Motivational characteristics of students in gifted ...

LEAIND-00922; No of Pages 8 Learning and Individual Differences xxx (2014) xxx–xxx

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Motivational characteristics of students in gifted classes: The pivotal role of need for cognition Elisabeth Meier a,1, Katharina Vogl b,1, Franzis Preckel b,⁎ a b

University of Munich, Germany University of Trier, Germany

a r t i c l e

i n f o

Article history: Received 4 January 2014 Received in revised form 30 March 2014 Accepted 8 April 2014 Available online xxxx Keywords: Achievement motivation Academic self-concept Need for cognition Investment traits Gifted classes Gifted education

a b s t r a c t We contrasted different motivational variables related to learning and achievement in order to identify which types of academic motivation predict students' attendance of a special class for the gifted (full-time ability grouping). We drew on a sample of 5th grade students in special classes for gifted and compared them to students in regular classes (N = 921; 31% in gifted classes) while controlling for confounding factors — that is, students' cognitive ability, academic achievement, sex, and parental level of education. Logistic regression analysis revealed that need for cognition (NFC) best predicted attendance of special classes for the gifted as compared to academic self-concepts, academic interests, or mastery and performance goals. Thus, it might be useful to explore NFC as an indicator for students' need for advancement options. In addition, our findings might stimulate the discussion on whether students high in NFC would benefit from being included in gifted programs. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Experts agree on the need for advancement options for gifted children at school (e.g., Rogers, 2007). However, the definition of giftedness remains a highly controversial issue (Dai, Swanson, & Cheng, 2011), which makes it difficult for schools to identify the gifted (Makel, Putallaz, & Wai, 2012). For intellectual giftedness, it is widely agreed that high cognitive ability is a central characteristic. In addition, various noncognitive factors are discussed as further indicators. According to multidimensional conceptions of giftedness (e.g., Gagné, 2004), the potential for extraordinary achievement (as one prominent understanding of giftedness) relies not only on high cognitive ability but also on noncognitive personality characteristics and environmental conditions. Intrapersonal characteristics highlighted in this context are, for example, self-regulatory strategies, control expectations, effort or motivational characteristics. The literature further highlights gifted students' need for cognitive challenge and their thirst for knowledge (e.g., Preckel, Götz, & Frenzel, 2010; Winner, 1996). This need could also be termed need for cognition (NFC), defined by Cacioppo and Petty (1982) as a tendency to engage in

⁎ Corresponding author at: University of Trier, Department of Psychology, 54286 Trier, Germany. Tel.: +49 651 201 4520; fax: +49 651 201 4578. E-mail address: [email protected] (F. Preckel). 1 The first two authors contributed equally to this work and are listed in alphabetical order.

and enjoy effortful cognitive endeavors. People with high NFC intrinsically devote their cognitive resources to thinking and they actively approach cognitively challenging situations (Fleischhauer, Enge, Brocke, Ullrich, & Strobel, 2010). NFC not only relates to what people are intellectually able to do but also corresponds to how they typically tend to invest their cognitive resources (von Stumm & Ackerman, 2013). Despite similarities in the description of NFC and gifted students' cognitive needs, to our knowledge NFC has not yet been investigated as a motivational characteristic of the gifted. In order to answer the question about who needs special advancement in school, it might be useful to explore who seeks special advancement. When parents of gifted children are asked about the reasons why their child attends a gifted class, they frequently highlight that gifted classes offer a better fit for their child's cognitive as well as motivational needs (Schneider, Stumpf, Preckel, & Ziegler, 2012). Gifted students are more likely to be underchallenged in regular classes (e.g., Emerick, 1992; Preckel et al., 2010) and accelerated and enriched curricula help to preserve interest in school and motivation to learn and prevent frustration, boredom, and subsequent demotivation (Baker, Bridger, & Evans, 1998; Feldhusen & Moon, 1992). In addition, these cognitively challenging programs might attract in particular students who are motivated to actively seek such challenge — like students with high NFC. In our study, we explored different motivational characteristics of students in regular classes and in gifted classes. We took into account motivational constructs which have already been established in giftedness research (i.e., academic self-concept, goal orientations, and

http://dx.doi.org/10.1016/j.lindif.2014.04.006 1041-6080/© 2014 Elsevier Inc. All rights reserved.

