When IQ is controlled, does motivation still predict achievement?

Intelligence 30 (2001) 71 – 100

When IQ is controlled, does motivation still predict achievement?$ Franc¸oys Gagne´a,*, Franc¸ois St Pe`reb a

Department of Psychology, University of Que´bec a` Montre´al, P.O. Box 8888, Downtown Station, Montreal, Canada H3C 3P8 b Clinique de consultation conjugale et familiale Poitras-Wright, Coˆte´ 32, rue St-Charles, ouest, suite 220, Longuevil, QC, Canada, J4H 1C6 Received 10 January 2000; received in revised form 18 June 2000; accepted 18 August 2000

Abstract Past research shows controversial results concerning relationships between cognitive abilities, motivation, and achievement. The present study aims to assess the unique contribution of motivation to academic achievement, after controlling for the predictive power of cognitive abilities. Over 200 female high school students completed two IQ tests, and three motivation-related measures (intrinsic, extrinsic, and persistence) twice during a semester. One parent and two teachers also rated twice each student’s three types of motivation. The results revealed among other things that (a) IQ and motivation were not correlated; (b) the parents’ ratings were only slightly related to their children’s own judgments; (c) the teachers’ ratings had doubtful predictive validity because they were strongly biased by the teachers’ knowledge of the students’ achievements; (d) cognitive abilities were by far the best predictor of school achievement; and (e) the students’ self-assessments of their motivation were not related to their academic achievement. These results question the belief of most educators about the crucial role of motivation as a determinant of scholastic achievement. D 2001 Elsevier Science Inc. All rights reserved. Keywords: Intelligence; Motivation; Intrinsic motivation; Volition; Persistence; Achievement; Academic achievement; Predictive validity; Parents; Teachers

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The study was conducted to become the second author’s PhD thesis. * Corresponding author. E-mail address: [email protected] (F. Gagne´).

0160-2896/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 0 1 6 0 - 2 8 9 6 ( 0 1 ) 0 0 0 6 8 - X

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1. Introduction In his famous book ‘‘Hereditary Genius,’’ Francis Galton (1869/1962) circumscribed the roots of eminence in the following way: By natural ability, I mean those qualities of intellect and disposition, which urge and qualify a man to perform acts that lead to reputation. I do not mean capacity without zeal, nor zeal without capacity, nor even a combination of both of them, without an adequate power of doing a great deal of very laborious work. (p. 77)

In Galton’s terms, reputation (talent, eminence) will emerge from proper qualifications (high capacities, gifts), urges and zeal (needs, passions), as well as the power for laborious work (will power, persistence). These ‘‘ingredients’’ of exceptional performance identified by Sir Francis Galton more than a century ago have reappeared again and again (a) in general models of school learning (e.g., Bloom, 1976; Carroll, 1963; Walberg, 1984), (b) in more specific models of talent emergence (e.g., Feldhusen, 1992; Gagne´, 1993; Renzulli, 1986; Tannenbaum, 1983), (c) in models of skill growth proposed by industrial psychologists (e.g., Atkinson, 1964; Kanfer & Ackerman, 1989; Vroom, 1964), and (d) in biographical analyses of eminent historical figures (e.g., Simonton, 1994). Cognitive aptitudes and motivational factors are probably the two most commonly mentioned determinants of academic achievement. Not only are they included in theoretical models, but they have appeared as independent variables in thousands of empirical studies of school learning, skill training in work settings, talent development in arts and sports, as well as longitudinal studies of occupational achievement. The most common belief within the general population is that both factors exert approximately equal causal influences on talent development (Gagne´ & Blanchard, submitted for publication). Attribution studies also show that both effort and ability are by far the two major causal attributions for both success and failure, not only in academics (Good & Brophy, 1990), but also in music (Austin & Vispoel, 1998) and sports (Biddle, 1993). To what extent does empirical research support these ‘‘equalitarian’’ beliefs? First, the very strong relationship between cognitive aptitudes, usually measured by some type of IQ test, and academic achievement is well-documented. In a survey of close to 3000 empirical studies of school learning, Walberg and his colleagues (see Walberg, 1984) computed an average correlation of .71 between various IQ measures and academic achievement. It emerged as the most powerful determinant, by far, among dozens of factors examined. Similarly, in her synthesis of many meta-analytic surveys of the predictive validity of IQ tests in work settings, Gottfredson (1997) argued (a) that the validity of intelligence measures applied to most occupations, (b) that it rose with job complexity, and (c) that ‘‘g can be said to be the most powerful single predictor of overall job performance’’ (p. 83). Recently, Schmidt and Hunter (1998) published for the field of personnel psychology a synthesis similar to that of Walberg’s. They analyzed the predictive validity of 19 distinct constructs and selection methods, including general mental ability (GMA) tests, work samples, interviews, assessment centers, job knowledge, job tryout, job experience, peer ratings, even graphology! In the case of GMA tests, they computed validity coefficients of .51 and .56 with job performance and job training, respectively. The relationship between motivation and achievement is also well-

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documented. In the above-mentioned review, Walberg (1984) and his colleagues found an average correlation of .34 between various indices of motivation and school learning. In terms of explained variance, this contribution is about one-fourth of the predictive power of IQ (r2 =.50 for IQ, and .12 for motivation). And it is probably a generous estimate since the text does not specify if the correlations used for the motivation component were independent contributions, that is after controlling for the predictive power of intelligence. In the Schmidt and Hunter synthesis, 2 of the 19 predictors analyzed belong to the general area of motivation, namely conscientiousness and vocational interests; their average validities with regard to job performance are .31 and .10, respectively. Again, these are zeroorder correlations. If cognitive abilities and motivation are both significant determinants of achievement, to what extent are they related to each other? Schmidt and Hunter (1998) cited Ones, Viswesvaran, and Schimdt (1993) to support their premise of a zero correlation between conscientiousness and IQ; they also cited Holland (1986) to justify a similar lack of correlation between interests and cognitive abilities. On the other hand, the gifted education literature is replete with statements to the effect that gifted children are more motivated; most checklists of ‘‘gifted’’ characteristics include curiosity, incessant questioning, and similar indices of intrinsic motivation (IM) to learn (e.g., Clark, 1996; Davis & Rimm, 1989; Gallagher, 1985). Some research also supports that relationship, showing significant differences between gifted children or adolescents and peers of average abilities on a variety of measures of motivation: intellectual curiosity, IM, achievement motivation, task orientation, and so forth (Gottfried & Gottfried, 1996; Janos & Robinson, 1985; Lloyd & Barenblatt, 1984; Vallerand, Gagne´, Sene´cal, & Pelletier, 1994). But, other studies report no correlation whatsoever between measures of these two constructs (e.g., Aspinwall & Taylor, 1992; Joswig, 1994; Spence, Pred, & Helmreich, 1989; Wong & Csikszentmihalyi, 1991). These contradictory results brought Shore, Cornell, Robinson, and Ward (1991) to express the following doubts: ‘‘There is insufficient explanation of how attention to intrinsic motivation might be different for the gifted compared with others. The implication in much of the literature that gifted children have more of it, or a superior kind, is not supported’’ (p. 215). The prediction problem is compounded by a major terminological ambiguity, since there is no agreed upon definition of the term gifted (Gagne´, 1995). Consequently, the gifted subjects in comparative studies are selected with a variety of procedures. Gagne´ argued that two types of measures are used more regularly: IQ tests (group or individual) and academic achievement scores (grades or standardized tests). In the framework of Gagne´’s Differentiated Model of Giftedness and Talent, IQ tests pinpoint intellectually gifted (IG) students, whereas grades identify academically talented (AT) students, or, in Gagne´’s (1995) words, IGAT students. These two groups overlap only partially. Consequently, many studies that compare ‘‘gifted’’ children with peers of average abilities are often comparing achieving gifted children. It follows that many of the significant differences observed between the two groups could be attributable as much to the superior cognitive abilities of these gifted children as to their academic talent. This methodological problem speaks eloquently in favor of efforts to disentangle the respective causal contributions of cognitive aptitudes (A) and motivation (M) to achievement.

