services jobs (most are “Realistic” or “Investigative”). As such, any Navy interest tool should measure basic interests (activities, styles, environments) specific to Navy enlisted ratings. Second, unlike voluntary civilian career choice, career choice in the armed services is highly constrained by minimum aptitude standards and job-fill requirements. Therefore, any developed inventory should reliably and accurately discriminate person-job fit among individual naval Ratings (or families of Ratings) to maximize utility for the classification and assignment process. Third, in contrast to civilian inventories using constructs made to span the “world of work at large” (p. 3), an inventory narrowly focused on enlisted skilled, technical, and industrial naval Ratings should be developed primarily via experienced Navy SME input (Farmer et al., 2006). Finally, the inventory should be organized around a flexible taxonomic model that is alterable as the Navy and related job fields change and evolve (Farmer, Watson, Alderton, Michael, & Hindelang, 2006).
JOIN development combined empirical and rational approaches to build a vocational interest model of global interest constructs and basic interest factors tailored to Navy-specific work content. The JOIN model incorporated elements from the Navy Vocational Information System (NVIS), NVII, and Air Force VOICE (Farmer et al., 2006), while also incorporating new content based on updated naval Rating job descriptions and SME input about potential Navy work activities, styles, and environments (Farmer et al., 2006). Iterative revisions to the JOIN interest model resulted in a hierarchical taxonomic structure (c.f., Schippmann, 1999) of broad, global functional communities, narrower job environments and styles, and specific work activities/tasks. JOIN items and corresponding images were developed around a computer-administered format for efficient administration at multiple locations including Military Entrance Processing Stations (MEPS), recruiting offices, or at home.
JOIN validation evidence
Initial psychometric evaluation showed the JOIN had satisfactory levels of internal consistency and reliability, showed differences between diverse demographics consistent with the extant vocational literature (i.e., gender, race, and ethnicity) (c.f. Su, Rounds, & Armstrong, 2009; Van Iddekinge et al., 2011), and was positively received by participants for navigability and ease of use. Critically, principal component analysis results indicated the JOIN could reasonably discriminate across enlisted naval Ratings (Farmer et al., 2006). A later analysis of 5,000 sailors from FY 2007 through FY 2013 indicated the JOIN was positively related to first-pass training class success, average performance evaluation, likelihood of being promoted to E-6, retention, and Sailor re-enlistment (HP Enterprise Services [HPES], 2013). Additional analysis of the sample further demonstrated the JOIN had significant, additive predictive value over ASVAB-based Navy Rating Identification Composite Scores (RCS) for career outcomes including average performance evaluation, paygrade advancement, and re-enlistment/retention (HPES, 2014). See Watson (this issue) in this issue for a thorough review of Navy JOIN interest tool development and validation.
Study 1: Development of the AF-WIN
AF-WIN taxonomy & content development
Given the predictive value of the Navy JOIN on Sailor performance, satisfaction, and re-enlistment (c.f. Chen & Jones, 2008; HPES, 2013, 2014, 2015), we used the JOIN as a starting point to develop a vocational interest tool for the Air Force’s 130+ enlisted career fields. The AF-WIN adapted the JOIN’s taxonomy into three levels – functional communities, job contexts, and work activities. (Chen & Jones, 2008; Farmer, Bearden et al., 2006; Farmer, Fedak et al., 2006; Farmer, Watson et al., 2006). The AF-WIN defines functional communities as specialized workgroups clustered by primary occupational mission focus and organizational function, job contexts as the environmental, procedural, stylistic, and structural job elements that define the setting and circ*mstances in which a job takes place, and work activities as general, observable tasks and behaviors performed on the job.
A team of Air Force personnel psychologists adapted JOIN taxonomy items and content based on the unique structure and needs of the Air Force using the October 2014 Air Force Enlisted Classification Directory (AFECD), O*NET content model worker characteristics and occupational requirements (National Center for O*NET Development, 2010), and “areas of interest” on airforce.com. Initial AF-WIN taxonomy definitions and items were reviewed by six HQ Air Force Recruiting Service SMEs with mastery knowledge of enlisted Air Force occupations for depth, breadth, and adequacy. AF-WIN taxonomy development and revision was an iterative process, resulting in 52 items: 12 functional communities, 12 job contexts, and 28 work activities.
Image collection
The AF-WIN uses job-relevant imagery to provide additional visual information and contextual framing for each survey item (Chen & Jones, 2008). A three-person team of personnel psychologists identified initial images using Air Force sources including airforce.com, af.mil, defenseimagery.mil, jbsa.mil, and the USAF Public Affairs office. Images were randomly assigned among team members and rated using a 5-point Likert scale for representativeness of each AF-WIN item (1 – Not at all Representative, 5 – Extremely Representative). Iterative consensus meetings resulted in a revised set of images that were reviewed qualitatively for image representativeness (i.e., accuracy, depth, and breadth), image repetition, and representation of diverse groups, resulting in 135 unique occupational images.
Website development
The AF-WIN survey is exclusively computer-administered. Each functional community, job context, and work activity item is presented with an item title, definition, set of 3–5 images, and a unidirectional 5-point Likert scale of interest, 1 – Uninterested to 5 – Very Interested. To reduce potential order effects, item presentation is randomized within taxonomy level and descriptive images are randomly ordered for each item. Survey completion results in a rank-ordered list of 132 enlisted career fields using a .0000 to .9999 “fit metric” where higher numbers indicate better fit. Fit metrics are based on an algorithm computing congruence between vocational interest inputs and SME-derived job profile markers, which are discussed in-depth in the next section.
Developing job profile markers
To enable AF-WIN person job-fit calculations, job profile markers were developed for each enlisted career field. Job profiles consist of 52 marker binary digits to indicate whether specific AF-WIN items are (1) or are not (0) applicable to a career field (Appendix A).
First data collection
Career Field Managers (CFMs) for 134 entry-level enlisted careers distributed the online survey via organizational listservs or directly to enlisted Airmen they identified as SMEs. Participants (N = 4,146) indicated via checkboxes the functional communities, job contexts, and work activities applicable to their career field. Due to inconsistencies in CFM involvement and SME response rate/completion across career fields, some career fields had representatives that provided substantially more feedback than others. After filtering for incomplete, random (e.g., selecting all or no options), and rushed (e.g., completing survey in an unrealistic amount of time) responding, the first data collection yielded 2,185 SME responses.
Initial job profile marker creation
The AF-WIN algorithm computes person-job fit scores based on participant interest using ratings from only applicable items, which become job profile markers. Job profile markers were identified using SME judgments of applicability of each survey item to their career field. Agreement among a minimum of 66.67% of SMEs was required to establish an item as a job profile marker. This methodology was applied to all 52 items, producing 134 job profiles with a broad range of total applicable markers across all career fields. Initially, this approach produced job marker profiles with as few as 13 markers and as many as 30 for different career fields. This imbalance meant that a particular marker would have much greater weight in a career field with only a few markers than in one with many.
To address this imbalance, applicable marker numbers were constrained to a more standardized set of “top” markers for each of the three taxonomy levels: 1–2 functional communities, 5–7 job contexts, and 5–7 work activities. Markers were selected “top-down” so that items with the highest percentage SME agreement were retained over lower agreement items. Unfortunately, several career field profiles could not be standardized due to lack of variance among SME agreement values (e.g., job context markers 5–8 might have equal SME agreement, preventing reduction to seven markers).
Second data collection
A second data collection was conducted to address remaining standardization issues, which were often due to having too few SME responses for a career field. Forty enlisted Airmen each from 87 career fields who were 1) on active duty status, 2) eligible for re-enlistment, and 3) had no upcoming separation or retirement date were randomly selected through the Air Force Surveys Office. Where career fields did not have 40 SMEs, all career field members were selected as participants. Sampling resulted in 3,191 SMEs and a final response rate of 18.99% (responder N = 606). As in data collection one, participants used checkboxes to identify items applicable to their career field, and those indicating they participated in data collection one were removed from the dataset. Also, as in data collection one, to prevent blank/rushed responses, participants were asked to select the top 3 functional communities, 8 job contexts, and 10 work activities applicable to their career field. Participants were additionally asked to rank-order their within-taxonomy choices from most to least applicable to their career field to serve as potential tie-breakers during the profile marker range standardization step.
