" HUMAN PERFORMANCE, 15(1/2), 1–2 Copyright © 2002, Lawrence Erlbaum Associates, Inc.
Introduction to the Special Issue: Role of General Mental Ability in Industrial, Work, and Organizational Psychology
Deniz S. Ones
Department of Psychology University of Minnesota
Chockalingam Viswesvaran
Department of Psychology Florida International University
Individual differences that have consequences for work behaviors (e.g., job performance) are of great concern for organizations, both public and private. General mental ability has been a popular, although much debated, construct in Industrial, Work, and Organizational (IWO) Psychology for almost 100 years. Individuals differ on their endowments of a critical variable—intelligence—and differences on this variable have consequences for life outcomes. As the century drew to a close, we thought it might be useful to assess the state of our knowledge and the sources of disagreements about the role of general mental ability in IWO psychology. To this end, with the support of Murray Barrick, the 2000 Program Chair for the Society for Industrial/Organizational Psychology (SIOP), we put together a debate for SIOP’s annual conference. The session’s participants were Frank Schmidt, Linda Gottfredson, Milton Hakel, Jerry Kehoe, Kevin Murphy, James Outtz, and Malcolm Ree. The debate, which took place at the 2000 annual conference of SIOP, drew a standing- room-only audience, despite being held in a room that could seat over 300 listeners. The questions that were raised by the audience suggested that there was room in the literature to flesh out the ideas expressed by the debaters. Thus, when Jim Farr, the current editor of Human Performance, approached us with the idea of putting together a special issue based on the “g debate,” we were enthusiastic. However, it occurred to us that there were other important and infor-
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mative perspectives on the role of cognitive ability in IWO psychology that would be valuable to include in the special issue. For these, we tapped Mary Tenopyr, Jesus Salgado, Harold Goldstein, Neil Anderson, and Robert Sternberg, and their coauthors. The 12 articles in this special issue of Human Performance uniquely summarize the state of our knowledge of g as it relates to IWO psychology and masterfully draw out areas of question and contention. We are very pleased that each of the 12 contributing articles highlight similarities and differences among perspectives and shed light on research needs for the future. We should alert the readers that the order of the articles in the special issue is geared to enhance the synergy among them. In the last article of the special issue, we summarize the major themes that run across all the articles and offer a review of contrasts in viewpoints. We hope that the final product is informative and beneficial to researchers, graduate students, practitioners, and decision makers. There are several individuals that we would like to thank for their help in the creation of this special issue. First and foremost, we thank all the authors who have produced extremely high quality manuscripts. Their insights have enriched our understanding of the role of g in IWO psychology. We were also impressed with the timeliness of all the authors, as well as their receptiveness to feedback that we provided for revisions. We also extend our thanks to Barbara Hamilton, Rachel Gamm, and Jocelyn Wilson for much appreciated clerical help. Their support has made our editorial work a little easier. Financial support for the special issue editorial office was provided by the Departments of Psychology of Florida International University and the University of Minnesota, as well as the Hellervik Chair endowment. We are also grateful to Jim Farr for allowing us to put together this special issue and for his support. We hope that his foresight about the importance of the topic will serve the literature well. We also appreciate the intellectual stimulation provided by our colleagues at the University of Minnesota and Florida International University. Finally, our spouses Saraswathy Viswesvaran and Ates Haner provided us the environment where we could devote uninterrupted time to this project. They also have our gratitude (and probably a better understanding and knowledge of g than most nonpsychologists). We dedicate this issue to the memory of courageous scholars (e.g., Galton, Spearman, Thorndike, Cattell, Eysenck) whose insights have helped the science around cognitive ability to blossom during the early days of studying individual differences. We hope that how to best use measures of g to enhance societal progress and well-being of individuals will be better understood and utilized around the globe in the next 100 years.
