the importance of motivation as a predictor of school achievement
TRANSCRIPT
Learning and Individual Differences 19 (2009) 80–90
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Learning and Individual Differences
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The importance of motivation as a predictor of school achievement
Ricarda Steinmayr ⁎, Birgit SpinathUniversity of Heidelberg, Department of Psychology, Hauptstraβe 47-51, D-69117 Heidelberg, Germany
⁎ Corresponding author. Tel.: +49 6221 547728; fax: +E-mail addresses: [email protected]
[email protected] (B. Spinat
1041-6080/$ – see front matter © 2008 Elsevier Inc. Aldoi:10.1016/j.lindif.2008.05.004
A B S T R A C T
A R T I C L E I N F OArticle history:
The present study examined Received 6 June 2007Received in revised form 25 February 2008Accepted 18 May 2008Keywords:School achievementIntelligenceMotivation
to which extent different motivational concepts contribute to the prediction ofschool achievement among adolescent students independently from intelligence. A sample of 342 11th and12th graders (age M=16.94; SD=.71) was investigated. Students gave self-reports on domain-specific values,ability self-perceptions, goals, and achievement motives. Hierarchical regression and relative weightsanalyses were performed with grades in math and German as dependent variables and intelligence as well asmotivational measures as independent variables. Beyond intelligence, different motivational constructsincrementally contributed to the prediction of school achievement. Domain-specific ability self-perceptionsand values showed the highest increments whereas achievement motives and goal orientations explainedless additional variance. Even when prior achievement was controlled, some motivational concepts stillproved to contribute to the prediction of subsequent performance. In the light of these findings, we discussthe importance of motivation in educational contexts.
© 2008 Elsevier Inc. All rights reserved.
1. Introduction
It is well-established that general school achievement is highlyrelated to general intelligence (e.g., Kuncel, Hezlett, & Ones, 2004).Given that general intelligence explains only about 25% of the variancein scholastic achievement (Kuncel et al., 2004), it is worthwhile to lookfor other concepts that might add to the explained variance.Motivation is one of the constructs thought to cover a share of schoolperformance variance not explained by intelligence. Despite theimportance that is often ascribed to motivation in school contexts,only a few studies have so far investigated the incremental validity ofmotivation above and beyond general intelligence. There is even lessresearch on how motivational constructs compare with intelligenceand prior performance as another important predictor of subsequentschool performance. Furthermore, there are only a few studiesinvestigating how much unique criterion-related variance can beattributed to certain motivational constructs, intelligence, and priorschool performance, respectively, considering criterion-related valid-ity shared by the different predictors. Studies of this kind allowestimating the relative importance of different predictors, providinginformation beyond incremental validity analyses (cf. LeBreton,Hargis, Griepentrog, Oswald, & Ployhart, 2007). The current studyaims at two goals. First, we investigate the incremental validity ofdifferent motivational constructs above and beyond intelligence.Second, we calculate the relative importance of these motivational
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constructs, intelligence, and prior school performance when all theseconstructs predict subsequent school achievement together.
1.1. Different concepts of motivation
Contemporary achievement motivation literature discusses a greatvariety of concepts all subsumed under the term motivation(cf. Murphy & Alexander, 2000). For the purpose of our investigationwe shall focus on three of the most prominent approaches: needachievement theory, expectancy-value theory, and goal theories. All ofthese theories have been extensively investigated in school settings(Covington, 2000; Eccles &Wigfield, 2002).We have chosen to neglecttheories that are either conceptually very close to the investigatedmotivational constructs [e.g., self-efficacy (Bandura, 1977) which isvery similar to ability self-perceptions] or have not been as extensivelyinvestigated with regard to academic achievement as the conceptslisted above (cf. Murphy & Alexander, 2000). In the following, we takea closer look at the above mentioned three motivational theories, themeasurement of their central constructs, and their relations to schoolachievement. One aspect important for our hypotheses is thetheorized generality or specificity of the motivational concepts. Thusthe following sections explain why we operationalize the variableseither domain-specifically or -generally.
1.1.1. Need for achievementMurray (1938) considered need for achievement as one of the basic
human needs. In his conception needs, such as need for achievement,are “a more or less consistent trait of personality” (Murray, 1938,p. 61). As need for achievement is part of a person's personality, it isthought to trigger behavior across different situations. Consequently,
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need for achievement is theorized to be domain-general and, thus,assessed without referring to a certain domain or situation. Need forachievement assessed in the vein of Murray's personology coversthoughts and behaviors associated with success, accomplishment, andovercoming obstacles. Jackson (1967) used Murray's personology asthe basis for his Personality Research Form (PRF) and one scaleassesses need for achievement. The PRF is one of the personalityquestionnaires most frequently used world wide and a gold standardin personality research.
Murray's concept of need for achievement was extended byMcClelland and others who founded the classical achievement motiveresearch (cf. McClelland et al., 1953). According to McClelland andcolleagues need for achievement is the result of an emotional conflictbetween the hope to approach success and the desire to avoid failure.Hope for success, on the one hand, is associated with positiveemotions and the belief to succeed. Fear of failure, on the other hand,is related to negative emotions and the fear that the achievementsituation is out of one's depth. The balance of these two motives isthought to determine the direction, intensity, and quality of achieve-ment-related behavior. Need for achievement according to McClel-land's is measured by describing affective experiences or associationslike fear or joy in achievement situations. Like Murray's need forachievement conception, hope for success and fear of failure are alsothought to possess omnibus importance in achievement-relatedbehaviors and are thus operationalized in a domain-general manner,i.e., referring to general achievement situations such as problemsolving. McClelland's andMurray's conceptions differ with regard to atleast two points. First, McClelland's conception of need for achieve-ment acknowledges that achievement situations are not onlycharacterized by the need or the hope to achieve (as in Murray'sconception) but also by the possibility to fail. Second, in contrast toMurray's conception of need for achievement McClelland stronglyunderlines the affective components of achievement situations.Therefore, need for achievement according to McClelland is assessedwith different instruments. A frequently used instrument in achieve-ment motivation research is the Achievement Motives Scale byGjesme and Nygard (1970).
Due to the fact that school largely consists of achievementsituations, need for achievement, both in the sense of McClellandand Murray, is triggered quite often. Since accomplishments in schoolcan only be achieved by learning or fulfilling the demands of school,students high on need for achievement or hope for success aresupposed to work hard to achieve. Correlations of achievementmotives assessed via self-report, both in the sense of McClelland andMurray, and academic success are mostly of weak to mediummagnitude in high school and college student samples (e.g., Paunonen& Ashton, 2001; Spangler, 1992).
1.1.2. Expectancy-value theoryIn the expectancy-value model of Eccles and her colleagues (Eccles
et al., 1983; Wigfield & Eccles, 2000) achievement-related behavior isexplained by expectancies for future success and the values ascribedto a task. Unlike need for achievement, both expectancies and valuesare conceptualized in a domain-specific manner, i.e., focusing onspecific tasks or subjects. This is in line with empirical researchdemonstrating that ability self-concepts and values have been shownto be domain-specific from the early school years on (e.g., Gottfried,1985; Wigfield et al., 1997).