Revised job profile marker creation
Data from both collections were combined2 and job marker profiles were created using a minimum 2/3rds agreement rule and top-down profile marker range standardization method. In rare instances where profile markers still could not be reduced to a desired standardized range, SME item ranking data served as a tie-breaker. Job profiles for 132 career fields were ultimately created with 1–2 functional community (M = 1.63, SD = .52), 5–7 job context (M = 6.69, SD = .96), and 5–7 work activity (M = 6.60, SD = 1.00) profile markers per career field.
Calculating AF-WIN job fit
Job marker profiles are used to compute person-job fit for each career field during administration of the AF-WIN. Specifically, the AF-WIN calculates person-job fit by multiplying participant item interest ratings (1–5) by a career’s corresponding dichotomous job profile markers (0–1). Item-level products are then averaged for functional community, job context, and work activity taxonomy levels, and a final, overall fit score is computed via a weighted average of taxonomy-level means.
Using the job marker profiles developed in Study 1, Study 2 explored the validity of the AF-WIN based on the relationship between AF-WIN job profile “fit” report rating and ranking results with self-report incumbent job satisfaction and re-enlistment intentions for the 132 career fields for which the AF-WIN provides matches.
Study 2: Incumbent concurrent validation of the AF-WIN
The primary purpose of developing the AF-WIN survey was to identify “good fit” career fields for enlisted Airmen, based on evidence that careers complementary to vocational interests improve desirable performance criteria (Van Iddekinge et al., 2011) and job satisfaction (Earl, 2015; Rottinghaus et al., 2009). Study 2 examined to what extent the AF-WIN can demonstrate this relationship using a concurrent validation study approach. Specifically, we administered the AF-WIN to current job incumbents to determine (a) the extent to which enlisted members’ current career field was identified as a relatively good or poor fit for their interests and (b) the extent to which fit in their current career field corresponded to level of job satisfaction/dissatisfaction and re-enlistment intentions. Demonstrating this fit-to-satisfaction and re-enlistment link would provide initial evidence the AF-WIN can accurately place Airmen into good fit enlisted career fields. Additionally, to guide potential future tool scoring revisions that might be needed to better align the tool’s identification of relative career field fit with individual interests, we analyzed results of the 52 individual AF-WIN survey items.
Study 2 method: Incumbent concurrent validation of the AF-WIN
Participants
Incumbent selection criteria were that the individual be on active duty status, be eligible for re-enlistment, and to not be scheduled to retire or separate before study completion. A stratified random sample of 180 incumbents (20 per enlisted grade) from each of 132 enlisted career fields was invited to participate in the study. If there were less than 180 career field members, or less than 20 members in an enlisted grade, then all in that career field or grade were invited. Usable responses were obtained from 4,222 individuals across all 132 career fields, M = 32.23, SD = 17.02. Only incumbents who completed the AF-WIN and job satisfaction ratings were included in analyses. By rank, the final sample included 14 E-1s (0.33%), 262 E-2s (6%), 630 E-3s (15%), 481 E-4s (11%), 629 E-5s (15%), 758 E-6s (18%), 808 E-7s (19%), 411 E-8s (10%), and 229 E-9s (5%).
Procedure
Incumbents were contacted via email with a link to the AF-WIN survey. After providing their survey responses, incumbents were shown, via the AF-WIN program, their personalized rank-ordered list of all 132 enlisted AF career fields from best (1) to worst (132) fit. Incumbents then rated level of job satisfaction in their current career field, indicated their intentions to re-enlist, and rated their satisfaction on other criterion-related “fit” questions. Survey responses were anonymous.
Measures
A series of five standard questions regarding job satisfaction and re-enlistment intent from the enlisted Air Force Occupational Analysis survey were used as criteria for measuring incumbent-career field fit. These measures appeared at the end of the survey after incumbents had completed the AF-WIN and obtained their job ranking report.
Job satisfaction
Overall job satisfaction was measured with the item “How satisfied are you with the sense of accomplishment you gain from your job?” Members responded using a 7-point Likert-type scale ranging from 1 – Extremely Dissatisfied to 7 – Extremely Satisfied. Higher scores reflected higher job satisfaction in their current career field.
Additional job-fit items
Additional job-fit items were assessed via the following questions: 1) “How interesting do you find your job?”, 2) “How well does your job utilize your talents?”, and 3) “How well does your job utilize your training?” Job interest was rated on a 7-point Likert-type scale ranging from 1 – Extremely Dull to 7 – Extremely Interesting while job training and talent utilization were rated on a 7-point Likert-type scale of 1 – Extremely Poor to 7 – Extremely Well. Higher scores indicated members felt 1) higher interest in their jobs and 2) that their talents and/or training were better utilized on the job.
Re-enlistment intent
Intention to re-enlist was assessed by the question “Do you intend to re-enlist at the end of your current enlistment?” Participants responded on a 4-point Likert-type scale ranging from 1 – Definitely Will not Re-enlist to 4 – Definitely Will Re-enlist. An alternative response category, will retire with 20 or more years of service, was also available. Higher scores indicated a greater intent to re-enlist.
Study 2 results: Incumbent concurrent validation of the AF-WIN
AF-WIN identified incumbent fit for current career field
To identify level of fit for incumbent career fields, we evaluated the extent to which their current career field was identified as a good fit relative to other career fields in their AF-WIN ranking report. Results showed 29.20% (1,233) of incumbents had their current career field listed among their top 10 “best fit” career fields (i.e., of 132), 47.42% (2,002) had their career field listed among their top 25 “best fit” careers, and 67.24% (2,839) had their career field listed among their top 50 career “best fit” careers.
Overall relationship between AF-WIN identified fit and incumbent satisfaction
We ran correlations between incumbent current job-fit values, job-fit ranking, and job satisfaction/re-enlistment intent variables. When examining re-enlistment intent, incumbents who indicated plans to retire after 20 years of service were excluded. Inter-correlations between AF-WIN job fit and job satisfaction/re-enlistment intent variables appear in Table 1. AF-WIN job fit was significantly related to incumbent job satisfaction (r = .38, p < .0001), interest (r = .43, p < .0001), talent utilization (r = .40, p < .0001), training utilization (r = .28, p < .0001), and re-enlistment intent (r = .22, p < .0001).
Table 1.
Relationships among AF-WIN identified career field fit, job satisfaction, and re-enlistment intent.
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|
| 1. AF-WIN Career Field Fit Value | – | ||||||
| 2. AF-WIN Career Field Fit Rank | −.71 | – | |||||
| 3. Job Satisfaction | .38 | −.39 | – | ||||
| 4. Interest | .43 | −.46 | .78 | – | |||
| 5. Talent Utilization | .40 | −.41 | .78 | .78 | – | ||
| 6. Training Utilization | .28 | −.28 | .66 | .63 | .72 | – | |
| 7. Re-enlistment Intent | .22 | −.15 | .37 | .34 | .35 | .29 | – |
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N = 4,222, except for Re-enlistment Intent where N = 2,372. Re-enlistment intent item excludes members indicating they will retire with 20+ years of service. All correlations were significant at p < .0001.
Given the AF-WIN is intended primarily for use by new recruits and early-career re-training Airmen, we additionally examined correlational relationships for just incumbents within enlisted grades E-1 through E-5 (N = 2,016). Similar results were demonstrated in the subset E-1 to E-5 sample with AF-WIN job fit values being significantly correlated with incumbent job satisfaction (r = .38, p < .0001), interest (r = .43, p < .0001), talent utilization (r = .41, p < .0001), training utilization (r = .26, p < .0001), and re-enlistment intent (r = .17, p < .0001).
Relationship of incumbent interest in relevant job markers with incumbent satisfaction
Overall incumbent item-level analysis
We also examined potential differences in incumbent job satisfaction for applicable (i.e., applicable to the incumbent’s current career field) versus non-applicable AF-WIN items. Based on existing AF-WIN career profiles created by a consensus of career field SMEs (see Study 1), incumbent job satisfaction was correlated with incumbent interest for both applicable and non-applicable items. Incumbent levels of interest should be positively correlated to overall job satisfaction for items identified as applicable to the incumbent’s career field. Conversely, incumbent levels of interest should be negatively or non-significantly correlated with overall job satisfaction for items not applicable to the incumbent’s career field. Ideally, the difference between these correlations should be significant. Using Fisher’s r-to-z transformation (Fisher, 1973), we conducted significance of difference testing between the two correlations for each AF-WIN item.
