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Journal of Educational Psychology 2001. Vol. 93. No. 4. 797-825

Copyright 2001 by the American Psychological Association, Inc. 0O22-O663A)l/$5.O0 DOI: 10.1037//0022-0663.93.4.797

Determinants of Individual Differences and Gender Differences in Knowledge Phillip L. Ackerman, Kristy R. Bowen, Margaret E. Beier, and Ruth Kanfer Georgia Institute of Technology The authors investigated the abilities, self-concept, personality, interest, motivational traits, and other determinants of knowledge across physical sciences/technology, biology/psychology, humanities, and civics domains. Tests and self-report measures were administered to 320 university freshmen. Crystallized intelligence was a better predictor than was fluid intelligence for most knowledge domains. Gender differences favoring men were found for most knowledge domains. Accounting for intelligence reduced the gender influence in predicting knowledge differences. Inclusion of nonability predictors further reduced the variance accounted for by gender. Analysis of Advanced Placement test scores largely supported the results of the knowledge tests. Results are consistent with theoretical predictions that development of intellect as knowledge results from investment of cognitive resources, which, in turn, is affected by a small set of trait complexes.

The prediction of individual differences in academic success has a long and distinguished history in the field of educational psychology. Binet's development of the intelligence scales for predicting academic performance has been heralded as one of the preeminent contributions of scientific psychology to education in the 20th century. The Binet scales and their progeny are not without controversy for a variety of reasons that go beyond the scope of this article. However, one central aspect of the Binet and other intelligence tests is that they are "norm referenced"; that is, an individual student's score on the test is only meaningful in the context of the larger population of age peers. In the original formulation, Binet and Simon (1905/1961) conceptualized their intelligence scales as representing the psychological method of assessment. They distinguished this approach from the pedagogical method of intelligence assessment (which aims to determine intelligence "according to the sum of acquired knowledge"; Binet & Simon, 1905/1961, p. 91). The choice of the psychological method by Binet has resulted in successful predictions of academic success for children and young adolescents. Some contemporary investigators even suggested that a measure of global intelligence (g) is sufficient for prediction across a wide variety of achievement criteria (e.g., Hunter, 1986), although others argued for differential prediction in certain cases

(e.g., Thorndike, 1986). Although the g perspective is attractive in terms of parsimony, measures of g (or even verbal and quantitative abilities) have been shown to be modest predictors of academic success for adults. Moreover, validity coefficients obtained for prediction of first-year college/university grades decline over the course of later college/university years, a fact recognized as early as the 1930s (e.g., see Wolf, 1939; also see Humphreys, 1968; but see Ackerman, 1994, for a different interpretation). It is our contention that g may be most useful in predicting academic success for children and adolescents, but that g becomes less important in predicting academic success as individuals reach young adulthood and beyond. This point is especially salient as educational evaluation shifts from a focus on abilities to a focus on achievement and competency assessment. Early education teachers may not agree that one major purpose of education is the development of knowledge as opposed to other goals (e.g., development of ability, critical thinking skills, learning styles, or more simply "nurturing" students; Alexander & Murphy, 1999, p. 434). At the secondary and postsecondary levels, however, domain knowledge has become an increasingly important criterion for assessing level of academic achievement, and domain knowledge has become an indicator of academic promise of applicants for higher education (e.g., the use of Advanced Placement and similar examination scores in the process of applicant selection and placement; see Watzman, 2000; see also the review by Hirsch, 1999, in the context of cultural literacy and academic achievement). One major question that arises from a focus on domain knowledge is, "What are the cognitive, affective, and conative determinants of individual differences in knowledge?" That is, although there is a large corpus of literature on these issues with respect to intelligence tests, far less is known about the determinants of individual differences in knowledge, especially for young adult and middle-aged students.

Phillip L. Ackerman, Kristy R. Bowen, Margaret E. Beier, and Ruth Kanfer, School of Psychology, Georgia Institute of Technology. This research was sponsored by a grant from The College Board and by National Institute on Aging Grant AG16648. We thank Dr. Wayne Camara for his encouragement and assistance, on behalf of The College Board, in supporting this research. We also thank Dean Joanne Brzinski of Emory University and Ms. Gwen Kea-Holloway of Georgia Tech for assistance in identifying study participants and the Kanfer-Ackerman Laboratory staff for assisting in experiment running and data coding. Correspondence concerning this article should be addressed to Phillip L. Ackerman, School of Psychology, Georgia Institute of Technology, Psychology Building, 274 5th Street, Mail Code 0170, Atlanta, Georgia 30332-0170. Electronic mail may be sent to phillip.ackerman® psych.gatech.edu.

Gender Differences in Knowledge Whereas intelligence tests (e.g., the major tests that provide IQ estimates) are constrained by design to yield equal mean scores for 797

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ACKERMAN, BOWEN, BEIER, AND KANFER

men and women (e.g., see Terman, 1916), domain-knowledge tests (which by design are content validated rather than norm referenced) do not usually yield equal mean scores for women and men. The implications of gender differences in domain knowledge on educational selection and placement at the postsecondary level and beyond (e.g., Advanced Placement [AP] tests, Graduate Record Examination subject tests, and professional certification tests) are both clear and somewhat chilling. One perspective on this issue is provided by examination of test statistics from The College Board (2000). The AP program involves a collaborative effort between high schools and colleges. High schools provide instruction in a variety of different collegelevel courses to selected students. The students complete AP examinations covering the college-level course material and submit the scores to colleges and universities. The tests are scored on a scale ranging from 1 to 5. Although there are some differences between postsecondary institutions regarding whether and how much college credit is provided, based on test scores, students obtaining scores of 4 or 5 are most often given college credit, whereas those obtaining scores of 1 or 2 are almost always denied credit (College Board, 2000). With a sufficient number of highscoring tests, the prospective college student can avoid the time and expense of one or two semesters of college. For the most recent test administration (May 2000), The College Board reported that 574,905 tests were administered to males and 667,419 tests were administered to females. Scores of 4 or 5 were obtained by 225,575 males but only 217,572 females. That is, even though 92,514 more tests were taken by females, males obtained more clear passing scores than females (39.2% vs. 32.6%). The implications are clear: Females who matriculate at colleges/universities are required to complete more coursework than males. At the individual test level, females perform more poorly on most tests (and not just in the math and science areas), with equal scores on a few tests (e.g., Art, English Language and Composition). Females only regularly perform better than males on the foreign language tests (e.g., see Willingham & Cole, 1997). This pattern of gender differences is further complicated by the fact that female performance in high school and college courses is often underpredicted by regression equations computed across genders. Research has also shown that girls and women tend to achieve higher course grades in high school and beyond in comparison to boys and men (see Dwyer & Johnson, 1997, for a review). Other sources of information have been gathered to help account for these differences. For example, extensive data exist that address gender differences on a wide variety of aptitude and achievement tests (see Cole, 1997; Stanley & Stumpf, 1997; Strieker, Rock, & Burton, 1991; Willingham & Cole, 1997; Willingham & Johnson, 1997). In terms of the AP tests, researchers have investigated testing format (multiple choice vs. free response), type of test content, use of calculators, AP course enrollment patterns, and many other sources of information (e.g., Breland, Danos, Kahn, Kubota, & Woltson, 1991; Mazzeo, Schmitt, & Bleistein, 1993; Stumpf & Stanley, 1996, 1997; Subotnik & Strauss, 1994/1995). Although test format, course taking, and similar variables can and have been used to address reasons for pervasive gender differences in AP test performance, several gaps are associated with this body of work. Investigators typically only consider a single domain of knowledge at one time (such as a single AP test). Thus,

little is known about how performance on a particular test relates to knowledge assessed on other tests. In studies of single domains, the possibility that knowledge in one domain (e.g., chemistry) is "supportive" of knowledge in another (e.g., physics) is neglected. For example, gender differences in chemistry might help account for some portion of gender differences in assessed physics knowledge. This concept can be expanded to include the entire repertoire of domain knowledge, such that gender differences in specific domain knowledge might be accounted for by gender differences in the patterns of supporting domain knowledge. Second, although much is known indirectly about interests (through course enrollment patterns), little is known about how interest differences relate to AP test performance. Gender differences in the structure of interests are well documented in the literature (e.g., see Lippa, 1998) and have also been found in personality measures. These personality and interest differences may help account for gender differences in test performance. Other researchers (e.g., Kanfer & Heggestad, 1997) have pointed to the possibility of gender differences in motivational traits and skills. For example, the mismatch between gender differences in AP test performance and gender differences in grades may be partly accounted for by gender differences in emotion control skills (which appear to be especially important when evaluation apprehension is elevated, such as during high-stakes testing). It has also been conjectured that differences in course-taking patterns at the high school level are at least partly responsible for gender differences on achievement test scores, especially in the math and science domains (e.g., see Ekstrom, Goertz, & Rock, 1988; Han, Cleary, & Clifford, 1993, for discussion). Two factors militate against this conjecture in the current context. First, it has been suggested that the gap between boys and girls in science and math course enrollment has been virtually eliminated. For example, researchers with the National Assessment of Educational Progress (National Assessment of Educational Progress, 1999) program have found that, for 17-year-olds, there are no significant differences between highest level of math courses taken (13% of both boys and girls had taken precalculus or calculus and 52% of girls had taken algebra II compared with 50% of boys). For science, the gap between boys' and girls' course taking had also been essentially eliminated by 1999. Data pertaining to percentages of boys and girls enrollments are consistent with this point, such as enrollment in general science (88% and 88%, respectively), biology (92% and 95%, respectively), chemistry (55% and 60%, respectively), and physics (18% and 16%, respectively). Therefore, persistent differences in knowledge between the gender groups cannot be simply explained by differences in direct curricular exposure to the material. Second, in the specific context of the study reported here, we only sampled students who had already completed a prescribed curriculum (e.g., specific AP courses in high school). Note that the controversy about whether girls are treated differently from boys in the classroom is not addressed by these two factors, for which course enrollment patterns are not diagnostic (e.g., see discussions by American Association of University Women, 1992; Sommers, 2000). Theory of Adult Intellectual Development To address the questions of the determinants of individual differences and gender differences in knowledge, we have adopted