Please cite this article as: Meier, E., et al., Motivational characteristics of students in gifted classes: The pivotal role of need for cognition, Learning and Individual Differences (2014), http://dx.doi.org/10.1016/j.lindif.2014.04.006

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E. Meier et al. / Learning and Individual Differences xxx (2014) xxx–xxx

interests). Additionally, we investigated NFC. By doing so, we were able to explore which motivational variables explain the attendance of gifted classes and we were able to contribute to our knowledge on the role of NFC in gifted education. 2. Theory and current research 2.1. Motivational variables related to learning and achievement Academic self-concept, goal orientations, and interest are core motivational constructs in educational research and they have also been theoretically or empirically linked to giftedness. Also, the impact of NFC on cognitive development and academic achievement is increasingly recognized (Richardson, Abraham, & Bond, 2012; von Stumm & Ackerman, 2013). However, NFC has not yet been linked to giftedness. 2.1.1. Academic self-concept The academic self-concept refers to a person's self-evaluation regarding a specific academic domain or ability (Marsh & Shavelson, 1985). Academic self-concept has beneficial effects on a wide range of educational variables and outcomes like coursework selection (Marsh, 1991), career aspirations (Nagengast & Marsh, 2012), academic emotions (Goetz, Frenzel, Hall, & Pekrun, 2008), and self-efficacy (Pajares, 1996). It is positively related to achievement (Marsh & O'Mara, 2008; Valentine & DuBois, 2005). Recent studies highlight the reciprocity of the effects of academic self-concept and achievement (e.g., Niepel, Brunner, & Preckel, 2014). Accordingly, with the exception of underachievers, gifted students are more likely to develop positive academic self-concepts (Hoge & Renzulli, 1993; Rost, 2009). There is no evidence suggesting differences in structure or development of academic selfconcepts of the gifted as compared to students of average ability (McCoach & Siegle, 2003; Plucker & Stocking, 2001). However, academic self-concept is affected by the average ability of the reference group or class of a student (big-fish little pond-effect; Marsh et al., 2008). Of note, for gifted classes both negative contrast effects of the high-ability reference group on academic self-concept and counterbalancing positive assimilation effects exist (Preckel & Brüll, 2010). 2.1.2. Academic interest Individual (academic) interests are relatively stable personal predispositions and positive affective orientations towards certain (academic) domains (Eccles & Wigfield, 2002). Academic interests relate to course selection (e.g., Köller, Schnabel, & Baumert, 2000), can improve the quality of learning, and promote intrinsic motivation towards a certain subject. They direct students' attention and enhance the quality of learning by the use of adaptive learning strategies (Hidi, Renninger, & Krapp, 2004; Krapp, 1999). Köller et al. (2000) highlighted reciprocal effects of academic interest and performance. In general, gifted students report higher academic interest than non-gifted students (e.g., Roznowski, Hong, & Reith, 2000), especially in mathematics (Pruisken & Rost, 2005; Vlahovic-Stetic, Vidovic, & Arambasic, 1999). 2.1.3. Goal orientations Goal orientations describe cognitive representations of individuals' goals and reasons for pursuing them (Pintrich, 2000). The 2 × 2 achievement goal framework by Elliot and McGregor (2001) distinguishes between mastery approach, mastery avoidance, performance approach, and performance avoidance goals. Individual preferences of goal orientations are well-established determinants of academic performance. Current research suggests positive effects of mastery approach goals (Elliot & McGregor, 2001; van Yperen, 2003) and performance approach goals (e.g., Barron & Harackiewicz, 2001) on achievement. Mastery approach goals positively affect career aspirations as mastery goal approach oriented students explore, develop, and realign their career choices based on preferences and interests (Creed, Tilbury, Buys, & Crawford, 2011). Mastery avoidance as well as performance avoidance