Results demonstrated significant, positive correlational relationships between incumbent self-report job satisfaction and AF-WIN items applicable to their current career field for 49 of 52 items. Items lacking this positive, correlational relationship included Work Activity item Operate Electronic Equipment and Job Context items Indoor and Work Independently.
Furthermore, results for 48 of 52 markers demonstrated incumbent interest in applicable items were significantly more positively correlated with job satisfaction than non-applicable items. Job Context item Work Independently and Work Activity item Operate Electronic Equipment both had negligible interest-to-satisfaction correlations regardless of item applicability, resulting in non-significant z-score values. Job Context item Work with Teams was also non-significant, likely due to a combination of a small interest-to-satisfaction correlation and extreme, uneven levels of item applicability (i.e., item was applicable to 96.40% of surveyed careers). Finally, interest-to-satisfaction correlations for Work Activity item Train People and Customers were similarly, significantly positive regardless of item applicability. This, along with a relatively even distribution of item applicability (53.36% applicable; 46.64% not applicable), contributed to no statistical difference between the two subsamples (see Tables 2 and and3).3). Results overall indicate the AF-WIN does appropriately differentiate incumbent job satisfaction for job markers applicable (versus not applicable) to incumbent career fields.
Table 2.
Relationship between incumbent interest in AF-WIN job markers (Taxonomy Elements) and career field job satisfaction.
| Job Marker Applicable to AFSC | Job Marker Not Applicable to AFSC | Fisher’s r-to-z | |||
|---|---|---|---|---|---|
| Taxonomy Element | N | r | N | r | Z |
| Functional Communities | |||||
| Aviation & Avionics | 886 | .39*** | 3,335 | −.05** | 12.17*** |
| Special Warfare & Operations | 270 | .36*** | 3,951 | −.01 | 6.16*** |
| Construction & Fabrication | 487 | .36*** | 3,734 | −.04* | 8.71*** |
| Cyber Systems | 274 | .34*** | 3,947 | −.04* | 6.25*** |
| Mechanical & Electrical | 740 | .38*** | 3,481 | −.01 | 10.07*** |
| Electronics | 524 | .28*** | 3,697 | .00 | 5.99*** |
| Health Care | 602 | .43*** | 3,619 | −.13*** | 13.37*** |
| Intelligence | 413 | .23*** | 3,808 | −.07*** | 5.84*** |
| Law Enforcement & Protection | 299 | .29*** | 3,922 | −.03* | 5.45*** |
| Transportation, Planning, & Logistics | 768 | .29*** | 3,453 | −.04* | 8.55*** |
| Personnel & Services | 952 | .22*** | 3,269 | −.12*** | 9.12*** |
| Weapons & Aerospace | 491 | .34*** | 3,730 | .02 | 7.04*** |
| Job Contexts | |||||
| Indoor | 3,635 | .02 | 587 | −.26*** | 6.31*** |
| Outdoor | 2,462 | .16*** | 1,760 | −.11*** | 8.61*** |
| Industrial | 1,229 | .29*** | 2,993 | −.02 | 9.28*** |
| Office | 2,673 | .11*** | 1,549 | −.19*** | 9.36*** |
| Mental | 2,774 | .04* | 1,448 | −.05 | 2.82* |
| Physical | 2,027 | .17*** | 2,195 | −.10*** | 9.06*** |
| Work Independently | 2,467 | .00 | 1,755 | −.04 | 1.22 |
| Work with Teams | 4,070 | .13*** | 152 | .10 | .33 |
| Hazardous | 2,047 | .11*** | 2,175 | −.09*** | 6.61*** |
| Non-Hazardous | 1,366 | .08** | 2,856 | −.09*** | 5.35*** |
| Predictable | 1,549 | .09** | 2,673 | −.10*** | 6.03*** |
| Unpredictable | 2,483 | .12*** | 1,739 | −.03 | 4.81*** |
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N = 4,222; *p < .05; **p < .001; ***p < .0001.
Table 3.
Relationship between incumbent interest in AF-WIN job markers (Taxonomy elements) and career field job satisfaction (Cont.).
| Job Marker Applicable to AFSC | Job Marker Not Applicable to AFSC | Fisher’s r-to-z | |||
|---|---|---|---|---|---|
| Taxonomy Element | N | r | N | r | Z |
| Work Activities | |||||
| Analyze Communications | 1,635 | .07* | 2,587 | −.07** | 4.65*** |
| Analyze Data | 1,834 | .07* | 2,388 | −.05* | 3.90*** |
| Analyze Documents | 1,609 | .09** | 2,613 | −.08*** | 5.50*** |
| Create Documents | 2,401 | .08*** | 1,821 | −.08** | 5.20*** |
| Direct Air & Spacecraft | 280 | .30*** | 3,942 | .03 | 4.61*** |
| Direct Emergency Response | 520 | .21*** | 3,702 | .02 | 4.08*** |
| Interact with People & Customers | 3,275 | .09*** | 947 | −.06 | 4.13*** |
| Maintain Air and Spacecraft | 531 | .53*** | 3,691 | .02 | 12.24*** |
| Maintain Data & Computer Systems | 341 | .38*** | 3,881 | −.06*** | 8.15*** |
| Maintain Documents | 2,855 | .09*** | 1,367 | −.05 | 4.32*** |
| Maintain Electrical Equipment | 295 | .47*** | 3,927 | .02 | 8.13*** |
| Maintain Electronic Equipment | 476 | .28*** | 3,746 | −.01 | 5.95*** |
| Maintain Facilities | 289 | .42*** | 3,933 | .00 | 7.31*** |
| Maintain Mechanical Equipment | 523 | .43*** | 3,699 | .02 | 9.31*** |
| Maintain Records & Resources | 863 | .18*** | 3,359 | −.03 | 5.44*** |
| Maintain Security | 94 | .53*** | 4,128 | .00 | 5.54*** |
| Maintain Supplies | 1,704 | .07* | 2,518 | −.03 | 3.15** |
| Maintain Weapons | 189 | .34*** | 4,033 | .04* | 4.20*** |
| Manufacture Facilities | 117 | .47*** | 4,105 | .02 | 5.17*** |
| Operate Data & Computer Systems | 502 | .21*** | 3,720 | −.04* | 5.45*** |
| Operate Electrical Equipment | 265 | .46*** | 3,957 | −.02 | 8.11*** |
| Operate Electronic Equipment | 715 | −.02 | 3,507 | −.01 | −.07 |
| Operate Mechanical Equipment | 853 | .33*** | 3,369 | .01 | 8.74*** |
| Operate Weapons | 228 | .39*** | 3,994 | .00 | 6.00*** |
| Produce Communications | 666 | .13** | 3,556 | −.02 | 3.73*** |
| Respond to Emergencies | 894 | .16*** | 3,328 | −.01 | 4.60*** |
| Serve People & Customers | 2,112 | .14*** | 2,110 | −.01 | 4.80*** |
| Train People & Customers | 2,253 | .11*** | 1,969 | .11*** | .07 |
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N = 4,222; *p < .05; **p < .001; ***p < .0001.
“Satisfied” incumbent item-level analysis
Occupational scale development and scoring for civilian vocational interest inventories (e.g., Strong Interest Inventory) are based on response differentiation of incumbents from the general population who voluntarily select into (and stay in) given occupations (Donnay et al., 2004). In contrast, the Air Force has career constraints including a) assigning careers based on “needs of the Air Force” (without taking the member’s preference into account) and b) requiring a multi-year service commitment without the freedom to change career fields in the same manner as civilians. These constraints conceivably reduce overall job satisfaction of military incumbents in their assigned Air Force career field. That is, a satisfied military incumbent would likely have higher interest in job markers associated with his/her respective career field compared to satisfied incumbents in another career field with different applicable job profile markers. Therefore, additional analyses were run using a subset of incumbents (N = 3,163) who indicated “neutral to positive” levels of satisfaction with their present career field (4 – Neither Satisfied nor Dissatisfied to 7 – Extremely Satisfied).
