1-s2.0-s0022440513000411-main

Journal of School Psychology 51 (2013) 517–533

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Journal of School Psychology

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Student–teacher relationship quality and academic adjustment in upperelementary school: The role of student personality

Marjolein Zee, Helma M.Y. Koomen⁎, Ineke Van der VeenUniversity of Amsterdam, The Netherlands

a r t i c l e i n f o

⁎ Corresponding author at:v Research Institute of ChilTel.: +31 20 5251524; fax: +31 20 5251200.

E-mail address: [email protected] (H.M.Y. KoACTION EDITOR: Kathy Moritz Rudasill.

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a b s t r a c t

Article history:Received 13 April 2012Received in revised form 4 May 2013Accepted 8 May 2013

This study tested a theoretical model considering students' personality traits as predictors ofstudent–teacher relationship quality (closeness, conflict, and dependency), the effects ofstudent–teacher relationship quality on students' math and reading achievement, and themediating role of students' motivational beliefs on the association between student–teacherrelationship quality and achievement in upper elementary school. Surveys and tests wereconducted among a nationally representative Dutch sample of 8545 sixth-grade students andtheir teachers in 395 schools. Structural equation models were used to test direct and indirecteffects. Support was found for a model that identified conscientiousness and agreeableness aspredictors of close, nonconflictual relationships, and neuroticism as a predictor of dependentand conflictual relationships. Extraversion was associated with higher levels of closeness andconflict, and autonomy was only associated with lower levels of dependency. Students'motivational beliefs mediated the effects of dependency and student-reported closeness onreading and math achievement.© 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Keywords:Student–teacher relationshipsUpper elementary schoolBig FivePersonalityMotivationAcademic adjustment

1. Introduction

The upper elementary school years bringmany new challenges and risks to young students' social, emotional, and academic lives.During this “grace period” between the securities of infancy and the stresses of puberty, students gradually become moreindependent from their teacher (Ang, Chong, Huan, Quek, & Yeo, 2008; Lynch & Cicchetti, 1997), establish a sense of personalidentity and competence (Baker, 1999), and face increasingly demanding academic tasks and social competition (Eccles et al., 1993).Many students negotiate this period without too many problems. For others, however, the challenges of upper elementary schoolmay cause the onset of a downturn in competence-related behaviors and motivation that may prevent them from succeedingacademically (Fredricks & Eccles, 2002).

Recent research suggests that the quality of student–teacher relationships may play a crucial role in helping students tonavigate the challenges of the upper elementary school years (Hamre & Pianta, 2001; Malecki & Demaray, 2006; Roorda, Koomen,Spilt, & Oort, 2011; Wang & Eccles, 2012). This quality is characteristically operationalized as a three-dimensional constructreflecting the level of closeness (encompassing warmth, support, and open communication), conflict (including discordance andnegativity), and dependency (including possessiveness and overreliance on the teacher) in the student–teacher relationship (seePianta, 1994; Pianta, Steinberg, & Rollins, 1995). When there are high levels of closeness and low levels of conflict anddependency, students are more likely to be motivated to succeed, to feel successful in educational pursuits and, consequently, toperform better than students without such supports (Baker, 2006; Furrer & Skinner, 2003; Roeser, Midgley, & Urdan, 1996;Wentzel, 1998). Additionally, positive student–teacher relationships may render older students far less vulnerable to antisocial

dDevelopment and Education, Universiteit van Amsterdam, PO Box 94208, NL-1090 Amsterdam, TheNetherlands.

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518 M. Zee et al. / Journal of School Psychology 51 (2013) 517–533

behavior, low self-esteem, and adjustment problems in later life (Herrero, Estevez, & Musitu, 2006; Wentzel, 2002). Student–teacher relationships may thus be protective against declines in both academic and socio-emotional functioning during thiscritical transition period.

Despite the importance of the student–teacher relationship quality for upper elementary students' school adjustment,relatively little is known about its predictors and consequences during this period. Thus far, studies investigating the link betweenstudent–teacher relationship quality and older students' academic adjustment have been typically focusing on teacher support.Less emphasis has been placed, however, on the myriad factors involved in student–teacher conflict and dependency, and thepotential effects of these negative relationship patterns on students' academic success. This lack of research is unfortunate, giventhat the quality of student–teacher relationships seems to be deteriorating by the time students reach the upper elementarygrades (e.g., Baker, 2006; Furrer & Skinner, 2003; Lynch & Cicchetti, 1997; Spilt, Hughes, Wu, & Kwok, 2012). To address this lackof evidence, the present study explored the contributions of student characteristics to the student–teacher relationship quality,the additive power of high- and low-quality student–teacher relationships as sources that may advance or hamper students'achievement, and the indirect effect of student–teacher relationship quality on students' achievement via the direct effect on theirmotivational beliefs in upper elementary school.