goals are mostly related to negative outcomes, such as fear of failure or negative self-determination (Creed et al., 2011). In general, goal orientations are independent of intelligence level (Bipp, Steinmayr, & Spinath, 2008; Payne, Youngcourt, & Beaubien, 2007). Studies regarding differences in goal orientations of gifted and non-gifted students are rare (Dai, Moon, & Feldhusen, 1998). While some studies do not find systematic differences (e.g., Ziegler, Heller, & Broome, 1996), a study by Stumpf and Schneider (2009) suggests that students in gifted classes show lower performance goal orientations than students of similar intelligence in regular classes. 2.1.4. Need for cognition (NFC) Individuals high in NFC are more likely to engage in and enjoy thinking, whereas persons low in NFC rather lack this tendency. NFC has been conceptualized as a general and relatively stable intrinsic motivational trait (Cacioppo & Petty, 1982). Cacioppo, Petty, Feinstein, and Jarvis (1996) literature review illustrates the extensive amount of empirical evidence suggesting that NFC is positively related to effortful information processing. NFC contributes not only to the acquisition of knowledge (Tidwell, Sadowski, & Pate, 2000) but also to the acquisition of complex skills (Day, Espejo, Kowollik, Boatman, & McEntire, 2007). NFC is positively related to performance in class (Bertrams & Dickhäuser, 2009; Elias & Loomis, 2002) and negatively related to underachievement (Preckel, Holling, & Vock, 2006). A current meta-analysis supports the positive relationship of NFC and university students' grade point average (Richardson et al., 2012). Several studies have linked higher NFC to higher intelligence (e.g., Cacioppo et al., 1996). More recent studies show that NFC is rather associated with fluid than with crystallized aspects of intelligence (Fleischhauer et al., 2010; Hill et al., 2013; von Stumm & Ackerman, 2013). However, correlations are usually small. NFC has not yet been studied in connection with giftedness even though the tendency to engage in and enjoy effortful cognitive endeavors (Cacioppo & Petty, 1982) strikes one as a very likely motivational characteristic of gifted (Lovecky, 1992; Preckel et al., 2010; Winner, 1996). 2.2. The present study We examined which motivational variable(s) explain(s) the attendance of special advancement options for the gifted at school. By doing so, we wanted to contribute to our knowledge about the role of NFC in gifted education. To provide a context for the study, we will briefly describe the gifted classes in Germany and the corresponding selection process. Some schools of the top track of the German secondary school system (Gymnasium) offer gifted classes in addition to regular classes from grade five on. In these classes the standard curriculum is presented at a faster pace (acceleration) and more in depth (enrichment) than in regular classes. Admission to gifted classes is usually based on multiple criteria (e.g., cognitive ability, motivational variables, prior achievement). The schools in our study employ similar multistage selection procedures for gifted classes: they require completion of an application form with general information on family and child (e.g., school career), previous school certificates, and the results of an intelligence test (usually, a minimum IQ of 120 is required). The selection process is completed by teacher observations of behavior during 1 or 2 days of probationary class. Applicants are selected in a conference of teachers, school psychologists, and school board members based on a partly compensatory strategy (that is, high achievement can partly compensate for an IQ below 120, and vice versa). Application for gifted classes is voluntary, which very likely leads to a preselected sample. Therefore, the question arises: Why do some highly able students seek special advancement options while other similarly intelligent students chose to attend regular classes? To add to our understanding about which motivational concept(s) might best explain the need for special advancement options we not only investigated core motivational constructs commonly related to learning, i.e., academic self-concept, mastery and performance goal orientations, and



academic interest, but also included NFC in our study. NFC seems to be closely related to the cognitive needs of gifted students described in the literature (e.g., need for cognitive stimulation, hunger for knowledge, enjoyment of complexity and abstract thinking). We expected higher scores for students in gifted classes as compared to students in regular classes for academic self-concept (e.g., Hoge & Renzulli, 1993; McCoach & Siegle, 2003) and interest in math (e.g., Pruisken & Rost, 2005). Little is known about differences in goal orientations of students in gifted classes and students in regular classes. Therefore, goal orientations were investigated in an explorative manner. For NFC we expected higher levels in gifted classes because of the resemblance of this construct to cognitive needs of the gifted (e.g., Winner, 1996). With respect to the explicatory power of the different motivational constructs for attending either a gifted or a regular class, with the research base being weak our analyses were exploratory. To our knowledge, our study is the first to investigate NFC and to contrast different motivational constructs in the context of gifted education. Furthermore, we controlled for confounding factors which relate to the attendance of gifted classes. Besides cognitive ability and achievement, which are explicit selection criteria for gifted classes, we controlled for sex and parental level of education. As research documents, boys are more likely to be admitted to gifted classes (e.g., Bianco, Harris, Garrison-Wade, & Leech, 2011) and parents with a higher level of education have more resources to support the intellectual development of their children (e.g., Parrish, 2004).

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classes (starting with grade 5). Students in gifted classes were significantly younger than students in regular classes (t(904) = 9.58, p b .001, d = 0.69). This was because some of those students took part in acceleration programs (e.g., grade skipping) in elementary school. Students' IQ was significantly higher in gifted classes (t(894) = - 21.28, p b .001, d = − 1.54). However, there was a large overlap of the distributions of cognitive ability across class types (see Table 1). This is because the selection for gifted classes is only partly based on IQ scores and application for these classes is self-selected by students and their parents. With respect to the parental level of education, the Mann–Whitney U-test showed significant differences in favor of students in gifted classes (z = −7.04, p b .00, r = −.25). 3.2. Procedure Data collection took place in class. Trained research assistants administered the self-report questionnaires, whereas psychologists administered the IQ tests. Students responded to the self-report questionnaires after their first month of secondary school (fifth grade). At the same time parents were given questionnaires and completed these within 2 weeks. Students' cognitive ability was assessed by an intelligence test 3 months after the start of fifth grade. 3.3. Measures Reliabilities of measures are presented in Table 2. For all measures, higher scores reflect a higher score in the corresponding construct.

