Michael J. Zickar and Hanyi Min
BOWLING GREEN STATE UNIVERSITY
In this chapter, we trace the beginnings of vocational interest measurement and the subsequent refinements of measures focusing in depth on several seminal measures (i.e., the Strong Vocational Interest Blank). Throughout this chapter, we discuss general trends but also concentrate on several key figures (i.e., Edward Strong and John Holland). In addition, we highlight several forgotten and less successful efforts to remind readers that history is not a linear and straight path from early days to the current situation. Finally, throughout this history we also focus on relevant sociopolitical and scientific events and contexts that influenced the measurement of vocational interests (see Zickar, 2015, for background on many of our historical decisions).
The beginning of the previous turn of the century was an exciting and tumultuous time in the business community, in governmental affairs, and in the fledging social sciences. Historians typically refer to the era in the United States from the 1890s to the 1920s as the Progressive Era as reformers in public and private industry tried to make the country better by improving and expanding access to education, addressing worker safety, advocating for the right of women to vote, tackling serious problems like alcoholism and drug use, and improving the country’s health through better food safety and nutrition. One of the first people to be concerned with vocational interests was Frank Parsons, a civil engineer for a railroad who then worked as a public school teacher before becoming a lawyer and then eventually a professor at Boston University and Kansas State Agricultural College. Mr. Parsons wrote and lectured on a variety of topics popular in the Progressive Era including abuses in the railroad industry, the importance of public ownership of utilities, and the need to expand democracy throughout our government (see Davis, 1969; Zytowski, 2001). Relevant to this chapter, one of the last passions of Parsons’s was the idea of matching people to appropriate careers and occupations. By finding the right jobs for people, he argued that both productivity and happiness would be maximized. The last book that Parsons published (actually it was published posthumously) was entitled Choosing a Vocation (Parsons, 1909). In that book, he advocated the importance of matching an individual’s aptitudes, abilities, ambitions, and limitations with the knowledge and requirements of jobs. In 1905, Parsons created a Breadwinner’s Institute, which offered courses for the working poor. In 1908, he created the Vocation Bureau of Boston, which was designed to educate the working poor to increase their job skills and to make better vocational decisions (see Brewer, 1942; Mann, 1950). Unfortunately Parsons died tragically early, soon after the founding of the Vocation Bureau. Inspired by the work of the Vocation Bureau of Boston, a National Vocational Guidance Association was created in 1913 and work was stimulated around the country to help people make better career decisions.
One problem was that there were few tools to help individuals make better occupational choices. Parsons searched the extant psychological literature at the turn of the century but did not find much help. One subsequent commentator stated that “when he went to the psychological laboratories for techniques he found that the cupboard was bare” (Paterson, 1938, p. 37). A few early efforts were made at assessing vocational interests. Jesse Davis, a principal in Grand Rapids public schools, created a Student Vocational Self-Analysis tool for use by 10th graders; this tool had a structured set of items (e.g., “What can you do better than others of your age?” and “Have you any weakness or temptation that would endanger your career in any particular vocation?”), though there was no proposed way to score these open-ended items (Davis, 1914). Soon after, psychologists developed the technology, tests, and techniques needed to provide guidance counselors with useful information about individuals.
Psychological testing began in earnest with the United States’ entrance into World War I (WWI). Prior to that time, the U.S. military was quite small and unprepared for the scale of combat faced on the European continent. Upon the entrance of the United States into WWI in April of 1917, the military was faced with the need to screen large numbers of applicants and place them into positions that were suited to their skills, talents, and abilities. Several psychologists, led by Robert Yerkes, took lessons learned from the recently published Stanford-Binet Intelligence Test (Terman et al., 1915), and developed a series of tools that could be administered in large groups to assess cognitive aptitude, temperament, and trade skills. Some of these tests were developed in time to be used operationally, whereas others were developed too late to be of use (see Yoakum & Yerkes, 1920).
Soon after the War, however, businesses and organizations started seeing the value of psychological testing to help them make better decisions (Katzell & Austin, 1992). Private consulting companies such as the Scott Company and the Psychological Corporation included key psychologists who had worked on the war testing effort and marketed psychological tests to industry for hiring purposes. Tests proliferated to measure a variety of constructs. Kornhauser and Kingsbury (1924), in an early survey postwar of psychological tests for industrial use highlighted tests designed to measure general intelligence, general mechanical aptitude, physical and motor abilities, sensory capabilities, character, temperament, and emotional traits. Several of these tests have names that are still well-recognized (e.g., Stanford-Binet), though many of them have faded into obscurity (e.g., Chicago Group Intelligence Test, Downey Will Temperament Test, and the Voelker Tests of Habits of Moral Trustworthiness). Although many of the complex statistical techniques (e.g., factor analysis) used to analyze tests had not yet been developed, nor had classical test theory been formulated, researchers such as Kornhauser and Kingsbury (1924), Freyd (1925), and Link (1919), among others, were advocating a scientific approach to test construction and evaluation.