INDIVIDUAL DIFFERENCES IN KNOWLEDGE

a theoretical approach to adult intellectual development, called PPIK (Intelligence-as-Process, Personality, Interests, and Intelligence-as-Knowledge; for additional details see Ackerman, 1996; see also Ackerman & Heggestad, 1997; Ackerman & Rolfhus, 1998, 1999; Goff & Ackerman, 1992; Rolfhus & Ackerman, 1999). Ackerman (1996) stated that the broad scope of adult intellect, which includes both postsecondary educational achievement and later achievement in occupational and avocational pursuits, is predicated on four components: intelligence as process (fluid-type abilities), personality traits, interests, and intelligence as knowledge. Ackerman (1996) further claimed that, as adulthood is reached, performance on achievement measures (e.g., grades, knowledge assessments) is more highly predicated on personality, interests, motivational traits and skills (see Kanfer & Heggestad, 1997), and verbal/crystallized abilities than on traditional measures of general reasoning and fluid intelligence. Ackerman's PPIK theory is essentially an "investment" theory, broadly consistent with that of Cattell (1971/1987). There are two major themes to this investment theory. First, adult intellect (as knowledge) is a result of effort devoted to the acquisition of information that may not be common to a dominant culture (e.g., such as the acquisition of knowledge in a collegiate "major" or acquisition of specific occupational knowledge). As such, crystallized knowledge becomes differentiated in both breadth and depth for different individuals as they reach early adulthood and beyond. Second, the direction and extent of effort devoted to knowledge acquisition are associated in part by "complexes" of ability and nonability traits. A trait complex is considered to be a broader form of what R. E. Snow called aptitude complexes. Snow (1963) defined aptitude complexes as "combinations of levels of some variables which are particularly appropriate or inappropriate for learning." Thus, the generalization is that trait complexes represent combinations of variables (especially cognitive, personality, and interest constructs) that are appropriate or inappropriate to intellectual development. A graphic depiction of the theory is shown in Figure 1, which illustrates how differentiated knowledge structures develop from fluid intelligence (Gf) and crystallized intelligence (Gc) influences, interacting with trait complexes. As shown, these trait complexes may have positive and negative influences on the development of domain knowledge. This work is also consistent with other models of academic learning (e.g., Alexander, Kulikowich, & Schulze, 1994), empirical data on the differentiation of abilities during adolescence (e.g., Atkin, Bray, Davison, Herzberger, Humphreys, & Selzer, 1977), and findings of knowledge determinants of academic success beyond college (e.g., Willingham, 1974). Ackerman (1996) also advocated a developmental pattern of the growth and decline of intellect that diverges from the extant approaches of g only or Gf and Gc. This pattern is illustrated in Figure 2. If one were to average across measures of Gf, Gc, and knowledge beyond the academic milieu (e.g., occupational and avocational knowledge), the trend of intelligence from young adult ages to middle age would be an increasing function rather than a declining function after young adulthood (as would be found from only averaging Gf and Gc, because the small increases in Gc with increasing age do not offset the larger declines in Gf). The Ackerman (1996) PPIK theory has Cattell's (1943, 1971/ 1987) constructs of Gf and Gc as important components. A reviewer of a previous version of this article pointed out that "some

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Intellectual Personality/Interest/SelfAbilities Concept Trait Complexes

Knowledge Structures

Business

— • Positive Influences ••> Negative Influences

Figure 1. Illustration of constructs and influences in the PPIK (Intelligenceas-Process, Personality, Interests, and Intelligence-as-Knowledge) theory (Ackerman, 1996). Gf (fluid intelligence) represents intelligence as process. Gc = crystallized intelligence. Trait complexes (including personality, interests, self-concept, ability) from Ackerman and Heggestad (1997). Positive and negative influences derived from the theory and supported by prior empirical data (Ackerman, 2000; Ackerman & Rolfhus, 1999; Beier & Ackerman, 2001; Rolfhus & Ackerman, 1999). "Negative influences" means that lower levels of one construct (e.g., Gc) lead to higher levels of the other construct (e.g., Clerical/Conventional trait complex).

educational researchers are less accepting of the crystallized/fluid distinction that helps to frame this investigation." Thus, some additional qualifications should be made. First, from our perspective, the physiological basis for Gf is not a necessary aspect of our

Avocational Knowledge , - - -.^"occupational /_ Knowledge 7

Traditional Assessment of Qc htelgenceas-process (Gf)

Adolescence Early Adit Mkjde MJL Late /

Age

Figure 2. Hypothetical growth/level of performance curves across the adult life span, for intelligence as process, traditional measures of Gc (crystallized intelligence), occupational knowledge, and avocational knowledge. Intelligence as process (GO and Gc are modeled after Horn, 1965. From "A Theory of Adult Intellectual Development: Process, Personality, Interests, and Knowledge," by P. L. Ackerman, 1996, Intelligence, 22, p. 229. Copyright 1996 by Elsevier Science. Reprinted with permission.

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ACKERMAN, BOWEN, BEIER, AND KANFER

approach to the construct (even though there is ample evidence from the neurological and the aging literatures indicating a functional division of abilities between process and content, such as Hebb's, 1942, Intelligence A and B). Moreover, extant theories of intelligence have analogous groupings of abilities, such as Vernon's (1950) k:m and v:ed factors. Finally, although there is some disagreement as to whether Gf and g are equivalent factors (e.g., Gustafsson, 1984), the consensus view among ability theorists and empirical researchers is that broad Gc factors and broad Gf (or g factors) can be found in appropriately designed studies (for a most extensive review and reanalysis of several hundred studies, see Carroll, 1993). Whether other aspects of Cattell's larger theory of intelligence are ultimately supported or disconfirmed in the literature is not directly relevant to the current study. Whether one chooses to call factors underlying selected ability measures Gf and Gc or some other terms is perhaps dependent on orientation; nonetheless, the ability measures do readily yield identifiable factors that are concordant with these terms as adopted by other researchers in the field (e.g., see McGrew & Flanagan, 1998). The one major exception to this approach, which is clear in the current empirical investigation (and similar investigations, see Ackerman, 2000; Ackerman & Rolfhus, 1999; Rolfhus & Ackerman, 1999), is that the Gf and Gc factors are substantially positively correlated, even in samples in which prior selection has occurred on ability measures (e.g., the SAT). This datum is inconsistent with Cattell's conceptualization of intelligence as an "incomplete" hierarchy, in which Gf and Gc are posited to be uncorrelated in adult samples (Cattell, 1971/1987). Ackerman and Heggestad (1997) conducted a meta-analysis and narrative review of the relations among ability, personality, and interest constructs. Ackerman and Heggestad reported that four groups of variables appeared to coalesce from the patterns of interrelations. The broad trait complexes were identified as Social, Clerical/Conventional, Science/Math, and Intellectual/Cultural. Examples of variables that make up these trait complexes are shown in Figure 3. Consistent with Snow's notion of "aptitude complexes," Ackerman and Heggestad (1997) conjectured that the acquisition of knowledge across a wide array of domains might be influenced by coherent patterns of individual differences on the variables in these trait complexes. Subsequent analyses and studies have included the constructs of self-concept and self-estimates of abilities because these constructs bridge the gap between objective measures of abilities and subjective judgments (Ackerman, 1997). Some investigators demonstrated that judgments of self-efficacy for task performance are only indirectly determined by objective ability measures but are directly determined by self-concept and self-estimates of ability, which are, in turn, partially determined by objective ability (e.g., Kanfer, Ackerman, & Heggestad, 1996). Thus, an individual's decision to engage in a task or attempt to acquire new knowledge may be influenced both by objective ability and the individual's subjective judgments of his or her ability. Within Ackerman's PPIK theory, these broad trait complexes are conjectured to have supportive or impeding influences on different domains of knowledge. For example, the Social trait complex (which is made up of extroversion-related personality constructs and both social and enterprising interests) is hypothesized to have a negative relationship with knowledge in the sci-

Trait Complexes #3: Science/Math,

(visual Perception

Math Reasoning

Realistic

Perceptual Speed Conventional Control

* £ Clerical/Conventional

/ Enterprising

Extroversion Social Potency Well-Being #1 Social

Abities (Bold) Interests (Helvetica) Personality (Italics)

Figure 3. Trait complexes, including abilities, interests, and personality traits showing positive commonalities. TIE = typical intellectual engagement. From "Intelligence, Personality, and Interests: Evidence for Overlapping Traits," by P. L. Ackerman and E. D. Heggestad, 1997, Psychological Bulletin, 121, p. 219. Copyright 1997 by American Psychological Association. Reprinted with permission.

ences and humanities. Individual differences on the Intellectual/ Cultural trait complex, which includes openness to experience and artistic interests, are expected to have substantial positive relations with the development of knowledge in the humanities domain and positive relations with knowledge in other domains (see Figure 1). It should be noted that, although trait complexes represent a kind of synergy among correlated constructs, they also represent the averaging of individual constructs. When the correlations among constructs within a trait complex are high or when the criteria to be predicted are broad, averaging is expected to amplify the respective relations between trait complexes and criterion variables. However, when the criteria are narrow or specific, or the constituent constructs in a trait complex are only modestly correlated, consideration of individual trait-level variables may have higher heuristic value. For a discussion of this issue within the framework of Brunswik symmetry, see Wittmann and SUB (1999). Overview of Present Study The broadly construed PPIK approach to adult intellect and the relations among abilities, personality, interests, and knowledge has been subjected to several investigations. In a study of college students (Rolfhus & Ackerman, 1996), significant and substantial overlap was found between key measures of ability, personality, and interest measures on the one hand and self-reported knowledge on the other hand. Subsequent studies of these relations, along with self-concept and self-reports of ability, were based on an in-depth series of objective knowledge tests in a sample of college students (Rolfhus & Ackerman, 1999), a sample of adults between 30 and 59 years old (Ackerman & Rolfhus, 1999), and a sample of adults (between ages 21 and 62 years) who had completed at least a baccalaureate level of education (Ackerman, 2000). The battery of knowledge tests was expanded to include measures of current events that have taken place between the 1930s and the 1990s (Beier & Ackerman, 2001).