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2. The multiple regression literature The relationships involving these two factors can be represented by two distinct causal models: an additive (A + M) model, and a multiplicative (A  M) model. 2.1. The additive causal model The more common and simple additive model guiding the empirical analysis of the causal relationships between aptitudes, motivation, and achievement underlies the question: ‘‘How much does motivation add to the prediction of school achievement after the predictive power of cognitive aptitudes has been controlled?’’ One might ask why precedence should be given to aptitudes in that causal chain; why not examine first the predictive power of motivation, then look for the proportion of the remaining variance explained by aptitudes? There are at least two answers: a theoretical one and a statistical one, both closely related. There is virtual unanimity among intelligence theorists that g reflects the ability to reason, solve problems, think abstractly, and acquire knowledge (Snyderman & Rothman, 1987). Gottfredson (1997) points out: ‘‘Intelligence is not the amount of information people know, but their ability to recognize, acquire, organize, update, select, and apply it effectively. In educational contexts, these complex mental behaviors are referred to as higher thinking skills’’ (p. 93). Similarly, Gagne´ affirms that ‘‘any structured LTP [learning, training, practicing] program uses as its starting point some appropriate NAT [natural] abilities, which it progressively transforms, adapts, and improves to create the skills and competencies that are characteristic of a given occupational field’’ (Gagne´, 1999, p. 115). By contrast, motivation plays the role of a facilitator or catalyst in the talent development process. In brief, the relationship between aptitudes and achievement is more direct. As we have seen above (Schmidt & Hunter, 1998; Walberg, 1984), that closeness is confirmed at the statistical level by much higher correlations between IQ and achievement than between motivation and achievement. That difference leads to the priority given to the stronger link in the causal chain. Surprisingly, very few studies can be found in the education literature that address the question of the independent additive contributions of these two major determinants of academic achievement. The following survey will distinguish microlevel studies from macrolevel ones. The former typically use an experimental design with small samples, measure achievement on a short learning task, and manipulate motivation differently in treatment and control groups. We will survey them first since their more artificial environment makes them somewhat less relevant to our goal of assessing the respective roles of aptitudes and motivation in real-life, long-term learning situations. A series of microlevel studies was conducted as part of a major research effort to validate Bloom’s (1976) model of mastery learning. The model itself included three causal constructs: cognitive entry behaviors (CEB), affective entry behaviors (AEB), and quality of instruction (QI). CEBs correspond essentially to past mastered learning, but can include aptitude test results. That encompassing definition of CEBs creates a problem similar to the one mentioned above with reference to the nondissociation of ‘‘pure’’ intellectually gifted (IG) students from academically talented ones (IGAT). AEBs include interests, attitudes toward school learning, self-views (e.g., confidence in one’s ability to learn, in resources to overcome difficulties), and so forth. Based on an

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extensive review of the school learning literature, Bloom theorized that the upper limits for the predictive validity of the first two factors (cognitive and affective antecedents) would be .70 (r2=.49) and .50 (r2=.25), respectively, with a combined predictive power of .80 (r2=.64). These values indicate that Bloom recognized (a) the much stronger predictive power of CEBs, (b) a significant overlap between the two constructs, and (c) a significant unique contribution of about .15 (.64–.49) for AEBs in accounting for academic achievement. In microstudies of mastery learning completed by five of his PhD students (see Bloom, 1976, Table 7.3, p. 192), the results revealed that in both the control and mastery learning groups the AEBs added virtually nothing to the predictive validity of CEBs. Because these students did not use cognitive ability tests as their CEB measures, but past learning scores, they increased significantly the predictive power of that component. Moreover, AEBs were assessed with a very short measure of interest for the subject matter. Bloom acknowledges that ‘‘the correlations for the cognitive plus affective entry characteristics are not much higher than those for the cognitive measures only,’’ blaming the ‘‘very limited measures of affect used in these brief studies’’ (p. 193). Still, it is very unlikely that more valid measures of AEBs would have brought the observed correlations from zero to anywhere close to Bloom’s theoretical predictions. For their part, Grabe and Latta (1981) examined the predictive power of aptitudes (past GPA and ACT score), achievement motivation, and effort, on end of semester achievement in two college courses. They measured achievement motivation with Mehrabian’s Resultant Achievement Motivation scale, while effort was assessed through the frequency of attempts at bettering one’s score on weekly quizzes (by redoing them), depending on one’s initial performance. They concluded that ‘‘appropriate effort was strongly correlated with student achievement, even when differences in student aptitude were controlled’’ (p. 7). Unfortunately, their measures of effort were themselves directly dependent on within-semester achievement, thus confounding their dependent and independent variables, and consequently inflating very substantially the causal relationship. The last microlevel study is a series of four very short experiments by Millman, Bieger, Klag, and Pine (1983). They were designed to test a corollary of Carroll’s (1963, 1989) model of school learning, namely that an increase in persistence will not alter learning efficiency. The authors used various motivators (encouragements, gifts, etc.) to manipulate the persistence of the children (Grades 2 to 4) in the treatment groups as they tried to memorize pairs of words. Academic aptitude was controlled in the selection process by asking teachers to propose students ‘‘who were most like each other in terms of their reading level and verbal ability in general’’ (p. 426). They found that, ‘‘as predicted by the model, none of the differences was statistically significant’’ (p. 425); but they observed that the increased induced persistence affected the amount of time spent on the task and, consequently, the amount learned (effectiveness). Only five macrolevel studies were found that tested the additive model. Gottfried (1979) administered a multiscale inventory of IM to 141 middle-school students (Grades 4 to 7). Each scale focused on a specific subject matter (reading, math, social studies, and science). After partialling out IQ, she found significant correlations, ranging from .20 to .41, between a given subject matter IM measure and the corresponding achievement score on the Stanford Achievement Test. The average r2 was close to .10. Because of these within subject matter pairings, the author argued that her results supported a nontrait, specificity account of IM. No

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gender differences were observed. Lloyd and Barenblatt (1984) gathered data from 455 high school sophomores to test five hypotheses on the relationships between intrinsic intellectual motivation (IIM), socioeconomic status, need achievement, intelligence, sex, and scholastic achievement. IQ measures came from student files, in which different tests scores were recorded (Lorge–Thorndike, Henmon–Nelson, Otis Beta). The custom-made IIM scale was comprised of 44 Likert-type statements; need achievement scores came from a subscale of the Edwards Personal Preference Schedule, and achievement was measured with the reading test from the High School Battery of the Metropolitan Achievement Tests. The two motivation measures, need achievement and IIM, were both equally correlated with IQ (.27), but only IIM had a substantially significant correlation with achievement (.37). The IQ’s predictive validity coefficient was .68. In a multiple regression, IQ explained 46% of the achievement variance, and IIM added a significant, but small, unique contribution of 3.6%; this corresponds to a 13:1 ratio between the two measures in terms of predictive power. Spence et al. (1989) administered to four large samples of college students (N > 900) a 7-item factorially independent subscale of the Jencks Activity Scale, called Achievement Strivings (AS); its name suggests a close relationship with the need achievement construct. The authors used SAT and GPA scores as measures of academic aptitude and scholastic achievement, respectively. The AS and SAT scores were not correlated, but both contributed to the prediction of GPA. The SAT’s unique contribution in terms of percentage of explained variance varied between .11 and .26, whereas the AS’s impact varied from .07 to .13. The SAT’s average contribution overall (.20) was about twice as large as that of the AS scale (.11). In their study of the relationships between personality, experience while studying, and academic performance, Wong and Csikszentmihalyi (1991) measured a large set of variables with close to 200 junior high school students: (a) scholastic aptitudes with the Preliminary Scholastic Aptitude Test (PSAT), a junior-high analogue of the well-known SAT; (b) a factor from the Personality Research Form, labeled Work Orientation, which ‘‘contained the personality characteristics that were believed to be important in academic performance — for instance, the motive to achieve, to control impulses, endure, etc.’’ (p. 547); (c) experiences felt during studying (one question each for IM, happiness, satisfaction with work, concentration, and unselfconsciousness) using the Experience Sampling Method (ESM); and (d) scholastic achievement with end-of-year GPA. The ESM uses random paging (during waking hours!); when paged, participants use a special form to indicate their ongoing activity, and answer the five questions. Focusing on studying activities, Wong and Csikszentmihalyi found that work orientation and IM were not related to each other or to aptitudes, and only the former predicted academic achievement. In multiple regression analyses, the PSAT was by far the best predictor, explaining approximately 20% of the variance in achievement, while work orientation, a measure analogous to persistence and will-power, explained between 5% (girls) and 10% (boys) of the GPA variance. Unselfconsciousness also had a unique, albeit small (4%), predictive power, and level of concentration while studying also contributed (7%), but only within the boys’ sample. It is important to point out that their nonhierarchical multiple regression design deleted any covariance between the predictors, thus reducing the SAT’s explanatory power. Finally, as part of a longitudinal study of the determinants of college adaptation and achievement, Aspinwall and Taylor (1992) examined the predictive role of