Given all of the previously mentioned excitement related to vocational guidance and the sudden availability of psychological tests, it was not surprising that tests designed for selection were soon promoted to be used for vocational guidance, as well as new tests being developed specifically for that purpose (see Harrington & Long, 2013). Kornhauser and Kingsbury (1924) noted “professional vocational counselors, whether connected or not with schools, have hitherto made much less use of psychological tests than it seems likely they will in the future” (p. 156). They noted that intelligence tests were used by counselors to help guide decision-making. In addition, researchers studied the relationship between intelligence test scores and occupational achievement; Fryer (1922) studied military data for 96 occupations and found the average intelligence level differed, ranging from the highest for engineer officer and lowest for laborer. Feingold (1923) examined high school students and investigated the match between their intelligence and the intelligence demanded of their occupational choice, finding that over 50% of students made bad vocational choices based on a mismatch. This focus on ability was important as it highlighted some of the occupations for which an individual might be successful, but these tests did little to identify which tests might be most satisfying, motivating, and interesting to individuals.
One individual, who received some of his key training during the Great War effort, would take the knowledge gained from test construction in ability tests and apply these techniques to develop the first prominent vocational interest inventory. In the following section, we present a brief history of Edward K. Strong and then detail his creation of the Strong Interest Inventory, as well as follow subsequent developments.
Edward K. Strong, according to his daughter, loved his work and he wanted others to enjoy their work, which might be the reason why he devoted a lot of time to develop Strong’s Vocational Interest Blank (SVIB; Hansen, 1987). Strong got his PhD degree from Columbia University in 1911. During WWI, Strong served as a member of the Classification of Personnel Committee in the Army. During this time, Strong began to “identify the need that individuals have for career guidance and vocational development, and he began to appreciate the efficiency of the army trade tests in determining a person’s fitness for a job” (Hansen, 1987, p. 120). After the war, Strong joined the Carnegie Institute of Technology, where a pool of 1,000 items used to differentiate people in various occupations was developed during a seminar conducted by Clarence S. Yoakum. The first Strong Vocational Inventory borrowed items from this item pool, as well as from several other existing vocational inventories, for example, the Occupational Interest Inventory and the Interest Report Blank (Donnay, 1997). Yoakum’s item pool inspired several other vocational inventories, the Occupational Interest Inventory (Freyd, 1922), the Interest Report Blank (Cowdery, 1926), and the Carnegie Interest Inventory (Carnegie Institute of Technology, 1920), though none of these inventories had the staying power of Strong’s inventory. In the fall of 1923, Strong moved to Stanford University and remained there for the rest of his career. At Stanford University, the first edition of the Strong Interest Inventory was published in 1927.
Strong’s original inventory was named Strong’s Vocational Interest Blank, and then changed to Strong-Campbell Interest Inventory. Now Strong’s original scales and other complementary scales developed later are referred to as the Strong Interest Inventory.
The first edition of the SVIB contained 10 Occupational Scales. The development of the SVIB was based on assumptions that interests are located on a dimension of liking and disliking, and that people interested in and people working in a particular occupation share similar likes and dislikes (Donnay, 1997).
The use of SVIB Occupational Scales rested on the differences in responses of members of various criterion groups and men-in-general or women-in-general groups (Strong, 1943, 1963). Reference groups of Strong’s Vocational Interest Blank were made up of 150–500 individuals from a specific occupation (Super & Crites, 1962). The reference group was selected based on several standards: (1) they needed to work in the occupation for at least three years, (2) they needed to be satisfied with their work, and (3) they needed to have minimum proficiencies for their work, such as certification (Campbell, 1971). Responses of reference groups and that of men-in-general, or women-in-general, were compared. Then based on how large differences were between these two groups, items were selected or assigned different weights on the scoring scale for that occupation (Campbell, 1971). Scores of the original occupation sample were standardized on a scale with mean of 50 and standard deviation of 10, and used as norm of scoring. Because standardized scores can be difficult to interpret, letter grades were assigned to respondents based the range of scores, including A, B+, B, B-, C+, or C (Strong, 1943).