INDIVIDUAL DIFFERENCES IN KNOWLEDGE Across these studies, individual differences in trait complexes (which have included ability, self-concept, personality, and interest measures) have been found to correlate positively with individual differences in knowledge (e.g., the Science/Math trait complex and the Intellectual/Cultural trait complex) or to negatively correlate with individual differences in knowledge (e.g., the Social trait complex). Moreover, the Science/Math and Intellectual/Cultural trait complexes have been used to demonstrate convergent and discriminant validity (e.g., the Science/Math trait complex has the highest positive correlations with physical sciences and technology knowledge, and the Intellectual/Cultural trait complex has the highest positive correlations with knowledge in the humanities). Although adult age differences in knowledge favoring middleaged adults were hypothesized and supported by the empirical data (Ackerman, 2000; Ackerman & Rolfhus, 1999), gender differences in knowledge were not a priori predicted. However, gender differences were found, favoring men, in nearly all of the academic knowledge domains (Bowen, Beier, & Ackerman, 2000). Many hypotheses could be offered to account for gender differences in these previous samples, such as an overabundance of full-time homemakers in the samples, differences in levels of education obtained by middle-aged adults, and so on. The current empirical study was, therefore, partly motivated by a desire to draw a narrower range of ages and a narrower range of educational experiences by women and men in the sample. Our goal was to examine gender differences in a group of participants for whom prior educational curricula were known to some degree (i.e., having completed specific AP courses in high school). Specific predictions can be made in the context of the PPIK theory (e.g., that, in comparison to Gf, Gc is a significantly better predictor of individual differences in domain-specific knowledge). Several other measures included in the study are exploratory. For the exploratory measures, only broad, qualitative predictions are considered. There are two contexts for the study: a broad context for predicting individual differences in intellectual development and a narrow context for drawing conclusions relevant to extant concerns in the educational community about the determinants of individual differences and gender differences in AP test performance. With the general and specific contexts in mind, we set out to recruit a sample of first-year college students (freshmen) who had completed at least one of the following target AP tests: English Literature, U.S. History, or Biology. In this way, a sufficient sample of participants was expected to be available to make comparisons based on the specific domains of learning experiences. Each of the participants completed an array of measures associated with cognition (e.g., Gf, Gc, perceptual speed [PS]), affect (e.g., extroversion, openness to experience, conscientiousness), and conation (e.g., vocational interests, motivational traits, and skills). After the assessment of trait and skill measures, each participant completed a battery of knowledge tests across 19 different domains (e.g., physics, art, world literature, current events) that also contained tests of the three "target" areas (English literature, U.S. history, and biology). Individual correlations among trait measures, abilities, and knowledge are presented first, followed by assessments that are based on evaluation of common and unique variance accounted for (e.g., multiple regression and structural equation modeling). In the Results section, we focus on the prediction of individual differ-

801

ences in knowledge. Subsequently, we focus first on attempts to document, and then account for, gender differences across the predictor and criterion variables. Finally, we address archival AP test data.

Hypotheses Several theory-based and inductive hypotheses can be made for the current study. 1. Knowledge tests. We expected that 19 knowledge tests would show substantial positive manifold (i.e., all tests were predicted to correlate positively with one another). We also expected four underlying factors that correspond to broad domains of knowledge, such as physical sciences/technology, humanities, civics, and law/business. We expected to find somewhat less differentiation among the individual tests than has been found with middle-aged adult samples (e.g., Ackerman, 2000; Ackerman & Rolfhus, 1999). 2. Ability tests. We designed the battery of ability tests to provide estimates of three factors: Gf, Gc, and PS. We expected that Gf measures would have the most substantial correlations with knowledge tests in the physical sciences/technology domains and positive, but smaller, correlations with the other knowledge tests. We expected Gc measures to have substantial positive correlations with all knowledge tests, with the largest to be found in the humanities and civics domains (Ackerman, 1996, 2000; Ackerman & Rolfhus, 1999). PS abilities were not expected to be correlated substantially with any of the knowledge tests. 3. Nonability traits. On the basis of the trait complex framework of Ackerman and Heggestad (1997), we expected that realistic and investigative interests would have positive correlations with knowledge in the physical sciences/technology domains (and with Gf). We further expected that investigative and artistic interests, along with Typical Intellectual Engagement (TIE) and Openness would have positive correlations with nearly all of the knowledge tests (and with Gc) and would have attenuated (closer to zero) correlations for the physical sciences/technology domains. We expected that measures associated with the Social trait complex (e.g., social and enterprising interests, extroversion, social closeness, femininity) and the Clerical/Conventional trait complex (conventional interests, conscientiousness, traditionalism, control) would have negative correlations with nearly all of the knowledge scales. We expected Clerical/Conventional trait complex scores to correlate positively with PS abilities. We expected that approachoriented motivational traits (desire to learn and mastery) would have positive correlations with many knowledge domains, whereas aversion-oriented motivational traits (worry and emotionality) were expected to have negative correlations with knowledge. 4. Gender differences. Regarding abilities, we expected that women would have lower scores, on average, on Gf, either equal means or slightly lower scores on Gc, and higher scores on PS. As for knowledge, we expected that women would have generally lower scores on the knowledge tests, with the largest differences (about .8-1.0 .05, and marginally higher verbal self-estimates of ability, t(3\2) = 1.20, p > .05. In the personality and interests domains, women had significantly higher levels of broad extroversion-related traits (including Social Closeness), r(316) = 4.16, p < .01, Femininity, f(310) = 3.07, p < .01, social interests, r(315) = 2.02, p < .01, and artistic interests, r(315) = 3.11, p < .01. Women also reported marginally higher levels of neuroticism/anxiety-related motivational traits of Worry, r(314) = 1.67, p > .05, and significantly higher levels of Emotionality, r(314) = 2.47, p < .05. From an activities and experiences perspective, women reported significantly higher levels of cultural, craft-related, and Other Extracurricular Activities, t(3ll) = 4.86, p < .01, but significantly fewer technology-related activities, r(311) = 8.44, p < .01. Gender differences summary. Predictions regarding gender differences (Hypothesis 4) were generally supported. Women per-

ACKERMAN, BOWEN, BEIER, AND KANFER

812

formed most poorly on Gf and Physical Sciences/Technology measures, although significant differences were found in the other domains as well. Women did perform more poorly on Gc than was anticipated but performed better than men on PS as predicted. Nonability measures also revealed ubiquitous gender differences; women showed lower levels of ability self-concepts and higher levels of extroversion-oriented personality and interests. Women also showed less frequent involvement in technology-related activities and greater levels of culture-related activities and craftrelated activities.

Stage 4: Multiple Regression Predictions of Knowledge The main justification for performing regression analyses with these data is that the sole presentation of raw Pearson productmoment correlations fails to take account of shared variance among predictor measures. The hierarchical regressions used in this section answer the question of "incremental predictive validity" for ability and gender measures, so that each additional predictor measure added to the equation is evaluated in terms of its unique valid variance in predicting the criterion. Furthermore, we have taken a more general approach to knowledge by using the four composite knowledge measures as criteria rather than the individual 19 knowledge tests. Although some information is lost by this approach, it is far more parsimonious to consider composites than individual scores. Hierarchical multiple correlations among gender, Gf, and Gc as predictors and knowledge composite scores as criteria are shown in Table 7. (Given that PS abilities had minimal validity for predicting knowledge test scores, PS abilities are not included in

these prediction equations.) These analyses were designed to address two central issues in the study: (a) the degree of validity of ability measures for predicting individual differences in knowledge across the domains and (b) whether, by taking account of individual differences in Gf and Gc levels, the amount of variance in the criterion measures accounted for by gender can be reduced. For example, if, when Gf and Gc are entered into the prediction equation, gender ceases to provide incremental predictive validity, then it is perhaps reasonable to consider that gender differences in knowledge can be explained by more "basic" gender differences in abilities. The goal of these analyses was to provide information on both individual and gender differences. The amount of variance in knowledge composite performance accounted for by gender is shown in the first column of Table 7. Two variable-entry strategies were adopted. In Method 1, Gf was the first variable in the prediction equation. In Method 2, Gc was the first variable in the equation. The reason for this procedure is that it allows comparison of two different theoretical perspectives. The first perspective (g only) would hold that the major source of predictive validity is Gf, given that it represents the closest assessment of "pure g" (Gustafsson, 1984; Jensen, 1998). As such, contributions of Gc to the prediction of knowledge performance would be expected to be relatively small, subsequent to Gf. The second perspective (Ackerman, 1996, 2000; Ackerman & Rolfhus, 1999) predicted that a difference will be found based on the topic domain under consideration. For Physical Sciences/Technology, Gf was expected to have the primary contribution to predicting knowledge, but for the remaining domains Gc was expected to have primary contribution,

Table 7 Summary of Hierarchical Regressions for Predicting Knowledge Scale Scores From Ability and Gender Step 0: gender only

Variable

Step 1: Gf

Step la: Gf + gender

Step 2: Gf+Gc

Step 3: Gf + Gc + gender

.104** .452** .001 (ns) .155** .006 (ns) .185** .008 (ns) .240**

.169** .517** .166** .320** .377** .556** .266** .497**

.092** .611** .003 (ns) .324** .002 (ns) .559** .004 (ns) .501**

.124** .574** .001 (ns) .312** .002 (ns) .559** .007* .492**

.067** .517** .010* .320** .000 (ns) .556** .012** .497**

.092** .611** .003 (ns) .324** .002 (ns) .559** .004 (ns) .501**

Method 1: Gf entered first Knowledge Composite Physical Sciences/Technology Biology/Psychology Humanities Civics

R2 to add Total R2 R2 to add Total R2 R2 to add Total R2 R2 to add Total R2

— .224** — .005 (its) .036** — .047**

— .348** — .154** — .179** — .232**

Method 2: Gc entered first Knowledge Composite Physical Sciences/Technology Biology/Psychology Humanities Civics

R2 to add Total R2 R2 to add Total R2 R2 to add Total R2 R2 to add Total R2

— .224** — .005 (ns) — .036** — .047**

— .450** — .311** — .556** — .485**

Note. Step 0 and Step 1 (raw Pearson product-moment correlation, df = 318). For each multiple regression, F to add is a 1 dft&sl. For total R2, Step la and Step 2 df = 1,318; Step 3 df = 3, 316. ns = not significant; Gf = fluid intelligence; Gc = crystallized intelligence. *p < .05. **p < .01.