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measures of optimism, psychological control and self-esteem with regard to motivation and subsequent achievement, while controlling initial aptitudes with the SAT. Motivation was assessed 3 months after admission to college with a 15-item locally written scale that combined questions about IM, persistence, school aspirations, and predictions of success in college. GPA scores were collected 2 years later. The authors used an a priori structural equation model with standardized parameter estimates; they found that the SAT had twice the predictive power of motivation (.50 vs. .25). A low unique causal relationship (.12) was observed between aptitudes and motivation. If the information is scarce in education, it appears much more abundant in the field of personnel psychology. One of the most extensive efforts at hierarchizing a large set of predictors of job performance was realized within the U.S. Army during the 1980s. Over 70 different predictors were measured: (a) general cognitive, spatial, and perceptual–psychomotor abilities; (b) temperament and personality; (c) vocational interests; and (d) job outcome preferences. The sample of over 4000 recruits covered nine different enlisted jobs. Two job performance indices were collected (core technical proficiency and general soldiering proficiency), as well as three measures of ‘‘personal achievement’’ (effort and leadership, personal discipline, and physical fitness/military bearing). In the course of the analysis process, the predictors were combined into two large composites: cognitive ability (11 variables) and temperament/interests (13 variables). The cognitive composite had validity coefficients of .65 and .69 with the two job performance measures; these coefficients only increased to .67 and .70 when the second composite was added, a substantially minuscule improvement (McHenry, Hough, Toquam, Hanson, & Ashworth, 1990). In their synthesis of the predictive validity of 19 predictors of job performance, Schmidt and Hunter (1998) estimated the unique contribution of each of them, that is how much they added to the prediction of job performance beyond the .51 average validity of GMA tests. Only three predictors increased the predictive power by at least .10: work sample tests (.12); integrity tests (.14), and structured employment interviews (.12). The two motivation-related constructs had smaller unique contributions: .09 for conscientiousness and .01 for vocational interests. The authors argued that an 18% gain in validity (.09/.51, but still a 6:1 ratio in predictive power) could be considered a substantial improvement. 2.2. The multiplicative causal model The multiplicative (A  M) model gives motivation the role of a moderator variable, so that the relationship between aptitudes and motivation becomes equivalent to the interaction effect in a two-way ANOVA. In other words, individual differences in motivation can modify the strength of the predictive validity of cognitive aptitudes with regard to achievement. The multiplicative model assumes that both aptitudes and motivation are essential for performance, so that there is a definite limit to the compensatory power of any of the two: if one of them is very low, performance will also be very low. Anastasi and Urbina (1997) reviewed past efforts to identify significant moderator variables of the predictive validity of IQ with regard to academic achievement, identifying the 1950s and 1960s as the most active period. The only consistent finding, according to them, was a small sex effect, with slightly higher predictive validity coefficients for women than men, especially at the college level. About the

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global effort to find moderator variables (including interests and motivation) of the strong relationship between aptitudes and academic achievement, Anastasi and Urbina concluded: ‘‘in the light of present knowledge, no variable can be assumed to moderate validities in the absence of explicit evidence for such an effect’’ (p. 156). Terborg (1977) also reviewed efforts to validate the multiplicative model, focusing on its application in the training of occupational skills. He pointed out that the A  M model was hypothesized by many scholars (e.g., Atkinson, 1964; Gagne´ & Fleishman, 1959; Heider, 1958, Vroom, 1964; all cited in Terborg, 1977), and that the additive and multiplicative sources were not mutually exclusive; in fact, both can be measured simultaneously, and account independently for a significant percentage of the variation in a performance measure. Terborg reviewed 14 empirical studies that attempted to verify the presence of both additive and multiplicative causal influences of performance; he stated that ‘‘two studies found clear support (. . .), six studies reported mixed findings, and six studies found no evidence for an interaction’’ (p. 192). Terborg conducted his own experiment, assessing ability, effort, role definitions, and the aptitude by motivation (effort) interaction. Abilities were measured by a composite score from (a) the Otis–Lennon, (b) a mechanical aptitudes test, (c) a test of study skills, and (d) a test of paragraph meaning from an achievement battery. Effort was measured through time-lapse movie films, and was operationally defined as ‘‘the percentage of time a person spent working at the material’’ (p. 199). He obtained a multiple correlation of .83 with his three variables (including interaction), but observed ‘‘that ability alone correlated r = .81 with performance and additional consideration of effort and role definitions increased the multiple R by only .02 units (. . .) inclusion of the multiplicative term did not significantly increase predicted performance variance’’ (p. 204).

3. Planning the new study 3.1. Critique of past research Whether one uses an additive or a multiplicative model of the relationship between aptitude and motivation, motivation’s independent contribution to the prediction of scholastic or occupational achievement appears limited. It is frequently nonexistent (Anastasi & Urbina, 1997; Bloom, 1976; McHenry et al., 1990; Terborg, 1977) or much less powerful than the independent contribution of cognitive abilities. At best, the ratio of these two contributions reaches 2:1 (Aspinwall & Taylor, 1992; Wong & Csikszentmihalyi, 1991); at worst, it can be as low as 13:1 (Lloyd & Barenblatt, 1984). The 4:1 and 6:1 ratios respectively extracted from Schmidt and Hunter’s (1998) and Walberg’s (1984) syntheses, probably upper-limit estimates, are more or less equidistant from the two extremes. Could this limited influence of motivation be explained by methodological problems? We purposely introduced in the literature review details that highlight some of these problems, the most common being the inconsistent operationalization of the variables. The problem was smaller in the case of aptitudes, although Bloom’s use of past achievement as CEB introduced an artificially high correlation with his dependent variable. Grabe and Latta (1981) did the same, mixing past GPA with the ACT to measure aptitudes. Such measures can hardly be labeled aptitude

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measures, at least in the more commonly acknowledged meaning of that concept (Anastasi & Urbina, 1997; Snow, 1992). There was also much variability in the way the motivation construct was defined and assessed. Measures included IM scales (Bloom, 1976; Lloyd & Barenblatt, 1984; Wong & Csikszentmihalyi, 1991), need achievement indices (Grabe & Latta, 1981; Spence et al., 1989), extrinsically induced motivation leading to persistence (Millman et al., 1983), effort defined as time on task (Terborg, 1977) or multiple trials (Grabe & Latta, 1981), and persistence included in composite measures (Aspinwall & Taylor, 1992; Wong & Csikszentmihalyi, 1991). Some used two distinct measures of motivation (Grabe & Latta, 1981; Lloyd & Barenblatt, 1984; Millman et al., 1983; Wong & Csikszentmihalyi, 1991), but the rationale for their inclusion was not always specified. Some instruments clearly circumscribed their target construct, like Spence et al.’s (1989) AS scale, Wong and Csikszentmihalyi’s (1991) work orientation factor, Schmidt and Hunter’s (1998) conscientiousness construct, or Lloyd and Barenblatt’s (1984) IIM scale; others looked more like a hodgepodge of weakly related concepts (e.g., Aspinwall & Taylor, 1992; McHenry et al., 1990). With such diversity, it is hardly surprising that few consistent results were found. Still, one trend emerges, namely the generally nonsignificant predictive power of IM (Bloom, 1976; Lloyd & Barenblatt, 1984) as opposed to the much larger impact of measures associated with the need to achieve and the will to persist (Aspinwall & Taylor, 1992; Schmidt & Hunter, 1998; Spence et al., 1989; Wong & Csikszentmihalyi, 1991). These divergent results fit perfectly well with Lyn Corno’s work; she distinguishes predecisional (decision making) and postdecisional (implementation) components in any goal-seeking process, labeling them motivation and volition, respectively. Broadly paraphrasing Corno and Kanfer (1993, pp. 303–305), these two constructs can be described as follows. Motivation comprises constructs and processes that affect decision making and choice with respect to an individual’s goals. The major determinants include individual differences in preferences, beliefs, expectancies, perceptions of outcome value, patterns of attribution, goal orientation, self-efficacy judgments, self-worth, and so forth; they have been used to distinguish between extrinsically and intrinsically motivated choice behavior and actions. Volition can be considered in terms of three broad construct/process clusters: (a) individual differences in action control processes, which refer to knowledge and strategies used to manage cognitive and noncognitive resources for goal attainment; (b) use of goal-related cognitions and flexible strategies for self-monitoring, self-evaluation, & self-regulation; (c) individual differences in dispositionally based volitional styles derived from factor-analytic views of personality (e.g., the will power or persistence factor mentioned in Digman, 1990). For their part, Deci and Ryan (1985) elaborated on the polarity between intrinsic (IM) and extrinsic (EM) motives, based on their self-determination theory. Intrinsically motivated behaviors are engaged in for their own sake — for the pleasure and satisfaction derived from their performance. (. . .) Extrinsically motivated behaviors, on the other hand, are instrumental in nature. They are performed not out of interest but because they are believed to be instrumental to some separable consequence. (. . .) Self-determination theory posits that the four types of extrinsic motivation [external, introjected, identified, integrated] result from the internalization processes having been differentially effective. The

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resulting regulatory styles thus fall at different points along an autonomy continuum that describes the extent to which they have been internalized and integrated (Deci, Vallerand, Pelletier, & Ryan, 1991, pp. 328– 329).

