Individual Differences

Individuals differ from one another behaviorally in myriad ways. Differential psychology, the scientific study of these individual differences, provides an organizational structure for this vast array of psychological attributes. By examining broad behavioral patterns and using systematic assessments of relatively stable personal attributes, differential psychology allows longitudinal forecasting of a variety of important life outcomes. Because much of the research in this area focuses particular attention on predicting long-term life outcomes, and because work is such a large and important feature of adult life, the relationships between many commonly investigated individual difference constructs and various aspects of work behavior (e.g., educational-vocational choice, acquisition of job-related knowledge, job performance, job satisfaction and tenure) are well understood.

Individual Differences Measurement Methods

Traditionally, the measurement of individual differences has relied on psychometric scales based on the aggregation of many items. Because any single item on a scale represents only a sliver of information about a personal attribute, aggregation is used to create a composite of several lightly correlated items. This approach distills the communality running through the items and constitutes highly reliable and useful information about the human characteristic under analysis.

Although individuals are commonly described in the more popular press in terms of types, implying that people are members of distinct categories (e.g., extraverts or introverts), individual difference variables are rarely observed as discrete classes. Rather, the majority of individuals are found near the center of a continuous distribution, with few observations at either extreme. The distributional pattern of most individual difference variables is well represented by the normal (bell-shaped) curve.

Major Domains of Individual Differences

The major dimensions of individual differences can be classified into three overlapping clusters: cognitive abilities, preferences (interests and values), and personality. Each will be reviewed in turn, but cognitive abilities will be focused on here because of their importance for industrial/organizational psychologists.

Cognitive Abilities

General Intelligence. The predominant scientific conceptualization of cognitive abilities involves a hierarchical organization. Various models of additional specific abilities have been proposed, but the hierarchical nature of human abilities is salient in each. For example, John Carroll factor analyzed more than 460 data sets collected throughout the 20th century and found a general factor g) at the apex that explained approximately half of the common variance among a heterogeneous collection of tests, revealing a communality running through many different types of more specialized abilities and the tests designed to measure them.

This general intelligence factor exhibits an extensive range of external correlates, implicating it as arguably the most scientifically significant dimension of human psychological diversity uncovered by differential psychology to date. It has repeatedly demonstrated its utility in the prediction of educationally and vocationally relevant outcomes, including the acquisition of job-related knowledge and job performance. For example, in a meta-analysis of 85 years of research on personnel selection methods, Frank Schmidt and John Hunter reported that g is the best single predictor of performance in job-training programs, exhibiting an average validity coefficient of .56. Schmidt and Hunter further reported that the validity of g in predicting job performance is second only to that of work sample measures. However, because the use of work samples is limited to use with incumbents and is much costlier to implement, g is usually considered more efficient.

The predictive validity of g in forecasting job performance varies as a function of job complexity, with stronger relationships among more complex positions. Hunter reports validity coefficients of .58 for professional and managerial positions, .56 for highly technical jobs, .40 for semiskilled labor, and .23 for unskilled labor. For the majority of jobs (62%), those classified as medium-complexity, a validity coefficient of .51 was observed.

Specific Abilities. The general factor of intelligence is supplemented by several more circumscribed, specific abilities that have demonstrated psychological importance. David Lubinski and his colleagues have shown that at least three add incremental validity to the variance explained by g: verbal, mathematical, and spa-tialabilities. The importance of specific abilities may be even more apparent at higher levels of functioning. In examinations of numerous job analysis data sets, for example, Linda Gottfredson found that, although the functional duties of jobs were characterized primarily by their cognitive complexity (i.e., demands on general intelligence), jobs requiring above-average intelligence were more dependent on profiles of specific abilities than were those jobs requiring average or below-average general intelligence.