Mean differences in incumbent interest between applicable and non-applicable job profile markers were compared using Cohen’s d effect sizes (Cohen, 1988), a validation methodology consistent with methods of validating civilian vocational assessments (Donnay et al., 2004). We used Cohen’s d rather than Fisher’s r-to-z transformation as we were interested in the absolute mean differences amongst only satisfied individuals. As shown in Tables 4 and and5,5, results were similar to the analyses conducted on the full set of respondents, with all but four items showing significant differences based on item applicability to incumbent career. The four items showing minimal differences in interest (d < .20) regardless of their applicability to incumbent career were Work Activities Operate Electronic Equipment (d = .17) and Train People & Customers (d = − .01), as well as Job Contexts Work Independently (d = .10) and Work with Teams (d = .03).
Table 4.
Relationship between incumbent interest in AF-WIN job markers (Taxonomy elements) and Career field job satisfaction.
| Satisfied Incumbents: Applicable Job Marker | Satisfied Incumbents: Non-Applicable Job Marker | Cohen’s d | |||||
|---|---|---|---|---|---|---|---|
| Taxonomy Element | N | M | SD | N | M | SD | Δ |
| Functional Communities | |||||||
| Aviation & Avionics | 662 | 3.44 | 1.42 | 2,500 | 2.44 | 1.34 | .74** |
| Special Warfare & Operations | 224 | 4.19 | 1.23 | 2,938 | 2.78 | 1.49 | .96*** |
| Construction & Fabrication | 370 | 3.49 | 1.41 | 2,792 | 2.22 | 1.26 | .99*** |
| Cyber Systems | 199 | 4.15 | 1.20 | 2,963 | 2.93 | 1.42 | .86*** |
| Mechanical & Electrical | 558 | 3.30 | 1.41 | 2,604 | 2.32 | 1.28 | .76** |
| Electronics | 370 | 3.77 | 1.17 | 2,792 | 2.60 | 1.34 | .89*** |
| Health Care | 449 | 4.12 | 1.15 | 2,713 | 2.66 | 1.44 | 1.04*** |
| Intelligence | 308 | 3.78 | 1.25 | 2,854 | 3.09 | 1.38 | .50** |
| Law Enforcement & Protection | 245 | 3.49 | 1.52 | 2,917 | 2.23 | 1.30 | .96*** |
| Transportation, Planning, & Logistics | 584 | 2.94 | 1.53 | 2,578 | 2.03 | 1.10 | .77** |
| Personnel & Services | 670 | 3.05 | 1.48 | 2,492 | 1.97 | 1.19 | .86*** |
| Weapons & Aerospace | 371 | 3.51 | 1.33 | 2,791 | 2.57 | 1.35 | .70** |
| Job Contexts | |||||||
| Indoor | 2,706 | 3.36 | 1.27 | 457 | 2.65 | 1.22 | .56** |
| Outdoor | 1,841 | 3.43 | 1.27 | 1,322 | 2.70 | 1.29 | .57** |
| Industrial | 905 | 3.11 | 1.22 | 2,258 | 2.44 | 1.15 | .57** |
| Office | 1,984 | 3.01 | 1.30 | 1,179 | 2.37 | 1.18 | .51** |
| Mental | 2,077 | 3.79 | 1.16 | 1,086 | 3.56 | 1.19 | .20* |
| Physical | 1,565 | 3.52 | 1.20 | 1,598 | 3.05 | 1.22 | .39* |
| Work Independently | 1,841 | 3.68 | 1.20 | 1,322 | 3.55 | 1.19 | .10 |
| Work with Teams | 3,043 | 3.66 | 1.16 | 120 | 3.63 | 1.17 | .03 |
| Hazardous | 1,543 | 3.17 | 1.34 | 1,620 | 2.42 | 1.26 | .58** |
| Non-Hazardous | 1,014 | 3.09 | 1.27 | 2,149 | 2.52 | 1.22 | .46* |
| Predictable | 1,121 | 3.04 | 1.18 | 2,042 | 2.80 | 1.20 | .21* |
| Unpredictable | 1,898 | 3.23 | 1.32 | 1,265 | 2.95 | 1.25 | .22* |
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N = 3,163; *small: .20-.49; **medium: .50-.79; ***large: .80+ (Cohen, 1988).
Table 5.
Relationship between incumbent interest in AF-WIN job markers (Taxonomy elements) and career field job satisfaction (Cont.).
| Satisfied Incumbents: Applicable Job Marker | Satisfied Incumbents: Non-Applicable Job Marker | Cohen’s d | |||||
|---|---|---|---|---|---|---|---|
| Taxonomy Element | N | M | SD | N | M | SD | Δ |
| Work Activities | |||||||
| Analyze Communications | 1,259 | 3.01 | 1.25 | 1,904 | 2.75 | 1.25 | .21* |
| Analyze Data | 1,383 | 3.17 | 1.29 | 1,780 | 2.88 | 1.25 | .23* |
| Analyze Documents | 1,206 | 3.04 | 1.28 | 1,957 | 2.65 | 1.20 | .32* |
| Create Documents | 1,778 | 2.83 | 1.27 | 1,385 | 2.37 | 1.17 | .37* |
| Direct Air & Spacecraft | 233 | 3.72 | 1.31 | 2,930 | 2.67 | 1.31 | .80*** |
| Direct Emergency Response | 395 | 3.56 | 1.37 | 2,768 | 2.64 | 1.30 | .71** |
| Interact with People & Customers | 2,435 | 3.22 | 1.30 | 728 | 2.72 | 1.24 | .39* |
| Maintain Air and Spacecraft | 391 | 3.45 | 1.33 | 2,772 | 2.28 | 1.23 | .94*** |
| Maintain Data & Computer Systems | 250 | 3.63 | 1.24 | 2,913 | 2.59 | 1.29 | .81*** |
| Maintain Documents | 2,068 | 2.46 | 1.20 | 1,095 | 2.05 | 1.11 | .35* |
| Maintain Electrical Equipment | 210 | 3.70 | 1.22 | 2,953 | 2.35 | 1.22 | 1.12*** |
| Maintain Electronic Equipment | 333 | 3.50 | 1.26 | 2,830 | 2.50 | 1.26 | .80*** |
| Maintain Facilities | 233 | 3.32 | 1.29 | 2,930 | 2.06 | 1.10 | 1.13*** |
| Maintain Mechanical Equipment | 385 | 3.35 | 1.29 | 2,778 | 2.36 | 1.23 | .80*** |
| Maintain Records & Resources | 622 | 2.76 | 1.26 | 2,541 | 2.33 | 1.19 | .36* |
| Maintain Security | 63 | 3.41 | 1.41 | 3,100 | 2.14 | 1.20 | 1.06*** |
| Maintain Supplies | 1,286 | 2.32 | 1.13 | 1,877 | 2.08 | 1.05 | .21* |
| Maintain Weapons | 131 | 3.37 | 1.48 | 3,032 | 2.35 | 1.27 | .80*** |
| Manufacture Facilities | 98 | 4.08 | 1.15 | 3,065 | 2.29 | 1.24 | 1.44*** |
| Operate Data & Computer Systems | 369 | 3.66 | 1.26 | 2,794 | 2.88 | 1.31 | .60** |
| Operate Electrical Equipment | 208 | 3.50 | 1.30 | 2,955 | 2.42 | 1.20 | .90*** |
| Operate Electronic Equipment | 543 | 3.09 | 1.27 | 2,620 | 2.87 | 1.24 | .17 |
| Operate Mechanical Equipment | 651 | 3.07 | 1.34 | 2,512 | 2.37 | 1.21 | .57** |
| Operate Weapons | 179 | 4.30 | 1.13 | 2,984 | 3.05 | 1.47 | .86*** |
| Produce Communications | 488 | 2.94 | 1.32 | 2,675 | 2.58 | 1.20 | .29* |
| Respond to Emergencies | 707 | 3.48 | 1.42 | 2,456 | 2.79 | 1.35 | .50** |
| Serve People & Customers | 1,602 | 2.76 | 1.32 | 1,561 | 2.31 | 1.24 | .35* |
| Train People & Customers | 1,667 | 3.19 | 1.30 | 1,496 | 3.20 | 1.31 | −.01 |
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N = 3,163; *small: .20-.49; **medium: .50-.79; ***large: .80+ (Cohen, 1988).