According to the Eccles model, expectations of future success arelargely determined by ability self-perceptions (synonyms are abilitybeliefs or ability self-concepts). The construct is measured by askingstudents how good they think they are at certain tasks or subjects.Academic ability self-concepts and academic achievement are usuallymoderately to highly correlated within domains (e.g., Guay, Marsh, &Boivin, 2003; Marsh, Parker, & Smith, 1983; Skaalvik & Valas, 1999).The importance of ability self-perceptions is further emphasized by
the body of research on causal ordering of ability self-concepts andschool achievement (e.g., Guay et al., 2003; Marsh & Yeung, 1997). Itwas shown that not only prior achievement influences ability self-perception but that prior ability beliefs influence subsequentachievement.
With reference to values, the model of Eccles and her colleaguesfocuses on intrinsic, importance, and utility values as the majorreasons for achievement behavior (Eccles & Wigfield, 1995). Taskvalues aremost commonlymeasured bymeans of asking an individualto rate how important, useful, or interesting a task is to them(cf. Jacobs, Lanza, Osgood, Eccles, &Wigfield, 2002). Interest is the bestinvestigated of the three values mentioned. Domain-specific correla-tions between interest and values, respectively, and school achieve-ment are weak to moderate (e.g., Gottfried, 1985, 1990; Lloyd &Barenblatt, 1984; Steinmayr & Spinath, 2007). This is in line withpredictions derived from expectancy-value theory (Eccles et al., 1983;Wigfield & Eccles, 2000) according to which values should be betterpredictors of choices in achievement contexts whereas ability self-concepts should bemore strongly correlated with actual performance.
1.1.3. Goal theoryIn the early literature on achievement goals, goals are separated
into learning and performance goals (e.g., Dweck, 1986; Nicholls,1984). Within this dichotomous framework, learning goals focus onthe development of competence while performance goals center thedemonstration of competence. Later on, Elliot and others furtherdistinguished between performance-approach (striving to demon-strate competence) and performance-avoidance goals (striving not todemonstrate incompetence) (Elliot & Church, 1997). Furthermore,some researchers include one component in their considerationswhich represents the opposite of high achievement motivation,namely the tendency of work-avoidance (e.g., Nicholls, 1984; Harack-iewicz, Barron, Carter, Lehto, & Elliot, 1997). Work-avoidance refers tothe goal to invest as little effort as possible.
Goals can be considered as situation-specific states or as cross-situationally consistent and stable-over-time traits. For example,Nicholls considered goal orientations as depending on differentnotions of success (Nicholls, Patashnick, & Nolen, 1985) and Dweckreasoned that goal orientations rely on implicit theories aboutintelligence (Dweck & Leggett, 1988). Both theoretical conceptionsimply that domain-general goal orientations influence achievement-related behavior in different situations, domains, or at different tasks inthe sameway. Thus, goal orientations are oftenmeasured in a domain-general way, although situationally specific measures might addvaluable information for specific situations. Experimental studiesshowed that, on average, an orientation towards learning goals leads toa better performance than an orientation towards performance goals(cf. themeta-analysis byUtman,1997). Furthermore, learning goals aretypically associated with higher achievement in real-life settings (e.g.,Greene & Miller, 1996; Meece & Holt, 1993). Performance-approachgoals are also positively related to achievement (e.g., Elliot &McGregor,1999; Lopez, 1999; Urdan, 2004), whereas performance-avoidancegoals are mostly negatively correlated with performance (Elliot &Church, 1997; Elliot, McGregor, & Gable, 1999; Zusho, Pintrich, &Cortina, 2005). Work-avoidance is consistently shown to be negativelycorrelated with achievement (e.g., Dupeyrat & Marine, 2005; Spinath,Stiensmeier-Pelster, Schöne, & Dickhäuser, 2002).
Summarizing the depicted motivational concepts and theirrelation to performance, the following observations are of specialimportance. First, all depicted motivational constructs are related toschool performance. Second, domain-specifically assessed motiva-tional constructs are typically stronger related to school achievementthan domain-generally assessed constructs. The former are moder-ately to highly associated with school performance whereas the latterare weakly to moderately correlated with academic achievement.Third, even though the utility of motivation for the prediction of
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achievement-related criteria has frequently been demonstrated,astonishingly few studies have investigated the importance ofmotivation above and beyond intelligence. This is in so far ofsignificance as some authors question the importance of motivationas a determinant of scholastic achievement (e.g., Gagné & St. Père,2002). Other authors regard intelligence as the predominant deter-minant of scholastic achievement (e.g., Kuncel et al., 2004), leaving noor little room for variance explanation by other constructs such asmotivation. A common way to prove the utility of a construct is theproof of its incremental validity (cf. LeBreton et al., 2007). It istherefore surprising to note, especially in school contexts, that onlyfew studies have examined whether motivation can predict academicachievement independently of intelligence and which motivationalconstructs show the highest increment in this regard.
1.2. The incremental power of motivational concepts predicting academicachievement
Intelligence is the most valid psychological predictor of academicachievement (e.g., Kuncel et al., 2004). The average correlation ofintelligence with school achievement is r= .50 (cf. Gustafsson &Undheim, 1996). The strength of the association depends, amongother factors, on the school performance criterion considered. Whenusing standardized achievement tests as achievement criterion,correlations are very high with coefficients ranging from .61 to .90(e.g., Deary, Strand, Smith, & Fernandes, 2007; Frey&Detterman, 2004;Rindermann, 2006). This high correlation can partly be attributed tomethodological variance shared by school achievement and intelli-gence tests. In contrast to standardized scholastic achievement tests,grades given by teachers are composites made up of different types ofinformation. Apart from just being a measure of knowledge they arealso a reflection of students' social behavior in the classroom, theirmotivation, and other aspects. Therefore, intelligence test scorescorrelate less high with grades. Concerning single grades, the highestcorrelation is usually the one between intelligence and mathematics(about r= .40; cf. Amthauer, Brocke, Liepmann, & Beauducel, 2001).Compared to general intelligence, domain-specific intelligence isusually more highly correlated with school performance in thecorresponding domains (e.g., Amthauer et al., 2001), although this isnot always the case (e.g., Spinath, Spinath, Harlaar, & Plomin, 2006).
For some motivational constructs it has been shown that, aboveand beyond intelligence, motivation explains variance in academicachievement (e.g., Gose, Wooden, & Muller, 1980; Schicke & Fagan,1994; Spinath et al., 2006). Addressing academic self-concept, Schickeand Fagan (1994) as well as Gose et al. (1980) demonstrated additionalvariance explanation independently of intelligence in samples ofelementary and middle-school students. Investigating intrinsic moti-vation, Gottfried (1990) as well as Lloyd and Barenblatt (1984) foundan incremental validity beyond intelligence for elementary and highschool students, respectively. In contrast to these findings, in a sampleof female high school students, a measure of intrinsic motivation didnot predict scholastic achievement over intelligence (Gagné & St. Père,2002). The reasons for these conflicting results are not quite clear.Possible explanations might be a restricted range of intrinsicmotivation in the sample from an all-girl high school, psychometricproblems of the measurement instrument (the authors mention a lackof construct validity) or the use of a domain-unspecific measure ofintrinsic motivation in the study by Gagné and St. Père (2002).