Specific abilities are relevant in the prediction of job performance, but they are also important in predicting the educational and vocational niches into which individuals self-select. This self-selection occurs even at extraordinary levels of general intellectual development. In a recent 10-year longitudinal study, for example, Lubinski compared the educational-vocational tracks chosen by three groups of profoundly gifted individuals (top 1 in 10,000 for their age): a high verbal group (individuals with advanced verbal reasoning ability, relative to their mathematical ability), a high math group (individuals with advanced mathematical reasoning ability, relative to their verbal ability), and a high flat profile group (individuals with comparably high verbal and mathematical abilities). Despite having similar levels of general cognitive ability, the three groups diverged in their professional developmental choices. High math participants were frequently pursuing training in scientific and technological professions, whereas high verbal participants were doing so in the humanities and arts. High flat participants were intermediate. Spatial ability provides unique information beyond g also in understanding development in educational and vocational contexts. It has been shown to be a necessary component in several career clusters, including engineering, the physical sciences, and the creative arts.

Preferences

Modeling preference dimensions (interests and values) can be helpful also for understanding how people approach and operate within educational-vocational environments. John Holland has proposed a model that is particularly useful for interests. The origins of this model stemmed from a theoretical, empirical keying methodology in which the likes and dislikes of incumbents across a variety of occupational categories were contrasted. Under the assumption that people in different occupations share common interests, which differentiate them from people in other occupations, measures of vocational interests compare an individual’s combination of interests with the average interest profiles of individuals from various occupational groups as a means for vocational counseling and selection. This empirical approach led the way to a more cohesive theory of interest that contributes valuable information regarding how people operate in learning and work environments.

Holland’s model of interests organizes six general occupational themes in a hexagon with one theme at each vertex in the hexagon. The themes are ordered according to their pattern of intercorrelations: Adjacent themes in the hexagon are more highly correlated to one another, whereas opposite themes are least correlated. This model is known as the RIASEC model, an acronym for the six themes represented in the hexagon: realistic, investigative, artistic, social, enterprising, and conventional. Individuals with high realistic interests exhibit preferences for working with things and tools; those with high investigative interests enjoy scientific pursuits; high artistic interests reflect desires for aesthetic pursuits and self-expression; social interests involve preferences for contact with people and opportunities to help people; individuals high in enterprising interests enjoy buying, marketing, and selling; and those with conventional interests are comfortable with office practices and well-structured tasks. Individuals’ relative normative strengths on each of the RIASEC’s general occupational themes are commonly assessed using the Strong Interest Inventory.

Although the generalizability of the RIASEC model has emerged repeatedly in large samples, Dale Prediger has suggested that the model can be reduced to two relatively independent bipolar dimensions: people versus things, and data versus ideas. People versus things may be superimposed on the social and realistic themes, respectively. Running perpendicular to the first dimension, the second dimension, data versus ideas, locates data between the enterprising and conventional themes and ideas between the artistic and investigative themes. The people versus things dimension represents one of the largest sex differences on a trait uncovered in psychology (a full standard deviation, with women scoring higher on the desire to work with people, and men, with things), revealing important implications for the occupations that men and women choose.

Values constitute another category of personal preferences germane to learning and work, which have demonstrated their utility in the prediction of both educational and occupational criteria. Values are validly assessed by the Study of Values, which reports the intraindividual prominence of six personal values: theoretical, economic, political, social, aesthetic, and religious. These dimensions provided an additional 13% of explained variance above the 10% offered by math and verbal abilities in the prediction of undergraduate majors in gifted youth assessed over a 10-year interval; moreover, this finding has recently been generalized to occupational criteria, measured in commensurate terms, over a 20-year interval. However, although preferences do seem to play an important role in predicting occupational group membership and tenure, once individuals self-select into occupational fields, the utility of preferences for predicting job performance in those fields is limited.