INDIVIDUAL DIFFERENCES IN KNOWLEDGE and Gf was expected to have a very small independent contribution to the prediction of individual differences in knowledge. For the assessment of gender as a predictive measure, it is considered after each step of the analysis. Thus, in Method 1, Gf is entered first (Step 1), followed by gender (Step la). Then Gc is added to the equation (without gender) (Step 2). Finally, gender is added to Gf and Gc (Step 3). A comparison can then be made between the independent contribution of gender (Step 0) and the "unique" contribution of gender (Step 3; R2 to add). Note that gender was not a significant independent predictor of Biology/ Psychology knowledge, and as such the unique contribution will be negligible (unless there is a suppressor effect). At the level of individual-differences predictions, the Ackerman (1996, 2000) PPIK-based predictions are largely supported. That is, for Biology/Psychology, Humanities, and Civics knowledge composites, entering Gc first (Method 2) drives the independent contribution of Gf to the prediction of knowledge to essentially a value close to zero (R2 to add = .010, .000, and .012, respectively). In fact, for these three domains, under Method 1, Gc accounted for a greater amount of variance than Gf, even when Gf was first entered into the prediction equation. In contrast, but consistent with predictions, the influence of Gf was substantial for predicting knowledge in Physical Sciences/Technology, even after Gc had been entered into the equation (R2 to add = .067). Overall, Gf and Gc together accounted for a range of 32% of the variance (Biology/Psychology) to 56% of the variance (Humanities). The fact that Gc had greater explanatory power than Gf for all of the knowledge domains is consistent with the PPIK theory. In addition, this finding is inconsistent with many theoretical approaches that equate Gf with "pure intelligence." It is important to note that our measures are multiple-choice tests that allow for recognition of the correct answer. Constructed-answer tests might be expected to yield higher correlations with Gf than were obtained in this study. For gender differences (ignoring the Biology/Psychology domain, for which there was no significant raw correlation with gender), entering either (or both) of the Gf and Gc measures effectively reduced the variance accounted for by gender. After both Gf and Gc were entered, there was no significant incremental validity accounted for by gender for either Humanities or Civics knowledge domains. For the Physical Sciences/Technology domain, gender still accounted for a significant and substantial amount of variance (R2 to add = .092), even after Gf and Gc were added to the equation. With an independent contribution of gender accounting for 22.4% of the variance, the amount to be explained was roughly cut in half after individual differences in Gf and Gc were accounted for.

Stage 5: Trait Complexes Another way to summarize the nonability predictor relations with both abilities and knowledge is to compute trait complex scores (Ackerman & Heggestad, 1997). By taking account of the communality of the different traits (e.g., self-concept, personality, interests, motivation), it is possible to assess differential associations between patterns of nonability differences and both objective ability measures and objective knowledge measures. To group the various measures in a parsimonious fashion, the nonability measures (excluding self-reports of specific knowledge domains) were subjected to an exploratory factor analysis. The same general

813

procedure for exploratory factor analysis described earlier was applied to these data. Although the parallel analysis criterion was interpreted as suggesting as many as 12 underlying factors (which is consistent with previous analyses of such diverse measures; see Ackerman 2000; Ackerman & Rolfhus, 1999), only five factors were extracted in keeping with a parsimony criterion. In contrast to the ability test factor analysis, an oblique solution did not result in improved simple structure. Thus, the factors were rotated to an orthogonal quartimax criterion. The factor loadings are shown in Table 8. Subsequent to the factor analysis computations, composites were formed with variables that had salient loadings on the factors. Some final decisions on inclusion or exclusion were made based on attempts to maximize discriminant validity and internal consistency reliability measures. The final result of these procedures was identification and computation of scores for five trait complexes, (with a list of variables that comprise each trait complex scale): (1) Science/Math/Technology (math self-concept, selfestimate of math, spatial self-concept, science self-concept, selfestimate of memory); (2) Verbal/Intellectual (verbal self-concept, self-estimate of verbal, self-estimate of knowledge, TIE, Openness to Experience, investigative interests, artistic interests); (3) Social Potency/Enterprising (Social Potency, Enterprising, Conventional); (4) Social Closeness/Femininity (Extroversion, Social Closeness, Bern Femininity); (5) Traditionalism/Worry/Emotionality (Traditionalism, Worry, Emotionality, Other Extracurricular Activities). Using variables with salient loadings from the factor solution, unit-weighted z-score composites were computed to obtain trait complex scores. Examination of the scores indicated that they were relatively independent of one another (no correlation exceeded r = .25). Even though there was little common variance between the five trait complex scores, it is possible that individual participants had high scores on more than one complex. As such, this is not a categorical classification of participants. A D = —.45 difference was found favoring men on the Science/Math/Technology trait complex, f(318) = 4.06, p < .01, and differences favoring women were found on three of the remaining four trait complexes: for Social Potency/Enterprising (D = .19, p < .05), Social Closeness/ Femininity (D = .58, p < .01), and Traditionalism/Worry/Emotionality (D = .40, p < .01). The difference between genders for Verbal/Intellectual trait complex (D = .17) was only marginally significant. Correlations between trait complex scores and both ability and knowledge composites were computed and are shown in Figure 5. Persons high on the Science/Math/Technology trait complex had higher scores on Gf and on the physical science/technology knowledge composite but also substantially higher Gk (overall knowledge) scores. However, persons high on the Verbal/Intellectual trait complex, although having lower Gf ability and Physical Sciences/Technology knowledge than those high on the previous trait complex, scored the highest on Gc, higher than the other groups on the remaining knowledge composites, and higher on Gk. The remaining three trait complexes were negatively associated with Gf, Gc, and overall knowledge (although there was very little difference between any of the trait complex scores on PS ability). Individuals with high Social Closeness/Femininity and Traditionalism/Worry/Emotionality trait complexes had low knowledge composite scores, but those individuals with high Traditionalism/ Worry/Emotionality scores had the lowest Gf and Gc abilities.

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Table 8 Factor Analysis for Self-Report Measures to Reveal Trait Complexes (Quartimax Rotation) Factor Self-report measures 1. Science/Math/Technology Math Self-Concept Self-Estimate Math Spatial Self-Concept Science Self-Concept Self-Estimate Memory Realistic Interests Emotion Control (positive) Technology Activities 2. Verbal/Intellectual Verbal Self-Concept Self-Estimate Verbal Self-Estimate Knowledge TIE Openness Investigative Interests Artistic Interests Desire to Learn Mastery Cultural Activities 3. Social Potency/Enterprising Social Potency Enterprising Interests Conventional Interests 4. Social Closeness/Femininity Extroversion Social Closeness Bern Femininity 5. Traditionalism/Worry/Emotionality Traditionalism Worry Emotionality Other Activities Craft Activities

I

II

III

IV

V

-.163 -.084 .117 .118 .086 .146 .266 .109

.765 .839 .720 .735 .555 .320 .577 .361

-.116 -.115 .007 -.187 .187 .024 .198 .073

-.001 -.008 -.083 -.086 .119 -.251 .132 -.337

.000 -.074 -.167 -.010 -.018 .253 -.144 .180

.652 .652 .479 .764 .726 .400 .642 .609 .396 .680

-.006 -.039 .135 .294 .113 .326 -.133 .333 .330 -.064

.145 .179 .309 -.019 -.197 -.339 -.077 -.081 .089 .053

.034 .027 -.048 -.021 .051 -.104 -.012 .066 .242 .006

-.263 -.349 -.111 .024 -.030 .238 .039 .193 .260 .209

.198 .113 -.201

-.098 -.051 .224

.605 .838 .566

.296 .135 -.063

-.130 .147 .303

.105 -.039 .134

.050 -.109 .081

.242 .123 -.064

.777 .689 .505

-.011 .092 .276

-.270 -.217 -.194 .127 .220

.012 -.190 -.398 -.023 -.029

.186 -.141 -.140 .184 .089

.271 .139 -.019 .076 -.007

.359 .421 .486 .358 .266

Note. Salient correlations are shown in bold (largest loadings > .300). Additional loadings that exceeded 300, but not the salient loadings, are shown in italics. TIE = typical intellectual engagement.