That quote suggests a single continuum of self-determination, with extrinsic motives occupying the negative end, and intrinsic motives the positive end. The above tripartite distinction guided our efforts to improve the assessment of the motivation components. Because of the lack of instruments to assess the volitional component, we chose to focus on one recurring construct clearly related with volition, namely persistence. The literature review shows much ambiguity between the concepts of effort and persistence. A survey of various definitions suggests that effort is usually associated with using one’s abilities to their fullest, or doing one’s best (Holloway, 1987), whereas persistence adds both a duration and an obstacle component: maintaining effort over time in order to overcome obstacles to the sought goal (Kanfer & Ackerman, 1989). Persistence is recognized as a stable dispositional trait, hence its presence in many personality tests (Digman, 1990). In spite of Corno’s work, persistence remains generally identified with motivation. This is why we decided to adopt the label ‘‘motivation-related constructs’’ to designate, as a group, the three independent variables (IM, EM, P) chosen to represent the motivational aspect of this study. 3.2. Design particularities The following methodological inputs were introduced to reinforce the correlational design: (a) two measures of cognitive abilities were used; (b) well-validated instruments were chosen to measure the three motivation-related constructs; (c) three sources of information (students, parents, and teachers) participated in the assessment of the motivation-related constructs, and (d) their ratings were collected twice during the semester; (e) a large sample was sought to maximize statistical power. Based on previous work by Rushton, Brainerd, and Pressley (1983), as well as Epstein (1986), we hoped to improve the reliability of both independent and dependent variables by aggregating data not only over time, but also over sources and instruments. Three hypotheses were formulated: (a) academic aptitudes will be, by far, the best predictor of scholastic achievement; (b) indices of motivation will add a statistically significant, but substantially small contribution to that predictive power; (c) aggregating data over, sources, constructs, and especially time, will slightly increase the unique predictive power of the motivationrelated constructs.

4. Method 4.1. Subjects Two hundred eight female students from nine classes in Grade 8 of an all-girl high school in the Greater Montreal area participated in the study; the students’ age varied from 12.3 to 14.10, with a mean of 13.5 (S.D. = 0.38). One parent of each student and a group of 15 of

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their teachers were also involved; parents had to assess the motivation and persistence of their child, while teachers did the same for groups of 25 to 30 participating students. The research design called for independent evaluations by two different teachers. 4.2. Instruments 4.2.1. Intelligence Two IQ measures were collected, using (a) Raven’s Progressive Matrices (Raven & Summers, 1986) and (b) the Otis–Lennon Mental Ability Test: Intermediate Form (Otis & Lennon, 1969). The Raven is a nonverbal test of inductive reasoning, recognized as one of the purest measures of g (Snow, Kyllonen, & Marshalek, 1984). It is not timed; its administration requires approximately 30 min. Good coefficients of reliability and validity have been reported (Raven & Summers, 1986). The Otis–Lennon is a well-known group test of academic aptitude (mostly verbal), with 80 multiple-choice items and a time limit of 45 min; adequate reliability and validity indices have been found (Otis & Lennon, 1969). 4.2.2. Motivation We chose a recent self-administered test of intrinsic–extrinsic school motivation, based on Deci and Ryan’s (1985) theoretical framework. Created by Vallerand, Blais, Brie`re, and Pelletier (1989), it is called E´chelle de Motivation en E´ducation: E´tudes Secondaires [School Motivation Scale: High School form]. Its 28 items are divided into seven 4-item subscales (see Appendix A for sample items), measuring three types of intrinsic (IM) motivation (S: stimulation, K: knowledge, A: accomplishment), three types of extrinsic (EM) motivation (Ex: external, Ij: introjected, Id: identified), as well as nonmotivation (Nm). Respondents judge the self-applicability of the statements with a 7-point Likert-type scale (1 = Does not correspond to me at all; 7 = Is just like me). It takes approximately 15 min to complete. The authors developed the following equation to create a global Relative Autonomy Index that ranges from 18 to + 18: [(2(S + K + A)/3) + Id] [(Ex + Ij )/2 + (2Nm)] (see Vallerand & Bissonnette, 1992, for a slightly modified version of the equation). That equation assumes a positive correlation between one EM subscale (Id ) and the three IM subscales, as well as negative correlations between the IM group and the other EM subscales plus the Nm subscale. In other words, the IM and EM constructs are not conceived as independent, but as the two poles of a continuum. The higher the score, the more the student is judged to attend school out of sheer pleasure, interest, and personal choice. The scale’s wording was slightly modified for the parent form. Because the teachers had to evaluate a whole group of students, a much shorter form was needed for this scale. Three items were kept, one for IM, EM, and Nm, respectively (see Appendix A); the same response scale was proposed. 4.2.3. Persistence A 10-item scale, called Ma Fac˛on de Travailler a` l’E´cole [How I Work at School], was created (see Appendix A for sample items). It was inspired by a similar scale in Le Test de Tendance Personnelle, a French Canadian adaptation of the Edwards Personal Preference Schedule (Gauthier, 1964). The same 7-point Likert-type self-applicability response scale was used. The scale takes about 5 min to complete; a global score is computed, namely the mean

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of the 10 answers after inverting responses to negatively worded statements. Again, minor wording changes were made for the parent form. The teacher questionnaire included one item to measure persistence (see Appendix A). The scale was pretested with a group of 70 students from a different grade in the same high school. The item means and variances were judged very satisfactory, as well as homogeneity (Cronbach’s a=.82). A principal components analysis confirmed its unidimensionality (St Pe`re, 1997). 4.2.4. Academic achievement The four major subject matters taught in that grade were chosen: French (mother tongue), Math, English, and History. The correlations between them ranged between .45 and .74, with a mean of .56, and justified pooling them into a single academic achievement score expressed in percentages. 4.3. Procedure About 360 students in the same grade were invited to participate in the study; they and one parent had to agree to complete the motivation and persistence scales twice during the semester, and allow access to IQ and achievement data. The participation rate was 58% (N = 208). Participating students received the Raven during a class period about a month after the beginning of the fall semester; the Otis–Lennon IQ was taken from the students’ files. Soon after, they completed both the motivation and persistence scales, and were given copies to be completed by a parent. The teachers also received the short 4-item questionnaire with which they had to evaluate the participating students in their class; 3 teachers out of 15 had to evaluate two groups. As planned, we obtained for each student two independent teacher ratings. Two months later, toward the end of the semester, the students, the parents, and the teachers again completed the motivation and persistence scales. At the end of the data collection, we had complete data for 205 students, 181 parents (9% loss), and 14 teachers. Academic achievement data were extracted from the school files for two consecutive assessment periods, one in the middle of the semester, the other at the end.

5. Results We will first examine basic statistical information concerning the various instruments; because of its structural difference, the teacher questionnaire will be analyzed separately. We will then look at the relationships between the ability- (IQ and achievement) and motivationrelated constructs before assessing the separate contributions of aptitude and motivation to school achievement. 5.1. Descriptive analyses 5.1.1. Determining independent variables Table 1 shows descriptive statistics for all the variables included in the data analyses. Percentiles were chosen for both the Otis–Lennon and Raven tests; they reveal a well-

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Table 1 Basic descriptive and psychometric statistics Variables

Mean

S.D.

