The Relationship Between Interests and Values in Career Decision Making

Abstract

Personal attributes such as interests and values are typically combined as part of an integrated career assessment to help clients examine, clarify, and integrate self-knowledge. Although most researchers agree there should be some relationship between these constructs, the empirical evidence is scarce and yields mixed results. The relationships between career interests (as measured by the Choices Interest Profiler) and work values (as measured by the Choices Work Value Sorter) were examined in this study using a sample of 57,032 individuals. Results showed good internal consistency reliabilities for career interests (all αs above .93), but extremely poor internal consistency reliabilities for work values (five of the six were negative). The low reliabilities for work values were due to the ideographic model for measuring work values. It is proposed that measuring work values nomothetically (as abilities and interests are measured) would improve the psychometric properties of values scales and make them more useful in career guidance. As would be expected, the correlations between career interests and work values were all close to zero.

Keywords

interests work values career assessment nomothetic measurement

The importance of vocational guidance, career education, and career counseling cannot be underestimated in contemporary society. Trends related to the global economy such as high unemployment rates, economic recession, advanced technology, and stagnant job creation have forced youth and workers in the United States to compete for jobs locally, nationally, and internationally. Career practitioners will need to help future workers become managers of their own careers and develop, refine, and implement career decisions on an intermittent, albeit recurrent basis. Making appropriate occupational and life role decisions will require a high level of self-understanding (Niles & Harris-Bowlsbey, 2013). Thus, career interventions to help clients examine, clarify, and integrate their self-concepts will be paramount. Career assessment can facilitate the self-discovery process and be used as a source of data in this process.

Career assessment has long been guided by Parson’s (1909) notion of person by environment fit (P×E). That is, self-understanding and knowledge of the world of work must be considered in relation to each other and the realities of the job market. Trait and factor approaches assume that traits (abilities, interests, values, and personality) can be measured objectively and correlated with job dimensions to predict work-related outcomes. This represents a modern, nomothetic approach to understanding an individual—traits can be classified, measured, averaged, compared, and generalized. Although this may be criticized from a postmodern standpoint, scientifically rigorous career assessment tools are supported by data, offer standard methods of interpretation, allow for comparison with a norm reference group, and help counselors determine the appropriateness of the instrument for their client. Moreover, career assessment tools can be used to predict future performance, discriminate occupational groups an individual resembles and has probability of success in, monitor career maturity or readiness to make a choice, and evaluate goal attainment (Herr, Cramer, & Niles, 2004).

Interests and values are two of the most frequently assessed individual difference variables within career counseling (Watkins, Campbell, & Nieberding, 1993). Interests, an individual’s preferences for certain activities, are a staple in career assessment tools and career counseling interventions (Hansen, 2005). Interests can be identified through direct questioning (expressed interests) or by providing a comprehensive interest inventory (measured interests). Expressed interests represent idiosyncratic, nonscaled, and idiographic responses, while responses on interest inventories are normative, scaled, and nomothetic (Lamiell, 1987). From a trait and factor perspective, one would assume that measured interests provide better predictive utility than expressed interests. However, the opposite is true; expressed interests better predict careers that people eventually pursue (Dolliver, 1969; Spokane & Decker, 1999). Silvia (2001) explained why this might be true. He proposed that expressed and measured interests are conceptually distinct—measured interests represent general attitudes and preferences while expressed interests represent specific vocational intentions to pursue a given occupation. Similar to Lent et al. (1994), Silvia suggested a causal relationship such that attitudes and preferences (measured interests) inform action intentions (expressed interests). He concluded that “their different predictive power reflects psychometric principles regarding abstract predictors and single-act criteria” (p. 383). That is, measured interests capture attitudes and preferences at varying levels of specificity (work in general, broad categories of work, and specific careers), while expressed interests refer to a very specific action intention. Thus, expressed interests will better predict career choice while measured interests will predict equally broad variables (Silvia, 2001).