3. Method 3.1. Participants Students came from seven schools located in two of Germany's federal states (Bavaria and Baden-Württemberg). Sample statistics are reported in Table 1. The final sample comprised 920 students (41.2% female) from 42 classes of two successive cohorts (beginning with the school year 2008/09). 281 students (30.5%) attended gifted

3.3.1. Academic self-concept Academic self-concepts in German and math were assessed with a German translation of the Self-Description Questionnaire by Marsh (1990). The constructs were measured by three (German) or four items (math). The response format consisted of a five-point Likert scale (1 = strongly disagree to 5 = strongly agree).

Table 1 Descriptive statistics for students in gifted classes and regular classes (N = 920). Variables

Cognitive abilitya Age Sex Female Male Level of education No school leaving certificate Lowest high school diploma Middle high school diploma Highest high school diploma College degree Math test achievementb German reading speedc Reading comprehensiond Academic self-concept math Academic self-concept German Mastery approach math Mastery avoidance math Performance approach math Performance avoidance math Mastery approach German Mastery avoidance German Performance approach German Performance avoidance German Interest in math Interest in German Need for cognition

Gifted classes (n = 281)

Regular classes (n = 639)

%

%

M (SD) 123.14 (10.66) 9.87 (0.74)

M (SD) 107.28 (10.12) 10.29 (0.52)

33.8 66.2

44.4 55.6

0.0 0.4 11.7 6.4 71.9

0.0 4.4 19.4 15.5 43.6

Effect sizee

−1.54 0.69 0.10 −0.26

27.08 (6.10) 785.99 (240.74) 26.62 (4.86) 4.34 (0.74) 4.22 (0.68) 4.51 (0.68) 4.59 (0.57) 3.35 (1.19) 3.76 (1.09) 4.24 (0.80) 4.36 (0.71) 3.15 (1.10) 3.63 (1.13) 4.27 (0.93) 3.67 (0.99) 3.82 (0.68)

20.61 (5.72) 652.41 (217.97) 22.10 (5.95) 4.00 (0.88) 4.03 (0.75) 4.54 (0.59) 4.61 (0.57) 3.55 (1.14) 3.96 (1.00) 4.35 (0.73) 4.46 (0.67) 3.39 (1.11) 3.87 (1.04) 4.06 (0.98) 3.75 (0.99) 3.48 (0.69)

−1.11 −0.80 −0.59 −0.40 −0.27 0.06 0.03 0.17 0.20 0.15 0.14 0.22 0.22 −0.22 0.09 −0.51

Note: a = KFT with M = 100 and SD = 15; b = possible range 0–46; c = possible range 0–1727; d = possible range 0–36; e = effect size d for all continuous variables, r for level of education, Phi-coefficient for sex.


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1. CT 1. Class type 2. Cognitive ability 3. Self-concept in math 4. Self-concept in German 5. Interest in math 6. Interest in German 7. Mastery approach in math 8. Mastery avoidance in math 9. Performance approach in math 10. Performance avoidance in math 11. Mastery approach in German 12. Mastery avoidance in German 13. Performance approach in German 14. Performance avoidance in German 15. Need for cognition 16. Math 17. Reading comprehension 18. Reading speed 19. Sex 20. Parental level of education

− .58⁎⁎ .18⁎⁎ .12⁎⁎ .10⁎⁎ −.04 −.03 −.02 −.08⁎ −.09⁎⁎ −.07⁎ −.07⁎ −.10⁎⁎ −.10⁎⁎ .23⁎⁎ .50⁎⁎ .35⁎⁎ .26⁎⁎ −.10⁎⁎ .25⁎⁎

2. CA .98 .25⁎⁎ .10⁎⁎ .10⁎⁎ −.12⁎⁎ .01 .03 −.06 −.05 −.08⁎ −.06 −.14⁎⁎ −.13⁎⁎ .19⁎⁎ .63⁎⁎ .45⁎⁎ .21⁎⁎ −.12⁎⁎ .21⁎⁎

3. MSC

.88 .09⁎⁎ .70⁎⁎ .00 .43⁎⁎ .31⁎⁎ .32⁎⁎ .26⁎⁎ .12⁎⁎ .14⁎⁎ .15⁎⁎ .10⁎⁎ .44⁎⁎ .36⁎⁎ .09⁎ .02 −.25⁎⁎ .04