N

Homogeneity

Abilities Raven Matrices Otis – Lennon Academic achievement 1 Academic achievement 2

74.7 81.8 79.4 80.1

22.08 19.72 9.12 8.36

203 197 208 208

– –

4.08 3.88 5.46 5.52 4.80 4.68

1.12 1.21 0.99 0.98 1.00 1.06

208 205 208 205 208 205

.91 .94 .77 .80 .81 .86

4.74 4.63 5.47 5.52 5.26 5.28

1.05 1.03 1.01 1.02 1.02 1.01

200 176 200 176 203 181

.92 .94 .76 .85 .87 .88

4.89 4.80 4.66 4.67 4.97 4.72

0.99 1.11 0.97 1.04 1.11 1.16

207 207 207 207 207 207

Motivation-related variables Student data IM 1 IM 2 EM 1 EM 2 P1 P2 Parent data IM 1 IM 2 EM 1 EM 2 P1 P2 Teacher data (1 item per construct) IM 1 IM 2 EM 1 EM 2 P1 P2

.80 .86

– – – – – –

Intrinsic motivation (IM), extrinsic motivation (EM), and persistence (P). 1, 2 = time periods. Teacher data correspond to pooled independent assessments by two teachers.

above average population, with about 45% of the scores on both tests exceeding the 90th percentile. In the case of the school motivation scale, correlation matrices of the subscales using the four data sets (two groups, two time periods) revealed that the authors’ equation for the computation of a global score could not be used because the assumption of negative correlations between the three IM subscales and two EM subscales (Ij, Ex) was not met. Four independent principal components analyses with Varimax rotations were then performed using the default options of SPSSx (Norusis, 1983), revealing three recurring factors. The first was called IM and grouped 16 items (S, K, A, and Id); the second was called EM because of high loadings from the 8 Ij and Ex items; the last one brought together the 4 Nm items. Due to very low item means and variances (see Fig. 1), that last factor was not retained. Thus, two scores, IM and EM, were created by averaging responses to the relevant items. The Table 1 data show a definite leniency effect, with most means about one unit above the scale’s midpoint of 4.00; we also observe good

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Fig. 1. Mean ratings of two groups for the seven IM/EM subscales, and of the three groups for the three motivation-related constructs (Times 1 and 2 pooled).

variation (S.D.), and satisfactory homogeneity coefficients (Cronbach’s a) for group comparison purposes. Results from the persistence scale reveal a similar leniency leaning. The homogeneity coefficients are equally satisfactory. Four separate principal components analyses, again using the default options of SPSSx and Varimax rotations, were performed (two groups, two times); the unipolar structure of the P scale was confirmed. Descriptive data for the 4-item teacher questionnaire appear at the bottom of Table 1. We first checked the comparability of the ratings given by the two teachers, and found just a few significant mean differences, as well as moderate correlations (from .18 to .51); teachers did agree better on their IM and P ratings than their EM ratings (St Pe`re, 1997). The two sets of ratings were judged similar enough to be pooled; Table 1 shows the pooled data. In line with the student and parent data, very low means and lack of variance in the rating of nonmotivation forced its exclusion from further analyses. 5.1.2. Stability and validity With its three sources of information on three different motivation-related constructs, as well as repeated measures over two time periods, the research design makes it possible to answer important questions concerning the reliability and validity of the data. How good is the short-term stability of the respondents’ ratings? How good is the convergent validity, which corresponds to the correlation between measures of a given construct by different ‘‘methods’’ (here sources of ratings)? How good is the discriminant validity, which corresponds to the correlation between measures of different constructs (here IM, EM, P) by the same source? Answers to these three psychometric questions appear in Table 2, which adopts the format of Campbell and Fiske’s (1959) multitrait – multimethod matrix of

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Table 2 Partial group by construct multitrait – multimethod matrix of correlations Students IM Students IM (.77) EM .11/.03 P .45/.44 Parents IM .32/.42 EM P Teachers IM .15/.14 EM P

Parents EM .13 (.76) .12/

P

.05

IM .43 .02 (.76)

.12/.20 .26/.38

Teachers EM

P

IM

.33

.15 .22

(.78) .35/.30 .48/.44

.36 (.68) .12/.09

.19/ .18/.24

P

.01 .23

.39 .01 (.74)

.19/.15 .07/.03

EM

.08 .39/.22

.00

.07 .15 .10

(.70) .50/.56 .86/.80

.36 (.46) .38/.51

.81 .01 (.70)

Values within parentheses along the main diagonal are short-term (2 months) stability coefficients. Validity coefficients from time periods 1/2 data are placed under the diagonal; the aggregated values (Times 1 + 2) appear above the diagonal. Discriminant validity coefficients surround the diagonal, whereas convergent validity coefficients appear in italics. N = 169. Significance levels: r  .15, P < .05; r  .20, P < .01.

correlations. Short-term stability coefficients are placed, within parentheses, on the main diagonal. Only the lower triangle would be used normally to insert validity coefficients; but, because of our repeated measures, we placed Times 1 and 2 coefficients below the diagonal, and the pooled (1 + 2) coefficients above. The three blocks clinging to the main diagonal correspond to discriminant validity coefficients, what Campbell and Fiske called heterotrait– monomethod data. Then, further away from the diagonal, there are, on each side, three trios of diagonally placed values, called monotrait – heteromethod coefficients, or measures of convergent validity. To avoid cluttering the table, we did not include the heterotrait– heteromethod data that would have filled the table; as expected, almost all of them were statistically nonsignificant or just barely so ( P < .05). Absent from Table 2, the short-term stability of the achievement scores was .89. The short-term stability coefficients are much lower than the corresponding homogeneity coefficients (see Table 1), but, except for the teachers’ EM scores, they are high enough for group comparison purposes. They are somewhat lower in the case of the teacher questionnaire, but still surprisingly high for correlations between single items. Ideally, discriminant validity coefficients should be as close to zero as possible, except when there is reason to believe that a pair of constructs are conceptually related. The three groups behave somewhat differently; but each group’s pattern remains almost the same over the two time periods. The students’ EM scores entertain no relationship whatsoever with either IM or P, but these last two are partially correlated (.45/.44). In continuity with their children’s responses, the parents’ IM and P scores are moderately correlated, while EM and P are independent; but, the parents perceive a low-to-moderate positive relationship between intrinsic and extrinsic motives (.35/.30). In contrast, the teachers’ discrim-

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inant validity coefficients are much higher; EM scores are moderately correlated with both IM and P, whereas the IM/P correlations are so high (.86/.80) that the two measures become undistinguishable. Ideally, the convergent validity coefficients should be almost as high as reliability allows; indeed, since the three groups are assessing the same motivational phenomena — the students’ IM, EM, and P, — these coefficients could be almost regarded as scorer reliability measures. The results are far from ideal. Whatever the time period, the three EM measures entertain no relationship between them. The coefficients for the IM and P measures are larger, but only a few of them barely reach the moderate level (r2  .10). The student–parent correlations are the least weak on average, especially with regard to IM.

5.1.3. Group comparisons The means in Table 1 indicate that the three groups might differ quite significantly in terms of their evaluations of the three motivation-related constructs, and that there might be also time effects. To answer these questions, the 18 IM, EM, and P variables in Table 1 were analyzed with a 3  3  2 (Group  Construct  Time) ANOVA, the three factors designated repeated measures. The group variable was considered a within subjects variable for the simple reason that the parents and teachers were assessing the students’ motivations, thus making the three sets of data interrelated with each other. The results appear in Table 3. The major main effect, a global difference between the means of the three motivational constructs,

Table 3 Repeated measures ANOVA comparing groups, constructs, and time periods Source

df

F

h2

17.61*** (2.49) 77.57*** (1.61) 9.54** (0.41) 66.92*** (0.94) 7.12*** (0.27) 2.73* (0.27)

.10

Between subjects The groups were introduced as a within subjects variable; see text. Within subjects Groups (G) S within-group error Constructs (C) S within-group error Time (T) S within-group error GC S within-group error CT S within-group error GCT S within-group error

2 334 2 334 1 167 4 668 2 334 4 668

.32 .05 .29 .04 .02

Values enclosed in parentheses represent mean square errors. Only statistically significant interactions are shown. * P < .05. ** P < .01. *** P < .001.