Persons who scored high on the Social Potency/Enterprising trait complex had slightly higher (but still lower than average) Gf and Gc scores and scored at the mean only on the Civics knowledge composite. Ackerman and Heggestad (1997) speculated that PS and math computation might be closely identified with their Clerical/Conventional trait complex. The current results (although admittedly taken from a talented college student population) might be interpreted as suggesting that the issue is far from settled. Suppose PS abilities are proposed to be a "compensatory" development in relation to Gf and Gc abilities. These data suggest that assertion may not be completely true. Individuals who were high on the Science/Math/Technology trait complex also tended to have higher PS scores than those individuals high on the other trait complexes. However, the remaining trait complexes were essentially uncorrelated with PS abilities (in contrast to the trait complex relations with Gf and Gc abilities). It appears that, at least in this sample, PS abilities are a very minor part of the equation leading from intelligence-personality-interests-motivation to knowledge. Trait complexes that largely reflected the Ackerman and Heggestad (1997) taxonomy were revealed through factor analysis of

self-report measures. Gender differences in trait complex scores reflected differences in the constellations of traits that are concordant with the extant literature. These trait complex differences were also concordant with the pattern of gender differences in ability patterns and knowledge across the four broad domains under investigation. Taken together, the gender differences and the trait complex correlates with Gf, Gc, and knowledge indicate that the trait complexes most associated with gender differences favoring women were associated with lower ability and knowledge scores. The trait complexes most associated with gender differences favoring men (or favoring neither group) were also associated with high ability and knowledge scores.

Stage 6: Structural Equation Modeling A structural equation model (SEM) was created using LISREL to examine the relationships between the latent knowledge and ability factors. The model is shown in Figure 6. In this model, Gf was placed causally prior to all other factors (including Gc). The model included direct relationships from Gf to Gc (in accordance with both the Cattell & Horn theory; e.g., Cattell, 1971/1987,

INDIVIDUAL DIFFERENCES IN KNOWLEDGE

815

0.50

0.50

0.40

0.40

0.30

O

0.30

._. 0.20

0.20

o

0.10

0.10

I

0.00

£ 0.00

-0.10

S

-0.20

-0.10 -0.20

-0.30

•A Knowledge Domain

-0.30



-0.40 &



-0.40 O Science/Math/Technology -0- Verbal/Intellectual — B - Social Potency/Enterprising —•- Social Closeness/Femininity • A- Traditionalism/Worry/Emotionality

Figure 5. Correlations between trait complex scores and both ability and knowledge composites. Gf = fluid intelligence; Gc = crystallized intelligence; PS = perceptual speed; Gk = overall knowledge composite. Lines on the graph are for illustrative purposes.

Horn, 1965; and with the PPIK theory, which considers Gf to be developmentally and causally prior to Gc; Ackerman, 1996) and from Gf to Physical Science/Technology knowledge. There were direct relationships from Gc to all four knowledge factors (Physical Science/Technology, Biology/Psychology, Humanities, and Civics). The model was designed to reflect the PPIK theory in that Gc and knowledge are posited to result from investment of fluid ability. This model fit was adequate, ^(396, N = 320) = 655.6, p < .01, root mean square error of approximation (RMSEA) = .045, nonnormed fit index (NNFI) = .92, comparative fit index (CFI) = .93. The model shown in Figure 6 was subsequently tested separately for men and women. Model fit for both groups was similar to that of the overall model and is considered adequate, for men, ^(396, N = 152) = 529.84, p < .001, RMSEA = .047, NNFI = .91, CFI = .92; for women, ^(397, N = 168) = 530.87,/? < .001, RMSEA = .045, NNFI = .92, CFI = .92. Although the model fits were indeed similar, differences were observed among the various significant paths. For women, the path from Gf to Physical Science/Technology knowledge was much larger than for men. That is, Gf was more strongly associated with science knowledge for women than it was for men. In contrast, Physical Science/Technology knowledge was more highly associated with Gc for men than for women. These findings suggest that Physical Science/ Technology knowledge may be more highly dependent on Gf for women and more a product of Gc (and Gf moderated through Gc) for men. The path from Gc to Humanities knowledge was also larger for women than for men. However, the path from Gc to Biology/Psychology knowledge was larger for men than for women. Overall, these patterns raise an interesting possibility: that Gc is a better determinant of knowledge in the sciences for men than it is for women.

Figure 6. LISREL structural equation model for ability factors and knowledge factors. Lines indicate significant path coefficients. Gf = fluid intelligence; Gc = crystallized intelligence.

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816

An additional structural equation model was constructed using LISREL to examine the relationships between the five trait complexes (Science/Math/Technology, Verbal/Intellectual, Social Potency/Enterprising, Social Closeness/Femininity, and Traditionalism/Worry/Emotionality) and the four knowledge factors. This model, shown in Figure 7, places the trait complexes as causally prior to the knowledge factors. We hypothesized that the relationships between Complex 1 (Science/Math/Technology) and the Physical Sciences/Technology knowledge factor would be significant and positive. We also hypothesized that the association between the Verbal/Intellectual trait complex and all of the knowledge factors would be significant and positive. Further, we expected generally negative paths from the remaining trait complexes to the various knowledge factors. For the most part, the test of the model confirmed our hypotheses. The Science/Math/Technology trait complex was significantly positively associated with Physical Science/Technology knowledge. The Verbal/Intellectual trait complex had positive and significant paths to all four of the knowledge factors. Most knowledge factors were also negatively and significantly associated with the remaining trait complexes, as expected. The model fit was adequate, ^(220, N = 320) = 354.10, p < .01, RMSEA = .044, NNFI = .92, CFI = .94. The model described previously was also tested for men and women separately. The model fit adequately with minor modifi-

T>*aft Complexes

Knowledge Factors

Figure 7. LISREL structural equation model for trait complexes and knowledge factors. Lines indicate significant path coefficients. Negative paths shown in dotted lines.

cations for both groups, for men ^(219, N = 152) = 304.43, p < .01, RMSEA = .051, NNFI = .89, CFI = .92; for women, ^(223, N = 168) = 303.78,/; < .01, RMSEA = .047, NNFI = .90, CFI = .92. The relationships among the knowledge factors and the trait complexes were consistent for both men and women with a few exceptions. For both men and women, fewer significant relationships emerged among the Social Potency/Enterprising, Social Closeness/Femininity, and Traditionalism/Worry/Emotionality trait complexes and the knowledge factors (in comparison to the model based on the entire sample). This result may be due to the reduction in variance on some variables when the sample was split by gender. The significant negative relationships found between Traditionalism/Worry/Emotionality and three knowledge factors (Physical Sciences/Technology, Biology/Psychology, and Civics) were all reduced to nonsignificant paths when the model was tested with women only. For men, one significant relationship between Traditionalism/Worry/Emotionality and Civics knowledge remained. The implication of these results is that, for women only, the individual differences in the Traditionalism/Emotionality/Worry trait complex are not instrumental in determining knowledge level outcomes.

Stage 7: Archival AP Tests Subsequent to the completion of the study, archival records of AP test performance were obtained for study participants. Of the 320 total, Educational Testing Service records were matched for only 295. The mean number of AP tests taken was 4.31 (SD = 1.98), with a range from only 1 test to a maximum of 10 tests. In addition to the 3 AP tests used as inclusion criteria (U.S. History, English Literature, and Biology), 3 other tests had adequate sample sizes (i.e., greater than 70 test takers) for further evaluation (U.S. Government, Chemistry, and Calculus AB). These additional 3 tests represent ex-post facto sampling, given that participants were selected for inclusion on other tests. As such, these test results may not be representative of test takers in general. Descriptive information on the AP tests, ordered by the number of participants who completed the tests, is shown in Table 9. The table includes sample sizes by gender, along with means, standard deviations, and mean gender differences calculated as Cohen's D. Small gender differences were found for Biology and Chemistry, and a medium effect size was found for Calculus. These results are largely consistent with the extant literature, given the additional consideration of our sample's restriction in range of talent (e.g., Stumpf & Stanley, 1996), except that we found larger gender differences in the AP Calculus AB test. It is important to keep in mind that all of the AP tests have both multiple-choice and open-ended question formats (including essay sections for some tests), but, in contrast, our knowledge tests only contained multiple-choice items. Consistent with findings by Breland et al. (1991), our knowledge tests had larger gender differences (favoring men) than were found on the AP tests for this sample (cf. Table 5). Across all of the possible AP tests, there was no significant difference in the number of tests completed by gender, f(293) = 1.43, p < .05, but concordant with the literature, there was a significantly higher average score obtained by men over women on the AP tests, f(293) = 2.91, p < .01. Correlations between AP exam test scores, knowledge tests, and abilities were computed and are shown in Table 10. In addition, a

INDIVIDUAL DIFFERENCES IN KNOWLEDGE

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Table 9 Descriptive Statistics for AP Tests Sorted by Frequency of Tests Taken Sample size AP test 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30.