1.1. Theoretical framework

There are a variety of perspectives, models, and approaches used in research on the effects of student–teacher relationship qualityon students' academic adjustment. The host of those, including transactional, developmental-systems, and self-determinationtheories, share the assumption that neither individual nor environmental factors exclusively determine students' developmentaloutcomes. Rather, these outcomes are assumed to be the product of bidirectional interactions between students and their socialenvironment (Pianta, Hamre, & Stuhlman, 2003; Ryan & Deci, 2002; Sameroff & Fiese, 2000). In examining processes that affectstudents' academic adjustment, this assertion is fundamental, as it points to the potential significance of the role that student featuresplay in modifying the social context, which, in turn, may also adjust students' behavior. Some of these student features are known tobe innate, such as personality traits that drive students' psychological needs for, among other things, relatedness (cf., Ryan & Deci,2002). Others, including students' motivational beliefs, values, and goals, may be more internalized through the influence of socialforces in the classroom, such as the student–teacher relationship. In concert, these student resources may promote or restrainstudents' active engagement and academic adjustment in the classroom. A comprehensive examination of such inherent andinternalized resources may thus advance understanding of how specific student characteristics interface with supports provided bythe teacher to make opportunities for learning available.

Guided by the tenets of transactional and self-determination theories, this study proposes a model within which research onthe effects of students' inner resources (i.e., personality) on social forces in the classroom (i.e., student–teacher relationshipquality), on the one hand, are combined with research on the association between student–teacher relationship quality andinternalized student resources (i.e., motivational beliefs) and achievement, on the other (see Fig. 1). Theoretical and empiricaljustification for each piece of the overarching model is given in the next sections.

1.2. Student–teacher relationships and academic adjustment in upper elementary school

The value of warm, high-quality student–teacher relationships for students' concurrent and subsequentmotivation and academicfunctioning is fairly well-established in prior research (Hamre & Pianta, 2001; Ladd, Birch, & Buhs, 1999; Roorda et al., 2011). Studiesshow that a sense of relatedness between students and teachers may provide students with internalized resources that enable themto regulate their own academic behavior, and to develop positive beliefs and attitudes about the self as learner (Baker, 1999, 2006;Reeve, Bolt, & Cai, 1999; Roeser et al., 1996). Such internalized resources—or motivational beliefs—include, among many others,students' self-efficacy, goal orientation, perceived competence, and task value. In the early elementary school grades, high-qualitystudent–teacher relationships have been connected to a range of positive outcomes that underlie students' motivation to learn, suchas school connectedness, perceived autonomy, and self-efficacy (Baker, 2006; Colwell & Lindsey, 2003; McWilliam, Scarborough, &Kim, 2003; Pianta, La Paro, Payne, Cox, & Bradley, 2002). Low-quality student–teacher relationships characterized by high levels ofconflict or dependency have, in contrast, consistently been associated with school adjustment problems in the cognitive, emotional,and behavioral domain (Hamre & Pianta, 2001; Mantzicopoulos, 2005; Palermo, Hanish, & Martin, 2007).

Mean levels of student–teacher relationship quality are likely to decline across the elementary school years. Typically, bothstudents and teachers tend to report gradual increases in conflict, and decreases in closeness by the time students reach the upperelementary grades (Baker, 2006; Jerome, Hamre, & Pianta, 2009; Spilt, Hughes et al., 2012; Spilt, Koomen, & Jak, 2012). A smallnumber of studies make it clear, however, that high-quality student–teacher relationships continue to play an important part inolder students' motivational beliefs and academic success (Furrer & Skinner, 2003; Jerome et al., 2009; Roorda et al., 2011).Empirical evidence indicates that students' need for relatedness increases during middle childhood, and that high levels of

Students’ Personality

Student-TeacherRelationship Quality

Motivational Beliefs

Academic Achievement

Fig. 1. Conceptual model predicting student–teacher relationship quality and academic adjustment.

519M. Zee et al. / Journal of School Psychology 51 (2013) 517–533

teacher support may diminish feelings of stress associated with increasing school complexity, changes in learning goals, and socialcomparison (Roeser et al., 1996; Wang & Holcombe, 2010). Students who feel that their efforts and skills are recognized by theteacher have been found to be more eager to explore and learn, to have higher self-esteem and confidence in their ability to learn(Herrero et al., 2006; Wang & Holcombe, 2010), and to have better achievement scores (DiLalla, Marcus, & Wright-Phillips, 2004;Roeser et al., 1996). When older students believe that their teachers care for them, they are also more likely to respond withgreater effort, to set numerous goals for themselves, and to exhibit greater compliance with teachers' behavioral and academicexpectations (e.g., Furrer & Skinner, 2003; Wang & Holcombe, 2010; Wentzel, 2002; Wolters, Yu, & Pintrich, 1996). In a study byGoodenow (1993), over one third of the variance in sixth-to eighth-grade students' interest in and expectations of their academicwork was explained by teacher support.

A small but growing number of studies have included tests of the hypothesis that students' motivation-related attitudes andbeliefs may mediate associations between student–teacher relationship quality and academic achievement (e.g., Ladd et al., 1999;Woolley, Kol, & Bowen, 2009). Furrer and Skinner (2003), for example, showed that students' beliefs about their effort, attention,and persistence were maintained through their sense of relatedness to teachers from third to sixth grade. In addition,Zimmer-Gembeck, Chipuer, Hanisch, Creed, andMcGregor (2006) revealed that the emotional quality of students' involvement inmiddle school mediated the association between student–teacher relationship quality and their school achievement. Similarresults were reported by Hughes, Wu, Kwok, Villarreal, and Johnson (2012) in a longitudinal study of children from third throughfifth grades. These researchers found that teacher-rated behavioral engagement and students' math competence beliefs mediatedthe effect of students' perceptions of closeness on math achievement. In addition, teacher-rated engagement was also found tofunction as a mediator in the relationship between student-perceived conflict and reading and math achievement. Collectively,this evidence implies that upper elementary students who feel securely connected to teachers are more likely to internalizepositive motivational beliefs about their schoolwork. These internalized resources, in turn, are expected to lead to greateracademic success.