Study 2 discussion: Incumbent concurrent validation of the AF-WIN
Results showed incumbents identified by the AF-WIN survey as a good match for their career field reported substantially higher levels of job satisfaction, re-enlistment intentions, job interest, and increased self-efficacy of one’s talents and Air Force training being well utilized compared to incumbents identified as a relatively poor match. These results were generally consistent for incumbents in both lower and higher enlisted ranks. Incumbent interest in 49 of 52 individual items also showed expected relationships to job satisfaction. Specifically, interest levels in applicable items were significantly, positively related with incumbent job satisfaction compared to non-applicable items, which yielded either negative or non-significant correlations.
Study 3: Perceptions of the AF-WIN among basic military trainees
With Study 2 establishing the psychometric value of the AF-WIN among job incumbents, Study 3 examined the perceptions of survey usability, accuracy, and utility via a sample of the target population – Air Force recruits still in the enlisted selection and classification pipeline. These individuals were to provide both critical information regarding survey credibility and potential impact of the AF-WIN on the target population. Specifically, if the AF-WIN is easy to navigate and use, is perceived as accurately matching careers with interests, and complements/improves the current enlisted Air Force accessions process, the survey should be positively received by the target demographic. Study 3 also content analyzed results of open-ended comments to determine how recruits might use their AF-WIN results during the enlisted selection and classification process.
Study 3 method: Perceptions of the AF-WIN among basic military trainees
Participants
Prior to attending technical training, all U.S. Air Force enlisted recruits attend Basic Military Training (BMT). BMT flights, Air Force basic training units consisting of approximately 50 Air Force trainees each, include both trainees recently assigned to or awaiting assignment to a career field. Therefore, BMT trainees in these flights constituted a unique, relevant sample to evaluate and provide feedback on the AF-WIN vocational interest tool.
Participants consisted of 1,018 BMT trainees selected from randomly-sampled male and female flights in their fourth to fifth week of training. Overall the study sample was 61.11% male with an average age of 21.11 years (SDage = 3.65). Of trainees who provided race data, racial backgrounds included Caucasian (63.89%), African American (20.11%), Asian American (6.27%), American Indian/Alaskan Native (1.41%), and Native Hawaiian/Pacific Islander (.76%); 7.57% self-identified as multiracial, and 19.20% of trainees indicated being of Hispanic/Latino ethnicity. After the removal of trainees unable to view their AFSC job ranking report or who did not have it displayed while responding to survey question, the final sample size was 886.
Procedure
Trainees completed the AF-WIN on a computer. Following administration of the AF-WIN (including trainee viewing of their AF-WIN generated job ranking report), participants answered a multiple-choice questionnaire to gauge their perceptions of AF-WIN accuracy, utility, and tool usability. Participants then provided responses to open-ended questions regarding potential use of the AF-WIN as well as providing feedback on how to improve the AF-WIN tool. Total administration time was approximately 50 min.
Measures
Demographics
Gender, race, ethnicity, and recruitment pathway (Guaranteed Training Enlistment Program [GTEP] vs. open Aptitude Index [AI]) were self-reported by participants. Demographic details were voluntary, so trainees could choose to leave one or more demographic questions blank.
Race
Participants selected all options that applied among the following choices: Caucasian, African American, Asian American, American Indian/Alaskan Native, and Native Hawaiian/Other Pacific Islander (Office of Management and Budget [OMB], 1997).
Ethnicity
Participants indicated whether or not they were of Hispanic/Latino ethnicity, defined as anyone of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin independent of race (OMB, 1997). Participants indicated Ethnicity in addition to selecting one or more racial groups.
Recruitment pathway
Enlisted recruits enter the Air Force through either the GTEP or open AI program, and are assigned careers based on Air Force-specific cognitive composite scores (Mechanical, Administrative, General, Electronics [MAGE]) derived from the Armed Services Vocational Aptitude Battery (ASVAB) (United States Air Force [USAF], 2013). The GTEP program reserves technical school training seats for recruits in a specific career field, for example, Bioenvironmental Engineering (4B0X1) or Paralegal (5J0X1). AFSCs typically have a percentage of accessions come from the GTEP program, though this proportion varies significantly by career field. In contrast, the open AI program only guarantees placement within one of the four USAF MAGE composite areas. For example, placement in the “Mechanical” open AI program means recruits are guaranteed one of 64 Mechanical AI jobs contingent on needs of the Air Force and training slot availability. This creates a relatively high degree of job certainty for GTEP recruits, but a low degree of job certainty for open AI recruits. Within our sample, 51.03% of trainees indicated entering through the GTEP process, and 48.97% indicated entering through the open AI program.
Job interest inventory
Trainees completed the AF-WIN in its entirety, viewed their generated AFSC job ranking report, and kept the report on-screen for the remainder of the study (c.f. Study 1 for overview of AF-WIN content and structure).
Multiple-choice feedback survey
This survey is composed of the following elements:
Satisfaction with current job-match process during recruitment
A single item assessed recruits’ satisfaction with the existing job-match process during Air Force recruiting (i.e., before implementation of the AF-WIN): “AF recruiters currently do a good job of matching recruits to AF careers that match their interests.” This item was rated using a 5-point Likert Scale (1 – Strongly Disagree to 5 – Strongly Agree).
“Top 10” job matches
A single item asked participants the number of career fields in the “top 10” (of 132) most compatible jobs reported in their AFSC job ranking report that interested them: “Of the first 10 jobs the survey tool matched you to, how many seemed to fit your interests?” Number of “top 10” jobs fitting trainee interests was rated using a 5-point Likert scale (1 – None, 2 – One to Three, 3 – Four to Six, 4 – Seven to Nine, 5 – Ten).
Perceptions of AF-WIN user interface, accuracy, and utility
Trainees rated the following dimensions: a) perceptions of AF-WIN tool usability/interface (8 items; e.g., format/layout, images, text, clarity, length; sample item: “The format/layout of the survey tool was easy to navigate”), b) tool accuracy (5 items; sample item: “The survey tool accurately matched my interests with enlisted Air Force careers”), and c) tool utility (13 items; sample item: “Taking this survey tool would be useful for people browsing Air Force careers on airforce.com”). AF-WIN usability, accuracy, and utility items were rated using a 5-point Likert Scale (1 – Strongly Disagree to 5 – Strongly Agree).
Short-answer feedback
Trainees were asked to provide open-ended feedback and comments about how to improve the AF-WIN. Additionally, trainees were asked the following open-ended question about how they would have used results of the AF-WIN to decide on career field choices:
If you had used a tool like the AF-WIN before entering the Air Force (which generated an AFSC job ranking report), how would you have used that report to decide which career field(s) to choose/rank?
Analyses
Quantitative and qualitative survey results are presented using basic, descriptive statistics to detail BMT trainee feedback and reaction to the AF-WIN survey tool. Where appropriate, means comparisons are made between Guaranteed Training Enlistment Program (GTEP) and open Aptitude Index (AI) recruitment pathways using independent-samples t-tests. Magnitude of mean differences were measured using Cohen’s d effect sizes for unequal sample sizes and interpreted using Cohen’s (1988) guidelines: Small d = .2, Moderate d = .5, and Large d = .8. Inter-rater reliabilities of content themes were assessed using Cohen’s kappa recommendations for nominal data: “moderate” (.41 – .60), “substantial” (.61 – .80), and “almost perfect” (.81 – .99) agreement (Cohen, 1960).
Study 3 results: Perceived utility of the AF-WIN among basic military trainees
BMT trainee reactions
Satisfaction with current recruiting job-match process
Recalling their experiences during their recruitment, trainees’ responses tended to fall into the “Neither Agree or Disagree” category for the question “AF recruiters currently do a good job matching recruits to AF careers that match their interests” (M = 3.19, SD = 1.19).