4. GSC

.75 .05 .59⁎⁎ .20⁎⁎ .19⁎⁎ .10⁎⁎ .15⁎⁎ .44⁎⁎ .36⁎⁎ .24⁎⁎ .25⁎⁎ .28⁎⁎ .06 .15⁎⁎ .16⁎⁎ .13⁎⁎ .04

5. MIN

6. GIN

.90 .24⁎⁎ .58⁎⁎ .39⁎⁎ .28⁎⁎ .25⁎⁎ .26⁎⁎ .21⁎⁎ .14⁎⁎ .14⁎⁎ .51⁎⁎ .19⁎⁎

.88 .30⁎⁎ .23⁎⁎ .13⁎⁎ .18⁎⁎ .63⁎⁎ .42⁎⁎ .29⁎⁎ .30⁎⁎ .36⁎⁎ −.08⁎

.00 −.02 −.19⁎⁎

−.04 .05 .12⁎⁎

.01

−.04

7. MMP

8. MMV

9. MMP

.77 .65⁎⁎ .31⁎⁎ .34⁎⁎ .56⁎⁎ .46⁎⁎ .21⁎⁎ .27⁎⁎ .41⁎⁎

.66 .31⁎⁎ .38⁎⁎ .44⁎⁎ .64⁎⁎ .26⁎⁎ .32⁎⁎ .32⁎⁎

.90 .74⁎⁎ .20⁎⁎ .28⁎⁎ .84⁎⁎ .70⁎⁎ .14⁎⁎

.02 −.02 .00 .00 −.02

.03 .03 .00 −.03 −.04

−.02 −.12⁎⁎ .03 −.09⁎⁎ −.07⁎

10. MPV

11. GMP

12. GMV

.82 .25⁎⁎ .35⁎⁎ .66⁎⁎ .78⁎⁎ .15⁎⁎

.84 .66⁎⁎ .33⁎⁎ .39⁎⁎ .35⁎⁎

.65 .39⁎⁎ .42⁎⁎ .26⁎⁎

−.04 −.05 .00 −.09⁎⁎

−.05 −.07 .01 .10⁎⁎

−.06 −.01 .01 .08⁎

−.06

−.04

−.00

13. GPP

.86 .77⁎⁎ .11⁎⁎ −.11⁎⁎ −.13⁎⁎ −.05 −.04 −.06

14. GPV

15. NFC

.88 .09⁎⁎ −.12⁎⁎ −.10⁎⁎ −.01 .00 −.05

.91 .22⁎⁎ .13⁎⁎ .14⁎⁎ −.12⁎⁎ .02

16. M

17. RC

18. RS

19. PLEd

.73 .37⁎⁎ .24⁎⁎ −.20⁎⁎ .18⁎⁎

.84 .35⁎⁎ −.12⁎⁎ .13⁎⁎

− −.01 .07

– .02

Note: 1. CT = class type (0 = regular/1 = gifted); 2. CA = cognitive ability; 3. MSC = academic self-concept in math; 4. GSC = academic self-concept in German; 5. MIN = interest in math; 6. GIN = interest in German; 7. MMP = mastery approach in math; 8. MMV = mastery avoidance in math; 9. MPP = performance approach in math; 10. MPV = performance avoidance in math; 11. GMP = mastery approach in German; 12. GMV = mastery avoidance in German; 13. GPP = performance approach in German; 14. GPV = performance avoidance in German; 15. NFC = need for cognition; 16. M = achievement in math; 17. RC = reading comprehension; 18. RS = reading speed; 19. Sex (0 = female/ 1 = male); 20. PLEd = parental level of education. ⁎p b .05. ⁎⁎p b .01.



Table 2 Reliabilities (in the diagonals) and correlations of predictors and the criterion class type.