Taken together, most of the reported studies are in favor of anincremental validity of motivation concerning the prediction ofacademic success. The same is true for a recent study by Spinathet al. (2006). The authors simultaneously investigated intrinsicmotivation and academic self-concept. When intelligence and bothmotivational constructs were entered in a regression analysis withdomain-specific academic achievement as the dependent variable,only ability self-concept showed incremental validity predicting
performance in English and math. Entered separately, both motiva-tional constructs showed an increment.
The meta-analysis by Robbins et al. (2004) gives further evidenceon the incremental power of different motivational constructs.Although the authors did not include any measures of intelligenceas predictors, it should be noted that the included cognitive measure,the SAT score, is highly correlated with intelligence (e.g., Frey &Detterman, 2004). Thus, the meta-analysis gives valuable evidence onthe importance of motivational measures above a cognitive measure.Achievement motivation, academic goals, and academic self-efficacywere investigated as motivational concepts. All of the motivationalconcepts were positively related to academic success, with academicself-efficacy showing the highest correlation and academic goals thelowest. The corrected correlation between academic self-efficacy andcollege GPA was even higher than the corrected correlation betweenthe cognitive predictors and the criterion.
Both studies, Robbins et al. (2004) and Spinath et al. (2006),provide valuable evidence on which motivational construct yields thehighest increment over intelligence or a related cognitive measure.Nevertheless, these studies also have some limitations. Even thoughRobbins et al. (2004) used the ACT they did not include a genuinemeasure of g when comparing the incremental validity of theinvestigated motivational constructs. Moreover, academic goals werenot further differentiated in learning, performance-approach, perfor-mance-avoidance, and work-avoidance goals. Considering thedescription of academic goals presented in Robbins et al., academicgoals are conceptionally close to learning goals. The questionwhetherthe other above mentioned goal orientations incrementally contributeto academic achievement remains unsettled. Other important motiva-tional concepts, such as values, were also not considered in the meta-analysis. Furthermore, the meta-analysis only concentrated on studiesin secondary education. Spinath et al. (2006) addressed the relativeimportance of ability self-concept and intrinsic motivation. Goalorientations and achievement motives were not investigated. Further-more, Spinath et al. (2006) examined elementary-school childrenaged nine. These results need to be replicated in different age groups.
Moreover, none of the aforementioned studies controlled for priorschool performance. If it was shown that, after controlling for priorachievement, motivation still predicted subsequent achievement overand beyond intelligence, this would be especially strong evidence forthe importance of motivation (Marsh, Byrne, & Yeung, 1999). Marsh,Trautwein, Lüdtke, Köller, and Baumert (2005) conducted a long-itudinal study with twomeasurement occasions considering grades inmath, a mathematical scholastic aptitude test, domain-specificintrinsic motivation, and ability self-concept. When testing allconstructs simultaneously in a structural equation model, abilityself-concept and performance in the math test still proved to beincrementally valid when controlling for prior grades inmathwhereasintrinsic motivation did not. These results should be replicated using ameasure of intelligence and considering domains beside math.
1.3. Hypotheses
The present study was designed to simultaneously explore therelative importance of some of the most prominent motivationalachievement constructs (achievement motives, goal orientations,ability self-concept, and values) in comparison to intelligence whenpredicting scholastic achievement. Motivational constructs differaccording to their theoretical backgrounds in whether they aredomain-general or domain-specific. Domain-specific motivationalconcepts as well as domain-general ones were tested againstdomain-specific intelligence measures and against each other whenpredicting domain-specific school performance (grades in math andGerman). Furthermore, domain-general motivational concepts weretested against general intelligence when predicting general schoolperformance (GPA). Moreover, we tested the incremental validity of
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intelligence and different motivational constructs when controllingfor prior school performance. Based on the findings reported above,the following hypotheses were derived:
(1) Achievement motives, goal orientations, ability self-concepts,and values incrementally contribute to the prediction of schoolperformance in math, German, and general school performancebeyond intelligence.
(2) Whenpredicting domain-specific school performance, domain-specifically operationalized motivational concepts show ahigher increment than domain-general ones with ability self-concepts showing the highest increment.
(3) When predicting general school performance, domain-generalmotivational constructs show a higher increment than whenpredicting domain-specific school performance.
(4) When controlling for prior school performance, motivationalconstructs and intelligence will still explain additional variancein math, German, and in general school performance.
2. Method
2.1. Sample and procedure
The sample was recruited from a German school preparingchildren for university (Gymnasium).1 The school was located in amid-sized town and its pupils can be considered as the typicalpopulation of this type of school in Germany (i.e., the majority beingCaucasian from medium to high socio-economic status homes). Inthree consecutive cohorts, 342 11th and 12th grade students weretested, 204 female and 138 male, with a mean age of 16.94 years(SD= .71; Range 16–19). Thus, the sample consisted of three fullstudent cohorts, and only students excused by amedical certificate didnot take part in the testing.
Testing took place on a day especially reserved for extra-curriculaactivities between September 2005 and 2006. Tests were adminis-tered at school. Students were separated into groups of about 20 andtested by trained students and research assistants. The test sessionslasted approximately 5 h, including breaks. Below, only those scalesrelevant for the present article are described.
2.2. Measures
First, we present the intelligence measure, then the general (needfor achievement and goal orientations) and the specific motivationalmeasures (ability self-concept and values) and last school perfor-mance as the performance criterion.
2.2.1. IntelligenceIntelligence was measured with the Intelligence Structure Test
2000 R, a well-established German multifactor intelligence measure(IST; Amthauer et al., 2001). The test offers domain-specific intelli-gence assessment for the verbal, numeric, and figural abilities as wellas an overall intelligence score (a composite of the three facets). Theoverall intelligence score is thought to measure reasoning as a higherorder factor of intelligence and can be interpreted as a measure of g.Construct validity has been demonstrated in several studies(Amthauer et al., 2001; Bühner, Ziegler, Krumm, & Schmidt-Atzert,2006; Steinmayr & Amelang, 2006). The test is standardized with amean score of 100 and a standard deviation of 10.
2.2.2. Achievement motivesAchievement motives were assessed using two different instru-
ments. The Achievement Motives Scale (AMS by Gjesme and Nygard;
1 We like to thank Brigitte Posselt, Peter Klein, and Harald Willert for their greatsupport and engagement.
Göttert & Kuhl, 1980) is a widely used instrument in Germanmotivation research and refers to the need for achievement concep-tion of McClelland and colleagues (1953) concentrating on affectiveexperiences or associations in achievement situations divided into“hope for success” and “fear of failure”. The two subscales “hope forsuccess” and “fear of failure” are measured by 14 items each. In thefollowing, we use these subscale titles when referring to resultsattained by means of the AMS scale. For the present study, we used ashort form measuring each construct with those seven items showingthe highest factor loadings. All items were answered on a 4-pointLikert scale ranging from „does not apply at all“ to „fully applies”.Example items for the two scales were “Difficult problems appeal tome.” and “Matters, that are slightly difficult, disconcert me.”