Worth noting is that the scoring of the SVIB should not focus on specific occupations, but on the patterns of scores. Score patterns allow interpretation of types of occupations rather than specific occupation. Also it is easier to see whether higher scores in one occupation are supported by high scores in other relevant occupations by looking at score patterns (Super & Crites, 1962). For example, it is more important to know that a respondent’s primary interest patterns are in the scientific and literary occupations with a secondary pattern in the social welfare field, than to know that he or she scored A’s as psychologist, physician, chemist, and other specific occupations (Super & Crites, 1962).
Results of the SVIB were proven to be consistent and stable over time. The average odd-even reliability coefficient of 36 revised scales was .88 for 285 Stanford senior males, with only one scale falling below .80 (Strong, 1943). And the average test-retest coefficient for an interval of a week was .91 (Strong, 1943). Also, studies indicated that reliabilities might differ for different types of respondents. For example: for 10th graders (135), average test-retest coefficient after two years was .57 for 7 typical scales; for 11th graders (813) after three years it was .71 (Van Fridagh Taylor, 1942). These results suggest that interests stabilize more as students get older.
Strong provided the first evidence of predictive validity for Vocational Interest Blank with a longitudinal study. Strong (1935) collected longitudinal data over a five-year interval from senior males at Stanford and found:
(1) Men continuing in an occupation obtain a higher interest score in it than in any other occupation; (2) Men continuing in an occupation obtain a higher interest score in it than men entering some other occupation; (3) Men continuing in an occupation obtain higher scores in it than men who change from that occupation to some other; and (4) Men changing from other occupations to occupation X score higher in X prior to the change than they did in other occupations. (Strong, 1935, p. 335)
Numerous researchers also examined concurrent and predictive validity of the SVIB following Strong’s early effort. Donnay (1997) summarized results of various validity studies and transformed them into consistent formats: percentage of good hits. Donnay (1997) showed that 24 studies reported direct and indirect good hits at the rate of 32% to 69%, which provided support for the validity of SVIB.
As one of the earliest scales still in use, Strong’s Vocational Interest Blank has gone through many revisions to avoid obsolescence. These revisions updated items of the original SVIB and expanded the number of Occupational Scales (Strong 1962; Strong, 1963; Donnay, 1997). Below are three major revisions of the SVIB.
In the 1968 revision, the first group of homogeneous content scales, Basic Interest Scales, were added to the Strong Interest Inventory. A few limitations of the Occupational Scales were hindering scale operation and interpretation: (1) further psychological interpretation of the empirical scales is difficult. For example, what does it mean if a respondent has interests that are similar to lawyers? (2) There was a large number of empirical occupation scales; and (3) the empirical scales were difficult to work with in research (Campbell, Borgen, Eastes, Johansson, & Peterson, 1968). The 22 Basic Interest Scales were developed aiming to address these limitations of the empirical Occupational Scales and to provide specific occupational areas that would be good fits for respondents.
Basic Interest Scales were built by identifying important clusters of interests represented in the SVIB (Campbell et al, 1968). Campbell (1971) pointed out that Basic Interest Scales were a supplement of the original Occupational Scales, with relatively less items and could be generalized beyond one particular occupation. Intercorrelations between items were examined. Then highly correlated items were treated as a cluster to represent one of the 22 general interests (e.g. Culinary Arts, Teaching and Education, and Medical Science).
The test-retest reliability of Basic Interest Scales was roughly equivalent to the SVIB Occupational Scale. Campbell et al. (1968) reported the average test-retest reliability was .89 for an interval of thirty days and .65 for an interval of two years. Seven concurrent and predictive validity studies (Donnay, 1997) showed that direct and indirect good hits of Basic Interest Scales at the rate of 21% to 75%, which was comparable with the Occupational Scales. Campbell et al. (1968) also reported the scores of Basic Interest Scales were related to occupational choice.