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can be deduced from the three curves on the right side of Fig. 1. Higher ratings are given to EM items (5.22) than to P items (4.96), whereas IM items receive the lowest ratings (4.53). To show the reasons for the higher popularity of EM over IM, we have included on the left part of Fig. 1 the means of the seven subscores — aggregated over time — from both the student and parent questionnaires. EM.Id (going to school to find a career I like and become competent in it) is judged by both groups to be the most relevant motive, whereas IM.S (learning for the fun and excitement) receives the lowest ratings from both groups. All other motives share generally comparable ratings. The second most significant main effect involves the three groups, with the parents expressing higher ratings overall (5.15) than either the teachers (4.82) or the students (4.76). The last main effect confirms that Time 1 ratings were slightly more positive than those made 2 months later (4.94 vs. 4.87). Fig. 1 shows the strongest interaction effect, the group by construct interaction. For each construct, a different group distinguishes itself from the two others, students in the case of IM, teachers in the case of EM, and parents in the case of persistence. The statistically significant, but not very substantial (h2=.04) construct by time effect is caused by an identical drop over time ( .12) in the IM and P ratings, counterbalanced by a small rise (.02) of the EM ratings. These changes can be easily extracted from

Table 4 Correlations of the three motivation-related constructs, by group, with the three ability measures, by time Raven Time 1 Aptitudes Otis – Lennon Raven

Otis – Lennon Time 2

Time 1

Academic achievement Time 2

.32***

Time 1

Time 2

.53*** .37***

.56*** .36***

Students IM EM P

.02 .05 .12

.02 .03 .16

.04 .00 .07

.03 .00 .08

.03 .05 .25**

.06 .01 .19*

Parents IM EM P

.08 .05 .10

.06 .04 .18*

.07 .07 .12

.10 .02 .15

.21** .12 .36***

.21** .01 .35***

Teachers IM EM P

.22** .21** .14

.17* .15 .06

.25** .26** .27**

.33*** .35*** .25**

.61*** .43*** .61***

.62*** .43*** .57***

N = 156. Times 1 and 2 for the Raven and the Otis – Lennon refer to the corresponding measures of IM, EM, and P. In the case of academic achievement, the Times 1 and 2 measures are paired with the corresponding motivationrelated measures. * P < .05. ** P < .01. *** P < .001.

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the Table 1 data. Finally, the significant G  C  T interaction effect is due to the fact that the IM drop over time is stronger in the students’ ratings, whereas the drop in the P ratings comes mostly from the teachers’ ratings (see Table 1). 5.2. Predicting school achievement As a first descriptive survey of the relationships between the independent variables and academic achievement, Table 4 shows the correlations between the three sets of motivationrelated measures and the three ability measures. First, the well-documented relationship between cognitive abilities and academic achievement is evident, especially in the case of the Otis–Lennon. Note the barely moderate correlation between the two cognitive ability tests, announcing possible independent contributions. Second, the students’ self-assessments of their motivation and persistence at two points in time show no relationship whatsoever with their cognitive abilities; moreover, only the persistence measures weakly predict academic achievement. The two sets of parent judgments are also unrelated to the child’s academic aptitudes, but the IM and P scores are significantly correlated with the students’ grades. Surprisingly, it is the teachers’ ratings, made with a psychometrically very fragile instrument, which produce by far the ‘‘best’’ results, not only as correlates of cognitive aptitudes, but even more as predictors of academic achievement. Based on (a) these suspiciously high correlations, (b) the moderate to high correlations between the three motivation-related measures obtained from the teachers (see Table 2), and (c) the lack of convergent validity between the students’ and teachers’ assessments, it became clear that the teachers’ ratings were unduly influenced by their knowledge of the students’ achievements. The construct validity of these ratings appeared so doubtful that we decided to exclude them from the last analytical step. To verify the three hypotheses mentioned earlier, a series of multiple regression analyses were performed. After excluding the teacher data, two sets of six motivation-related variables (3 constructs  2 groups) were available for each time period, plus the two academic aptitude tests. A semihierarchical model was adopted (Norusis, 1983). The Raven and Otis–Lennon scores were entered as Step 1, in that sequence. We judged that the purest available measure of cognitive abilities should have priority over an instrument that had been specifically designed to maximize its predictive validity with regard to scholastic achievement (Anastasi & Urbina, 1997). Then, assuming that the students were the best judges of their own motivation and persistence, we entered the students’ IM, EM, and P scores as a second step, without specifying a particular order among them. The last step brought in the three parental measures of motivation, again in no specific order. We did not use the stepwise procedure in SPSSx (Norusis, 1983), but the ‘‘forward’’ command so that a previously retained variable would not be deleted later, in the same step or in a subsequent step. Four successive multiple regressions were performed, first for Times 1 and 2 separately, then with the student and parent data separately aggregated over time, and finally by also aggregating the two aptitude measures. The basic assumptions of regression analysis (linearity, normality, and homoscedasticity) were checked and found to be respected; no outliers were discovered using the Mahalanobis distance index (Tabachnick & Fidell, 1989). The results appear in Table 5.

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Table 5 Multiple hierarchical regressions of ability and motivation-related indices on school achievement Variables

B

b

sr2

R & R2

Time 1 1. Raven Matrices 2. Otis – Lennon 3. Persistence (students) 4. Persistence (parents)

0.085 0.185 1.120 2.390

.204 .388 .120 .271

.13*** .17*** .04* .06***

R = .63 R2 = .40 adj. R2 = .39

Time 2 1. Raven Matrices 2. Otis – Lennon 3. Persistence (parents)

0.067 0.205 2.136

.171 .466 .257

.14*** .22*** .06***

R = .65 R2 = .42 adj. R2 = .41

Times pooled 1. Raven Matrices 2. Otis – Lennon 3. Persistence (students) 4. Persistence (parents)

0.071 0.203 0.684 2.555

.179 .455 .075 .288

.15*** .21*** .03 .07***

R = .68 R2 = .46 adj. R2 = .45

Times and aptitudes pooled 1. Raven + Otis – Lennon 2. Persistence (students) 3. Persistence (parents)

0.263 0.618 2.547

.506 .068 .288

.33*** .03 .07***

R = .66 R2 = .43 adj. R2 = .42

* P < .05. *** P < .001.

The first regression, using the Time 1 data, shows that (a) the two aptitude measures account for 30% of the academic achievement variance, (b) only persistence scores (students and parents) add any significant contribution to the prediction equation, and (c) the ratio of their respective contributions is 3:1. Note that the parents’ assessment of their child’s persistence appears a more powerful predictor of academic achievement than the students’ own judgment, even though we gave priority to the students’ scores. The second regression equation, which uses end of semester measures of motivation, persistence, and achievement, generally confirms the first one, except that the students’ P scores no longer contribute significantly to the prediction of academic achievement. Here, the aptitude/motivation ratio for predictive power is 6:1. The aggregation of the two successive measures slightly improves the predictive validity; the squared multiple correlation increases from .42 to .46. This is mainly attributable to the reapparition of the students’ P scores among the predictors. But, note that while their contribution had to be statistically significant when these scores were entered in Step 3, the significance disappears when the parents’ P scores are included in the equation. In other words, if we had used the stepwise procedure instead of the forward one, the students’ P scores would have been deleted from the final regression equation. The fourth multiple regression attempted to verify if we could improve the predictive validity by aggregating the two aptitude measures; the result was negative. Similarly, we tried aggregating the student and parent data and found no significant change in predictive power (R2 change from .43 to .42). In brief, the best

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predictive equation, with both times combined, gives aptitudes at least five times (.36/.07) more predictive power than any combination of motivation-related measures. 6. Discussion We will first discuss the results related to the three hypotheses, then examine some peripheral yet interesting observations. 6.1. The three hypotheses We predicted (a) that cognitive aptitudes would be a strong predictor of school achievement, (b) that motivation would also contribute, but to a much smaller degree, to the prediction equation, and (c) that some slight improvement in predictive power would occur thanks to data aggregation. Only the first hypothesis was clearly confirmed. 6.1.1. Aptitudes Together, the Raven Matrices and Otis–Lennon produced a multiple correlation in the .55 to .60 range depending on the data set. The hierarchical method advantages the Raven since it receives the variance shared with the Otis–Lennon; in a stepwise approach, it ranks second, well behind the Otis–Lennon, and its contribution in terms of explained variance drops slightly below 10%. Still, that unique contribution remains at least as important as that of any motivation-related variable assessed in this study. As noted in the literature review, such results are quite common, especially in the case of the Otis–Lennon, whose mostly verbal items were chosen to maximize its ability to predict school achievement. The fact that the nonverbal inductive reasoning measured by the Raven Matrices contributes significantly, albeit more marginally, to the prediction was hoped for, but not strongly expected; that positive effect reinforces the strength of general cognitive abilities as a predictor of academic achievement. 6.1.2. Motivation The second hypothesis was just barely confirmed, thanks to a statistically significant contribution of the persistence measures, especially the parents’ P scores. Their unique contribution ranged between 6% and 10%, approximately equivalent to that of the Raven Matrices. Compared to the explanatory power of aptitudes, it is, at best, three or four times smaller. Again, such a result is perfectly in line with a majority of the past studies reviewed (e.g., Aspinwall & Taylor, 1992; McHenry et al., 1990; Schmidt & Hunter, 1998; Spence et al., 1989; Wong & Csikszentmihalyi, 1991). But, there is a problem; the construct validity of that small significant contribution of the parents’ persistence scores is doubtful. Let us assume, as we did for the hierarchical multiple regressions, that the students’ P scores are the most valid representation of the ‘‘real’’ persistence of these high school students; it is a very plausible assumption in view of the distancing process between parents and children at the start of adolescence. Keeping that assumption in mind, we observe first that the students’ and parents’ persistence measures, at both points in time, are not even moderately correlated (see