The Self-Directed Search (Holland, 1971, 1985), Strong Interest Inventory (Harmon, Hansen, Borgen, & Hammer, 1994), and the Campbell Interest and Skill Survey (Campbell, Hyne, & Nilsen, 1992) are the three most frequently used interest inventories (Hansen, 2005). All three are rooted in Holland’s (1997) theory of vocational personalities and work environments and delineate scales to measure the six Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (RIASEC) personality/interest types along with corresponding work environments. The six interest types are arranged on a hexagon; types that are closer to each other are more highly related. Holland’s four theoretically derived diagnostic indicators (congruence, consistency, differentiation, and identity) provide a useful organizational strategy and ideas for interpretation and discussion with the client. Of these concepts, congruence has received the most empirical attention. P×E interest congruence has been shown to be related to persistence, success, and performance (Meir, Esformes, & Friedland, 1994; Richards, 1993; Smart, 1997). The relationship between P×E interest congruence and job satisfaction has been extensively researched, but with mixed results (Spokane, Meir, & Catalano, 2000). A recent meta-analytic study of P×E interest congruence and job satisfaction yielded a correlation of .17 (Tsabari, Tziner, & Meir, 2005). Hansen (2005) offers four measurement issues that may explain the surprisingly low correlations between these constructs: restricted range in people’s job satisfaction scores, moderation of other variables (e.g., occupational level, career stage, career salience), the use of a global satisfaction measure, and the measurement of interests from a higher order (Holland Code) versus a lower order (occupational scale). Although there are some measurement issues associated with congruence, the clinical utility of this concept and Holland’s interest structure in general cannot be underestimated.

Although vocational interests have been the most studied concept in vocational research (Hackett, Lent, & Greenhaus, 1991), career practitioners recognize the importance of including multiple personal attributes into an integrated assessment. Values make an important contribution to career exploration, decision making, and satisfaction (Dawis & Lofquist, 1984; Judge & Bretz, 1992) and should therefore be included in the counseling process. Historically, values have been viewed as beliefs that motivate people to act in a manner that is consistent with their self-concept (Allport, 1961). In synthesizing research data regarding values, Brown and Crace (1996) made several propositions outlining the role of values in decision making. For example, they assert that high-priority values are more critical to decision making than low-priority values. At the same time, if values are not fully crystallized or the outcomes are unclear, difficulties will arise and the choices made will be tentative (e.g., choosing a liberal arts major). Another proposition is that individuals develop a small number of work values. They highlight measurement research demonstrating a six-factor (Bolton, 1980; Lofquist & Dawis, 1978), eight-factor (Macnab & Fitzsimmons, 1987), and 14-factor (Braithewaite & Law, 1985) solution for work values. A third proposition is that values provide cognitive filters through which reinforcers can be perceived and evaluated. Thus, assessing high-priority values can reveal the meaning of work, why people work, and what motivates them. From a strengths-based perspective, helping clients learn how to compare their values to rewards offered in the workplace could be an empowering career management strategy.

The best known approach to values assessment within the work domain is based upon the Theory of Work Adjustment (Dawis & Lofquist, 1984). Within this theory, individuals possess needs—basic requirements that may be satisfied through reinforcement from the work environment. Values are the result of factoring lower order needs; thus, values are reference dimensions for needs. The Theory of Work Adjustment proposes six values based upon their measure, the Minnesota Importance Questionnaire (MIQ; Gay, Weiss, Hendel, Dawis, & Lofquist, 1971). These values are achievement (seeks accomplishment), comfort (seeks comfort and a nonstressful environment), status (seeks recognition and prestige), altruism (seeks harmony and service to others), safety (seeks predictability), and autonomy (seeks initiative). Each of these values can be satisfied to a greater or lesser degree by reinforcers available through the work environment. Analysis of reinforcers present in the work environment has yielded three reinforcement factors (Shubsachs, Rounds, Dawis, & Lofquist, 1978): (1) achievement–autonomy–status (self-reinforcement), (2) safety–comfort (environmental reinforcement), and (3) altruism (social reinforcement). The predictions of the Theory of Work Adjustment are straightforward: Individuals will have the greatest satisfaction in those work environments that reinforce their values. Indeed, satisfaction within the theory is partially defined as the correspondence between the work environment’s reinforcers and the individual’s needs (which are summarized as values; Dawis, 2005).