In 1974, David P. Campbell first combined separate forms for male and female into a single-sex form, called the Strong-Campbell Interest Inventory (Campbell, Crichton, Hansen, & Webber, 1974). The 1974 revision increased the number of Occupational Scales to 124 and added scales that assessed Academic Comfort and the personality trait of Introversion-Extroversion. Most importantly, the 1974 revision developed the General Occupational Themes. The General Occupational Themes incorporated Holland’s theory of six personality types for classifying individuals and occupations: Realistic, Investigative/Intellectual, Artistic, Social, Enterprising, and Conventional (RIASEC). Campbell and Holland (1972) used the General Occupational Themes to analyze the vast Strong archival data and reported that the mean scores conformed well. This indicates “the first use of theory framework to organize and interpret the earlier empirical efforts of Strong” (Donnay, 1997, p. 12). The General Occupational Themes showed predictive validity nearly as high as the Occupational Scales for males, with direct and indirect good hits at the rate of 31% to 69%. But it was less predictive for females with the rate of good hits ranging from 13% to 53% (Donnay, 1997).
Four Personal Style Scales were added to the Strong Interest Inventory in the 1994 revision, measuring respondent’s broad living and working preferences. The four scales were Work Style, Learning Environment, Leadership, and Risk Taking/Adventure (Harmon, DeWitt, Campbell, & Hansen, 1994). Researchers had found that there was a relationship between vocational interest and personality (Costa, McCrae, & Holland, 1984). In addition, within the field of Industrial and Organizational (IO) psychology in general, the inclusion of the five-factor model as well as the development of several popular Big Five personality inventories had resulted in the usage of personality in all aspects of work-related psychology (see Hough & Oswald, 2008). The inclusion of Personal Style Scales reflected this trend of incorporating personality into vocational interest measurement and interpretation (Borgen & Harmon, 1996). The four Personal Style Scales were found to differentiate occupational and educational groups consistent with theoretical expectations (Harmon et al., 1994).
Another innovation of the 1994 revision was development of the Skills Confidence Inventory (SCI) as a companion to the traditional interest inventory booklet. The development of the SCI was a combination of measurement for interest and self-efficacy in a particular interest dimension (e.g., interest and self-efficacy in Realistic occupations), in order to better interpret vocational interests (Betz, Harmon, & Borgen, 1996). The result of the SCI was a reflection of respondent’s confidence level in the six Holland themes. Betz et al. (1996) reported that score patterns of SCI were consistent with predicted group and occupational differences and within-occupation score profiles were consistent with the Holland interest codes of each occupation, which supported the validity of the SCI. Betz et al. (1996) suggest that the measurement of interest-based self-efficacy can be used by counselors in giving career advice.
The Strong Interest Inventory has had the longest history of any currently used psychological test (Donnay, 1997). Kuder (1977, p. xii) wrote “the firm foundation of scholarly research provided by Strong for further advance in interest measurement is so extensive that it is almost impossible to do anything in the field that does not have some background in Strong’s work.”
Many studies Strong conducted on vocational interests as early as the 1930s or 1940s are still meaningful for researchers today. For example, Strong realized there were gender differences in vocational interests and published separated forms of interest inventory for male and female. Strong (1943) also found that interests had cross-cultural similarity. Moreover, Strong (1955) pointed out that career counseling should not be restricted to youth and should occur all through life at the time when a majority thought career counseling was only appropriate for high school and college students.
The methods Strong used to develop Strong’s Vocational Interest Blank were also influential for later studies. Strong’s contrasted-groups method of empirical scale construction was adopted to develop many well-known and widely used questionnaires, such as the Minnesota Multiphasic Personality Inventory (Donnay, 1997; Hathaway & McKinley, 1940). Strong’s criterion-related measurement used in the SVIB along with Kuder’s content-related measurement represent the foundation of vocational interest measurement (Donnay, 1997).
Although we devote a large amount of space to Strong’s work, other vocational interest inventories were developed and had various degrees of success. For example, in the Buros Mental Measurement Yearbook of 1940, seven tools were mentioned, including Strong’s inventory, as well as Cleeton’s Vocational Interest Inventory (1935), which used the original 1,000-item pool from Yoakum to create a 670-item inventory that provided scores for occupational groups. Other inventories included Brainard and Stewart’s Specific Interest Inventory (1932), Thurstone’s Vocational Interest Schedule (1935), and Le Suer’s Occupational Interest Blank (1937). In addition, the Diagnostic Interest Blank was a projective interest inventory with 80 incomplete sentences that were scored on a variety of dimensions (Forer, 1948). Excepting Strong’s inventory, these tests generally fell out of favor in a short amount of time. The one other test from that time that had staying power was the Kuder Preference Inventory (Kuder, 1939) which was developed by Dr. Frederic Kuder.