1.3. Predictors of student–teacher relationship quality in upper elementary school

The odds of students having a high-quality student–teacher relationship with their teacher appear to be determined, at least inpart, by (parents' and teachers' perceptions of) students' inner dispositions and behaviors in the classroom, such as temperamentand personality (e.g., Birch & Ladd, 1998; Rudasill & Rimm-Kaufman, 2009; Saft & Pianta, 2001; Stuhlman & Pianta, 2002;Wentzel, 2002). Longitudinal studies on student–teacher relationship quality show that from preschool to upper elementarygrades, teacher-reported measures of closeness, and especially conflict for individual students, are fairly stable across teachers(e.g., Baker, 2006; Jerome et al., 2009; Spilt, Hughes et al., 2012; Spilt, Koomen et al., 2012). Moreover, Jerome et al. (2009) notedthat conflictual student–teacher relationships are more determined by student features than any other fluctuating aspects ofteachers or the school environment. These findings call attention to the need for further exploration of fairly stable resources thatstudents bring to their relationships with teachers.

Recently, the idea that genetically-based traits may lead to differences among students in the quality of their relationships withteachers has attracted increasing research interest (e.g., Birch & Ladd, 1998; Koenig, Barry, & Kochanska, 2010; Rudasill &Rimm-Kaufman, 2009; Saft & Pianta, 2001; Shiner & Caspi, 2003; Stuhlman & Pianta, 2002). The handful of researchers interested inpersonality differences in relation to student–teacher relationship quality and academic adjustment have described these differencesaccording to common tenets of temperament, such as effortful control, shyness, and anger (Justice, Cottone, Mashburn, &Rimm-Kaufman, 2008; Rudasill & Rimm-Kaufman, 2009; Rudasill, Rimm-Kaufman, Justice, & Pence, 2006). Personality traits have, bytradition, been distinguished from temperamental aspects, as personality traits are assumed to be rooted in temperamental variationsin emotion, motor reactivity, and attention that are already present from birth onward (De Pauw & Mervielde, 2010; Mervielde, DeClercq, De Fruyt, & Van Leeuwen, 2005; Rothbart, 2007). Temperamental variations are biologically-based, and largely determinechildren's reaction to the environment and the processes that regulate them (Rothbart, 2007). The dynamic interplay betweenchildren's temperament and their environmental experiences forms the basis of children's personality. Compared to temperament,personality is considered wider in scope, focusingmore on comprehensive, higher-order traits that account for behavioral variationsamong children and adolescents (Mervielde et al., 2005). In addition, whereas temperament mainly comprises a child's emotionaland attentional capacities, personality also involves cognitive andmotivational aspects. These aspects of personality include a child's“developing cognitions about self, others, and the physical and social world, as well as his or her values, attitudes, and copingstrategies” (Rothbart, 2007, p. 207). Following this line of reasoning, it may thus be assumed that students' personality traits not onlypredispose them to engage in and value student–teacher relationships differently. These inner resources may, in turn, also have aninfluence on differences in students' internalized motivational beliefs.

Despite the apparent contrast between temperament and personality, there is accumulating evidence that the common tenetsof temperament show evident correspondence with personality traits of children aged 3 to 12 (Goldberg, 2001; Mervielde, Buyst,& De Fruyt, 1995). An overview of temperament and personality of Mervielde et al. (2005) and De Pauw and Mervielde (2010)reveals that at least three factors of the Big Five Model of Personality (e.g., Costa & McCrae, 1992), including extraversion,neuroticism, and conscientiousness, are evidently complementary to dimensions of temperament. As Mervielde et al. (2005)contend, however, autonomy and agreeableness have largely failed to be recognized by temperament models, while emerging askey dimensions in personality research, both for youngsters and for grown-ups. To our knowledge, comprehensive research onfactors of the Big Five model has not yet been integrated with studies conducted on student–teacher relationships. Asimultaneous exploration of all Big Five personality traits in relation to student–teacher relationship quality may help to integrate


evidence from different lines of research, as well as move the field forward by generating new hypotheses about the yet unclearroles of personality factors such as autonomy and agreeableness.