English Literature U.S. History Calculus AB Chemistry Biology U.S. Government English Language Calculus BC Physics B Physics Mechanics European History Microeconomics Psychology Statistics Macroeconomics Computer Science A Spanish Language French Language Physics, Elec, and Magnet Computer Science AB Latin Virgil Comparative Government Art History German Latin Literature Environmental Science Art (Drawing) Art (General) Music Theory Spanish Literature Average no. of AP tests taken Average score on AP tests taken

M

SD

Male

Female

Male

Female

Male

Female

D

92 95 87 58 41 34 29 29 25 24 15 17 11 12 11 10 9 3 11 7 3 5 3 2 1 1 1 2 0 0

118 106 75 47 47 45 33 28 20 16 24 22 19 16 7 6 5 10 2 3 7 4 1 2 3 2 1 0 2 1

3.40 3.40 3.80 3.40 4.02 3.50 3.55 4.03 3.36 3.75 3.80 3.59 4.09 4.08 3.82 2.70 2.56 3.00 3.64 2.71 2.67 3.40 4.00 3.00 4.00 4.00 3.00 2.50 — — 4.48 3.49

3.35 3.24 3.19 3.06 3.57 3.29 3.30 3.96 3.00 3.19 3.54 3.32 3.68 3.56 3.28 2.33 3.20 2.50 2.50 4.00 4.00 3.50 4.00 4.00 3.67 2.00 3.00 — 2.00 4.00 4.15 3.20

().94 .11 1.03 1.21 1.11 (3.99 .15 1.40 .04 .19 .08 ().71 .30 .31 ().87 .64 .24 .00 .63 .50 .53 .67 .00

1.00 1.08 1.22 1.20 1.25 1.27 0.81 1.26 1.26 1.11 1.10 1.25 0.88 0.96 1.11 1.75 1.48 1.27 2.12 1.00 0.82 0.58 —

-.06 -.15 -.55 -.28 -.38 -.19 -.25 -.05 -.31 -.49 -.24 -.27 -.37 -.45 -.54 -.22 .47 -.44 -0.60 1.01 1.08 0.08

0.58 — ().71 — — .84 ().80

— — — — 2.09 0.89

— — — — -0.17 -0.34

Note. Mean gender differences (£>) larger than ±.50a units are shown in bold. Negative D scores indicate higher scores for men. Dashes indicate no entry (means), no variance (standard deviation), or cannot be computed (D). AP = Advanced Placement; Elec = electricity; magnet = magnetism.

mean AP test score was computed for each participant (including all AP tests recorded for the participants). It is important to note that the scores on the AP tests range from 1 to 5 in whole numbers only. Therefore, the magnitude of correlation between these individual tests and other variables is very likely underestimated because of the crudeness of the AP scale scores. Knowledge tests that "corresponded" to the AP tests tended to have substantial correlations (e.g., the correlation of U.S. Government knowledge scale with AP U.S. Government was .586). The smallest corresponding correlation was for the two biology tests (r = .355). There are several noteworthy aspects of Table 10. First, consistent with the knowledge scale analysis, correlations between the Gc composite and AP test performance were higher for English Literature, U.S. History, U.S. Government, Biology, and mean AP performance than were corresponding correlations with the Gf composite. Essentially equivalent correlations for Gc and Gf were found for the AP Chemistry test, and a higher Gf correlation was found for the AP Calculus AB test. Second, the ubiquity of positive manifold and the principle of aggregation are clearly demonstrated in the knowledge scale composite correlations with individual AP tests and with the mean AP score across tests.

Aggregating across both sets of measures produced a substantial correlation (r = .590). Although the table entries appear to indicate that apparently dissimilar knowledge contents are positively correlated (e.g., physics and biology or music and chemistry), raw correlations do not indicate the unique contributions of each knowledge domain for predicting AP test performance. We computed a series of multiple regression prediction equations for each of the six AP tests. The main expectation was that knowledge in other domains provides incremental predictive validity over and above traditional measures of ability. We first entered Gf and Gc measures into the prediction equation. Next, we performed a forward stepwise entry procedure for each of the knowledge tests (excluding any "equivalent content" tests; e.g., U.S. Literature was not entered for predicting AP English). The results of these analyses are described rather than tabled for space considerations. Although multicollinearity among the predictors and the relatively small subsamples with particular AP exam scores suggest these results should be taken as illustrative rather than decisive, the results supported the general expectation. Both positive and negative predictors (suppressor variables, given the positive raw correlations among these measures and the AP tests)

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Table 10 Correlations Between Knowledge Scales, Composites, Abilities, and AP Exam Scores AP exams Variable

English Literature

U.S. History

U.S. Government

Biology

Chemistry

Calculus AB

Mean AP score

Physics Electronics Technology Astronomy Chemistry Physical Science/Technology Psychology Biology Biology/Psychology World Literature Art U.S. Literature Current Events (1990s) Geography Music Western Civilization Humanities U.S. Government Business/Management Economics Law U.S. History Civics Gk Gf Gc Gender

.252** .056 .171* .215** .232** .257** .289** .347** .391** .468** .325** .410** .210** .168* .229** .287** .459** .340** .156* .262** .202** .287** .359** .448** .341** .584** -.027

.210** .067 .140* .219** .218** .240** .204** .304** .313** .402** .270** .376** .284** .288** .197** .441** .490** .400** .291** .305** .180* .463** .468** .486** .192** .420** -.074

.294** .268* .314** .247* .220 .381** .397** .345** .440** .411** .138 .416** .490** .220 .182 .286* .434** .586** .362** .390** .536** .542** .641** .570** .434** .533** -.090

.478** .337** .353** .330** .436** .524** .211* .355** .347** .261* .345** .331** .305** .491** .107 .509** .478** .356** .066 .344** .174 .370** .383** .554** .263* .464** -.188

.529** .258** .269** .253** .535** .521** .426** .354** .474** .285** .181 .184 .225* .155 .285** .268** .331** .277** .176 .318** .236* .346** .376** .494** .450** .464** -.136

.440** .182* .297** .194* .345** .389** .239** .144 .225** .143 .141 .081 .143 .167* .132 .287** .225** .331** .199* .317** .112 .171* .303** .349** .348** .248** -.266**

.449** .193** .300** .293** .381** .440** .395** .434** .503** .394** .270** .336** .325** .281** .243** .455** .496** .425** .259** .421** .322** .419** .576** .590** .445** .534** -.167**

Note. Ns differ by Advanced Placement (AP) test: English Literature, 210; U.S. History, 201; U.S. Government, 79; Biology, 88; Chemistry, 105; Calculus AB, 162. Mean AP score is an arithmetic average of scores on all AP tests taken by the participants (M = 3.33, SD = .86). Composites are shown in italics. Corresponding tests correlations are shown in bold. Gender codes: 1 = male, 2 = female. Gf = fluid intelligence; Gc = crystallized intelligence; Gk = composite of all knowledge tests. *p < .05. **p < .01.

were found. For example, in the AP Government test, positive incremental predictions were provided by knowledge in U.S. history, current events (1990s), law, and psychology, whereas art knowledge was a negative predictor, again, after Gf and Gc had been entered into the equation. Together, these knowledge measures increase the amount of variance accounted for from 31.1% (Gf and Gc only) to 54.8% (for Gf, Gc, and selected knowledge measures). Overall, these results lead us to suggest that individual differences in breadth and depth of knowledge beyond the target measure contribute to the prediction of AP test performance. It is not entirely clear, however, from these data whether the knowledge is supportive (e.g., as might be the case for the role of physics knowledge for the AP Chemistry test) or whether the predictive validity is obtained through differential interests and course engagement (e.g., as might be the case for the negative role of electronics knowledge in the AP English Literature and AP U.S. Government tests). The raw correlations between each of the five trait complexes and the six selected AP tests (and the mean AP score) are shown in Table 11. We interpret these results as very consistent with our earlier interpretations of the knowledge tests results. The Science/ Math/Technology and Verbal/Intellectual trait complexes are positively associated with AP test performance. The Science/Math/

Technology trait complex is significantly associated with AP Biology, AP Chemistry, and AP Calculus. The Verbal/Intellectual trait complex is significantly associated with AP English Literature, U.S. History, U.S. Government, and Biology. The three remaining trait complexes were negatively associated with AP test performance, either narrowly (as in the case of Social Potency/ Enterprising with AP Chemistry) or broadly (as in the case of Traditionalism/Worry/Emotionality). At the level of aggregated mean AP scores, the first two trait complexes had positive correlations, whereas the others showed negative correlations, consistent with expectations. Multiple regressions were computed for the trait complex scores predicting AP test performance, again with Gf and Gc first entered into the prediction equation. The results were largely consistent with the main predictions. The Science/Math/Technology trait complex had significant incremental prediction in the case of AP Biology and AP Calculus; the Verbal/Intellectual trait complex had incremental prediction for AP English Literature. A positive contribution was made by the Social Potency/Enterprising trait complex for AP U.S. Government. Negative associations were found for Social Potency/Enterprising and Traditionalism/Worry/ Emotionality trait complexes in predicting AP Chemistry. AP U.S.

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Table 11 Correlations Between Trait Complex Scores and Advanced Placement (AP) Exam Scores AP exams Trait complex I. II. III. IV. V.

Science/MauVTechnology Verbal/Intellectual Social Potency/Enterprising Social Closeness/Femininity Traditionalism/Worry/Emotionality

*p < .05.

English Literature

U.S. History

U.S. Government

Biology

Chemistry

Calculus AB

Mean AP score

.119 .463** -.042 -.032 -.204**

.084 .304** .009 -.087 -.162*

.121 .277* .114 -.229* -.336**

.302** .240* -.161 -.145 -.128

.329** .142 -.355** -.248** -.381**

.381** .071 -.060 -.043 -.094

.235** .265** -.141* -.192** -.227**

**p < .01.

History was the only AP test in which trait complex scores had no significant incremental prediction validity. Given that significant gender differences in trait complex scores were found and also given that the SEM analyses suggested the potential of different gender relations between trait complexes and knowledge, we were interested in whether the relations between the trait complex scores and the criterion variables were different for the men and women of this sample. Separate correlations were computed for men and women between the trait complex scores and a subset of knowledge, ability, AP, and SAT measures. These results are shown in Table 12. The patterns of correlations for men and women are very similar when correlating trait complex scores and the various criterion variables. That is, using a reasonably conservative threshold of r = ± .20 for salience (which is beyond the a = .01 level for a Type I error rate), there are relatively few differences between the genders for the salient correlations between trait complexes and the other variables. More specifically, the Science/Math/Technology trait complex showed equivalent and positive correlations for men and women. The Verbal/Intellectual trait complex showed a similar pattern but generally larger correlations for women compared with men. Traditionalism/ Worry/Emotionality was a negative predictor of knowledge and ability, more strongly for men in the knowledge and ability domains and more strongly for women in the AP exam composite.