To pursue a career in vocational guidance, Frederic Kuder received a master’s degree in education at the University of Michigan. After graduation, he went to Procter & Gamble where he worked with Marion Richardson to develop a scale used to measure the effectiveness of sales personnel, work that resulted in the Kuder-Richardson index of reliability, one of the first statistical indexes of that important psychometric concept. During the Great Depression, P&G closed their research shop and he went to Ohio State University where he studied with Herbert Toops, receiving his dissertation in 1937. He began his academic career at the University of Chicago where he worked as a university examiner; while at Chicago he developed the Kuder Preference Record interest inventory, which he continued to refine after moving to Duke University (see Zytowski & Austin, 2001).
The Kuder Preference Record (KPR) was originally published by the University of Chicago Bookstore (later by Science Research Associates) and included 330 pairs of forced-choice items where respondents had to express their preference between pairs of activities such as “Visit the U.S. Senate” or “Visit an art museum.” The inventory was scored on seven basic interests: Scientific, Computational, Musical, Artistic, Literary, Social Service, and Persuasive. One of the key selling points to the KPR was a self-scoring edition that included a stylus and a specialized answer key that could be used to generate gender-specific percentile scores (see Zytowksi, 2014). Before computerized editions of tests that would make collection and scoring of data extremely easy, such practical advantages of tests were often important in helping marketing. Subsequent editions of the KPR added scales for Mechanical and Clerical, and Outdoor. The KPR remained in print until 2002. Kuder-related products still remain in use, with Kuder, Inc. providing many other career-planning tools.
Borgen (1986) identified the Strong Vocational Interest Blank and the Kuder Preference Record as two of the “Big Three” interest inventories, scales that had the largest impact in the field. The third of the Big Three, John Holland’s Self-Directed Search is the focus of the next section.
John L. Holland was one of the most prominent theorists in the field of vocational interests and warrants a short biographical foray. Holland grew up in Omaha Nebraska and received bachelor’s degrees in psychology, French, and mathematics. After graduating in 1942, he served in the Army for 3.5 years. Given his psychology major, he was given the responsibilities to interviewing hundreds of new recruits, as well as administering psychological tests. This experience led him to start thinking that people fall into a relatively small number of types (Hansen, 2011; Weinrach, 1980). Holland got his PhD degree in psychology in 1952 at the University of Minnesota, in a department well-versed in scale development and individual differences. Even though Holland is well known as a theorist for his six typologies in vocational interests, he was also “an effective empirical researcher and a psychologist who valued the importance of applied psychology” (Hansen, 2011; p. 1213). He started his career by working in applied settings such as the Counseling Center at the Western Reserve University and the American College Testing Program. In 1969, he moved to Johns Hopkins University where he stayed until retirement. This variety of applied and academic settings might have been responsible for his work, which was both theoretically important, rigorously conducted, and with significant applied implications.
The limitations of existing vocational interest inventories, such as Strong’s Vocational Interest Inventory (SVIB; Strong, 1927) and the Kuder Preference Record (KPR; Kuder, 1939), motivated Holland to develop the Vocational Preference Inventory (VPI). First, the existing inventories were difficult to interpret and extrapolate beyond what their occupation scales assessed directly (Nauta, 2010). Meanwhile, it took a long wait to score existing vocational interest inventories, a concern for someone like Holland who had experience using inventories in counseling settings (Nauta, 2010; Weinrach, 1980). Finally, the existing inventories did not allow easy linkage between the respondent’s score and environments (Nauta, 2010). Thus, the VPI was constructed aiming to give a maximum amount of reliable and valid information with a minimum amount of testing and scoring time, skill, and expense (Holland, 1958).
The VPI was published in 1958. Holland obtained the formulations of VPI by reviewing 30-plus Occupational Scales of the SVIB and writing brief interpretations (Weinrach, 1980). Holland considered the VPI as a personality inventory using occupational title for content. Subjects respond to 300 items in the VPI by indicating either “interest” (appeal) or “dislike.” Result of the VPI reflects respondents’ feelings and attitudes about occupational titles (Holland, 1958).
Holland (1958) provided evidences of reliability and validity of the VPI. The internal consistency coefficients of the VPI ranged from .72 to .95 and .68 to .90 for male and female samples, respectively. Test-retest reliability coefficients ranged from .70 to .87. Also, the VPI significantly differentiated a number of defined criterion groups, which proved construct validity of the inventory. Moreover, the VPI scales were found to be consistent with the SVIB and KPR (Weinrach, 1980).