1.3.1. Contributions of the Big Five to student–teacher relationship qualityGiven its leading role in psychological and educational research (Costa & McCrae, 1992; De Pauw & Mervielde, 2010), this study

focuses on the factors of the Big Fivemodel. Including thismodel in our studymay offer valuable insights into both the variation in thequality of student–teacher relationships, and students' academic adjustment, as it encompasses both interpersonal (i.e., extraversion,agreeableness, and neuroticism) and cognitive (i.e., conscientiousness and autonomy) capacities. Of these, interpersonal aspects ofstudents' personality seem to be the most relevant for the quality of student–teacher relationships (Graziano, Jensen-Campbell, &Hair, 1996). Extraverted persons, for instance, are viewed as effective in social interactions and display friendly, assertive, andgregarious behavior. Evidence has shown that students high in extraversion are more likely to experience positive affect duringinteractionswith their teacher, are ready to seek helpwhen needed, and engagemore actively in joint activities (Bidjerano & YunDai,2007. By spending more time with others, extraverts may actively create opportunities for warm and cooperative student–teacherrelationships in the course of achieving success (Diener, Larsen, & Emmons, 1984; LePine & Van Dyne, 2001).

Extraversion has been shown to be correlated with several distinct temperamental lower-order traits, such as shyness,sociability, dominance, and activity level (Mervielde et al., 2005). There is some evidence from studies on the link betweenshyness and student–teacher relationship quality suggesting that teachers experience their relationship with shy children as lessclose and more dependent than those with more extraverted behaviors (e.g., Arbeau, Coplan, & Weeks, 2010; Thijs & Koomen,2009). Moreover, results from Rudasill and Rimm-Kaufman (2009) indicate that socially inhibited children are less likely toinitiate interactions from teachers than their more sociable peers. Other research (e.g., Saft & Pianta, 2001; Wentzel, 1991) hasalso found that teachers generally seem to favor students who display extraverted, spontaneous, and companionable behaviors,relative to students with more introverted or shy conduct.

Agreeable persons are commonly perceived as friendly, compliant, courteous, and tolerant (Barrick & Mount, 1991). They tendto be more cooperative and generally have higher quality interpersonal interactions, as they minimize interpersonal conflict bybeing less hostile, or by provoking less aggression from others (Asendorpf & Wilpers, 1997; Barrick, Stewart, & Piotrowski, 2002;Graziano et al., 1996). In so doing, agreeable students may experience more satisfying social environments themselves, which inturn initiates higher levels of motivation to work on school-related tasks, and may better prepare them for the academicchallenges they face over the course of development (Furrer & Skinner, 2003; Hair & Graziano, 2003).

Whereas both agreeable and extraverted persons are commonly perceived as effective in social interactions, neurotic individualstend to reflect poor emotional adjustment in the formof stress, anxiety, and depression and are prone to negative affect (Koenig et al.,2010). A number of temperamental lower-order traits, including anxious distress (i.e., self-directed anxiety, guilt, and fear) andirritable distress (i.e., externally-directed irritability, anger, and hostility), have been associated with neuroticism (Mervielde et al.,2005). Such temperamental traits may act as catalysts for poor student–teacher relationships by hindering positive interactions,expressing negative attitudes towards the teacher, and limiting teachers' ability to be sensitive and responsive to students' signals(Little &Hudson, 1998). Previous research on temperament has shown, for instance, that irritable and hostile behaviors are associatedwith less warm and more forceful and over-dependent student–teacher relationships, concurrently and prospectively (Birch & Ladd,1998; Howes, Phillipsen, & Peisner Feinberg, 2000; Ladd & Burgess, 1999; Little & Hudson, 1998), and lower achievement scores(e.g., Laidra, Pullmann, & Allik, 2007). Furthermore, in a study of Graziano, Reavis, Keane, and Calkins (2007) it was found thatneurotic, emotionally unstable students are likely to be rated by teachers as difficult to handle, requiring more energy from theteacher to control their behavior and to assist them with engaging in classroom activities. Students scoring high in neuroticism maytherefore have lower quality student–teacher relationships in the classroom. Thus, whereas extraversion and agreeablenessmay havea prominent position in sustaining high-quality student–teacher relationships, neuroticism will probably result in more conflictualand dependent student–teacher relationships.

Compared to interpersonal aspects, cognitive aspects of students' personality are most often linked with motivational aspectsin relation to student learning. Conscientiousness, which comprises temperamental capacities such as orderliness, responsibility,attention and self-control (Mervielde et al., 2005), has, for example, been found to be positively correlated with motivation(Chamorro-Premuzic & Furnham, 2003), self-regulation (Bidjerano & Yun Dai, 2007), and perceived competence for learning(Ntalianis, 2010) across all educational levels. Although links between conscientiousness and student–teacher relationshipsquality have hardly been established, there is some evidence connecting this trait to high-quality student–teacher relationships.Because highly conscientious students are meticulous and achievement-oriented, they tend to accomplish their goals by beingmore caring and sociable towards others, adapt more easily to implicit and explicit social norms, and invest more in long-termrelationships than their less conscientious peers do (Asendorpf & Wilpers, 1998; Noftle & Shaver, 2006). This pattern is likely toresult in enhanced self-esteem, motivation, and appreciation by teachers and peers, which may in turn engender reciprocation inthe form of increased achievement (e.g., Chamorro-Premuzic & Furnham, 2003; Laidra et al., 2007; Steinmayr & Spinath, 2008).