“Top 10” job matches
Trainees, in response to the question “Of the first 10 jobs the survey tool matched you to, how many seemed to fit your interests?”, indicated, on average, between “4 to 6” (coded as “3”) and “7 to 9” (coded as “4”) jobs seemed to fit their interests (M = 3.75, SD = .94) (Figure 1). Sixty-three percent of trainees indicated at least seven of 10 jobs fit their interests, and over 99% of trainees indicated at least one job in the AF-WIN AFSC “top 10” ranking report fit their interests.
Figure 1.
Number of AF jobs in the “top 10” results of their AF-WIN AFSC ranking report trainees indicated having a significant interest.
User-interface/usability
Means for the seven of eight items assessing trainee perceptions of user-interface/usability ranged from 4.25 to 4.63 on a 5-point Likert-type scale, with 82.16% to 94.32% of trainees indicating they “Moderately Agree” or “Strongly Agree” the tool 1) format/layout was easy to navigate, 2) had clear and easy-to-understand instructions, 3) contained visually appealing and informative images, 4) had clear and informative text descriptions, and 5) was interesting to take. Furthermore, 76.50% of trainees responded “Moderately Disagree” or “Strongly Disagree” to the item, “The length of the survey tool was too long” (M = 1.95, SD = 1.06), indicating the survey could potentially be expanded to include additional content.
Perceived accuracy
Item means for five items assessing perceived accuracy ranged from 4.19 to 4.41 on a 5-point Likert-type scale, with 83.03% to 91.27% of trainees indicating they “Moderately Agree” or “Strongly Agree” the AF-WIN tool accurately matched their interests to compatible enlisted Air Force careers. Trainees reported agreement with the statements: the “AFSC job ranking report identified career options that seemed to fit my interests” (M = 4.41, SD = .85), and “The survey tool did a good job of identifying my career interests” (M = 4.35, SD = .88).
Perceived utility
AF-WIN utility was evaluated in terms of perceptions of overall utility, specific utility (e.g., with respect to airforce.com, or talking with recruiter), and effects of tool use on participants. Perceptions of general utility ranged from 4.53 to 4.67 on a 5-point Likert-type scale indicating 86.78% to 95.48% of trainees indicated they “Moderately Agree” or “Strongly Agree” the AF-WIN has general utility as an information and recruiting tool. Trainees indicated the AF-WIN had high utility with respect to “learning more about which Air Force jobs are compatible with my interests” (M = 4.61, SD = .65), and “With so many career fields in the Air Force … help[ing] recruits choose a job that best matches their interests” (M = 4.67, SD = .63).
Perceptions of utility for specific purposes were similar, with means ranging from 4.29 to 4.71 on a 5-point Likert-type scale. Results indicated 77.80% to 96.61% of trainees “Moderately Agree” or “Strongly Agree” the AF-WIN tool would be useful for “those who want to enlist but know little about the Air Force” (M = 4.62, SD = .66), “people browsing Air Force careers on AirForce.com” (M = 4.71, SD = .55), and when “meeting with an Air Force recruiter” (M = 4.67, SD = .62).
Trainees also indicated the potential, positive effects survey implementation might have on the Air Force accessions process, with means ranging 3.89 to 4.29 on a 5-point Likert-type scale. Trainees indicated the AF-WIN tool could be used to communicate the “Air Force cares about the well-being of their enlisted Airmen” (M = 4.29, SD = .81). Notably, 66.06% of trainees indicated Moderate to Strong agreement with the question “The results of the AFSC job ranking report have helped me feel more confident about the career choices I’ve made so far”. The somewhat lower trainee ratings (M = 3.89) on this item indicates some trainees may have had reductions in confidence of their existing career paths after viewing their AF-WIN results. That is, trainees with final job assignments may have found alternate “good fit” jobs in their report. Results indicate there is room for improving trainee confidence in career choices via early implementation of the AF-WIN in the accessions process.
GTEP vs. AI differences
As noted earlier, there is disparity in job certainty between GTEP and open AI recruitment paths – the former guarantees a career-specific job while the latter guarantees only a job within a broad aptitude area. Therefore, we examined whether recruitment pathway influenced survey responses due to degree of certainty of specific career field placement. In most cases, no differences were found between GTEP and open AI trainees. However, open AI trainees more strongly agreed (M = 4.11, SD = 1.03) that “taking this survey tool helped me identify Air Force jobs I might like that I previously didn’t know existed” than GTEP trainees ((M = 3.96, SD = 1.09), t(873) = −2.15, p < .05, d = − .15). Additionally, open AI recruits more strongly agreed with the statement “I wish a survey tool like this was available when I went through the recruitment process” (M = 4.61, SD = .72) than did GTEP recruits ((M = 4.45, SD = .90), t(873) = −2.83, p < .005, d = − .19). Results indicated increased uncertainty in job placement results in greater demand for a vocational interest tool such as the AF-WIN.
Short-answer feedback
For the short-answer question “If you had used a tool like the AF-WIN before entering the Air Force (which generated an AFSC job ranking report), how would you have used that report to decide which career field(s) to choose/rank?”, two raters sorted open-ended trainee responses (N = 744) into broad usage themes. Trainee responses were coded as “present” or “absent” for one or more usage themes, with nine broad themes emerging with at least “moderate agreement” among raters, Cohen’s krange = .52 to 1.00 (Cohen, 1960).
As shown in Table 6, trainees most often indicated they would use the AF-WIN ranking report to “research/review AFSCs more or use as a reference to consider” (42.61%), “help create/prioritize a top 5–10, top picks, or best matches list” (35.48%), or “identify AFSCs I didn’t know about or consider more career fields” (16.67%). In contrast, 15.05% of trainees indicated “the AFSC Ranking Report would not have made much difference/changed my decision” and 0.94% indicated AF-WIN results would encourage them to delay enlistment in the Air Force (e.g., to await availability of training class seats for a better fit career field). Additionally, trainees provided open-ended feedback requesting improvements to the tool such as including mandatory entry requirements (cognitive/physical/medical), career-specific videos, technical training and enlistment contract length requirements, and job availability.
Table 6.
Ways trainees would have used AF-WIN job ranking report if available during recruitment.
| I would have used my AFSC Ranking Report to … | N Responses w/Theme | Theme in % Responses | Rater % Agreement | Cohen’s Kappa | |
|---|---|---|---|---|---|
| 1 | Research/review AFSCs more OR use as a reference to consider | 317 | 42.61% | 86.83% | .71 |
| 2 | Help create/prioritize a top 5–10, top picks, or best matches list | 264 | 35.48% | 81.59% | .52 |
| 3 | Identify AFSCs I didn’t know about OR consider more career fields | 124 | 16.67% | 91.94% | .64 |
| 4 | The AFSC Ranking Report would not have made much difference/changed my decision | 112 | 15.05% | 93.82% | .71 |
| 5 | Choose a different/more specific job OR would have gone in a different direction within a certain field | 81 | 10.89% | 93.41% | .53 |
| 6 | Identify AFSCs of interest more quickly/easily | 55 | 7.39% | 97.58% | .79 |
| 7 | Ask the recruiter more questions OR consult on list with recruiter OR discuss job availability with recruiter | 36 | 4.84% | 97.98% | .73 |
| 8 | Motivate me to try harder to score well on the ASVAB | 13 | 1.75% | 99.60% | .87 |
| 9 | Justify waiting for a certain job to be made available | 7 | 0.94% | 100% | 1.00 |
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N = 744.
Study 3 discussion: Perceived utility of the AF-WIN among basic military trainees
Results demonstrated BMT trainees perceived the AF-WIN survey tool as being both an accurate reflection of their interests and having utility within the Air Force classification process. Furthermore, trainees indicated the AF-WIN is easy to navigate, and responses to the Likert question about survey length (M = 1.97) suggest the AF-WIN survey was not overly lengthy. In sum, Study 3 demonstrated that, in addition to benefits to classification, there is demand among recent recruits for tools like the AF-WIN in the accessions process. Furthermore, the AF-WIN may improve perceptions of fairness, honesty, and consideration for recruit interest in the Air Force accessions process.