As a second established measure of achievement motives weemployed the need for achievement scale of the Personality ResearchForm referring to Murray's view (1938) on need for achievement (PRFby Jackson, 1974; Stumpf, Angleitner, Wieck, Jackson, & Beloch-Till,1985). We use the term need for achievement (PRF) when referring tothis scale in the following. The PRF need for achievement scaleassesses a different aspect of need for achievement than the AMS scaleand has a stronger focus on thoughts and behaviors associated withhigh need for achievement than on emotions. The PRF scale consists of16 items measuring an individual's tendency to be ambitious,determined, striving etc... Unlike the dichotomous answering formatused in the original PRF we used a 4-point Likert Scale ranging from“strongly disagree (1)” to “strongly agree (4)”. Example items are “Ioften aim high” and “I don't bother working while others have fun”.
2.2.3. GoalsStudents' goal orientations were assessed by means of a German
self-report measure (“Skalen zur Erfassung der Lern-und Leistungs-motivation”, SELLMO; Spinath et al., 2002). SELLMO contains foursubscales measuring learning goals (e.g., “In school it is important forme to learn as much as possible.”), performance-approach (e.g., “Inschool it is important to me that others think I am smart.”), andperformance-avoidance goals (e.g., “In school it is important to me notto give wrong answers to questions of the teacher.”) as well as work-avoidance (e.g., “In school it is important to me to do as little work aspossible.”). All scales beside the performance-approach scale (sevenitems) consist of eight items and were answered on a 5-point Likertscale ranging from “totally disagree (1)” to “totally agree (5)”.
2.2.4. Ability self-perceptionDomain-specific ability self-perceptions were assessed by four
items per domain. Students were asked to indicate on a 5-point Likertscale how good they thought they were at different activities in mathor German (“In math (German) I know little/a lot.”). Construct validityof the measures was already demonstrated (cf. Schöne, Dickhäuser,Spinath, & Stiensmeier-Pelster, 2002; Steinmayr & Spinath, 2007).
2.2.5. ValuesSubjective task values were assessed by means of three items per
domain representing the three value components of the Eccles model(e.g., Wigfield & Eccles, 2000), i.e. intrinsic value, importance, andutility. Students were asked to indicate the values ascribed to mathand German on a 5-point Likert scale. The items were the following:“Howmuch do you like doing math/German?”, “For me, being good inmath/German is not at all/very important.”, “In general, how useful iswhat you learn in math/German?”. Construct validity of the measureshas already been demonstrated (cf. Steinmayr & Spinath, 2007).
2.2.6. School performanceSchool achievement was operationalized by means of the German
and math grades for domain-specific and grade point average forgeneral school performance. Grades range from 1 to 6 with 1indicating an outstanding and 6 an insufficient performance. For the
Table 1Means (M), standard deviations (SD), internal consistencies (α), and intercorrelations among all predictors
Descriptives Intercorrelations
M SD a NI G MASP GASP MV GV HoS FoF PRF LG PAG PAV WAG
IntelligenceVerbal 103.7 9.63 .73 .29 .64 .11 .01 .06 .06 .11 − .15 .00 .04 − .02 .05 .17Numeric (NI) 103.2 9.23 .90 .82 .54 − .21 .38 − .15 .22 − .18 .09 − .05 .05 .04 .13General (G) 103.6 9.57 .90 .48 − .17 .33 − .19 .22 − .23 .06 − .02 .01 .02 .16
Ability self-perceptionMath (MASP) 3.12 1.02 .95 − .43 .81 − .25 .31 − .30 .21 .02 − .03 .00 .01German (GASP) 3.70 .75 .90 − .37 .55 .05 .01 .11 .15 .16 − .03 − .13
ValuesMath (MV) 3.22 .97 .79 − .12 .34 − .23 .34 .20 .07 .04 − .09German (GV) 3.73 .78 .75 .08 .02 .18 .29 .10 − .01 − .25
Achievement motivesHope for Success (HoS) 2.62 .58 .85 − .34 .54 .38 .17 − .10 − .26Fear of Failure (FoF) 1.70 .60 .88 − .23 − .09 .05 .37 .21Need for Achievement (PRF) 2.67 .34 .78 .39 .22 − .07 − .43
GoalsLearning (LG) 50.99 8.56 .78 .30 − .05 − .35Performance-approach (PAG) 49.13 9.12 .81 .55 .14Performance-avoidance (PAV) 49.43 9.88 .88 .43Work avoidance (WAG) 48.78 9.19 .88
Notes: N=342; The intelligence test was standardized with M=100 and SD=10 and the goal orientation scales withM=50 and SD=10. The achievement motives scales range from 1to 4, and all other scales range from 1 to 5 with 1 indicating lower ability perceptions and values. Correlations≥ |.11|, pb .05; correlations≥ |.14|, pb .01.
2 Here and in the following, we use the word “prediction” or “predict” withoutsuggesting any kind of causal relations.
3 We like to thank Jeff W. Johnson, Personnel Decisions Research Institutes, Inc.,Minneapolis, USA, for providing us with the necessary SPSS Syntax for calculatingrelative weights.
4 We also performed multiple regression analyses using structural equationmodelling (SEM). The results concerning the incremental validity of the differentmotivational constructs beyond the particular intelligence measure marginally differedfrom the ones presented here. The determination of relative weights based on SEM is,to our knowledge, not yet possible.
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analyses reported here, grades were recoded so that higher numbersrepresent better performance. For prior school performance, studentswere asked to bring a copy of their last report card (made anonymous).For subsequent school performance, the school delivered the reportcards (made anonymous) at the end of the term for all students. All but14 report cards could be assigned to the students (14 students forgotor refused to report their names, thus study code and name could notbe matched).
3. Results
3.1. Descriptives and intercorrelations
Means (M), standard deviations (SD), and internal consistencies (α)of all measures are presented in Table 1. Internal consistencies for thecognitive ability test and the self-report measures aremostly good andat least satisfactory.
With regard to our hypotheses, it is important to check forindications of range restrictions in the central variables because suchrestrictions would influence their predictive power. The meanintelligence score of the sample was about four standard pointshigher compared to the reference values of a norm sample (a total ofN=882 students of the same age and education). Nevertheless, thestandard deviation of our sample was comparable to the one expectedfor a full student population sample. Concerning the motivationscales, reference values were only available for the SELLMO. Means ofall four scales were comparable to the ones of the reference group (atotal of N=1806 students of the same age; no different normvalues forschool type). A slight restriction of range applied to all goal orientationscales, but only the variance of the learning goal scale differedsignificantly from the one of the norm sample (F(1804;340)=1.36 pb .01).Taken together, we assume that there were no range restrictions forintelligence or motivation in the present sample.