Autonomy has been associated with tendencies towards seeking novel academic experiences, independence, originality, andalso with intelligence (McCrae & Costa, 1987; McCrae & John, 1992). This trait has been marked by self-determination theorists asa crucial psychological need that is essential for facilitating students' social and academic adjustment (e.g., Deci & Ryan, 2000,2008). Empirical work of Verschueren, Buyck, and Marcoen (2001), for instance, has revealed that students are more likely toinitiate positive and conflict-free student–teacher relationships when they have dispositions towards curiosity, classroomexploration, and self-determination. Because persons higher in autonomy seem to be more open to change, and willing to transfernew skills and behaviors learned in one domain to benefit another, they tend to be more creative in developing solutions when


conflict arises (Wayne, Musisca, & Fleeson, 2004). Conflict in the classroom is thereby likely to be reduced, resulting in betterstudent–teacher relationships and higher achievement scores (e.g., Laidra et al., 2007; Paunonen & Ashton, 2001). Students'autonomy may therefore not only contribute to the development of high-quality student–teacher relationships. Also, it mightinitiate the kind of teacher support that children need to become motivated to succeed.

1.4. Present study

To summarize, the purpose of the present study was to explore student features predicting the student–teacher relationshipquality, as well as the additive power of high- and low-quality relationships as sources that may advance or hamper students'adjustment during upper elementary school. Specifically, a model (see Fig. 1) was tested positing that (a) students' inner resources(i.e., Big Five personality traits) predict the quality of student–teacher relationships, and (b) student–teacher relationship qualityindirectly affects students' achievement via the direct effect on their internalized resources (i.e., motivational beliefs).

2. Method

2.1. Participants

The current study was conducted using data from the first wave of the national COOL-cohort study, which started in theacademic year of 2007–2008 in the Netherlands. COOL is a prospective longitudinal research project in which about 38,000students from kindergarten, grade 3, and grade 6 are tested every three years in language, reading, and mathematics. Extensiveinformation about a number of attitudinal, motivational, and background characteristics is collected as well (Driessen, Mulder,Ledoux, Roeleveld, & Van der Veen, 2007). In the Netherlands, elementary education—intended for 4- to 12-year-old students—isorganized by eight age-level cohorts, or groups, in which students typically have the same teacher throughout the school day.Most students are 11 or 12 years old when they enter grade 6 (group 8), the final year of primary school. Grade 6 in particularmarks the beginning of a challenging and important transition period for Dutch students. During this period, students arefrequently undergoing profound changes in their sense of self and are struggling with dense curricular demands (Lynch &Cicchetti, 1997; Resnick et al., 1997). Teacher support may have protective benefits for this age group of students, as teachers mayserve as a safe haven and transmit important values and personal advice to students (Rhodes, Grossman, & Resch, 2000; Roeser &Eccles, 1998). In addition, as it is the last year before students shift to junior high school, their teachers have to make importantdecisions about the type and level of secondary education most suited to them. Generally, teachers make such recommendationson the grounds of an aptitude test called the Final Elementary Education Test, developed by the Dutch National Institute forEducational Measurement (CITO), which is taken by the vast majority of Dutch students. Students' concerns about aptitude,evaluation, and teachers' expectations are likely to be enhanced by issues such as the pressure of this aptitude test, making goodquality student–teacher relationships even more important in upper elementary classrooms (e.g., Ang et al., 2008). The presentstudy, therefore, made use of a subset of the nationally representative sample, to examine student–teacher relationships withsixth-graders. A total of 8545 students from 1001 classes in 395 schools were included for analyses.

Demographics of the sample indicated that 50.7% of the students were boys, and the mean age was 11.6 years at the start ofthe study (range = 8.0 to 13.0 years, SD = 0.6 years). Teachers indicated that 20.1% of the students had special educationalneeds. For 6.9% of the students, the teachers did not indicate whether their students had special needs or not. Information fromthe school administrators about student ethnicity was available for 96.8% of the children. A total of 78.8% of the students were ofDutch origin, and 18.1% were of non-Dutch origin. Mothers' educational background, in terms of the highest level of educationcompleted, was available for 93.3% of the cases. In total, 9.0% of the mothers had finished primary school, 23.9% finishedpre-vocational secondary education, 41.7% finished senior vocational education, and 18.6% finished higher education.

2.2. Measures

2.2.1. Student–teacher relationship qualityBoth teachers and students completed rating scales measuring their perceptions of student–teacher relationship quality.

2.2.1.1. Teacher's perspective of student–teacher relationship quality. Teachers' perceptions of the quality of their relationship witheach of their students were estimated using an authorized Dutch translated and slightly adapted version of the Student–TeacherRelationship Scale (STRS; Koomen, Verschueren, & Pianta, 2007). This instrument was especially developed to assess relationshipquality for 3- to 12-year-old students. Like its original, the adapted STRS has shown to be represented by three distinct factors thatare referred to as the Closeness, Conflict, and Dependency subscales (Koomen, Verschueren, van Schooten, Jak, & Pianta, 2012).Closeness measures the extent to which teachers feel their relationship with a student to be characterized by warmth, openness,and proximity, with items such as “I share an affectionate and warm relationship with this child.” Conflict and Dependencymeasure negative aspects of student–teacher relationships, which are those in which teachers observe the relationship withstudents to be overly conflictual, or in which teachers experience the child to show clingy and demanding behavior. Exampleitems are “This child and I always seem to be struggling,” and “This child reacts strongly to separation from me,” respectively.