3.3.2. Academic interest The scales interest in German and interest in math were obtained from the project PALMA (Pekrun et al., 2007). Both constructs were operationalized using three items. Participants responded to the items on a 1 (strongly disagree) to 5 (strongly agree) point Likert scale. 3.3.3. Performance and mastery goal orientations Goals were assessed for the domains of math and German. With the exception of mastery avoidance goals, we used a German translation of the Achievement Goal Questionnaire by Elliot and Church (1997). Mastery avoidance goals were measured by a scale developed by Preckel (2008). To assess mastery approach goals, mastery avoidance goals, performance approach goals, and performance avoidance goals in German and math, respectively, students responded to three items each using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). 3.3.4. Need for cognition NFC was assessed with the German adaptation of the Cacioppo/ Petty-scale for adolescents (Preckel, 2014). The scale consists of 19 items such as “I really enjoy a task that involves coming up with new solutions to problems.” Items were answered on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). 3.3.5. Cognitive ability Students' cognitive ability was measured with the KFT 4-12 + R (Heller & Perleth, 2000), which is a German adaptation of the Cognitive Abilities Test developed by Thorndike and Hagen (1971). The KFT 412+R assesses reasoning in the verbal, numerical, and figural domain. The 90-minute short version of the test was administered in class using a paper-and-pencil format. 3.3.6. Academic achievement Achievement in German and math was measured by standardized achievement tests. Reading comprehension was assessed by administering the FLVT 5–6 (Souvignier, Trenk-Hinterberger, Adam-Schwebe, & Gold, 2008), and reading speed was measured by the LGVT 6–12 (Schneider, Schlagmüller, & Ennemoser, 2007). Achievement in math was assessed with a test developed by Weiß and Schneider (2009) measuring curriculum dependent knowledge as well as general math ability. 3.3.7. Parental level of education We used parents' self-report of their highest level of education. In Germany a three-tier school system is used. Diplomas can be received at each level. Parental level of education was coded using values of 1 through 5, where 1 = no school leaving certificate, 2 = lowest high school diploma, 3 = middle high school diploma, 4 = highest high school diploma and 5 = college degree. 3.4. Data analysis We tested differences between class types by t-tests (Bonferroni correction: Alpha level for the 13 motivational variables was set at .05/13 = .004). To investigate our binary outcome (attendance of gifted or regular class) we used logistic regression. First, we analyzed every single construct family (academic self-concepts, goal orientations, academic interests, NFC) as well as the control variables separately. Secondly, we ran a full model taking the overall contribution of each variable into account while controlling for all other variables. The full model allows the evaluation of the combined variables and the evaluation on the incremental predictive validity of each construct. Mastery goals in math and interest in German were excluded from the analysis because they did not relate to class type (see Table 2). Because we paid particular attention to the motivational concepts, we decided to impute missing data of motivation scales using maximum likelihood estimation (EM algorithm). The number of cases with missing data for single items varied from 6.7% to 24.0%. Little's MCAR test

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(χ2 = 9423.95, df = 11684, p = 1.00) indicated that the data were probably missing completely at random. Cases with missing values for the IQ test, achievement measures, or demographic data were listwise deleted. 4. Results Only the correlations of academic self-concept in math with interest in German and academic self-concept in German with interest in math were not significant. All other motivational variables under study were positively correlated (see Table 2). Correlations were sufficiently low to exclude multicollinearity. 4.1. Mean differences between class types Table 1 provides means, standard deviations, and Cohen's d effect sizes of group differences. Students in gifted classes had higher academic self-concepts than students in regular classes (math: t(634.49) = − 6.03, p b .001, d = − 0.40; German: t(585.38) = 3.91, p b .001, d = −0.27) and they reported more interest in math (t(918) = −3.10, p = .002, d = −0.22) but not in German (t(918) = 1.19, p = .235). There were no mean differences between the class types for both facets of mastery goal orientation in math (approach: t(478.97) = 0.74, p = .460; avoidance: t(918) = 0.47, p = .640) and in German (approach: t(918) = 2.05, p = .041; avoidance: t(918) = 2.01, p = .045). Performance goals in math did not differ significantly between groups (approach: t(918) = 2.39, p = .017; avoidance: t(496.99) = 2.65, p = .008). Regarding performance goals in German, there were small differences in favor of the students in regular classes (approach: t(918) = 3.11, p = .002, d = 0.22; avoidance: t(918) = 3.11, p = .002, d = 0.22). Students within gifted classes reported higher NFC (t(918) = − 7.07, p b .001, d = −0.51). 4.2. Explaining class attendance Coefficients and related statistics for the construct family models and the full logistic regression model are presented in Table 3. Odds ratios (OR) indicate odds associated with one-unit increase of the coefficient. For interpretation purposes we did not standardize the predictors. Comparison of the impact of coefficients is possible by contrasting Wald statistics (Urban, 1993). Nagelkerke's R2 is reported as Pseudo R2 statistic which approximates variance interpretation of R2 in multiple linear regression analysis (Tabachnick & Fidell, 2007). The fit between the actual and predicted class membership is evaluated by the percentage of cases correctly classified by the model (Wright, 1995). 4.2.1. Construct family models As expected, cognitive ability was positively associated with attendance of a gifted class. For each one-unit increase in cognitive ability, the odds of attending a gifted class increased by 1.17. With increasing achievement in math and reading comprehension the odds increased, while the odds ratio of reading speed was 1.0. Boys were more likely to attend gifted classes than girls. For comparisons of the parental level of education, the lowest high school diploma in the German three-tier system was used as reference. Only the coefficient for college degree was significant, i.e., the odds of attending a gifted class were 20 times higher for students whose parents had college degrees. Regarding the motivational variables in the construct family models, academic self-concepts, interest in math, and NFC positively predicted attendance of gifted classes. Students with higher self-concepts in math and German were 1.66 resp. 1.43 as likely to attend a gifted class. With increasing interest in math, the odds of attending a gifted class increased by 1.27. For each one-unit increase in NFC the odds increased by 2.15.