The Empirical Relationship Between Interests and Values

A systematic approach to career counseling combines information from multiple sources simultaneously to help clients gain a more complete picture of themselves. However, the decision about which assessment tools to combine is left to clinical judgment. Rounds (1990) suggested using a combination of interest and value measures as both play important yet independent roles in predicting job satisfaction. He highlighted theoretical similarities between Holland’s (1985) theory of careers and Dawis and Lofquist’s (1984) theory of work adjustment. For example, both are P×E fit theories with satisfaction as a principal outcome. Each has testable propositions, well-established measures, and the ability to compare results with occupations. In a sample of adult clients, Rounds found that work values can add incremental validity to the prediction of job satisfaction over and above that accounted for by interests.

Theoretically, people have differed on the question of which is more fundamental to work satisfaction. As Smith and Campbell (2009) note, some have proposed that interests influence values (e.g., Blustein & Flum, 1999), while others have proposed that values are the more fundamental construct and precede interests (e.g., Strong, 1955). Thorndike, Weiss, and Dawis (1968) found strong significant relationships between interests and values (r = .78 and r = .74 for canonical variates). However, Katz (1993) suggested that the strong correlations between these constructs were due to the use of value-like items in older interest inventories. Leong, Hardin, and Gaylor (2005) found that values were important predictors of specialty interest in male and female second-year medical students. In a large sample of adolescents, Rottinghaus and Zytowski (2006) found that values explained only 6% of the variance in interests. Even if it is small, most researchers agree that there should be some relationship between career interests and work values (e.g., Bobek & Gore, 2004). Peterson, Mumford, Borman, Jeanneret, and Fleishman (1995) note, “The idea is that individuals who are motivated will perform well, and that interests and values are important parts of motivation” (Ch.11, p. 1). Surprisingly, empirical studies of the relationship between career interest and work values are scarce.

One exception is a study by Sagiv (2002) in which he examined the relationship between Holland’s six interest types (R, I, A, S, E, and C) and Schwartz’s 10 value types (Hedonism, Achievement, Power, Security, Conformity, Tradition, Benevolence, Universalism, Self-direction, and Stimulation). Twenty-two of the 26 correlations were in the expected direction and 15 were statistically significant. As an example, Enterprising interests were positively correlated with Power and Achievement and negatively correlated with Universalism. Opposing patterns of relations were also hypothesized. For instance, given their placement on Holland’s hexagon, Social and Enterprising interests were hypothesized to share some values but in different ways. Consistent with his hypothesis, Sagiv found that Enterprising interests were positively correlated with Power, whereas Social interests were negatively correlated with Power. In interpreting these results, Sagiv suggested that Social and Enterprising interests reflect similar abilities and skills but differ in the underlying motivation. Overall, results of this study indicate that vocational interests systematically and moderately correlate with basic values.

Smith and Campbell (2009) used data from the U.S. Department of Labor’s O*NET data set and developed a values characterization for each of the O*NET (i.e., Holland) interest categories. Correspondence analysis and canonical correlation were then carried out to determine the relationship between interest and values categories. The value profile plots revealed the following: (1) Conventional and Realistic interest types had similar value profiles, with the values of Support and Working Conditions being the two highest values; (2) Investigative and Artistic interest types had similar value profiles, with the values of Achievement and Independence being the two highest values; and (3) Social and Enterprising interest types had similar value profiles, with relatively flat value profiles, except that the Social interest type had a solitary peak on the Relationships value. Smith and Campbell also found substantial canonical correlations (between .883 and .413, all p’s < .01) between four of the six linear composites of interests and values. One aspect of the Smith and Campbell study that is worth noting is that the interest and value profiles studied were for occupations, not for individuals. However, it does support the notion that at least some interest types are associated with certain specific values.