In the COOL cohort-study (Driessen et al., 2007), 5 items for each subscale were selected on the basis of the highest factorloadings reported in earlier research (Koomen et al., 2012). All items were rated on a 5-point Likert type scale, ranging from 1


(definitely does not apply) to 5 (definitely applies). Investigators using the adapted STRS have reported satisfactory reliability andconstruct validity evidence for the STRS, from preschool to upper elementary school, and across gender and age (e.g., Doumen,Koomen, Buyse, Wouters, & Verschueren, 2012; Koomen et al., 2007, 2012). In these studies, Cronbach's alphas ranged between.88 and .93 for Closeness, .88 and .91 for Conflict, and .77 and .82 for Dependency. Internal consistency scores in the present studywere .86 for Closeness, .93 for Conflict, and .91 for Dependency, respectively, and therefore indicate good reliability.

2.2.1.2. Students' perspective of student–teacher relationship quality. Considering that the student–teacher relationship is a dyadicconstruct, its quality was also measured from the perspective of the student. Accordingly, students answered 7 questionsconcerning well-being with respect to the relationship with their teacher which primarily measure positive aspects of thestudent–teacher relationship (Peetsma, Wagenaar, & De Kat, 2001). This student-reported Closeness scale was rated on a 5-pointLikert-type scale, ranging from 1 (definitively not true) to 5 (definitively true). Items that made up this scale included statementssuch as “Usually, my teacher knows how I feel” and “I have a good relationship with my teacher.” Cronbach's alpha of this scalewas satisfactory (α = .78).

2.2.2. Outcome variablesStudents' academic achievement was obtained from their performance on individually administered multiple-choice tests for

reading comprehension and mathematics. Both instruments, developed by the Dutch assessment institute CITO, are nationallynormed achievement tests, designed to screen and determine the current level of reading comprehension and mathematics in grade6. The reading comprehension test, which consists of 35 multiple-choice items, gives an indication of proficiency in the areas ofconceptual reasoning and practical reading ability. The math test, which consists of 32 multiple-choice items, was designed to tapimportant aspects of mathematics taught in mainstream classrooms, such as geometry, multiplication, and addition (Driessen et al.,2007). The reading comprehension test (α = .91) and math test (α = .92) had excellent reliability. Moreover, van Boxtel, Engelen,and de Wijs (2011) evaluated construct validity among several versions of the Final Elementary Education Test, of which the mathand reading comprehension test are part. They concluded that the test content and structure were consistent across different testversions, and that the tests were highly predictive of children's IQ (correlations between .72 and .78).

According to the guidelines required by the CITO (CITO, 2008), students' answers were first calculated into raw scores. Usingproper tables for students' age, the raw scores were then converted into age-standard ability scores. These ability scores are basedon Item Response Theory and take the number and complexity of items of the reading comprehension andmath test into account.In the present study, these ability scores were used to indicate students' achievement in both subjects.

2.2.3. Predictor variablesStudents completed rating scales measuring their self-perceptions of their personality traits.

2.2.3.1. Personality traits. Students' personality traits were measured using the Five Factor Personality Inventory (FFPI; Hendriks,Hofstee, & De Raad, 1999; Hendriks, Kuyper, Offringa, & Van der Werf, 2008). The FFPI is a 100-item self-report questionnairedeveloped to evaluate a person's position on the psycho-lexically based facets of Extraversion, Agreeableness, Conscientiousness,Neuroticism, andAutonomy. Items thatmade up thismeasurewere rated by students on a five-point Likert-type scale, ranging from1(not at all applicable) to 5 (entirely applicable), with higher scores indicating higher values on the five personality dimensions. Someslight adjustments were made to the FFPI to make its itemsmore suitable for upper elementary students. In two items, references to“people” were substituted by “other children.” Additionally, all 100 items were rephrased from the third person into first-personsingular (Hendriks et al., 2008). Example items for each respective dimension are “I like to chat” (Extraversion), “I respect others'feelings” (Agreeableness), “I do things according to a plan” (Conscientiousness), “I can't take my mind off my problems”(Neuroticism), and “I can easily link facts together” (Autonomy). The psychometric properties of this slightly adapted version of theFFPI have been demonstrated to be sufficiently suited for use in this specific age group of students. In a sample of 12- to 13-year oldstudents, Hendriks et al. (2008) reported sufficient reliability (the mean α across the five factors was .70), and showed that thestructure of the adapted FFPI was construct-valid across gender and educational level. In the present sample, Cronbach's alpha valueswere as follows: Extraversion, .76; Agreeableness, .80; Conscientiousness, .79; Neuroticism, .74; Autonomy, .65. Composite scores forthe five FFPI subscales were used to represent students' placement on the five personality factors.

2.2.4. Mediator variablesStudents completed rating scales measuring their perceptions of both their motivational goals and expectancies.