General discussion
In this paper, we described the development, validation, and feedback steps taken to create the Air Force Work Interest Navigator (AF-WIN), a vocational inventory designed to assess person-job fit for 130+ enlisted Air Force occupations. After review of the military vocational interest literature, we identified the Navy’s JOIN inventory as a suitable starting point for interest tool development. We began by describing development of the AF-WIN from initial adaptation of the JOIN taxonomy and methodology through generation of Air Force-specific items, text, images, and job profile markers from enlisted SMEs. We then presented a concurrent validation study in which the AF-WIN was administered to job incumbents of 132 enlisted career fields to assess job satisfaction and re-enlistment intentions. Critically, item-level analyses demonstrated incumbent satisfaction was positively related to applicable job profile markers, and unrelated or negatively related to non-applicable job profile markers for 49 of 52 items. AF-WIN fit scores were also positively related to re-enlistment intentions (r = .22), job satisfaction (r = .38), job interest (r = .43), talent utilization (r = .40), and training utilization (r = .28). Finally, we provided results of initial beta-testing with BMT trainees on perceptions of AF-WIN accuracy, utility, and ease of use. Trainees felt the AF-WIN was accurate, had utility for Air Force recruitment processes, and was easy to interact with and navigate.
Results demonstrated the AF-WIN has significant potential utility for the enlisted Air Force classification process. Considering the strong link between interest, task performance (Nye et al., 2012, 2017; Van Iddekinge et al., 2011), and job satisfaction (Earl, 2015; Rottinghaus et al., 2009), successful placement of qualified Airmen into good-fit career fields has potential to reduce overall technical training cost and Airman attrition. Furthermore, in light of constraints in the armed forces accessions process (e.g., cognitive/physical/medical requirements, needs of the service, training availability), AF-WIN implementation may increase recruit perceptions of accessions fairness and transparency via consideration of vocational interest.
Limitations and future developments
Some limitations of the current study should be noted. First, both the Navy JOIN and AF-WIN interest inventories assess person-job fit via congruence between participant vocational interest and binary job profile markers. While “applicable/not applicable” job profile markers successfully identify good-fit jobs for both Navy and Air Force populations, binary job profiles potentially limit inventory accuracy. For example, O*NET recommends assessing level, importance, and frequency of generalized work activities (Jeanneret, Borman, Kubisiak, & Hanson, 1999) and work contexts (Strong, Jeanneret, McPhail, Blakley, & D’Egidio, 1999). Therefore, development of job profile markers with multipoint ordinal scales may be more informative, assessing both applicability as well as magnitude of job context or work activity to a job. Second, due to the recency of AF-WIN development and implementation, long-term impacts of the use of the tool (e.g., on performance appraisals and re-enlistment rates) cannot yet be assessed. Planned AF-WIN validation procedures include ongoing assessment of Study 3 trainees through first-term enlistment and evaluation of an AF-WIN recruiter pilot test program. Finally, the AF-WIN currently lacks a taxonomy level assessing basic job attributes and requirements like deployment frequency, variety of duty locations, or potential for international assignments. These military-specific items were frequently requested by SME incumbents and BMT trainees. AF-WIN taxonomy revisions and updates are forthcoming, with additional items and a newly-developed job attributes taxonomy level being piloted.
Additional future developments include development of an officer version of the AF-WIN to assist in early college career preparation and ranking of non-rated line officer career fields – a departure from recent military interest inventories which have primarily focused exclusively on enlisted careers. Future developments also consist of assessment of incremental predictive validity of AF-WIN job fit above those derived from cognitive aptitude measures (HPES, 2014, 2015) and measures of Big Five personality characteristics for criteria such as job satisfaction, work performance, and re-enlistment intent. Eventual results of incremental validity studies should inform development of predicted training and career success models that integrate aptitude, personality, and vocational interest.
Acknowledgments
The authors would like to gratefully acknowledge the contributions of both Emily Prim and Ashley Goethe for their assistance in content-coding open-ended responses for Study 3.
Appendix A: Truncated Example of AF-WIN Job Profile Markers.
| Functional Communities (Ntotal = 12) | Job Contexts (Ntotal = 12) | Work Activities (Ntotal = 28) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AFSC Code | Air Force Specialty Title | Special Warfare & Operations | Mechanical & Electrical | Personnel & Services | Weapons & Aerospace | Mental | Work with Teams | Hazardous | Predictable | Analyze Commun-ications | Direct Air & Spacecraft | Interact with People & Customers | Respond to Emergencies |
| 1C4X1 | Tactical Air Control Party (TACP) | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 |
| 1U0X1 | Remotely Piloted Aircraft Sensor Op | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 |
| 2M0X3 | Missile and Space Facilities | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 |
| 2R1X1 | Maint. Management Production | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0 |
| 2W0X1 | Munitions Systems | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 2W1X1 | Aircraft Armament Systems | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 |
| 3A1X1 | Administration | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 |
| 3E8X1 | Explosive Ordnance Disposal | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 |
| 5J0X1 | Paralegal | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 7S0X1 | Special Investigations | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 |
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Notes
1.Supplementary materials from the following studies are available upon request by contacting the corresponding author at james.johnson.271@us.af.mil. Materials include (Study 1) (1) side-by-side comparison of AF-WIN and Navy JOIN survey items, (2) images of AF-WIN survey pages with results page; (Study 3) (1) full-text BMT feedback survey questions with means/standard deviations, (2) select sampling of trainee responses to open-ended AF-WIN “use” question.
2.Before combining data from collection one and two, we visually compared job marker profiles for each career field based on 1) collection one-only data, 2) collection two-only data, and 3) combined collection one and two data. If profiles from data collection one and two were reasonably similar, the combined profile was used. In instances where profiles were different, Career Field Managers were contacted and asked to determine which markers were most reflective of their career field.
Disclosure statement
No potential conflict of interest was reported by the authors.
References
- Alley, W. E. (1978). Vocational interest-career examination; Use and application in counseling and job placement (AFHRL-TR-78-62). Brooks Air Force Base, TX: Air Force Human Resources Laboratory. [Google Scholar]
- Alley, W. E., & Matthews, M. D. (1982). The vocational interest career examination: A description of the instrument and possible applications. The Journal of Psychology, 112, 169–193. doi: 10.1080/00223980.1982.9915374 [CrossRef] [Google Scholar]
- Armstrong, P. I., Smith, T. J., Donnay, D. A., & Rounds, J. (2004). The strong ring: A basic interest model of occupational structure. Journal of Counseling Psychology, 51(3), 299–313. doi: 10.1037/0022-0167.51.3.299 [CrossRef] [Google Scholar]
- Chen, H. T., & Jones, L. A. (2008). Predictive validity and reliability of the Navy vocational interest inventory: Job opportunities in the navy (JOIN) (Technical Note No. 08–3). Millington, TN: Navy Personnel Research, Studies, and Technology Division, Bureau of Naval Personnel. [Google Scholar]
- Clark, K. E. (1955). The use of interest measures with naval enlisted personnel. Minneapolis: University of Minnesota Press. [Google Scholar]
- Clark, K. E. (1961). The vocational interests of nonprofessional men. Minneapolis: University of Minnesota Press. [Google Scholar]
- Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46. doi: 10.1177/001316446002000104 [CrossRef] [Google Scholar]
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences. San Diego, CA: Academic Press. [Google Scholar]
- Donnay, D. A. C., & Borgen, F. H. (1996). Validity, structure, and content of the 1994 strong interest inventory. Journal of Counseling Psychology, 43(3), 275–291. doi: 10.1037/0022-0167.43.3.275 [CrossRef] [Google Scholar]
- Donnay, D. A. C., Morris, M. L., Schaubhut, N. A., & Thompson, R. C. (2004). Strong interest inventory® (Revised ed.). Mountain View, CA: Consulting Psychologists Press, Inc. [Google Scholar]
- Earl, K. (2015). Interest congruence and job satisfaction: A quantitative review. Retrieved from University of Illinois at Urbana-Champaign, Educational Psychology Web site:http://hdl.handle.net/2142/72842
- Farmer, W. L., Bearden, R. M., Fedak, G. E., Watson, S. E., Lightfoot, M. A., Alley, W. E., … Heggestad, E. D. (2006). Development of a Navy job-specific vocational interest model (Technical Note No. 07–2). Millington, TN: Navy Personnel Research, Studies, and Technology Division, Bureau of Naval Personnel. [Google Scholar]
- Farmer, W. L., Fedak, G. E., Bearden, R. M., Lightfoot, M. A., McBride, J. R., Heggestad, E. D., … Rounds, J. (2006). Navy interest inventory: Approach development (Technical Note No. 07–4). Millington, TN: Navy Personnel Research, Studies, and Technology Division, Bureau of Naval Personnel. [Google Scholar]
- Farmer, W. L., Watson, S. E., Alderton, D. L., Michael, P. G., & Hindelang, R. (2006). Improving person-job congruence during the classification process: Item development and initial testing of a pictorial interest instrument (Technical Note No. 06–8). Millington, TN: Navy Personnel Research, Studies, and Technology Division, Bureau of Naval Personnel. [Google Scholar]
- Fisher, R. A. (1973). Statistical methods for research workers (14th ed.). New York, NY: Hafner Publishing. [Google Scholar]
- Guilford, J. P., Christensen, P. R., Bond, N. A., Jr., & Sutton, M. A. (1953). A factor analysis study of human interests (Research Bulletin No. 53–11). Lackland Air Force Base, TX: Human Resources Research Center, Air Research and Development Command. [Google Scholar]
- Guilford, J. P., Christensen, P. R., Bond, N. A., Jr., & Sutton, M. A. (1954). A factor analysis study of human interests. Psychological Monographs: General and Applied, 68(Whole No. 375), 1–37. doi: 10.1037/h0093666 [CrossRef] [Google Scholar]
- Hanson, M. A., Paullin, C. J., Bruskiewicz, K. T., & White, L. A. (2003,
November ). The army vocational interest career examination. Paper presented at the annual conference of the International Military Testing Association, Pensacola, FL. [Google Scholar] - Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 35–45. doi: 10.1037/h0040767 [CrossRef] [Google Scholar]
- Holland, J. L. (1973). Making vocational choices: A theory of careers. Englewood Cliffs, NJ: Prentice Hall. [Google Scholar]
- Holland, J. L. (1994). Self-directed search assessment booklet. Odessa, FL: Psychological Assessment Resources. [Google Scholar]
- Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa, FL: Psychological Assessment Resources. [Google Scholar]
- Hough, L. M., Barge, B., & Kamp, J. (2001). Assessment of personality, temperament, vocational interests, and work outcome preferences. In Campbell J. P. & Knapp D. J. (Eds.), Exploring the limits in personnel selection and classification (pp. 111–154). Mahwah, NJ: Erlbaum. [Google Scholar]
- HP Enterprise Services . (2013,
October 31). An analysis of the value of JOIN survey results in predicting sailor career success using historical data. Prepared for Navy Selection and Classification Office (N-132G). Contract Number: N00189-11-D-Z019. - HP Enterprise Services . (2014,
January 22). A limited analysis of the incremental value of using JOIN survey results above RIDE scores for predicting sailor career success. Prepared for Navy Selection and Classification Office (N-132G). Contract Number: N00189-11-D-Z019. - HP Enterprise Services . (2015,
March 16). An assessment of the impact of gender on JOIN effectiveness as a Sailor career metric predictor. Prepared for Navy Selection and Classification Office (N-132G). Contract Number: N00189-11-D-Z019. - Ingerick, M., & Rumsey, M. G. (2014). Taking the measure of work interests: Past, present, and future. Military Psychology, 26(3), 165–181. doi: 10.1037/mil0000045 [CrossRef] [Google Scholar]
- Jeanneret, P. R., Borman, W. C., Kubisiak, U. C., & Hanson, M. A. (1999). Generalized work activities. In Peterson N. G., Mumford M. D., Borman W. C., Jeanneret P. R., & Fleishman E. A. (Eds.), An occupational information system for the 21st century: The development of O* NET (pp. 105–125). Washington, DC: American Psychological Association. [Google Scholar]
- Kuder, G. F. (1939). Kuder preference record–form A. Chicago, IL: University of Chicago Bookstore. [Google Scholar]
- Losey, S. (2016,
October 8). Air Force recruits most new airmen since Vietnam. Retrieved from Air Force Times Web site: https://www.airforcetimes.com/articles/air-force-recruiting-tops-33k - National Center for O*NET Development . (2010). The O*Net Content Model. Retrieved from O*Net Online website: https://www.onetcenter.org/content.html
- Nye, C. D., Su, R., Rounds, J., & Drasgow, F. (2012). Vocational interests and performance a quantitative summary of over 60 years of research. Perspectives on Psychological Science, 7(4), 384–403. doi: 10.1177/1745691612449021 [PubMed] [CrossRef] [Google Scholar]
- Nye, C. D., Su, R., Rounds, J., & Drasgow, F. (2017). Interest congruence and performance: Revisiting recent meta-analytic findings. Journal of Vocational Behavior, 98, 138–151. doi: 10.1016/j.jvb.2016.11.002 [CrossRef] [Google Scholar]
- Office of Management and Budget . (1997,
May 12). Revisions to the standards for the classification of federal data on race and ethnicity. Federal Register 62FR58781-58790. - Oliver, J., Whetzel, D. L., McCloy, R. A., & DeSimone, J. A. (2011). Preview/job exploration feasibility analysis (AFCAPS-FR-2011-00XX). JBSA Randolph, TX: Air Force Personnel Center. [Google Scholar]
- Rottinghaus, P. J., Hees, C. K., & Conrath, J. A. (2009). Enhancing job satisfaction perspectives: Combining Holland themes and basic interests. Journal of Vocational Behavior, 75, 139–151. doi: 10.1016/j.jvb.2009.05.010 [CrossRef] [Google Scholar]
- Schippmann, J. S. (1999). Strategic job modeling: Working at the core of integrated human resources. Mahwah, NJ: Lawrence Erlbaum Associates. [Google Scholar]
- Strong, E. K., Jr. (1927). Vocational guidance of executives. Journal of Applied Psychology, 11(5), 331–347. doi: 10.1037/h0075674 [CrossRef] [Google Scholar]
- Strong, E. K., Jr. (1943). Vocational interests of men and women. Stanford, CA: Stanford University Press. [Google Scholar]
- Strong, M. H., Jeanneret, P. R., McPhail, S. M., Blakley, B. R., & D’Egidio, E. L. (1999). Work context: Taxonomy and measurement of the work environment. In Peterson N. G., Mumford M. D., Borman W. C., Jeanneret P. R., & Fleishman E. A. (Eds.), An occupational information system for the 21st century: The development of O* NET (pp. 105–125). Washington, DC: American Psychological Association. [Google Scholar]
- Su, R., Rounds, J., & Armstrong, P. I. (2009). Men and things, women and people: A meta-analysis of sex differences in interests. Psychological Bulletin, 135, 859–884. doi: 10.1037/a0017364 [PubMed] [CrossRef] [Google Scholar]
- Tracey, T. J., & Rounds, J. B. (1993). Evaluating Holland’s and Gati’s vocational-interest models: A structural meta-analysis. Psychological Bulletin, 113(2), 229. doi: 10.1037/0033-2909.113.2.229 [CrossRef] [Google Scholar]
- United States Air Force . (2013). Classifying military personnel (Officer and Enlisted) (Tech. Rep. No. AFI-36-2101). Washington, DC: Secretary of the Air Force. [Google Scholar]
- Van Iddekinge, C. H., Putka, D. J., & Campbell, J. P. (2011). Reconsidering vocational interests for personnel selection: The validity of an interest-based selection test in relation to job knowledge, job performance, and continuance intentions. Journal of Applied Psychology, 96(1), 13–33. doi: 10.1037/a0021193 [PubMed] [CrossRef] [Google Scholar]
- Van Iddekinge, C. H., Roth, P. L., Putka, D. J., & Lanivich, S. E. (2011). Are you interested? A meta-analysis of relations between vocational interests and employee performance and turnover. Journal of Applied Psychology, 96, 1167–1194. doi: 10.1037/a0024343 [PubMed] [CrossRef] [Google Scholar]
- Watson, S. E. (this issue). Job opportunities in the Navy (JOIN). Military Psychology, 32, 101–110. [Google Scholar]
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