HUMAN PERFORMANCE, 15(1/2), 3–23 Copyright © 2002, Lawrence Erlbaum Associates, Inc.
g2K
Malcolm James Ree
Center for Leadership Studies Our Lady of the Lake University
Thomas R. Carretta
Air Force Research Laboratory Wright-Patterson AFB, Ohio
To answer the questions posed by the organizers of the millennial debate on g, or general cognitive ability, we begin by briefly reviewing its history. We tackle the question of what g is by addressing g as a psychometric score and examining its psychological and physiological correlates. Then tacit knowledge and other non-g characteristics are discussed. Next, we review the practical utility of g in personnel selection and conclude by explaining its importance to both organizations and individuals.
The earliest empirical studies of general cognitive ability, g, were conducted by Charles Spearman (1927, 1930), although the idea has several intellectual precursors, among them Samuel Johnson (1709–1784, see Jensen, 1998, p. 19) and Sir Francis Galton (1869). Spearman (1904) suggested that all tests measure two factors, a common core called g and one or more specifics, s1, … sn. The general component was present in all tests, whereas the specific component was test unique. Each test could have one or more different specific components. Spearman also observed that s could be found in common across a limited number of tests allowing for an arithmetic factor that was distinct from g, but found in several arithmetic tests. These were called “group factors.” Spearman (1937) noted that group factors could be either broad or narrow and that s could not be measured without also measuring g. As a result of his work with g and s, Spearman (1923) developed the principle of “indifference of the indicator.” It means that when constructing intelligence tests,
Requests for reprints should be sent to Malcolm James Ree, Our Lady of the Lake University, 411 S. W. 24th Street, San Antonio, TX 78207–4689. E-mail: reemal@lake.ollusa.edu
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the specific content of the items is not important as long as those taking the test perceive it in the same way. Although the test content cannot be ignored, it is merely a vehicle for the measurement of g. Although Spearman was talking mostly about test content (e.g., verbal, math, spatial), the concept of indifference of the indicator extends to measurement methods, some of which were not yet in use at the time (e.g., computers, neural conductive velocity, psychomotor, oral–verbal). Spearman (1904) developed a method of factor analysis to answer the vexing question: “Did each of the human abilities (or ‘faculties’ as they were then called) represent a differing mental process?” If the answer was yes, the different abilities should be uncorrelated with each other, and separate latent factors should be the sources for the different abilities. Repeatedly, the answer was no. Having observed the emergence of g in the data, an eschatological question emerged. What is g? Although the question may be answered in several ways, we have chosen three as covering broad theoretical and practical concerns. These are g as a psychometric score, as psychological correlates of g, and as physiological correlates of g.
PSYCHOMETRIC g Spearman (1904) first demonstrated the emergence of g in a battery of school tests including Classics, French, English, Math, Pitch, and Music. During the 20th century, many competing multiple-factor theories of ability have surfaced, only to disappear when subjected to empirical verification (see, e.g., Guilford, 1956, 1959; Thurstone, 1938). Psychometrically, g can be extracted from a battery of tests with diverse content. The correlation matrix should display “positive manifold,” meaning that all the scores should be positively correlated. There are three reasons why cognitive ability scores might not display positive manifold—namely, reversed scoring, range restriction, and unreliability.
Threats to Positive Manifold
Reversed scoring. Reversed scoring is often found in timed scores such as reaction time or inspection time. In these tests, the scores are frequently the number of milliseconds necessary to make the response. A greater time interval is indicative of poorer performance. When correlated with scores where higher values are indicative of better performance, the resulting correlation will not be positive. This can be corrected by subtracting the reversed time score from a large number so that higher values are associated with better performance. This linear transformation will not affect the magnitude of the correlation, but it will associate better performance with high scores for each test.