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Table 3 Logistic regression results for the construct family models and the full model. Construct family models Model Control variables Cognitive ability CA MC Achievement tests Math Reading comprehension Reading speed MC Student's sex Male MC Level of education Lowest high school diplomaa Middle high school diploma Highest high school diploma College degree MC Motivational variables Academic self-concept Math German MC Interest in math MC Goal orientations Performance approach math Performance avoidance math Mastery approach German Mastery avoidance German Performance approach German Performance avoidance German MC Need for cognition MC Full model FMC

Full model

B

SE

Wald

OR

B

SE

Wald

OR

0.16⁎⁎ −19.11⁎⁎

0.01 1.32

197.92 209.62

1.17 0.00

0.12⁎⁎

0.01

73.93

1.13

0.15⁎⁎ 0.10⁎⁎ 0.00⁎⁎

−7.70⁎⁎

0.02 0.02 0.00 0.62

76.17 26.78 8.88 153.45

1.16 1.11 1.00 0.00

0.05⁎ 0.03 0.00⁎⁎

0.02 0.02 0.00

5.70 1.64 7.26

1.05 1.03 1.01

0.45⁎⁎ −1.10⁎⁎

0.15 0.12

9.05 85.37

1.57 0.33

0.30

0.25

1.50

1.35

2.01 1.63 3.01⁎⁎ −1.67⁎⁎

1.04 1.05 1.02 0.27

48.48 3.76 2.41 8.69 38.86

7.45 5.09 20.35 0.19

1.09 1.02 1.95

1.10 1.11 1.07

15.14 0.98 0.85 3.32

2.96 2.77 7.02

0.51⁎⁎ 0.36⁎⁎ −4.42⁎⁎ 0.24⁎⁎ −1.83⁎⁎

0.10 0.11 0.60 0.08 0.34

27.06 11.16 53.53 9.40 28.86

1.66 1.43 0.01 1.27 0.16

−0.12 0.23

0.22 0.17

0.31 1.87

0.88 1.26

−0.15

0.19

0.63

0.86

0.09 −0.08 −0.07 −0.01 −0.17 −0.05 0.30 0.77⁎⁎ −3.62⁎⁎

0.13 0.13 0.13 0.14 0.14 0.13 0.50 0.11 0.43

0.47 0.44 0.32 0.01 1.44 0.17 0.35 45.36 71.06

1.09 0.92 0.93 0.99 0.85 0.95 1.34 2.15 0.03

0.59⁎⁎

0.20

8.80

1.81

−20.86⁎⁎

1.84

129.04

0.00

a

Note: MC = model constant; FMC = full model constant; OR = odds ratio. Sex: 0 = female, 1 = male; = reference category. ⁎ p b .05. ⁎⁎ p b .01.

4.2.2. Full model In this model, we included all variables that were significant predictors of class attendance in the family construct models. Cognitive ability, math achievement, reading speed, and NFC were significant positive predictors of attendance of gifted classes. Reading comprehension, sex, parental level of education, academic self-concepts, as well as interest in math did not incrementally predict attendance of gifted classes above the other predictors. For each one-unit increase in cognitive ability, the odds of attending a gifted class increased by 1.13. With increasing achievement in math and reading speed, the odds increased by 1.05 and 1.01, respectively. Taking Wald statistics into account, following cognitive ability, NFC was the second most important predictor of attending a gifted class. For each one-unit increase in NFC, the odds of attending a gifted class increased by 1.81. Nagelkerke's R2 of the full model was .54. The percentage of the overall accuracy of classification was 81.7. Of those students who attended a gifted class, 66.7% were correctly identified as such (i.e., sensitivity) while 89.1% of those students who attended a regular class were correctly identified (i.e., specificity).