Personality

Empirical examinations of personality use trait models to understand a person’s typical interpersonal style and behavioral characteristics. These models have historically relied on a lexical approach that assumes that important dimensions of human personality are encoded in human language. This method has been fruitful: Lewis Goldberg, among others, has factor analyzed the lexicons of many languages and found a five-factor model of personality with remarkable similarities across cultures (see also investigations by Robert McCrae and Paul Costa). Although the labels for each of the factors have varied, similar underlying constructs consistently emerge: extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience. Extraversion is characterized by terms such as talkative, sociable, or not reserved; agreeableness by good-natured, cooperative, or not cold; conscientiousness by responsible, thorough, or not disorganized; neuroticism (sometimes referred to as emotional stability, reversed) by anxious, emotional, or not calm; and openness to experience (sometimes referred to as culture or intellect) by imaginative, reflective, or not narrow. The normative standing of individuals on each of the dimensions of the five-factor model of personality is commonly assessed using the NEO Personality Inventory, although an analogous instrument, the IPIP-NEO (IPIP is International Personality Item Pool), is available in the public domain at http://ipip.ori.org.

Collectively (and sometimes individually), these broad dimensions of personality are valid predictors of occupational training and subsequent performance. For example, across multiple occupational categories, conscientiousness alone exhibits validity coefficients in the low .20s for predicting training and job proficiency. However, when conscientiousness was assessed in conjunction with emotional stability, Denise Ones and her colleagues have documented a coefficient of .41 for predicting job performance. This particular combination of personality factors, conscientiousness and emotional stability, is found in tests of integrity commonly used in personnel selection. (Robert Hogan and his colleagues reviewed these and other studies of personality in selection in 1996.)

Relationships among Attributes

Although each of the major classes of individual differences—cognitive abilities, preferences, and personality—has traditionally been examined in isolation from the other two, these classes are not independent. Cognitive abilities, preferences, and personality traits tend to covary systematically to create constellations of personal attributes; and these complexes have interdependent developmental implications. Phillip Ackerman, for example, has proposed a theory of adult development that models the dependencies among individual difference attributes to describe how intellectual processes and knowledge are relevant to occupational performance across the life span. The cornerstones of Ackerman’s theory are intelligence-as-process, personality, interests, and intelligence-as-knowledge. Intelligence-as-process regulates the complexity and density of the knowledge assimilated, whereas the development of intelligence-as-knowledge is guided by interest and personality attributes. Thus, intelligence-as-process, through interactions with interests and personality, fosters intelligence-as-knowledge.

How each individual attribute operates in a given person will vary according to his or her full constellation of attributes. Because all three classes of individual differences—cognitive abilities, preferences, and personality—influence the development of particular knowledge structures over time, great variability exists among the knowledge bases of individuals who are similar on some dimensions but dissimilar on others. For example, two individuals with similar ability profiles, but with contrasting interests and personality traits, might exhibit markedly diverse behavioral patterns. Using a multidimensional approach to individual differences has important implications for understanding professional development: Richard Snow has outlined the importance of trait complexes in educational contexts, and Rene Dawis and Lloyd Lofquist have done so in a discussion of taxons in vocational settings.

Although vocational counseling and personnel selection frequently attend to individuals’ strengths and salient interests and personality traits, another feature of personal profiles is relevant to these applications. Just as an individual’s strengths and preferences influence the niches people self-select into and their subsequent likelihood of acceptable job performance and satisfaction with those occupations, their weaknesses and dislikes are relevant here, too. At the individual level, relative weaknesses and dislikes influence domains that people choose to avoid, but these attributes likely influence subsequent performance and satisfaction-related job tenure, as well.

Conclusion

Individual differences attributes and the constellations they form differentially attune people to contrasting educational-vocational opportunities (affordances for learning and work). From an individual’s perspective, an appreciation of one’s cognitive abilities, preferences, and personality provide invaluable insight for directing one’s career development in personally rewarding ways. From an organizational perspective, one may use this information—available through measures of individual differences—to estimate the likelihood of desirable work behavior (e.g., citizenship, job performance, satisfaction, and tenure). Creating optimal niches for personal development and satisfaction (for the individual) and meeting the environment’s goals and demands (for the organization) may be achieved simultaneously using an individual differences approach.

References:

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