In order to check for multicollinearity, correlations betweenintelligence and motivational measures were inspected. The magni-tude of these correlations ranged from weakly negative (r=− .23) tohighly positive (r=.54). The strongest associations between intelli-gence and motivation was found for the two domain-specific
constructs (ability self-perceptions and values), whereas goals yieldedthe weakest associations with intelligence.
3.2. Regression analyses
We separately predicted2 school performance in two specificdomains (math and German) and for school in general (results cf.Tables 2, 3, and 4). First, hierarchical regressions were performed withdomain-specific and general school achievement, respectively, asdependent variables. In each regression analysis, the focusedintelligence measure (numeric, verbal, and general intelligence,respectively) was entered in the first step and the specific motivationscale in the second. We also determined relative weights of allpredictors when predicting the three different performance criteria byall variables simultaneously (cf. LeBreton et al., 2007). This procedureallows the determination of the relative importance of each predictorin a multiple regression analysis in the presence of multicollinearity.When dividing the specific relative weight of a variable by the totalvariance explanation (R2) of all predictors, this rescaled relativeweight describes the percentage of predicted criterion varianceuniquely attributed to this variable.3
Models a and b in Tables 2 and 3 present the results of thehierarchical regression analyses for the domain-specific motivationalconstructs ability self-perception and values whereas models c to iincluded the general motivational constructs (for Table 4 the latterapplies to models a to g). The results for achievement in math as thedependent variable are shown in Table 2, for German in Table 3, andfor general school performance in Table 4.4
Table 2Hierarchical regression of school performance in math on measured numeric intelligence and different motivational constructs
Beta t p R R2 ΔR2 ΔF(df) Δp
Model 1 Numeric intelligence .33 6.41 .00 .33 .11 .11 41.14 (01, 327) .00Model 2a Numeric intelligence .00 .03 .98
Ability self-perception math .64 12.66 .00 .64 .40 .30 160.22 (1, 326) .00Model 2b Numeric intelligence .14 2.85 .01
Values math .54 11.48 .00 .61 .37 .26 131.81 (1, 326) .00Model 2c Numeric intelligence .31 5.75 .00
Hope for success .13 2.37 .02 .35 .12 .02 5.61 (1, 326) .02Model 2d Numeric Intelligence .31 5.80 .00
Fear of Failure − .14 −2.58 .01 .36 .13 .02 6.65 (1, 326) .01Model 2e Numeric intelligence .31 6.18 .00
Need for achievement (PRF) .23 4.57 .00 .41 .17 .05 20.87 (1, 326) .00Model 2f Numeric Intelligence .34 6.69 .00
Learning goals .17 3.37 .00 .38 .14 .03 11.33 (1, 326) .00Model 2g Numeric intelligence .34 6.45 .00
Performance-approach goals − .04 − .75 .45Model 2h Numeric intelligence .34 6.45 .00
Performance-avoidance goals − .06 −1.06 .29Model 2i Numeric intelligence .35 6.58 .00
Work-avoidance goals − .08 −1.58 .12
Notes: N=328 (N was reduced to 328 since 14 report cards could not be assigned to the students). Variables printed in italics were not included in the prediction.
85R. Steinmayr, B. Spinath / Learning and Individual Differences 19 (2009) 80–90
In line with our first hypothesis, most of the motivational variablescontributed to the prediction of school achievement over and aboveintelligence. In some cases motivation even outperformed intelligenceas a predictor. Contrary to our expectations, performance goals did notcontribute incrementally to the prediction in any of the threedomains. All variables considered simultaneously when calculatingthe relative weights explained 46%, 21%, and 28% of the total variancein math, German, and GPA (Table 5). Ability self-concepts in math andGerman had higher shares of uniquely explained variance inachievement than intelligence. Numeric intelligence even becameinsignificant as a predictor when self-perceptions were entered in thehierarchical regression analysis (cf. Table 2).
Table 3Hierarchical regression of school performance in German on measured intelligence anddifferent motivational constructs
Beta t p R R2 ΔR2 ΔF(df) Δp
Model 1 Verbal intelligence .23 4.41 .00 .23 .05 .05 18.24(1, 327)
.00
Model 2a Verbal intelligence .23 4.41 .00Ability self-perceptionGerman
.30 5.76 .00 .37 .14 .09 33.17(1, 326)
.00
Model 2b Verbal intelligence .25 4.78 .00Values German .28 5.49 .00 .36 .13 .08 30.11
(1, 326).00
Model 2c Verbal intelligence .21 3.92 .00Hope for success .13 2.40 .02 .26 .07 .02 5.77
(1, 326).02
Model 2d Verbal intelligence .23 4.21 .00Fear of failure .00 .02 .99
Model 2e Verbal intelligence .23 4.38 .00Need for achievement(PRF)
.22 4.17 .00 .32 .10 .05 17.38(1, 326)
.00
Model 2f Verbal intelligence .21 4.08 .00Learning goals .23 4.38 .00 .32 .11 .05 19.19
(1, 326).00
Model 2g Verbal intelligence .23 4.28 .00Performance-approachgoals
.07 1.21 .23
Model 2h Verbal intelligence .23 4.33 .00Performance-avoidancegoals
− .07 −1.22 .23
Model 2i Verbal intelligence .25 4.65 .00Work-avoidance goals − .13 −2.45 .02 .26 .07 .02 5.98
(1, 326).02
Notes: N=328. Variables printed in italics were not included in the prediction.
In line with our second hypothesis, when predicting domain-specific achievement, the domain-specific motivational measuresshowed higher increments than domain-general ones with abilityself-concepts being the most important predictors (see Tables 2and 3). Relative weights analyses yielded markedly larger shares ofuniquely explained variance for domain-specific ability self-percep-tions (math=43.7%, German=26.0%) and values (math=32.3%, Ger-man=16.5%) than for any domain-general motivational construct(largest for learning goals and German achievement =13.2%).Whether this difference is significant cannot be decided becauseone limitation of relative weight analysis is that it does not allowtesting for significant differences between coefficients (cf. LeBretonet al., 2007).
In line with our third hypothesis, when predicting general schoolachievement, most domain-general motivational constructs showed ahigher increment above intelligence than when predicting domain-
Table 4Hierarchical regression of general school achievement on measured generalintelligence and domain-general motivational constructs
Beta t p R R2 ΔR2 ΔF(df) Δp
Model 1 General intelligence .35 6.65 .00 .35 .12 .12 44.21(1, 327)
.00
Model 2a General intelligence .29 5.58 .00Hope for success .25 4.82 .00 .42 .18 .06 23.27
(1, 326).00
Model 2b General intelligence .32 5.90 .00Fear of failure −.12 2.27 .02 .37 .13 .01 5.17
(1, 326).02
Model 2c General intelligence .33 6.74 .00Need for achievement(PRF)
.36 7.33 .00 .50 .25 .13 53.71(1, 326)
.00
Model 2d General intelligence .35 7.07 .00Learning goals .28 5.53 .00 .44 .20 .08 30.62
(1, 326).00
Model 2e General intelligence .35 6.63 .00Performance-approachgoals
.04 .71 .48
Model 2f General intelligence .35 6.71 .00Performance-avoidancegoals
− .10 −1.87 .06
Model 2g General intelligence .37 7.16 .00Work-avoidance goals −.16 3.15 .00 .38 .15 .03 9.94
(1, 326).00
Notes: N=328. Variables printed in italics were not included in the prediction.