The purpose of this study was to further examine the empirical relationship between interests and values and support their combined use in career assessment. In this study, we focused on data provided by the XAP Corporation from the Choices program. Choices is a web-based career guidance system that meets the comprehensive system standards of quality for Computer Information Delivery Systems (CIDS) established by the Association of Computer-Based Systems for Career Information (2008). Of particular interest to us was information about work interests (as measured within Choices by the Interest Profiler) and work values (as measured within Choices by the Work Importance Locator [WIL]). Similar to Sagiv (2002) and Smith and Campbell (2009), we hypothesized specific relationships between the six Holland interest types and six work values (based on the MIQ) such that (1) the Realistic interest scale would correlate significantly with the Working Conditions value scale (in O*NET; this corresponds to the Comfort scale in the MIQ); (2) the Investigative interest scale would correlate significantly with the Achievement value scale; (3) the Artistic interest scale would correlate significantly with the Independence value scale (in O*NET; this corresponds to the Autonomy scale in the MIQ); (4) the Social interest scale would correlate significantly with the Relationships value scale (in O*NET; this corresponds to the Altruism scale in the MIQ); (5) the Enterprising interest scale would correlate significantly with the Recognition value scale (in O*NET; this corresponds to the Status scale in the MIQ); (6) the Conventional interest scale would correlate significantly with either the Working Conditions scale (in O*NET; this corresponds to the Comfort scale in the MIQ, and includes Compensation) or the Support scale (in O*NET; this corresponds to the Safety scale in the MIQ, and includes Supervision and Clear Company Policies in the Work Environment) or both; and (7) other interest scales would correlate with nonsignificantly other value scales.

Method

Participants

Participants were 57,032 individuals who completed career interest and/or work values assessments using the Choices (XAP Corporation, 2010) CIDS. The data were provided to us by XAP Corporation in deindividuated form, with all participant identifiers removed. Because the data set was deindividuated, demographics were not available. However, the majority of individuals completing the Choices instruments in any given year are high school students.

The data set was cleaned, removing all those participants who did not respond to every interest and value item. After cleaning, the data set consisted of 52,253 participants. The results that are reported below are based upon this subset of 52,253 participants with complete data.

Materials

The Choices Interest Profiler

The Choices Interest Profiler is a computerized translation of the paper-and-pencil O*NET Interest Profiler used as part of the Choices (XAP Corporation, 2010) CIDS. The content is the 180 items used in the O*NET Interest Profiler, and participants respond to each item with either “Like,” “Unsure,” and “Dislike.” Items marked as Like are scored as 1 point, while other responses receive zero points. Items are presented on a computer via a web interface. Participants’ responses are computer scored and their results presented in bar graph format. For the current study, we recomputed scores on each of the Holland interest types from individual item responses to verify the accuracy of the interest scale scores.

The Choices Work Values Sorter

The Choices Work Values Sorter is an adaptation of the paper-and-pencil O*NET WIL used as part of the Choices (XAP Corporation, 2010) CIDS. The WIL is a card sorting activity consisting of 20 cards, each card containing a “need statement” related to one of the six work values, and a Work Value Card Sorting Sheet. The card sorting sheet has columns labeled for five levels of importance (1 = most important to 5 = least important) with spaces for four statements under each. In the Choices Work Values Sorter, the 20 statements are presented one at a time on virtual cards that can be picked up and placed on a sorting grid with the click of a computer mouse. Items can be moved and replaced on the card sorting sheet until a satisfactory solution to the sorting task has been achieved.

Results are scored as follows: (1) statements placed in the most important column receive 5 points; (2) statements placed in the more important column receive 4 points; (3) statements placed in the somewhat important column receive 3 points; (4) statements placed in the less important column receive 2 points; and (5) statements placed in the least important column receive 1 point. Point values are then summed for statements under each of the six work values, and multiplied by a weighting factor to put all value scores on the same scale (the individual work values have differing number of statements associated with them, ranging from working condition, which has six statements, to achievement, which has two statements). For the current study, we recomputed scores for each of the MIQ-based value scales to verify the accuracy of the value scale scores.