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Table 2). Moreover, as shown in Table 4, it is the parents’ P ratings that better predict school achievement; in terms of percentage of (zero-order) explained variance, that difference is large: about 5% for the students’ P scores, as opposed to 12% for the parents’ P scores. When both variables are entered in the multiple regression, the students’ P scores, even though they are given priority, do not contribute significantly (except slightly in the Time 1 data set) to the prediction of academic achievement; that priority makes them benefit from any shared predictive power with the parents’ P scores. Why do the parents’ P ratings predict school achievement better than the students’ theoretically more valid self-ratings? The most plausible hypothesis is a contamination of the parents’ perception of their child’s motivation by their knowledge of her achievement, an effect identical to the much stronger one observed in the teacher data. There is a very strong common sense belief that persistent effort and success are closely related. It shows not only in popular sayings (e.g., Edison’s famous quote ‘‘Genius is 1% inspiration and 99% perspiration,’’ or ‘‘If one has the will, one can succeed’’), but also in the scientific literature. For instance, as mentioned earlier, research in the field of attribution theory has confirmed repeatedly the major role of effort as a perceived cause of success. Consequently, if the parents observe that their child is achieving, they will tend to readjust their persistence judgments to a slightly higher level, doing the opposite when the child fails. These influences are sufficient to inflate the correlation between their persistence ratings and achievement, and create a low-level artificial causal relationship. Without such knowledge of the dependent variable, these scores would probably correlate no better with school achievement than the students’ own P judgments. That bias is quite common in psychometrics; its exact opposite, called criterion contamination, happens when raters of job performance are influenced by their knowledge of the ratee’s performance on selection tests (Anastasi & Urbina, 1997). In summary, these results show no significant contribution of intrinsic or extrinsic motives, whether assessed by the students themselves or rated by their parents, to the prediction of academic achievement. Academically talented students are not more intrinsically motivated nor are they more (or less) extrinsically motivated than their peers who achieve at average or below average levels. Even the slightly significant impact of persistent efforts was questioned because it came from the less valid parents’ ratings instead of the students’ self-perceptions. 6.1.3. Aggregation The design of this study made possible three forms of data aggregation: over time, over sources (students, parents, and teachers), and over constructs (aptitudes). When we hypothesized that aggregation would slightly improve the overall predictive validity of the motivation-related constructs, we targeted time — because of usually strong stability coefficients — as the most promising of the three types. Aggregation works best when the covariance is large and reflects the same true score, while the rest of the variance is just random measurement error instead of the nonrandom measure of a different construct. Pooling the three successive motivation-related measures for both students and parents did increase the multiple correlation from .65 to .68, a substantially limited improvement. The two other aggregation attempts proved unsuccessful. When the two aptitude scores were merged into a global aptitude percentile, that construct’s predictive power slightly decreased (see Table 5); a similar decrease was observed when we pooled the students’ and parents’

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measures of the three motivation-related variables. It must be pointed out that in both cases the correlations were low (usually < .35), barely high enough, according to Epstein (1986), to warrant such aggregation. Moreover, the strong bias in the parents’ ratings did not help. All in all, these results indicate that the success of an aggregation strategy rests on rather demanding criteria. 6.1.4. Methodological considerations It is not the goal of the present study to explain why motivation or persistence contribute so little to the prediction of scholastic achievement; we leave such reflections to motivation theorists. We only aimed to show, as so few others did before us, that the predictive power of these constructs was limited. And the results are clear, especially in view of efforts to ensure reliable and valid measures of all the relevant constructs. Granted that the teacher data lack validity, and that the parental information is also partly questionable. But, these two sources were included as independent estimates of the ‘‘real thing,’’ namely the students’ motivation and persistence. Consequently, their exclusion does not jeopardize the methodological strength of this study. In fact, the comparisons between sources lead to important observations about the value of outside judgments (see below). The core data are the aptitude measures and the students’ own judgments of their motivation and persistence; and we believe that our instruments stand comparison with the best ones used in past studies. First, the Otis–Lennon and Raven Matrices are both well-validated tests of cognitive abilities. Some could argue that we had a skewed distribution with a large percentage of high scores, but reduced variation attenuates correlation coefficients. So, we could argue that our multiple correlation of .60 is a lower-bound estimate of the true relationship. Second, the IM, EM, and P scores show good reliability; the short-term stability coefficients are somewhat lower than the corresponding homogeneity coefficients, but still high enough for group comparisons. Third, in terms of content validity, the IM and EM scales were created by Vallerand et al. (1989) from a respected theory of school motivation (Deci & Ryan, 1985). Similarly, the P scale was inspired by a valid scale in a well-recognized personality test (Edwards, 1959; Gauthier, 1964). Finally, the discriminant validity data confirmed the independence of the constructs, except for a theoretically defensible moderate relationship between IM and persistence; it makes sense that as students find more pleasure in academic learning, they will tend to invest more energy to understand and master the more difficult concepts. In brief, we doubt that the lack of predictive power could be attributable to invalid measurement of the key concepts. 6.2. Additional observations 6.2.1. The nature of school motivation In their own judgment, students attend school more for extrinsic motives than for intrinsic ones (see Fig. 1); not that learning and discovering are not interesting to some extent for many of them, but these motives are judged less important than the more practical goals of finding a career that will be interesting (EM.Id) and well-paid (EM.Ex). Among intrinsic motives, IM.S (the excitement of learning) gets the lowest mean; nonmotivation is almost nonexistent, but a large S.D. (1.03) suggests that some students might be seriously questioning their schooling process. The parents’ ratings follow a very similar pattern, but they perceive their child as

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more motivated intrinsically and more persistent on average than the students perceive themselves. The more positive parental assessments of IM and persistence can be interpreted in different ways. First, they could represent the observable part of the child’s motivation, what she decides to show publicly to her parents. It is understandable that these students would be hesitant to manifest outwardly too much lack of interest or persistence, not only to preserve their own self-esteem, but also as a form of thoughtfulness toward parents who are meeting the added cost of a private education. Second, it could be the result of a parental clemency bias brought out by their limited knowledge of their child’s motivation level; unsure about the ‘‘real’’ level, they would give her the benefit of the doubt. Third, it might also reflect selective perception on the part of the parents who want to believe that the choice of a costlier education has a positive impact on their child’s IM and persistence. The tendency to identify more readily with extrinsic than with intrinsic motives is not specific to this sample, but was observed by the authors of the instrument (Vallerand et al., 1989). In view of the relative loftiness of intrinsic motives compared to extrinsic ones — as shown so clearly in Deci and Ryan’s (1985) use of them as endpoints on a self-determination continuum, with IM placed at the ‘‘good’’ end, — one would expect that the students, influenced by social desirability pressures, would have given higher ratings to IM motives. The fact that they did not does not necessarily mean that social desirability had no impact. The students’ ratings might still be somewhat inflated; only a more in-depth approach would reveal the extent of that potential bias. Finally, the significant but small decrease in motivation over the course of the semester is a phenomenon well known to teachers and professors. Because of its minor importance, we did not check whether it had been scientifically documented. It is interesting that the students report a decrease in interest (IM), whereas the teachers perceive a decrease in persistence. Could the teachers’ focus result from the better observability of persistence in daily school life? 6.2.2. Motivation and aptitudes The total lack of relationship between the students’ IQ and their motivational level is another important result of this study. Not a single one of the 12 correlation coefficients (see Table 4) attains minimal statistical significance, not to say minimal substantial significance (r2  .05). These results contradict a very common belief among educators, namely that intellectually gifted students show more IM and task commitment than average ability students. At the same time, they confirm Shore et al.’s (1991) questioning of that belief. Persistence is the only variable that predicts, and just barely, academic achievement. On second thought, that lack of relationship can be easily explained. It is a well-known fact that the high school curriculum offers little challenge to bright students, a fact that has been amply demonstrated in the U.S. (Archambault et al., 1993; Reis et al., 1993); now, the Quebec curriculum largely overlaps its U.S. counterpart. The lack of challenge impacts on both IM and persistence. It decreases the desire to learn in those students who were hoping for new and fascinating knowledge presented at a fast rate. In the framework of Vygotsky’s (1978) theory, they are seldom working at the upper limit of their zone of proximal development. On the other hand, bright students can achieve very well without even trying. It is clear that the above observations do not apply to all students. Some of them maintain IM even in dreary school environments, in the same way that some high achievers show high persistence and