Table 5Relative weights (RW) and percentage of explained criterion variance (%) for allpredictors with and without including prior school achievement
Without prior schoolachievement
With prior school achievement
Predictor RW % RW %
M G GPA M G GPA M G GPA M G GPA
Prior Schoolachievement
.26 .17 .51 44.6 50.7 73.8
Specificintelligence
.04 .05 .10 9.1 24.6 35.1 .03 .04 .06 5.1 11.9 7.9
Hope forsuccess
.01 .01 .03 1.7 3.7 11.4 .01 .01 .03 1.3 2.0 3.7
Fear of failure .01 .00 .01 2.3 .5 4.3 .01 .02 .01 1.5 .3 1.7Need forachievement
.03 .02 .08 6.1 11.6 29.8 .02 .02 .05 3.6 5.5 7.6
Learning goals .02 .03 .04 3.3 13.2 14.6 .01 .00 .03 2.0 6.3 4.0Performance-approach goals
.00 .00 .00 .6 .9 1.0 .00 .00 .00 .3 .8 .2
Performance-avoidance goals
.00 .00 .00 .4 1.4 1.4 .00 .00 .00 .3 1.0 .5
Workavoidance
.00 .00 .01 .6 1.4 2.3 .00 .00 .01 .3 .8 .7
Abilityself-concept
.20 .05 43.7 26.0 .13 .04 22.4 12.5
Values .15 .03 32.3 16.5 .11 .03 18.6 8.1
Notes. M=Math; G=German; GPA=General School Achievement.
86 R. Steinmayr, B. Spinath / Learning and Individual Differences 19 (2009) 80–90
specific achievement (see Table 4). This was true for hope of success,need for achievement (PRF), learning goals, and work-avoidance goalsbut not for fear of failure, performance-approach goals, and
Table 6Hierarchical regression of school performance in math on prior math achievement,measured numeric intelligence and different motivational constructs
Beta t p R R2 ΔR2 ΔF(df) Δp
Model 1 Prior math achievement .72 18.64 .00 .72 .52 .52 347.51(1, 327)
.00
Model 2 Prior math achievement .70 16.55 .00Numeric intelligence .06 1.48 .14
Model 3a Prior math achievement .53 10.56 .00Numeric intelligence .03 .61 .54Ability self-perceptionmath
.28 5.48 .00 .75 .56 .04 27.94(1, 325)
.00
Model 3b Prior math achievement .57 12.17 .00Numeric intelligence .02 .42 .68Values math .26 5.56 .00 .75 .57 .05 30.91
(1, 325).00
Model 3c Prior math Achievement .70 16.18 .00Numeric intelligence .06 1.50 .13Hope for Success − .01 − .32 .75
Model 3d Prior math achievement .69 16.23 .00Numeric intelligence .05 1.26 .21Fear of failure − .06 −1.37 .17
Model 3e Prior math achievement .68 15.72 .00Numeric intelligence .06 1.50 .14Need for achievement(PRF)
.09 2.14 .03 .73 .53 .01 4.60(1, 325)
.03
Model 3f Prior math achievement .68 16.25 .00Numeric intelligence .07 1.70 .10Learning goals .09 2.43 .02 .73 .53 .01 5.90
(1, 325).02
Model 3g Prior math achievement .70 16.48 .00Numeric intelligence .06 1.45 .15Performance-approachgoals
.01 .19 .85
Model 3h Prior math achievement .70 16.50 .00Numeric intelligence .06 1.52 .15Performance-avoidanceGoals
− .04 − .92 .36
Model 3i Prior math achievement .70 16.45 .00Numeric intelligence .06 1.51 .13Work-avoidance goals − .02 − .38 .70
Notes: N=328. Variables printed in italics were not included in the prediction.
performance-avoidance goals. Relative weight analysis showed thatgeneral intelligence proved to be the most important predictor ofgeneral school performance (35.1%), followed by need for achieve-ment (PRF) (29.8%) as well as learning goals (14.6%), and hope forsuccess (11.4%).
According to our fourth hypothesis, we tested whether motiva-tion and intelligence still predicted subsequent school performanceafter controlling for prior school performance. Again, we performedhierarchical regression analyses. First, the specific prior schoolperformance was entered in the analyses, then the particularintelligence measure and last the different motivational constructs.Tables 6–8 present the results of the hierarchical regressionanalysis.
In all analyses, prior school performance explained most variancein subsequent school achievement. After controlling for prior schoolachievement, intelligence contributed to the prediction of Germanachievement and general school achievement but not in math. Needfor achievement (PRF) and learning goals proved to be incrementallyvalid for all three achievement criteria when controlling for priorperformance and specific intelligence. Ability self-perceptions andvalues also explained additional criterion variance in the domain-specific criteria. According to the relative weights analyses, priorschool performance explained by far the largest share of variance insubsequent school performance (44.6%–73.8%), followed by intelli-gence (5.1%–11.9%), need for achievement (PRF) (3.6%–7.6%) andlearning goals (2.0%–6.3%) (cf. Table 5). Thus, the incrementalimportance of the different predictors was comparable to the one
Table 7Hierarchical regression of school performance in German on prior Germanachievement, measured verbal intelligence and different motivational constructs
Beta t p R R2 ΔR2 ΔF(df) Δp
Model 1 Prior German achievement .49 10.17 .00 .49 .24 .24 103.55(1, 327)
.00
Model 2 Prior German achievement .47 9.73 .00Verbal intelligence .17 3.61 .00 .52 .27 .03 13.00
(1, 326).00
Model 3a Prior German achievement .41 8.51 .00Verbal intelligence .18 3.83 .00Ability self-perceptionGerman
.21 4.29 .00 .56 .31 .04 18.39(1, 325)
.00
Model 3b Prior German achievement .42 8.67 .00Verbal intelligence .19 4.07 .00Values German .19 4.02 .00 .55 .31 .04 16.14
(1, 325).00
Model 3c Prior German achievement .45 9.39 .00Verbal intelligence .16 3.38 .00Hope for success .09 1.90 .06
Model 3d Prior German achievement .47 9.71 .00Verbal intelligence .17 3.58 .00Fear of failure .01 .14 .89
Model 3e Prior German achievement .44 8.90 .00Verbal intelligence .18 3.75 .00Need for achievement(PRF)
.13 2.77 .01 .54 .29 .02 7.70(1, 325)
.01
Model 3f Prior German achievement .43 8.78 .00Verbal intelligence .17 3.55 .00Learning goals .13 2.71 .01 .54 .29 .02 7.35
(1, 325).01
Model 3g Prior German achievement .47 9.60 .00Verbal intelligence .17 3.58 .00Performance-approachgoals
− .02 − .44 .66
Model 3h Prior German achievement .47 9.82 .00Verbal intelligence .18 3.70 .00Performance-avoidancegoals
− .09 −1.79 .08
Model 3i Prior German achievement .46 9.40 .00Verbal intelligence .19 3.83 .00Work-avoidance goals − .07 −1.52 .13
Notes: N=328. Variables printed in italics were not included in the prediction.