The hexagonal RIASEC typology is the core of Holland’s theory and also one of the most influential theories in vocational psychology. The typology is often referred to by individuals who have no training in psychology and might be the second most popular psychological typology after the Myers-Briggs personality types. The typology came out of elaboration of the VPI rationale; the rationale for the first 6 scales of the VPI formed a primitive account of the RIASEC typology. Then when Holland and Whitney were searching for a way to order the typology by recording the correlations between the different scales of the VPI, the hexagonal model was built (Weinrach, 1980).
The main idea of Holland’s theory is that people resemble a combination of the six orientations: Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (abbreviated to RIASEC).
Each orientation represents a somewhat distinctive life style which is characterized by preferred methods of dealing with daily problems and includes such variables as values and interests, preferences for playing various roles and avoiding others, interpersonal skills and other personal factors. (Holland, 1959, p. 36)
Each person has his/her own hierarchy or quasi-serial order of orientations according to those orientations’ relative strengths (Holland, 1959). When making vocational choices between different classes of occupations, people actively search for environments that satisfy their hierarchy of orientation based on their knowledge for themselves and their environments (Holland, 1959).
Holland (1959) also developed a corresponding typology for occupational environments in his theory. Holland based this part of his theory on Linton’s (1945) notion that environment can be shaped by people in it. Holland pointed out that environments were dominated by one type of people and people would behave in a way that was consistent with their typology (Holland, 1997). People would find environments reinforcing and satisfying when environment patterns resembled their personality pattern (Holland, 1997). Also, the parallel typologies of person and environments provided theory framework for development of Self-Directed Search, which will be more specifically discussed below.
Moreover, Holland pointed out that people and environment would interact with each other to influence behavior. Person-environment congruence was found to predict a person’s occupation choices (Holland, 1997) and occupation stability (Donohue, 2006). Employed adults in the process of changing their career move in a direction of greater congruence (Oleski & Subich, 1996). This idea would later be elaborated upon by Benjamin Schneider in his Attraction-Selection-Attrition (ASA) model (Schneider, 1987).
The Self-Directed Search (SDS) is an extension of Holland’s RIASEC model and “a practical, self-help device—a pair of booklets that helps a person summarize who he/she seems to be and explore some occupational alternatives” (Weinrach, 1980; p. 408). The result of the SDS is a three-letter summary code, which represents a preference style. The order of the three letters is hierarchical, with the first letter representing the strongest preference (Weinrach, 1996).
The development of SDS was a long process. After the VPI, Holland kept constructing interest inventories believing that interest inventories could be made more helpful to clients (Nauta, 2010). Holland constructed the Personal Survey and the ACT College Guidance Profile before SDS. The personal survey was developed to assess people’s resemblance for different typologies; however, this scale did not seem to outperform VPI; the ACT College Guidance Profile was a combination of many different sections, which made the scale difficult to self-score or direct compare scores of different sections (Weinrach, 1980). He then constructed the SDS in 1971 by “looking at old item analyses; old monographs in which life goals, self-ratings, activities, personality scales had been organized around the VPI; selected SVIB scales; and field of study” (Weinrach, 1980, p. 409). Later revisions of the SDS simplified the scoring procedure (Nauta, 2010) and reduced sex differences (Weinrach, 1980).
Nauta (2010) reported that test-retest reliability of the SDS was from .73 to .88 for an interval of 12 weeks. Gottfredson and Holland (1975) examined the validity of the SDS by looking at the percentage of correctly predicted career choice categories for each predictor: hits for the SDS range from 25.5% to 52.1% for men and from 49.5% to 72.4% for women.
The RIASEC typology, VPI, and SDS are the major part of Holland’s theory and measurements, but Holland’s theory and instruments were not limited to these. Holland’s theory also incorporated self-evaluation and self-knowledge. Holland (1959) pointed out that people’s choices within an occupation class were a function of self-evaluation and ability to perform adequately in their chosen environment.
In addition to measurements for person, such as SDS, Holland also constructed instruments for assessing environments with respect to the RIASEC types. For example, the Position Classification Inventory (Gottfredson & Holland, 1991) and the Environmental Assessment Technique (Astin & Holland, 1961) were developed to classify work and educational environments using the RIASEC types. With these measurements classifying environments into RIASEC, respondents could have a better idea about whether the environment typology is congruent with their personality typology, therefore facilitating their career decisions.