2.2.4.1. Motivational beliefs. To capture the multifaceted nature of students' motivation-related beliefs, both their goals andexpectancies were considered as motivators of academic achievement. The Task Motivation Scale (Seegers, Van Putten, & DeBrabander, 2002) was used to evaluate students' motivational goals. This instrument is a self-report instrument composed of 5items, whichmeasure the extent to which students focus onmastering learning tasks and on learning opportunities in the contextof school. All items were rated on 5-point Likert scales that range from 1 (definitively not true) to 5 (definitively true). Examples ofitems are “I feel satisfied when I have learned something in school that makes sense to me,” and “I feel satisfied when I havelearned something new in school.” Support for the construct validity of the Task Motivation Scale has been provided by Hornstra,Van der Veen, Peetsma, and Volman (2013), who found that the Task Motivation Scale reflected the same construct over time andacross gender, ethnicity, and students' socioeconomic status. In the present sample, the Cronbach's alpha of this measure was .75.


Students rated their expectancies about their capability to perform academic tasks in the classroom using a translated version ofthe Academic Efficacy subscale from the Patterns of Adaptive Learning Survey (PALS; Midgley et al., 2000). The 6 items of thisself-reportmeasurewere scored on a 5-point Likert scale, ranging from1 (definitively not true) to 5 (definitively true). Statements suchas “I'm certain I can figure out how to do themost difficult classwork” and “I can do almost all thework in class if I don't give up”wereincluded in this scale. The Academic Efficacy subscale from PALS has been widely applied and there are some studies to suggest thatthe Academic Efficacy subscale from PALS shows adequate construct validity. For example, the negative association betweenAcademic Efficacy and personal performance-avoidance goals found in a study of Middleton and Midgley (1997) implies that theAcademic Efficacy subscale is related in expected ways to other measures from PALS. Moreover, in previous studies (e.g., Midgley etal., 2000), the Academic Efficacy subscale displayed sufficient reliability (α = .78). In the present sample, the Cronbach's alpha of thismeasure was also .78, and therefore sufficient as well.

2.2.5. CovariatesThe effects of student gender, ethnicity, special education needs (SEN), and socioeconomic status (SES) were controlled for in the

hypothesized model, given their associations with the student–teacher relationship quality and academic adjustment (e.g., Baker,2006; Rudasill et al., 2006; Saft & Pianta, 2001). Becausematernal education has previously been demonstrated to be a good indicatorof a number of school-related outcomes (e.g., Magnuson, 2007; Saft & Pianta, 2001), this variablewas used as a proxy of students' SES.Maternal education comprised four categories: no more than primary education, secondary prevocational education, seniorsecondary vocational education, and higher education. Information about ethnicity was collected from the school administrators, andbased on the country of birth of student's mothers. Given the small proportions of ethnic groups other than Dutch in the sample,preliminary analyses of variance were performed to determine whether these minority groups differed with regard to student–teacher relationship quality, motivational beliefs, and academic achievement. The results showed no significant differences (p > .05).Therefore, ethnic minorities were treated as one group and contrasted with the Dutch majority group. Information about studentswith SEN was obtained from the teachers. Following Pijl, Frostad, and Flem (2008), SEN refers to “various (combinations of)impairments and/or difficulties in participating in education” (p. 389). In the present study, students with SEN were operationalizedas those who received some sort of special education based on an individual education plan (IEP). Eligibility criteria for IEPs werevisual and hearing impairments, mental and physical handicaps, behavioral problems, and developmental disorders, such as autism.Accordingly, a dichotomous single-indicator variable was used, requesting the teacher to indicate whether or not the student wasadmitted to an individual education plan. Gender was dummy coded, such that girls were assigned a value of 1 and boys a value of 0.

2.3. Procedures

Data from the first wave of COOL were collected in three phases. First, between April and September 2007, 2800 schools received aformal letter of invitation to take part in the COOL cohort-study. Of these schools, 550 were ultimately recruited. After gaining theschool's agreement to research participation, informed consent was obtained from the parents by providing them with a writtenaccount of the study's purposes and a permission form in their native language that could be returned to the student's school. In thesecond phase (September 2007), extensive data about students' background characteristics were obtained from the schooladministrators. In the third and last phase (January–April 2008), students' scores on mathematics and reading comprehension weregathered by e-mail from the teachers. During that same period, questionnaires regarding personality, student–teacher relationshipquality, and motivational beliefs were administered to teachers and students by research assistants. Both students and teacherscompleted these questionnaires in their own classrooms. The response rate of the student-completed questionnaires was 94.9% andcompleted teacher-reported questionnaires were available for 94.3% of the sample. Nonparticipation of students and teachers wasdue to absences or sickness at the time of data collection.

2.4. Statistical analyses

The datawere not independent, as theywere nestedwithin classrooms and corresponding teachers.1 To avoid underestimation ofstandard errors, structural equation modeling (SEM) procedures for complex survey data were warranted in examining thehypothesized theoretical model (Muthén & Muthén, 2007). Unlike traditional linear modeling techniques, SEM procedures forcomplex survey data are quite flexible in that they allow for the simultaneous estimation of direct and indirect influences inhierarchically clustered data, and the adjustment of measurement errors by using latent constructs (Kline, 2011; Preacher, Zyphur, &Zhang, 2010).Model fittingwas performed inMplus, version 6.11, usingmaximum likelihood estimationwith robust standard errors,and amean-adjusted chi-square statistic test (MLR; Muthén &Muthén, 2007). A check of the data for statistical assumptions showedno problems.2 Missing data on the continuous variables (b6%) were handled by use of the EM algorithm, after finding that Little'sMCAR test was not statistically significant, p = .901 (Tabachnick & Fidell, 2007).