Table 8Hierarchical regression of general school performance on prior general schoolachievement (GPA), measured general intelligence and domain-general motivationalconstructs
Beta t p R R2 ΔR2 ΔF(df) Δp
Model 1 Prior GPA .82 25.55 .00 .82 .67 .67 652.99(1, 327)
.00
Model 2 Prior GSA .79 23.33 .00General intelligence .08 2.42 .02 .82 .68 .006 5.87
(1, 326).02
Model 3a Prior GSA .78 22.03 .00General intelligence .08 2.26 .03Hope for success .04 1.06 .29
Model 3b Prior GSA .79 23.15 .00General intelligence .07 2.03 .04Fear of failure − .05 1.66 .11
Model 3c Prior GSA .76 20.84 .00General intelligence .09 2.59 .01Need for achievement(PRF)
.07 2.00 .05 .83 .68 .004 4.00(1, 325)
.05
Model 3d Prior GSA .77 21.87 .00General intelligence .09 2.65 .01Learning goals .07 2.01 .05 .83 .68 .004 4.02
(1, 325).05
Model 3e Prior GSA .79 23.31 .00General intelligence .08 2.42 .02Performance-approachgoals
− .02 − .72 .47
Model 3f Prior GSA .79 23.16 .00General intelligence .08 2.48 .01Performance-avoidancegoals
−.04 −1.35 .18
Model 3h Prior GSA .79 22.82 .00General intelligence .08 2.28 .02Work-avoidance goals .01 .42 .68
Notes: N=328. Variables printed in italics were not included in the prediction.
87R. Steinmayr, B. Spinath / Learning and Individual Differences 19 (2009) 80–90
which calculated relative weights without considering prior schoolperformance.
4. Discussion
The results of the present study emphasize the general importanceof motivation in school contexts. Furthermore, they give evidence onwhich motivational construct to choose when predicting scholasticperformance. In the following, we discuss the implications of theresults along the hypotheses and look at some limitations of our study.
4.1. Incremental contribution of motivation to the prediction of schoolachievement
In line with our hypotheses, most of the motivational constructscontributed to the prediction of school success beyond intelligence.Only performance goals did not add to the variance explanation.Relative weight analyses showed that domain-specifically assessedability self-concepts and values explained most predicted domain-specific achievement variance. In both domains, ability self-conceptsexplained even more unique variance than intelligence. One mightargue that this result is a consequence of ability self-concepts beingmere reflections of domain-specific school achievement as putforward in the skill-development theory (cf. Marsh et al., 1999).However, this argument can be refuted by the fact that the twodomain-specific ability self-concepts still predicted grades whencontrolling for prior school performance as put forward in the self-enhancement approach and as already shown by other authors (e.g.,Marsh et al., 1999). Furthermore, motivational constructs nearlyexplained as much unique variance in general school performanceas intelligence. Consequently, we demonstrated that motivation is apredictor of school performance whose relative importance is at leastcomparable to intelligence irrespective of the considered domain.
In accordance with the expectancy-value model of Eccles et al.(1983), we assumed that ability self-perceptions would be morestrongly associated with academic achievement than values. Indeed,ability self-concepts explained more additional variance in perfor-mance measures than values in the present sample, even though thedifferences were rather small. Consequently, the present results byand large replicate the findings by Spinath et al. (2006). However, theeffects predicted on the basis of the expectancy-value model weremore pronounced in the elementary-school sample investigated bySpinath et al. than in the present adolescent sample. Whether theseresults reflect a developmental component in the determination ofschool performance through ability self-concepts and values shouldbe investigated longitudinally.
With regard to the specificity of motivational measures it wasshown that domain-specific motivational constructs explained morevariance in specific school performance than domain-generallyassessed motivational constructs. Domain-general motivational con-structs proved to be good predictors of general school performance.These results can be explained by the principle of correspondence byAjzen and Fishbein (1977) and Ajzen (1988). The principle ofcorrespondence states that the extent towhich predictor and criterionare related depends on the extent to which both variables correspondin action, target, time, and context.When predictor and criterion differin their degree of specificity, shared variance is diminished. Hence,when predicting general school performance, general motivationalconstructs are useful predictors with need for achievement (PRF),learning goals, and hope for success showing the highest increments.When predicting domain-specific school achievement, domain-specific constructs are the more useful predictors.
We further hypothesized that somemotivational constructs wouldpredict school performance independently from prior school perfor-mance and intelligence. Again, ability self-concepts and values as wellas learning goals and need for achievement (PRF) still contributed tothe prediction of school performance after controlling for priorachievement and intelligence. Once more, the relative importance ofthe motivational constructs was comparable to the one of intelligence.Our results are in line with studies investigating reciprocal effectsbetween academic achievement and ability self-concept that foundboth self-enhancement and skill-development effects (e.g., Guay et al.,2003; Skaalvik & Hagtvet, 1990). Even though we did not test for skill-development effects, our results showed that prior ability self-conceptinfluenced subsequent performance when controlling not only forprior performance but additionally controlling for intelligence.Consequently, ability self-concept predicts subsequent achievementnot only beyond prior achievement but also beyond prior achieve-ment and intelligence which strongly supports the self-enhancementeffect. Since the same was true for other motivational variables,teachers trying to realize the self-enhancement effect in the classroomshould not only concentrate on enhancing the ability self-concept inorder to improve achievement but should also concentrate on values,learning goals, and need for achievement. When intending to drawcausal conclusions about the influence of motivation on schoolperformance, the demonstration of such longitudinal effects is animportant first step. Our data are in line with the assumption thatmotivation has a causal influence on school performance that isindependent from prior achievement and intelligence. Nevertheless,to put such causality assumptions to the test, more elaboratelongitudinal or even experimental designs are needed.
For any longitudinal approach controlling for prior schoolachievement, the problem of the usually very high temporal stabilityof school achievement arises. This has direct implications for thesearch of potential causal determinants because the more stable aconstruct is the lower is the possibility of finding variables thatcontribute to its prediction beyond earlier parameter values. It followsthat, when prior scholastic achievement is controlled for, the extent towhichmotivation is able to contribute to the prediction is restricted by
88 R. Steinmayr, B. Spinath / Learning and Individual Differences 19 (2009) 80–90
the high stability of the criterion. Thus, the fact that, in the presentstudy, motivation still influenced succeeding school performanceunderlines its importance.
One final result we want to put attention to is that whenperformance in math was predicted and prior achievement wascontrolled, motivation but not intelligence contributed to theprediction. Since math performance is often thought to be highlycognitive in nature, the importance of motivation is most interesting.This result is especially important considering the potential malle-ability of motivation via educational processes. Compared to intelli-gence or more specific abilities, motivation may be more easilyinfluenced by situational factors, such as salient classroom goals(Midgley, Anderman, & Hicks, 1995). Thus, when teachers aim atimproving students' performance, enhancing their motivation mightbe as important as the conveyance of knowledge.