None of the correlations with either Social Potency/Enterprising or Social Closeness/Femininity were positive. However, the correlations for both trait complexes tended to be stronger for men than for women, especially for the Social Closeness/Femininity trait complex. These results are consistent with the findings of the SEM approach discussed earlier, but this analysis extends the SEM approach by suggesting that the patterns of supportive and impeding trait complexes are largely the same for men and women (with perhaps the exception of Social Closeness/Femininity, which appears to be more of an impeding trait complex for men than it is for women).

Discussion In the discussion of any empirical study, it is appropriate to attempt to answer the question, "What do we know now that we did not know before the study was conducted?" Before we address this question, it is appropriate to review briefly what we knew (or thought we knew) before starting this study. First, we knew that women tend to do more poorly on AP-type tests compared with men. Second, we knew that women tend to perform more poorly on average than men on measures typically associated with Gf (e.g., math and spatial reasoning). We also knew that differences are smaller between men and women on measures associated with

Table 12 Correlations Between Trait Complex Scores and Knowledge, Ability, AP Mean Scores, and SAT Exam Scores

Trait complexes Men only I. Science/Math/Technology II. Verbal/Intellectual III. Social Potency/Enterprising IV. Social Closeness/Femininity V. Traditionalism/Worry/Emotionality Women only I. Science/Math/Technology II. Verbal/Intellectual III. Social Potency/Enterprising IV. Social Closeness/Femininity V. Traditionalism/Worry/Emotionality

Physical Sciences/ Biology/ Technology Psychology Humanities

Civics

Gk

Gf

Gc

SAT-m

SAT-v

AP mean

.394** .343** -.162* -.236** -.300**

.145 .280** -.321** -.194* -.225**

.010 .414** -.165* -.124 -.105

.229** .329** .196* .115 .351** .441** .074 .428** -.033 - . 1 8 1 * -.224** -.170 -.181* -.220** -.060 -.183* - 3 2 5 * * -.283** -.287** -.317**

.324** .283** -.185* -.138 -.204**

.166* .279** -.257** -.123 -.204**

.072 .536** -.128 -.159* -.260**

.197* .216** .308** .212** 387** .109 .208** .468** .522** .144 .540** .050 .458** .334** .078 -.125 -.046 -.117 -.122 -.128 -.124 -.145 -.178* -.140 -.111 -.102 - . 1 7 1 * -.193* -.181* -.268** -.145 -.262** -.201* -.303** -.260**

305** .120 .198* -.042 368** .208* -.203* -.252** -.123 -.051 -.131 -.105 -.180* -.375** -.125

Note. Correlations larger than ± .200 are shown in bold. Gf = fluid intelligence; Gc = crystallized intelligence; Gk = general knowledge (unit-weighted z score sum of all 19 knowledge measures); AP = Advanced Placement; SAT-m = Scholastic Aptitude Test—math; SAT-v = SAT—verbal. *p < .05. **p < .01.

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Gc, and in some samples women perform better on such verbal measures. Finally, we knew that several nonability measures are valid predictors of individual differences in knowledge. What we did not know before we conducted this study is how various cognitive (ability), affective (personality), and conative (motivational) trait measures might jointly account for both individual differences and gender differences in the depth and breadth of knowledge assessed across 19 different domains. We expected that Gc would be a more effective predictor of knowledge in every area but Physical Sciences/Technology and that Gf would be a more effective predictor of knowledge in Physical Sciences/Technology. We also expected that traits previously identified as positive correlates of knowledge in other samples of young and middle-aged adults would also correlate positively with knowledge in the current sample of talented young adults. What we did not know, however, was how much variance in knowledge would be respectively accounted for by individual differences in the various trait measures. We also did not know whether we could reduce the amount of variance accounted for by gender by focusing on these and other individual differences measures. What we learned is that gender differences in performance on Civics and Humanities knowledge domains could be virtually completely accounted for by individual differences in Gf and Gc. For the Physical Sciences/Technology domain, the picture obtained is somewhat more complicated. Inclusion of nonability predictors (e.g., personality, interests, self-concept, and motivational traits and skills) reduced the amount of variance accounted for by gender to 1.9%, a significant but far smaller amount of the variance than the initial 22.4% variance accounted for by gender, before consideration of any of the trait measures. Together, the ability and nonability trait measures accounted for nearly 80% of a knowledge composite aggregated across all 19 tests. Less aggregated composites were less well predicted, as could be expected, ranging from 40% variance accounted for in Biology/Psychology to 71% variance accounted for in the Physical Sciences/Technology domain. A potentially important method of analyzing the communality among various trait families was provided by the trait complex perspective. To the degree that individuals had higher or lower standing on the five trait complexes, they often had different patterns of breadth and depth of knowledge. These results are more interesting in a developmental framework, given that individuals are hypothesized to develop more coherent patterns of trait similarities as they proceed from late adolescence to adulthood. Such measures should be considered in a longitudinal forum to validate this particular perspective. When archival records of AP test performance were examined in concert with the wide array of data obtained in this study, we found that significant incremental predictions (over and above traditional ability measures) for the AP tests were provided by measures of the depth and breadth of knowledge in both similar and dissimilar domains. These results are consistent with two major hypotheses that are not mutually exclusive and that require further study: (a) Knowledge in various domains is "supportive" for knowledge in a target domain (e.g., knowledge of physics may be "supportive" for calculus knowledge or knowledge of current events may be "supportive" for knowledge of U.S. government); and (b) differential course-taking patterns give rise to constella-

tions of knowledge that are supportive or impeding for knowledge in particular target areas. The raw correlates and incremental validity associated with the five trait complexes also appeared to show interesting trends. Consistent with earlier research (e.g., Ackerman, 2000), Science/ Math/Technology and Verbal/Intellectual trait complexes were positive correlates of test performance, whereas Social Potency/ Enterprising, Social Closeness/Femininity, and Traditionalism/ Worry/Emotionality trait complexes were negative correlates of test performance. These trait complexes might be used to distinguish between academically positive and academically negative families of personality, interest, and self-concept variables. Such results are consistent with the broad investment approach inherent in the PPIK theory to adult intellectual development.

Conclusions and Implications The results described in this study are consistent with our prediction that it is possible to better account for individual and gender differences in performance on knowledge tests like the AP measures. Additional research is clearly needed, but these initial results point to gender differences in broad intellectual abilities (Gf and Gc) and to gender differences in several nonability traits and experiences as instrumental determinants of gender differences in knowledge. Ironically, intelligence theorists have steered clear of considering gender differences in overall intelligence. Although pursuit of this particular issue is fraught with potential for controversy, resolution of this conundrum is perhaps central to accounting for gender differences in broad aptitude tests, such as the SAT. This multifaceted approach to adult intelligence appears to have promise over the traditional g-oriented or verbal/quantitative approaches, especially in terms of improving educational and occupational selection and in tailoring training and education to adults of differing backgrounds and genders.

Individual Differences With the identification of supportive and impeding trait complexes and their influences on the development of knowledge structures, we suggest several avenues for potential remediation interventions. One possibility is to better match groups of individuals (by dominant trait complexes) with instruction that is tailored to their personality or interest patterns. This represents a strategy for implementing education in light of expected aptitude-treatment interactions, although the trait complex approach is potentially more comprehensive than, for example, Snow's (1989) discussion of achievement-via-conformance versus achievement-via-independence aptitude complexes. Instruction could perhaps build on the orientations of learners, especially for those with high levels of impeding trait complexes. Another possibility is to focus the educational experience to develop supportive trait complexes by structuring learning experiences that build self-concept and interests that are associated with supportive trait complexes. If Holland's (1959) assertions regarding the development of interests are correct, then increasing the number of positive experiences in these domains will, in turn, generate increments in interest for the respective topics, which will develop a positive feedback loop. A third possibility is that educational curricula might shift the focus from general skills (such as critical thinking) to the development of

INDIVIDUAL DIFFERENCES IN KNOWLEDGE domain knowledge. Moreover, it may be beneficial to allow for more opportunities for specialized domain learning at high school levels of education to allow students to develop greater levels of expertise that, in turn, will be beneficial for future domain learning. Thus, it may be beneficial to some students to prepare for AP-type tests earlier in the high school experience or for a longer duration (e.g., some schools now provide AP courses at the sophomore, junior, and senior levels of high school). For selection purposes, the use of trait complex assessments may yield improved indicators for predicting success in AP-type courses. When placements are limited, such assessments may increase the probability of student success and reduce the number of failures. Similarly, trait complex assessment might be used to support identification of at-risk students for special attention and potential remediation.

Gender Differences For AP tests, there is nearly 7% difference in rates of scores of 4 or 5 between men and women. With more than 1 million students tested each year, these differences translate into a substantial impact on large numbers of women in college each year in terms of both their placement in courses and the number of on-campus courses that must be completed to obtain a baccalaureate degree. Gender differences in academic knowledge assessments outside of the AP experience are largely consistent with these score differences. A remedy that simply takes account of these differences would be to guide more women to courses of study in which gender differences are small or in which women tend to obtain higher scores (e.g., art, foreign languages). However, such a remedy seems unsatisfactory in the context of the goal of raising the achievement levels of women across science, civics, and humanities domains. For this goal to be reached, differential curricular emphases between genders that address supportive and impeding trait complexes might be needed. Interventions that focus on enhancing interests and self-concept in the Science/Math/Technology and Verbal/Intellectual domains may be especially useful for female students. To the degree that level of domain knowledge is a critical determinant of graduate school success (e.g., see Willingham, 1974) and future occupational success, a gender gap in domain knowledge may very well account for the larger disparities between enrollment levels in graduate and professional study and in levels of achievement beyond the educational milieu. In the final analysis, it may be that those in the educational community need to do a better job of emphasizing the importance of domain knowledge to female students.