SELECTION AND COGNITIVE ABILITY
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Range restriction. Range restriction is the phenomenon observed when prior selection reduces the variance in one or more variables. Such a reduction in variance distorts the correlation between two variables, typically leading to a reduction in the correlation. For example, if the correlation between college grades and college qualification test scores were computed at a selective Ivy League university, the correlation would appear low because the range of the scores on the college qualification test has been restricted by the selectivity of the university. Range restriction is not a new discovery. Pearson (1903) described it when he first demonstrated the product–moment correlation. In addition, he derived the statistical corrections based on the same assumptions as for the product–moment correlation. In almost all cases, range restriction reduces correlations, producing downwardly biased estimates, even a zero correlation when the true correlation is moderate or strong. As demonstrated by Thorndike (1949) and Ree, Carretta, Earles, and Albert (1994), the correlation can change sign as a consequence of range restriction. This change in sign negates the positive manifold of the matrix. However, the negation is totally artifactual. The proper corrections must be applied whether “univariate” (Thorndike, 1949) or “multivariate” (Lawley, 1943). Linn, Harnish, and Dunbar (1981) empirically demonstrated that the correction for range restriction is generally conservative and does not inflate the estimate of the true population value of the correlation. Unreliability. The third threat to positive manifold is unreliability. It is well known1 that the correlation of two variables is limited by the geometric mean of their reliabilities. Although unreliability cannot change the sign of the correlation, it can reduce it to zero or near zero, threatening positive manifold. Unreliable tests need not denigrate positive manifold. The solution is to refine your tests, adding more items if necessary to increase the reliability. Near the turn of the century, Spearman (1904) derived the correction for unreliability, or correction for attenuation. Application of the correction is typically done for theoretical reasons as it provides an estimate of the correlation between two scores had perfectly reliable measures been used.
Representing g Frequently, g is represented by the highest factor in a hierarchical factor analysis of a battery of cognitive ability tests. It can also be represented as the first unrotated principal component or principal factor. Ree and Earles (1991) demonstrated that any of these three methods will be effective for estimating g. Ree and Earles also demonstrated that, given enough tests, the simple sum of the test scores will pro1Hunter and Schmidt (1990) noted, “Since the late 1890s, we have known that the error of measurement attentuates the correlation coefficient” (p. 117).
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duce a measure of g. This may be attributed to Wilks’s theorem (Ree, Carretta, & Earles, 1998; Wilks, 1938). The proportion of total variance accounted for by g in a test battery ranges from about 30% to 65%, depending on the composition of the constituent tests. Jensen (1980, pp. 216) provided an informative review. Gould (1981) stated that g can be “rotated away” among lower order factors. This is erroneous, as rotation simply distributes the variance attributable to g among all the factors. It does not disappear. Interested readers are referred to a text on factor analysis. To dispel the charge that g is just “academic intelligence” (Sternberg & Wagner, 1993), we demonstrate a complex nexus of g and nonacademic activities. The broadness of these activities, ranging from accident proneness to the ability to taste certain chemicals, exposes the falsehood that g is just academic intelligence.
PSYCHOLOGICAL CORRELATES OF g Several psychological correlates of g have been identified. Brand (1987) provided an impressive list and summary of 48 characteristics positively correlated with g and 19 negatively correlated with g. Brand included references for all examples, listed later. Ree and Earles (1994, pp. 133–134) organized these characteristics into several categories. These categories and examples for each category follow:
• Abilities (analytic style, eminence, memory, reaction time, reading). • Creativity/artistic (craftwork, musical ability). • Health and fitness (dietary preference, height, infant mortality, longevity,
obesity).
• Interests/ choices (breadth and depth of interest, marital partner, sports
participation).
• Moral ((delinquency (–)*, lie scores (–), racial prejudice (–), values). • Occupational (income, military rank, occupational status, socioeconomic • • • • •
status). Perceptual (ability to perceive brief stimuli, field-independence, myopia). Personality (achievement motivation, altruism, dogmatism (–)). Practical (practical knowledge, social skills). Other (accident proneness (–), motor skills, talking speed). * Indicates a negative correlation.
Noting its pervasive influence on human characteristics, Brand (1987) commented, “g is to psychology as carbon is to chemistry” (p. 257). Cognitive and psychomotor abilities often are viewed as unrelated (Carroll, 1993; Fleishman & Quaintance, 1984). This view may be the result of dissimilarity of appearance and method of measurement for cognitive and psychomotor tests.