Holland’s theory had a great influence on research and practice in vocational psychology. One commentator stated: “Prior to the introduction of Holland’s theory, the majority of the research on test development was atheoretical and empirical. Holland’s theory brought order to the entire field and provided a much sharper and more clear-cut focus” (Hansen, 2011, p. 1215).
Holland’s works not only represented a breakthrough in the field at his time, but also has had continuous influence in vocational psychology. Nauta (2010) calculated articles in various counseling-related and career-related journals between 1999 and 2009 and found that 47% of the articles published in the Journal of Career Assessment, 32% of those in the Career Development Quarterly, 18% of those in the Journal of Vocational Behavior, and 18% of those in the Journal of Career Development had cited Holland’s work at least once.
Although this chapter has focused on relatively distant history, we did want to summarize some recent history, especially drawing connections with this earlier history. Here we summarize a few key contributions to the vocational interest literature.
The United States Department of Labor created the Dictionary of Occupational Titles (DOT) during the throes of the Great Depression to help better categorize the fundamental attributes of jobs so that unemployed workers could be matched with appropriate jobs (see U.S. Department of Labor, 1993). In 1990, the secretary of labor commissioned an advisory panel to evaluate the DOT and recommend improvements. That panel recommended the development of a new system that provided technical and scientific improvements as well as a computerized database system that would integrate various types of occupational information. This panel led to the creation of the Occupational Information Network, also known as O⋆NET. O⋆NET is an internet-based system that provides job analytic data on a large number of jobs and links characteristics of jobs to individual assessments in a way that facilitates use by both employers and job-seekers (see Peterson et al., 2001). Within the O⋆NET, occupations are assessed using the Interest Profiler (Lewis & Rivkin, 1999), which is based on Holland’s RIASEC taxonomy. In addition, Rene V. Dawis’s 6-dimension taxonomy of work values, which is assessed by the Work Importance Locator (McCloy et al., 1999), was used to describe common values for each occupation (Dawis, 1991). Occupations are described on other characteristics such as education required, physical skills needed, and many other aspects.
O⋆NET is a significant leap forward in the matching of individuals to relevant jobs because it combines individual assessments with occupational information. Converse, Oswald, Gillespie, Field, and Bizot (2004) demonstrated the utility of matching individuals as assessed through O⋆NET tests with occupations. Specifically, the integration of abilities and personality data along with correspondent interest data along with job requirement data allow for identification of occupations that are consistent with a person’s abilities, personality, and interest. The development of the O⋆NET, its comprehensiveness, and its accessibility have significantly advanced the practice of person-job matching. The work of Holland continues through the internet age.
In the early days of vocational interests, statistical theory was just developing with statistical technology lagging behind. As statistical practice developed, vocational interest researchers took advantage of the new insights provided. For example, Strong used exploratory factor analysis to refine his individual scales. As a further extension of the role of statistics, Jackson (1977) used exploratory factor analysis to develop the scales and dimensions of the Jackson Vocational Interest Survey. In one step further, as factor analysis has developed to include confirmatory methods, Armstrong, Rounds, and Hubert (2007) applied confirmatory factor analysis on an old dataset (Guilford, Christensen, Bond, & Sutton, 1953). They found that the data did not fully support Holland’s RIASEC model and investigations into alternative basic interest typologies were recommended.
There have been several other creative usages of modern psychometric and statistical techniques to develop important insights into the nature of vocational interests. Structural equation modeling (SEM) has been used to examine short-term consistency in vocational interest, given SEM can help disentangle the effect of measurement error and true consistency (Gaudron & Vautier, 2007). SEM was also used to investigate the relationship between vocational interests and other constructs, such as career choice (Tokar & Jome, 1998) and career indecision (Guay, Senécal, Gauthier, & Fernet, 2003). The ability to untangle measurement error and construct-relevant variance provides an important advance as does the rigorous approach to goodness-of-fit that allows researchers to determine whether hypothesized structures fit the data. In addition, more recent advances in applying SEM-based approaches to longitudinal data and multilevel data will likely provide more insights into the measurement of vocational interests.