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Appendix Knowledge Tests and Ability Tests Knowledge Testsa 1. Art: a test requiring identification and interpretation of art and architecture from around the world from images displayed on the computer monitor. Items require identification of works with artists and artistic styles and movements. Typical items require the examinee to identify the Taj Mahal or the movement that Salvador Dali's work reflects. 2. Astronomy: broad areas of astronomy, including observational tools and techniques, structure of the solar system, structure of the universe, and physical principles that govern astronomical observations. Typical items require the examinee to identify the planet nearest the sun or techniques to estimate stellar distances. 3. Biology: a broad range of biology, at the cellular, organismal, and ecological levels. Questions range from an understanding of food chains to the function of meiosis and mitosis. 4. Business/management: business management principles and their application. A typical item might ask about job analysis or types of organizational structure. 5. Chemistry: the content of a first-year college course in chemistry, from the structure of the atom to standard laboratory procedures. 6. Current events (1990s): A test of current events that occurred between 1990 and 1999 (inclusive). Questions assessed knowledge in the

domains of art/humanities, politics/economics, popular culture, and nature/ science/technology. (This test is described more fully in Beier & Ackerman, in press.) 7. Economics: both micro- and macroeconomics. Questions range from simple understanding of supply and demand to reasons underlying the Federal Reserve's monetary policy. 8. Electronics: basic principles of electricity and their applications in electrical equipment and circuitry. Typical questions might ask the function of a transformer, the proper fuse size for residential light circuits, or techniques for testing voltage in a circuit. 9. Geography: world geography, including the location of mountains, rivers, oceans, cities, nations, and biomes. Approximately one half of the items are maps. A typical item requires matching a city to a letter on a map or typing in the name of a country shaded in red.

a These descriptions are adapted from Rolfhus and Ackerman (1999) and Ackerman (2000). (Note that the term "U.S." has replaced "American" to make the test content more clear.)

(Appendix continues)

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10. Law: basic principles of law and more advanced criminal, civil and business law. Items require an understanding of basic constitutional rights to more complex contract and commercial law. 11. Music: basic music terminology and styles, instruments, and composers. About one third of the test involves identification of classical music pieces played over headphones. Items range from simple identification of a woodwind instrument to selecting the composer of a particular piano sonata. 12. Physics: basic physical principles and their applications. Items address both classical and quantum physics, thermodynamics, and atomic structure. 13. Psychology: the content from an introductory college course in psychology. Questions range from personality theory, clinical assessment tools, and neuronal structure to learning theory and behaviorism. 14. Technology: assessment of understanding for a wide range of modern technologies. Questions range from how microwave ovens and televisions work to an understanding of superconductivity. 15. U.S. government: the structure of U.S. government, function of various government units, and the American political system. Typical questions ask about the significance of the Roe v. Wade decision of 1973 or the nature of the president's veto power. 16. U.S. history: U.S. history from pre-Revolutionary times to the present. Fifty percent of this test was derived from two College Board examinations: American History I: Early Colonization to 1877 and American History II: 1865 to the Present. 17. U.S. literature: a broad range of U.S. writers, playwrights, and poets from Revolutionary times to the present. Questions require identification of authors and works from Walt Whitman to Kurt Vonnegut and an understanding of literary styles and movements such as transcendentalism. 18. Western civilization: major political, philosophical, and economic events in Europe from Ancient Greece to the Cold War. Typical items relate to the French Revolution or the genesis of World War II. 19. World literature: non-American literature and poetry, primarily classic Western literature. Items address ancient Greek plays and mythology through 20th-century authors such as George Orwell and D. H. Lawrence.

Ability Tests Fluid Intelligence 1. Word Problem Solving: This is a test of math word problems. The test has one part, with a 5-min time limit (created by D. Lohman; see Ackerman & Kanfer, 1993) 2. Primary Mental Abilities (PMA) Number Series: This a test of inductive reasoning in which a series of numbers generated by a rule is provided and the next number is the series is to be identified. The test has one part, with a 4-min time limit (Thurstone, 1962) 3. Spatial Analogy: A four-term multiple-choice test of analogical reasoning with spatial content similar in structure to verbal analogy tests (i.e., A:B:: C: a,b,c,d). This test has one part, with a 9-min time limit (created by P. Nichols; see Ackerman & Kanfer, 1993). 4. Necessary Facts: This is a problem-solving test that does not actually require solution of the problem. Participants must determine whether sufficient information is presented in the problem for a solution to be calculated or, instead, what information is missing. The test has two parts, each with a 4 Vi-min time limit (created by D. Lohman; see Ackerman & Kanfer, 1993). 5. Educational Testing Service (ETS) Diagramming Relations: In this logical reasoning test, a list of three objects is presented. Participants must choose a set of overlapping circles that best represents the relations among the three objects. This test has two parts, each with a 4-min time limit. (ETS Kit: Ekstrom, French, Harman, & Derman, 1976).

Crystallized Intelligence 1. ETS Extended Range Vocabulary Test: This is a classic vocabulary test in which individuals are presented with a word and must choose the word that most closely matches it. This test has two parts, each with a 7-min time limit (ETS Kit: Ekstrom et al., 1976). 2. Wechsler Adult Intelligence Scales-Revised (WAIS-R) Information Test: This test of general knowledge was adapted directly from the WAIS-R Information Test for group administration. Participants attempt to complete all items. There is one part to this test, with a 5-min time limit (Wechsler, 1981). 3. ETS Word Beginnings: This is a test of verbal fluency in which participants are given three letters and asked to produce as many words that begin with these letters as time allows. This test has two parts, each with a 3-min time limit (ETS Kit: Ekstrom et al., 1976). 4. MAB Comprehension: This is a test of common cultural knowledge. Each item asks for the correct response to or the rationale behind everyday situations, cultural conventions, or practices. This test has one part, with a 7-min time limit (Jackson, 1985). 5. MAB Similarities: This is a test of verbal knowledge. Each item presents two words, and participants must select the option that best describes how the two words are alike. This test has one part, with a 7-min time limit (Jackson, 1985). 6. Cloze: Three Cloze tests were also included in the ability battery. The Cloze tests were constructed from passages selected from college-level textbooks, as follows: (a) U.S. history (Rice, Krout, & Harris, 1991, p. 25); (b) U.S. literature (DiYanni, 1994, p. 55); and (c) Biology (Campbell, 1996, p. 913). Passages were originally selected to be around 250 words in length (M = 255.5 words). Following the technique originated by Taylor (1953), a "structural" (Ohnmacht, Weaver, & Kohler, 1970) Cloze test was constructed. This entailed leaving the first and the last sentences of the passage intact. Starting with the second sentence, every fifth word was deleted (regardless of its grammatical or contextual relationship) and replaced with an underlined blank ten spaces long. This procedure has been shown by Taylor (1957) to be superior to lexical deletion (in which words are deleted based on their relationship to the text) in efficiency of construction as well as equal or superior in its correlation to some ability measures. Cloze tests in this study included M = 41 blanks. Participants were instructed to read through the passage and fill in the blanks with the words that best fit into the sentence. If participants did not know the exact words that fit in the blank, they were instructed to guess. Participants were given 10 min to complete each Cloze test. Credit was given for either the actual missing word or for words that fit the gist of the paragraph (and were grammatically correct in the context of the text). Scores for the Cloze tests reflected the total number of actual or "gist" words. Participants were not penalized for obvious misspellings of words (i.e., when the misspelling did not change the meaning of the intended word). No credit or penalty was given to words that did not make sense within the context of the paragraph or for words that were grammatically incorrect. (Ackerman, Beier, & Bowen, 2000, p. 111). 7. Completion: A procedure identical to that of developing Cloze tests was used to develop the completion tests. Four completion tests were presented: (a) U.S. history (Rice et al., 1991, p. 157); (b) U.S. literature (DiYanni, 1994, p. 77); (c) Biology (Campbell, 1996, p. 227); and (d) Narrative (DiYanni, 1994, p. 362). The completion test passages averaged 248.5 words in length with an average of 42 blanks. Completion tests differed from cloze tests in their administration [based on a design introduced by Terman, 1906], Specifically, participants were instructed to listen to the passage read in its entirety without looking at the completion test form. After the passage was read, participants were shown the completion test form

INDIVIDUAL DIFFERENCES IN KNOWLEDGE and instructed to fill in as many of the missing words as possible. If they did not remember the exact words, participants were instructed to guess. Participants were given 8 min to fill in the completion test form. Scoring for the completion tests was different than that of the Cloze tests, where participants received equivalent credit for exact matches and gist matches. Completion tests were scored by giving participants two points for every exact match and one point for words fitting the gist meaning of the paragraph (as long as the words were grammatically correct). Thus, one difference between the scoring of the Cloze and Completion tests was that extra credit was accorded in the Completion test for the exact word in the text. Again, participants were not penalized for obvious misspellings of words. (Ackerman et al., 2000, pp. 111-112)

Perceptual Speed 1. Name Comparison: This test presented the examinee with a list of pairs of names, some of which were an identical match and some that had subtle differences. The task was to place an " = " for the pairs with an identical match and an "X" for the pairs that did not match. Three 1 Vi-min

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parts were administered (Ackerman & Cianciolo, 2000; Ackerman & Rolfhus, 1996). 2. Finding a and t: This test presented several paragraphs of text (in Italian). The examinee was instructed to circle every word that contained both an "a" and a "t." Three 2-min parts were administered (Ackerman & Cianciolo, 2000; Ackerman & Rolfhus, 1996). 3. ETS Subtraction and Multiplication: This test presented alternating rows of either two-digit subtraction problems or two digit X one digit multiplication problems. Two 2-min parts were administered (ETS Kit: Ekstrom et al., 1976). 4. Coding: In this test, examinees were presented with sets of wordnumber code lists (seven items each) followed by a column of words and number options. The task was to identify the correct code that matched each of the prompt words. Three 1 V4 parts were administered (Ackerman & Cianciolo, 2000; Ackerman & Rolfhus, 1996).

Received September 27, 2000 Revision received February 5, 2001 Accepted February 5, 2001 •