4.2. Interplay between variables
We found a moderate negative association between ability self-perceptions in math and German (r=− .43). Correlations betweenability self-perceptions in math and German mostly range aroundr= .06 (Marsh & Hau, 2004). The moderately negative correlationbetween math and German self-concepts in our study might beexplained with the age of the investigated sample. Skaalvik and Valas(1999) found that the correlations between domain-specific abilityself-concepts become more and more negative from grade three toeight. A possible explanation might be a more differentiated under-standing of one's abilities. This explanation is in line with the modeldepicted in Shavelson, Hubner, & Stanton (1976) that postulates moredifferentiated ability self-concepts with increasing age. A recent studyby Marsh, Trautwein, Lüdtke, Köller, & Baumert (2006) with morethan 4000 German pupils of approximately the same age as oursample also reports a negative correlation of r=− .29 between mathand German ability self-concepts.
Another interesting finding concerns the comparison of the twooperationalizations of need for achievement. Need for achievement(PRF) explained more variance in general school performance (29.8%)than hope for success (11.4%) and nearly explained as much variancein general school performance as general intelligence (35.1%). Need forachievement (PRF), assessed in the sense of Murray (1938), addressesa broad spectrum of concrete cognitions or behaviors related to highachievement such as resolving to achievemore than others or workingwhile others are out and having fun. In contrast, hope for success isoperationalized by assessing the positive valence attributed toproblem solving or other achievement-related situations. The strongfocus on affects attributed to achievement situations when assessingneed for achievement in the sense of McClelland might diminish theassociation between need for achievement and scholastic perfor-mance since the scales only measure a certain aspect of achievementsituations. According to the present study, need for achievementseems to be a better predictor of academic achievement whenoperationalized by a broader spectrum of achievement-related issuesthan by means of items mainly covering feelings associated withachievement situations.
4.2.1. Limitations and outlookAddressing the question of whether any motivational constructs
incrementally contribute to the prediction of school achievementbeyond intelligence, onemight argue that different results would havebeen obtained when using standardized tests of academic achieve-ment as a criterion rather than school grades. As reported above,intelligence tests and standardized academic achievement tests aremore highly related than intelligence tests and grades. When onepredictor is highly related to the criterion, the possibility for otherdeterminants to additionally contribute to its predictions is dimin-ished. This would probably have been the case for motivational
constructs competing with intelligence in the prediction of scholasticaptitude test performance. Nevertheless, two arguments must beconsidered that led to the choice of school grades as the criterion foracademic achievement. First, in most German schools standardizedacademic achievement tests are not employed at all at present.Therefore, student evaluations as well as selection or allocationdecisions solely rely on grades. Thus, even though standardizedacademic achievement tests might be purer criteria of attainedknowledge, grades play a more important role in students' daily lifeat the time being and constitute the most important criterion forschool success of the sample investigated. Second, grades are knownto be valid predictors of future academic and vocational success (e.g.,Robbins et al., 2004; Roth, BeVier, Switzer, & Schippmann, 1996). Apossible explanation might be that grades are thought to cover morethan pure conveyed knowledge, e.g. also effort and motivation(cf. Marsh, 1996, p. 153). As depicted above, these variables triggerachievement-related behaviour in achievement situations. Thus,whatever it is that is captured by grades but not by scholastic aptitudetests, it is important and research is needed to make it predictable.Nevertheless, future research should also include scholastic aptitudetests as a criterion for school achievement.
The presented data were cross-sectional and just partly long-itudinal since we only measured school achievement twice. Weconsider the presentation of the cross-sectional data important forseveral reasons. First, most studies on the incremental validity ofmotivational concepts beyond intelligence are cross-sectional innature. The presentation of cross-sectional data allows directcomparisons between the results of prior studies and the presentone. Second, there is a dearth of studies investigating the incrementalvalidity of motivation beyond and above intelligence and only a fewstudies have so far investigated an adolescent sample. Comparisons ofstudies investigating elementary-school children and adolescentstudents cross-sectionally might give valuable hints on developmen-tal effects in the interplay of the investigated variables. Third, cross-sectional data reflect the interplay of variables at a certain point oftime. Even though these data provide no information on the causalordering of the variables, they reflect the amount of shared and uniquevariance and variance explanation of and through these variables.
When investigating the competing importance or causal orderingof constructs (cf. Marsh et al., 1999; Marsh & Yeung, 1997) it is clearthat longitudinal designs offer a methodological advantage over cross-sectional designs. For example, given that ability self-concepts arestrongly influenced by prior performance, failing to control for priorperformance would lead to a substantial overestimation of the role ofability self-concepts in the prediction of school grades. We also knowthat prior ability self-concepts influence subsequent achievementindependently from prior performance (e.g., Guay et al., 2003). Hence,not controlling for prior ability self-concept leads to an overestimationof the role of prior school performance in predicting subsequentperformance. In this case, variance in subsequent performanceactually attributed to prior ability self-concepts is explained viaprior school performance. Since we did not control for prior abilityself-concepts the role of prior scholastic achievement in the predictionof subsequent school grades might be overestimated and the role ofability self-concepts might be underestimated in our study. This isprobably true for all motivational variables we considered in our studysince they are all thought to be related to school performance. Hence,in the case of our study design it is important to report both the cross-sectional as well as the partly longitudinal data in order to gain arealistic view on the importance of the chosen predictors.
Nevertheless, further studies should control for both priormotivation and prior school performance. In order to disentanglethe interplay between motivation, intelligence, and achievementfurther research should follow the guidelines of Marsh and Yeung(e.g., 1997) measuring each variable at least twice and followingfurther methodological advances such as using structural equation
89R. Steinmayr, B. Spinath / Learning and Individual Differences 19 (2009) 80–90
modelling and measuring each variable with a least three indicators.Such longitudinal designs would allow for more conclusive tests of apotential causal influence of motivation on school achievement.
In the present paper, the motivational constructs were operatio-nalized as suggested by the underlying theory either domain-specifically or domain-generally. For example, according to Murrayand McClelland, need for achievement is a personality trait and aperson should be achievement motivated either in all domains or innone. Nevertheless, the general items could be reworded to include aspecific context. For example, the AMS item “Difficult problems appealto me.” could be reworded to “Difficult problems in math appeal tome.” The present study does not allow us to draw conclusions asregards the relative importance of each construct when assessingthem at the same level of generality. Future studies should comparethe predictive power of the constructs assessed at the same level ofgenerality and include further motivational constructs that were notincluded in the present study. A possible design should cover acomplete crossover of the investigated motivational concepts andlevel of generality, i.e., need for achievement, ability self-concepts,values, and goals would be assessed with regard to motivation ingeneral, at school, and for different subjects. Even though such adesign would not necessarily assess each motivational construct inaccordance with its theoretical foundation, it would allow for a morestraightforward comparison of the predictive power of the concepts. Itcan be assumed that the specific operationalizations of motivationalconstructs explain more variance in corresponding criteria thangeneral motivational constructs.
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