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effort even when they could achieve almost as well with little involvement. In other words, there are complex phenomena in action as well as important individual differences, and their effect is to minimize the relationship between IQ and motivation, as well as between motivation and academic achievement. 6.2.3. The intrinsic/extrinsic continuum As quoted earlier, Deci and Ryan (1985) present IM as the most desirable form of motivation, corresponding to self-determination and maturity; EM is judged to be a less evolved or mature form. Following that theoretical position, the authors of the scale we used computed from their instrument a single global score, called a self-determination score or relative autonomy index, in which the three IM subscales (A, K, S ), plus the EM.Id subscale, had positive weights, whereas the two other EM subscales (Ex, Ij) and Nm were given negative weights. In other words, the more self-determined a person is, the more importance she should give to intrinsic motives. The fact that the EM.Id items side with those of the three IM subscales to create an IM factor already signals some ambiguity in the IM vs. EM distinction. It seems clear from the participants’ answers, both students and parents, that going to school to find an interesting occupation and become competent in it (EM.Id) is closely related to finding pleasure and interest in acquiring new knowledge (IM.K), and wanting to become more competent and achieving as a learner (IM.A). The means-to-end orientation measured by EM.Id clearly appeals more to both students and parents. It is also interesting that the IM and EM constructs, contrary to theory, are not negatively correlated. The students implicitly see them as independent, while the parents’ ratings make them positively correlated. These results show that it is possible for intrinsic and extrinsic motives to be pursued in parallel, for the intrinsic pleasure of learning to coexist with the desire to find a well-paying job (EM.Ex) or to prove to oneself that one can attain the goal of a high school diploma (EM.Ij). This is not the place for a full-fledged reexamination of Deci and Ryan’s self-determination continuum, but our results clearly question that central aspect of their theory. Moreover, other recent empirical work (e.g., Hoekman, McCormick, & Gross, 1999; Nicholls, 1992) supports that questioning. 6.2.4. The validity of external judges The present study has shown that using outside evaluators to assess a person’s internal state of mind can be a risky business, the more so when the evaluators have limited direct information about the traits or behaviors they are asked to assess. We were hoping for moderately high correlations (>.50) between pairs of scores, but found that most of them were not even statistically significant at the .01 level (see Table 2). Teachers were especially poor at assessing both motivation and persistence; their judgments had virtually nothing in common with the students’ self-perceptions nor with the parents’ judgments. The situation was even worse when the measures were pooled over time. Note for example that the moderate correlations between the teachers’ EM and P ratings (.38/.51) disappear when the Times 1 and 2 data are pooled (see Table 2), a sure sign of low reliability. Even though we had to expect reduced stability coefficients because only one question per construct was used, we did not expect (a) the unduly high discriminant validity coefficients observed, (b) the quasi-total lack of convergent validity with the student data, as well as (c) suspiciously high correlations with

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achievement measures. These results, totally divergent with those of the two other sources, brought us to hypothesize that the teachers, because of their limited information about the students’ thoughts and feelings, relied heavily on the only clear knowledge available, namely school grades, to infer the students’ level of motivation (IM and EM), as well as persistence. Indeed, if we had included the teachers’ scores in a stepwise multiple regression analysis, their IM and P scores would have dethroned the two IQ measures from their top rank as predictors of academic achievement (see Table 4). In summary, it is a clear case of ‘‘hindsightful’’ prediction; and we doubt that a longer instrument would have much improved their ratings. Such bad results might be partly explained by the fact that our judges were specialist teachers and met their students for only a few hours (3 to 8) each week. Still, we doubt that elementary school teachers, who work full-time with the same students, would fare much better as judges of these complex constructs. In a nutshell, there is much ground for caution in using teachers’ or parents’ assessments of the students’ internal states, like mood, needs, or goals, in fact anything that cannot be easily observed.

7. Conclusion The nonsignificant contribution of motivation to the prediction of school achievement found in the present study corroborates results from many past investigations in both school and work settings (e.g., Bloom, 1976; McHenry et al., 1990; Schmidt & Hunter, 1998; Terborg, 1977). More significant, the students’ own judgments of their motivation and persistence did not predict achievement even when cognitive aptitudes were not controlled. In fact, the motivation-related measures were not even related to intelligence, contradicting a common belief among educators and scholars (Gagne´ & Blanchard, submitted for publication; Janos & Robinson, 1985). These results should be extrapolated with caution. First, the study was conducted in an all-girls high school; boys might behave differently. Anastasi and Urbina (1997) mentioned a small, but consistent moderating sex effect of the predictive validity of aptitude tests. They speculated that male students might find more interest in nonacademic activities; ‘‘these interest differences would introduce additional variance in their course achievement and would make it more difficult to predict achievement from aptitude test scores’’ (p. 183). Second, the sample came from a private school with somewhat selective admission criteria, as shown by the high IQ scores. More regular school settings could lower not only the aptitude measures, but also the motivation scores, creating more variance and, possibly, increasing the predictive power of motivation-related constructs. Third, these results were obtained in a school setting. Compared to other fields of talent development, like arts, sports or business, schools have one special characteristic: attendance is compulsory, thus has little to do with interest. By contrast, most youngsters do choose to practice sports, learn a musical instrument, develop a talent in visual arts, and so forth. If they become bored, or do not perform at an expected level, they can always abandon and switch to another activity. Consequently, studies in school settings need to be compared with parallel ones in these other fields, with special attention given to designs that control for field-related aptitudes. Fourth, these results should not be extrapolated to talent development at exceptional levels of excellence; at such levels, motivation and persistence seem to play a much more significant

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role (Hemery, 1986; Simonton, 1994). For instance, Lewis Terman compared the 150 most successful and 150 least successful participants of his immense longitudinal study of talent development, when they attained mid-life; he found that they did not differ to any extent in intelligence as measured by tests. But, there were significant differences: ‘‘the four traits on which they differed most widely were ‘persistence in the accomplishment of ends,’ ‘integration toward goals,’ ‘self-confidence,’ and ‘freedom from inferiority feelings.’ In the total picture the greatest contrast between the two groups was in all-round emotional and social adjustment, and in drive to achieve’’ (Terman & Oden, 1959, pp. 148–149). Finally, this study exemplifies the importance of intelligence as a causal agent in human behavior. That causality has been demonstrated well beyond the school walls, in a large variety of social phenomena, from unemployment, incarceration, and illegitimate births (Gottfredson, 1997), to single motherhood, HIV infection, and credulity (Gordon, 1997). Yet, IQ is almost never included as a covariate in social science research. Lubinski and Humphreys (1997) made a strong plea for its regular inclusion, contending ‘‘that, if consulted more often, the construct of general intelligence would contribute to understanding many puzzling human phenomena, because successive gradations of intelligence reflect successive degrees of risk’’ (p. 159). More scholars in education should heed that advice, and examine the antecedent causal role of intelligence with regard to popular psychoeducational constructs, like academic self-concept, self-efficacy, confidence, self-regulation, and many others. It would be interesting to see how much of their demonstrated relationship with school achievement would remain if cognitive abilities were controlled. Such controlled studies are hard to find; yet, they are badly needed if we want to refine our understanding of the determinants of achievement and success, not only in school settings, but in all other domains where youths and adults alike strive toward competence and excellence.

Acknowledgments The authors sincerely thank the administrators, faculty, and students (as well as their parents) of College Regina Assumpta, in Montreal, for their precious help in the data collection.

Appendix A A.1. Sample items from the school motivation scale I go to school because. . . S: K: A: Id: Ij:

I really like going to school. I find much pleasure and satisfaction in learning new things. of the pleasure I get from surpassing myself in my studies. high school studies will prepare me better for the career I have chosen. I want to prove to myself that I can complete with success a high school program.

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Ex: I need at least a high school diploma if I want to find a well-paying job later. Nm: honestly, I don’t know; I really feel like I’m losing my time in school. A.2. The 4-item teacher questionnaire IM: This student goes to school for the pleasure she feels when she is learning new things; she shows much interest for school subjects. EM: This student goes to school to get rewards (e.g., good grades, a better salary later, a present promised by the parents). Nm: This student does not know why she is going to school; she really feels that she is losing her time. P: When facing difficulties in school, this student persists; she keeps at it and puts in more efforts to overcome these difficulties and attain her goal. A.3. Sample items from the persistence scale (1) When I begin a complex homework, I always give my best to complete it. (3) When I meet with some difficulties in my school work, I abandon rapidly and do something else. (5) When I have some difficulty with a homework, I take all the time it takes to complete it well. (8) Sometimes, I do not complete difficult homework.

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