Item response theory (IRT) is a psychometric framework that uses a statistical model to relate properties of items and latent traits to item response behavior (see Drasgow & Hulin, 1990). This approach, in contrast to classical test theory, allows researchers to choose between various models and provides rigorous tests of fit to see which particular model fits best (Zickar & Broadfoot, 2008). In addition, IRT allows researchers to use powerful psychometric tools such as computerized adaptive testing and differential item functioning (DIF), tools that could be useful in vocational interest measurement. Tracey (2010) used IRT to develop a short version of the Personal Globe Inventory and delete items with DIF across either gender or ethnicity. IRT is extremely useful in determining how best to shape a test to maximize psychometric information and minimize the number of items, and to identify items that function differently across groups. Tay, Drasgow, Rounds, and Williams (2009) applied a new IRT model—the generalized graded unfolding model (GGUM)—to three vocational interest inventories: the Occupational Preference Inventory, the Interest Profiler, and the Interest Finder, and they found that GGUM showed better fit across all three inventories. This research is important and should spur additional research. The unfolding model that Tay et al. (2009) used relies on a psychometric model radically different from traditional psychometric approaches. Under this model, the probability of affirming an item depends on the distance between a person’s latent trait and an item’s location parameter. People can reject an item because they are too low on a latent trait or too high on the latent trait, relative to the item. This is different from traditional models in which more is always better. This assumption seems appropriate with ability items in which having more of an ability should result in a higher likelihood of answering an item correctly. With vocational interests, however, it may be possible that a person who rejects an item may have too much of a particular type of interest or too little of it. Tay et al.’s (2009) interesting findings should be explored in future work.
Several researchers have applied more than one method to investigate the structural validity of vocation interest models. Nagy, Trautwein, and Lüdtke (2010) used the randomization test of hypothesized order relations (RTOR) and confirmatory factor analysis (CFA) to three vocational interest models with a German sample: Holland’s circular model, Gati’s (1979, 1982, & 1991) hierarchical model, and Rounds and Tracey’s (1996) alternative hierarchical model. They found RTOR supported all three models, whereas CFA only supported Holland’s model. Although these two techniques did not provide identical results, the ability of contemporary statistical techniques to compare the model fit of various models has the potential to clarify the underlying structure of vocational interests. Gupta, Tracey, and Gore (2008) examined Holland’s RIASEC model across five ethnic groups, Caucasian/Euro-Americans, African Americans, Asian American, Latinos, and Native Americans, using four different methods, RTOR, multidimensional scaling (MDS), Circular Stochastic Process Model with a Fourier series correlation function (CSPMF), and circular unidimensional scaling (CUS). Their results indicated that nonparametric methods showed more support for Holland’s RIASEC model whereas SEM-based ones showed less support. Because these studies indicated that different methods might lead to different results, application of multiple statistical methods and investigation into potential reasons for inconsistency were recommended. As methodology keeps developing, we expect that these new methods will continue to be applied to vocational interest measurement. The ability to evaluate goodness-of-fit is perhaps the most useful advance of these various new techniques.
Another area of focus in recent research is exploring the relation between occupational interests and job performance. Early researchers such as Holland and Strong did consider the relationship of interests with job performance, specifically that the congruence between job demands and persons interests could result in increased performance. Advances in meta-analysis, however, provided the stimulation of being better able to synthesize the results across a large number of primary studies. Hunter and Hunter (1984) meta-analyzed the relation between interests measured by the Strong Interest Inventory and job performance and found a corrected correlation of only ρ = .13, discouraging future research. Recent researchers, however, have focused on how occupational interests may correlate with a wider variety of criteria. Van Iddekinge, Putka, and Campbell (2011) found cross-validated Rs that varied from .25 to .46 across a variety of criteria, including job knowledge, job performance, and intent to stay at an organization. In addition, they found that vocational interests predicted criterion even after controlling for cognitive ability and Big Five personality traits. Nye, Su, Rounds, and Drasgow (2012), in a meta-analysis of 60 primary studies, found that interest inventory scores correlated with performance and persistence in both academic and work contexts. In addition, they found that congruence measures correlated even higher with these outcomes. These studies suggest that measurement of vocational interests is not only helpful for providing individuals with information that may improve their vocational decision-making, such information may be useful for helping organizations make better hiring decisions. Much more research needs to be done about using interest inventories in this manner, but this research is promising.
In this chapter, we traced the beginnings of vocational interest measurement to the Progressive Era’s urge to improve living conditions, as well as the passion for psychological testing that grew out of the World War I efforts. The history of vocational interest inventories is one that combines a concern for psychometric theory and testing along with a notion for using these tests to help individuals make better life decisions. As can be seen in our brief review of some of the major test creators, they combined these two passions. We believe the history of vocational testing is an area that psychologists can point to with pride, an area where their scientific rigor was used to help improve the lives of countless individuals.
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