5. Discussion In the present study, we contrasted different motivational characteristics of students in regular classes and in gifted classes. Our research aim was to identify which type of academic motivation predicts

students' attendance of a gifted class while controlling for confounding factors, namely students' cognitive ability, academic achievement, sex, and parental level of education. We explored motivational constructs including academic self-concept, goal orientations, and academic interest, which have already been established in giftedness research, and added NFC. As expected, students in gifted classes reported higher academic self-concepts, higher interest in math, and higher NFC than students in regular classes. We found no group differences in goal orientations in math and only small differences in German in favor of students in regular classes. In addition, students in regular classes reported slightly higher performance goals in math and German. In part, these findings might be explained by the fact that our sample only comprised students from the highest track of the German secondary school system who are of above average ability and academic motivation. Group differences might have been too small to become significant. Our findings revealed that attendance of gifted classes is positively related to students' NFC, academic self-concept and interest in math, but is independent of students' goal orientations. When including all predictors into one model (full model), only NFC further explained the residual variance. That is, for students who are comparable with respect to their cognitive ability, academic achievement, sex, parental education level, academic selfconcept and interest, NFC significantly adds to the explanation for who attends a gifted class. Academic self-concept and interest did not incrementally add to the prediction of attendance of a gifted class. This finding might be explained by the fact that academic self-concept



and interest are strongly associated with achievement and share a lot of variance. In conclusion, among the motivational constructs under investigation, our results identify NFC as the most important predictor of the attendance of gifted classes. Persons high in NFC engage in and enjoy effortful cognitive endeavors. They are characterized by a need for cognitive challenge and a thirst for knowledge. Knowing these characteristics of individuals high in NFC, it seems very likely that students who seek advancement options in school have a high NFC. Limitations, practical implications and directions for future research Before discussing our findings, we want to point out some limitations of our study. First, methodological constraints may limit the generalizability of findings: participants in this study did not represent a randomized sample. Particularly, students were not evenly distributed across sex or age categories, and therefore, findings regarding sex and age should be interpreted with caution. Furthermore, the results of this study are specific to gifted classes in Germany as described in this paper. Generalization to other programs for gifted children is not possible without further testing. Secondly, the study represents a crosssectional analysis. Accordingly, results cannot be interpreted causally. Longitudinal studies monitoring students during the transition from elementary school to secondary school are necessary to investigate causal predictors of educational choices. Future research needs to control for additional variables – such as parental involvement – that might influence the decision for seeking special advancement options. For example, children high in NFC might motivate their parents to get more involved in their academic development. Also, parents who are high in NFC themselves might support the development of their children's NFC and might be more involved in their academic development. It might be useful to investigate these possible interactions in future research. Nevertheless, our results suggest that students of gifted classes are characterized by a higher NFC than students in regular classes. Assuming that NFC is a general and relatively stable intrinsic motivational trait (Cacioppo & Petty, 1982), individual levels of NFC can be considered a preexisting condition, which may serve as an important motivation for students to seek and/or to be selected for special treatment in school. This finding is in line with the conceptualization of NFC as an investment trait that explains stable individual differences in the propensity to seek out learning opportunities, which, in turn, positively affects cognitive development (von Stumm & Ackerman, 2013) and academic performance (von Stumm, Hell, & Chamorro-Premuzic, 2011). It might be useful to grant children with a high NFC access to special treatment in school (see Gottfried & Gottfried, 2004) since those who actively seek special advancement options may be those who particularly need and benefit from them. In addition, further research is needed to explore NFC (or related constructs like intellectual curiosity or typical intellectual engagement; e.g., von Stumm et al., 2011) as an indicator of giftedness. Many researchers in the field of gifted education adhere to a multidimensional view of giftedness (Dai et al., 2011). In our view, NFC seems to be a fruitful construct to be explored in this context. For example, children with a high level of cognitive ability and different levels of NFC could be compared in their educational needs and intellectual development. Different ability profiles of gifted students (e.g., general vs. domain specific giftedness) could be related to students' NFC. In addition, the domain specificity of NFC could be an interesting topic for future research as many motivational constructs are better conceptualized as domain general as well as domain specific (e.g., for academic self-concept see Brunner et al., 2010). That is, educational practice could benefit from investigating educational implications for persons with various constellations of different levels of cognitive ability and NFC more closely. The results certainly recommend including NFC in counseling practice for gifted children. The assessment of NFC can be useful to

7

determine if a child enjoys and seeks cognitive challenges and can therefore be helpful when considering special advancement options. As already mentioned, it might be useful to explore educational implications for persons with various constellations of different levels of cognitive ability and NFC. Longitudinal research is necessary to investigate the development of students differing in their NFC in diverse educational settings. Our findings suggest paying more attention to NFC in the educational context and will hopefully stimulate further research in this field. Acknowledgment The data used in this study was taken from the PULSS Project (team in alphabetical order: Bettina Harder, Monika Motschenbacher, Franzis Preckel, Wolfgang Schneider, Eva Stumpf, Susanne Trottler, Katharina Vogl, Christina Weiß, Albert Ziegler), a two-cohort longitudinal study on school achievement and academic development at the beginning of secondary education. 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