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Several recent studies, however, have shown a modest relation between cognitive and psychomotor ability (Carretta & Ree, 1997a; Chaiken, Kyllonen, & Tirre, 2000; Rabbitt, Banerji, & Szymanski, 1989; Ree & Carretta, 1994; Tirre & Raouf, 1998), with uncorrected correlations between .20 and .69. Although the source of the relations between cognitive and psychomotor ability is unknown, Ree and Carretta (1994) hypothesized that it might be due to the requirement to reason while taking the tests. Carretta and Ree (1997b) proposed that practical and technical knowledge also might contribute to this relation. Chaiken et al. (2000) suggested that the relation might be explained by the role of working memory capacity (a surrogate of g; see Stauffer, Ree, & Carretta, 1996) in learning complex and novel tasks.
PHYSIOLOGICAL CORRELATES OF g A series of physiological correlates has long been postulated. Hart and Spearman (1914) and Spearman (1927) speculated that g was the consequence of “neural energy,” but did not specify how that mental energy could be measured. They also did not specify the mechanism(s) that produced this energy. The speculative physiological causes of g were “energy,” “plasticity,” and “the blood.” In a similar way, Thomson (1939) speculated that this was due to “sampling of mental bonds.” No empirical studies were conducted on these speculated causes. Little was known about the human brain and g during this earlier era. Today, much more is known and there is a growing body of knowledge. We now discuss the correlates demonstrated by empirical research. Brain Size and Structure There is a positive correlation between g and brain size. Van Valen (1974) found a correlation of .3, whereas Broman, Nichols, Shaughnessy, and Kennedy (1987) found correlation in the range of .1 to .2 for a surrogate measure, head perimeter (a relatively poor measure of brain size). Evidence about the correlation between brain size and g was improved with the advent of more advanced measurement techniques, especially MRI. In a precedent-setting study, Willerman, Schultz, Rutledge, and Bigler (1991) estimated the brain size-g correlation at .35. Andreasen et al. (1993) reported these correlations separately for men and women as .40 and .45, respectively. They also found correlations for specific brain section volumes such as the cerebellum and the hippocampus. Other researchers have reported similar values. Schultz, Gore, Sodhi, and Anderson (1993) reported r = .43; Wickett, Vernon, and Lee (1994) reported r = .39; and Egan, Wickett, and Vernon (1995) reported r = .48. Willerman and Schultz (1996) noted that this cumulative evidence “provides the first solid lead for understanding g at a biological level of analysis” (p. 16).
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Brain myelination has been found to be correlated with g. Frearson, Eysenck, and Barrett (1990) suggested that the myelination hypothesis was consistent with brighter people being faster in mental activities. Schultz (1991) found a correlation of .54 between the amount of brain myelination and g in young adults. As a means of explanation, Waxman (1992) suggested that myelination reduces “noise” in the neural system. Miller (1996) and Jensen (1998) have provided helpful reviews. Cortical surface area also has been linked to g. An early postmortem study by Haug (1987) found a correlation between occupational prestige, a surrogate measure of g, and cortical area. Willerman and Schultz (1996) suggested that cortical area might be a good index based on the studies of Jouandet et al. (1989) and Tramo et al. (1995). Eysenck (1982) provided an excellent earlier review. Brain Electrical Potential Several studies have shown correlations between various indexes of brain electrical potentials and g. Chalke and Ertl (1965) first presented data suggesting a relation between average evoked potential (AEP) and measures of g. Their findings subsequently were supported by Ertl and Schafer (1969), who observed correlations from –.10 to –.35 for AEP and scores on the Wechsler Intelligence Scale for Children. Shucard and Horn (1972) found similar correlations ranging from –.15 to –.32 for visual AEP and measures of crystallized g and fluid g. Speed of Neural Processing Reed and Jensen (1992) observed a correlation of .37 between neural conductive velocity (NCV) and measured intelligence for an optic nerve leading to the brain. Faster NCV was associated with higher g. Confirming replications are needed. Brain Glucose Metabolism Rate Haier et al. (1988) observed a negative correlation between brain glucose metabolism and performance on the Ravens Advanced Progressive Matrices, a highly g-loaded test. Haier, Siegel, Tang, Able, and Buchsbaum (1992) found support for their theory of brain efficiency and intelligence in brain glucose metabolism research. However, Larson, Haier, LaCasse, and Hazen (19..."
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