Results and Discussion

Internal Consistency Reliabilities

Because correlations presuppose the existence of reliable measures, we decided to calculate the internal consistency reliabilities for each interest and value scale from the Choices assessment instruments. Table 1 presents coefficient αs for each of the six interest scales and six value scales. As can be seen from the table, interest scales demonstrate high internal consistency (all α above .93). Value scales, in contrast, show little to no internal consistency (five of the six calculated αs are actually negative!).

Table 1.

Internal Consistencies (Coefficient αs) for Interests and Values Scales.

Interest scale	α	Value scale	α
Realistic	.953	Working conditions	−.604
Investigative	.950	Achievement	.437
Artistic	.950	Independence	−.167
Social	.951	Relationships	−.680
Enterprising	.930	Recognition	−.234
Conventional	.957	Support	−.197

Why were values so poorly measured by the Choices Work Values Sorter? We believe this is an artifact of the card sorting procedure used. When certain values are ranked high, other values must be ranked lower. No more than four values can occupy the most important column in the card sort; likewise, no more than four values can occupy the least important column. Because placing value statements in one category constrains the placement of other values, an inherent negative correlation exists among ranking of values. This reveals itself in the low coefficient αs.

Both the O*NET Importance Locator and the MIQ (Gay et al., 1971) approach the measurement of values from an idiographic (i.e., within individual) perspective. From this perspective, what is important is not the absolute level of an individual’s score on a particular value, but rather which values are more important and which values are less important. These preference rankings can then be related to the preference rankings of job incumbents in particular professions (or the ratings given by experts such as industrial organizational psychologists for reinforcement patterns within particular professions). Combining interests and values to predict a dependent variable, such as job satisfaction, can be done (e.g., Rounds, 1990), but only using secondary measures such as interest congruence and value correspondence.

Interests and abilities are both typically measured as nomothetic (across individuals) constructs. We propose that information on values would be much more useful, and much easier to relate to other important career-related constructs, if it were measured nomothetically. This could be accomplished via a Likert-type scale procedure, whereby individuals indicate their level of agreement with each value statement on a multipoint scale. Scores could then be combined into the value scales proposed by the MIQ, or combined in empirically determined scales, defined through factor analysis. The result, we believe, would be value scales with acceptable psychometric properties. These scales could then be directly related to other career relevant information, or used jointly to predict important outcome variables such as job satisfaction, without resorting to secondary variables. It should be pointed out that these secondary variables make additional theoretical assumptions that may be only partially met, and therefore may have greater error associated with them than primary variables such as values and interests.

A second issue that adversely affects reliability is reduced scale length. Other things being equal, longer scales produce more reliable measures. The number of items for each of the work values scales produced by the Choices Work Values Sorter was never more than 6 (e.g., Working Conditions, 6 items; Achievement, 2 items; Independence, 3 items; Relationships, 3 items; Recognition, 3 items; and Support, 3 items). Even under optimal measurement procedures, such short scales would produce value measures of dubious reliability.

Correlational Results

It should come as no surprise that our measures of interests and values were essentially uncorrelated. We present these correlations in Table 2. Hypothesized relationships are presented in boldface. The median correlation between interests and values that were predicted to be related was .001, while the median correlation between interests and values that were not predicted to be related was .005. No interest and value shared more than two tenths of 1% of variance (i.e., r ²). While this is not surprising, given that work values were measured unreliably (from a nomothetic perspective), we present Table 2 for two reasons: (1) the sample size in our study was unusually large and (2) calculating the correlations between career interests and work values, as they are currently being measured, is a straightforward approach in which many researchers and practitioners would expect to observe significant relationships.

Table 2.

Correlations for Interests and Values Scales.

Values	Interests
Values	Realistic	Investigative	Artistic	Social	Enterprising	Conventional
Working conditions	0.002	0.006	0.006	−0.007	0.008	0.013
Achievement	−0.028	−0.012	0.005	−0.002	−0.030	−0.041
Independence	0.009	−0.010	−0.001	−0.003	0.019	0.020
Relationships	0.015	0.010	0.006	0.001	0.012	0.004
Recognition	0.017	0.022	−0.007	0.020	0.024	0.036
Support	−0.013	−0.016	−0.009	−0.007	−0.031	−0.031

Note. All hypothesized relationships have been presented in boldface.

Implications for Research and Practice

Values have been shown to have utility in career counseling (e.g., Rounds, 1990) but are currently measured very differently than interests and abilities. Our attempt to look for partial overlap between values and interests was thwarted by differences in the measurement theory underlying these two important constructs as well as scale length issues. Given that most individual differences variables in psychology are measured via nomothetic, rather than ideographic, means, we suggest that effort be spent developing such measures for values. The significant work of the MIQ (Gay et al., 1971) need not be abandoned; it need only to be supplemented by applying alternative measurement techniques to the values specified. Such a procedure would make measured values more directly comparable to measured interests and abilities.

We had hoped our findings would shed some light on the relationship between interests and values and support their combined use. While we were unable to assess these relationships properly, we would venture to guess that with a more reliable measure of values, some of our hypothesized relationships would be supported similar to results found by Sagiv (2002) and Smith and Campbell (2009). It should be noted that both of these studies employed either direct or transformed measures of values that approximate a continuous rather than nomothetic measurement strategy. At the same time, we are left to wonder whether the overlap between interests and values is all that complete. First, interests and values may tap slightly different aspects of P×E fit. Interests specify the activities we like or dislike, while values speak to the importance of activities or aspects of the work environment. For example, we may not like close supervision, but we may be willing to tolerate it if we value compensation sufficiently, and if close supervision yields high compensation.

Second, developmental issues may moderate the relationship between interests and values. Younger individuals may have fewer difficulties specifying interests than specifying values, especially as those values relate to the work environment. High school students have fairly clear notions of what they like to do and do not like to do (i.e., interests), but it may be hard for a high school student to specify at least some work values (e.g., whether one would value working in an office vs. working in the field) until they have some work experiences to solidify their value structure. It may well be that the relationship between interests and values strengthens as individuals age and gain experience.

Our findings have important implications for practice within vocational guidance, career education, and career counseling. First, the examination of P×E fit necessitates the use of psychometrically sound assessment tools that can provide accurate diagnostic and predictive information. Given that we found the Choices Work Values Sorter lacks internal consistency, we recommend counselors interpret this measure with caution and use it as a base for further inquiry. If values were measured in ways similar to interests and abilities, scores would reflect the relative importance of each value, not a forced rating whereby some values receive a low rating by default and are thus artificially truncated. Measuring values nomothetically would allow counselors to help clients understand their full range of values and think about how they may fluctuate over their life course. Another important issue associated with “fit” is how the fit is determined. It would be important for counselors to understand the algorithm used to link value scores to specific occupations and how this differs from the algorithm linking interest scores to occupations. Knowing these differences would ensure a much more accurate interpretation of assessment data and would guide more appropriate use of these instruments with students and clients. Finally, there is an implicit assumption that the combined and additive use of multiple individual difference variables produces better career outcomes. Although our research findings cannot speak to this, it makes intuitive sense that multiple sources of data will enhance self-discovery, meaning making, and integration of the self-concept. Adding a values measure to an interest assessment can reveal a client’s motivation to pursue particular interests, reveal important work reinforcers, and provide a language to evaluate reinforcers and determine satisfaction. Thus, the addition of a measure of values can serve to empower the client as an important lifelong career management strategy. Hopefully, our results serve as an impetus for empirically exploring these issues more fully.

Footnotes

Authors’ Note

The analyses reported were part of a doctoral dissertation in the Department of Educational Psychology at the University of Utah, presented by the first author. Data used in this study were kindly provided by Bridges Transitions Co., Culver City, CA. Bridges Transitions Co. is a subsidiary of XAP Corporation.

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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