1 Intraclass correlation coefficients at level two (class level) were in the range of .04–.24 and intraclass correlations at level three (school level) were in therange of .00–.02. Given the relatively small proportion of variance associated with the third level of hierarchy, a two-level model was estimated in the presentstudy.

2 Tests of skewness and kurtosis were nonsignificant for all variables included in the study. Data were found to meet assumptions regarding multivariatenormality and linearity.


Given the relatively large sample size and the exploratory nature of the study, models were fitted using a cross-validationprocedure, involving randomly selecting calibration and validation samples,3 estimating the hypothesized model with thecalibration sample and then refitting the model to the validation sample. In order to determine whether the hypothesized modelholds across the two samples, the fit indices and beta-coefficients of the two samples were compared (e.g., Yuan, Marshall, &Weston, 2002).

2.4.1. Measurement modelPrior to analyzing the hypothesized structural model, a measurement model was tested in two steps to evaluate the fit of the

hypothesized latent variables and to find evidence for the internal validity and common factor structure of the measures (Kline, 2011).First, amodel was estimated that only contained items that were used as indicators of the five latent constructs (i.e., Motivational Beliefs,student-reported Closeness, and the three teacher-reported constructs of student–teacher relationship quality). Secondly, after evidencewas found supporting the factor structure for the latent constructs, the remaining single indicator variables (i.e., students' personalitytraits, their reading and math scores, and covariates) were included in the model. To achieve model identification, the firstunstandardized factor loading of each construct was fixed to equal 1.0, and all latent variable variances and covariances were allowed tobe freely estimated. The error variances of the single indicators were set to zero, as perfect measurement of each variable was assumed.

2.4.2. Structural modelThe hypothesized structural model was evaluated in three steps. The first step involved fitting the hypothesized structural model

withmain effects only. Covariates and additional parameters were added stepwise. The second step entailed a cross-validation of thismodel. Cross-validation of complex structural models is acknowledged to be essential, given that model respecification in the initialsamplemight have been capitalized on chance aspects of the data (e.g., Yuan et al., 2002). Multiple group analyseswere performed totest the correspondence between the two samples. Equality constraints were gradually imposed on both the measurement andregression coefficients. In the third step, two alternativemodels were estimated to test for mediation. First, a direct effects model wasfitted, inwhich the effects of the student–teacher relationship quality onmath and reading comprehensionwere freely estimated andthe effects of themediators constrained to equal zero. Second, a partial mediationmodel was fitted, in which theMotivational Beliefsfactor was inserted back in the model. For ease of interpretation, reading and math achievement were centered around their grandmean, and their error variances were allowed to covary. Because aspects of the student–teacher relationship were assumed to beindicators of a similar construct (i.e., student–teacher relationship quality) and were generally reported by the same source, theirfactor covariances were freely estimated as well.

2.4.3. Model goodness-of-fitThe overall goodness-of-fit of themodels was evaluated by themean-adjustedχ2 test, with nonsignificant chi-squares indicating

satisfactory fit. Given the large sample size and statistical power of the test, however, even a trivial discrepancy between the expectedand the observed model may lead to rejection of the model (Chen, 2007). Therefore, additional fit indices were calculated, includingthe root mean square of approximation (RMSEA). Values≤ .05 reflect a close fit, and≤ .08 a satisfactory fit (Browne & Cudeck, 1993).The comparative fit index (CFI) was also obtained, with values ≥ .90 indicating satisfactory fit, and values ≥ .95 indicating close fit(Bentler, 1992). The fit of the measurement components of the model was evaluated by inspecting themodification indices, residualcorrelations, and their associated summary statistic SRMR (standardized root mean square residual). Values≤ .08 indicate relativelygood fit of the model to the data (Kline, 2011).

Differences in model fit were tested with the Satorra–Bentler scaled chi-square difference test (TRd; Satorra, 2000; Satorra &Bentler, 2010), with nonsignificant chi-squares indicating equivalent fit, and the CFI-difference, with CFI changes ≥ .02 beingindicative of model nonequivalence (Cheung & Rensvold, 2002). Additionally, the RMSEA-based root deterioration per restriction(RDR), and expected cross-validation index (ECVI)-differences were calculated using the computer program NIESEM (Dudgeon,2003), along with their corresponding 90% confidence intervals. When RDR-values do not exceed .05, they indicate an essentiallyequivalent fit. ECVI-differences between two hierarchically nested models are considered equal when their 90% confidenceinterval does not include zero (e.g., Oort, 2009).

3. Results

3.1. Measurement model

The first measurement model with latent constructs did not reach a satisfactory fit to the data, χ2 (485) = 7446.92, p b .001,RMSEA = .058 (90% CI [.057, .059]), CFI = .87, SRMR = .053. In order to diagnose potential sources of misfit, the correlationsbetween the residuals and modification indices were inspected. Six residuals appeared to be over-predicted by the model.Stepwise addition of these correlation residuals resulted in a more satisfactory model: χ2 (479) = 3718.47, p b .001, RMSEA =.040 (90% CI [.038, .041]), CFI = .94, SRMR = .048. In the second iteration of the measurement model that included singleindicators as well as the latent constructs, the model also yielded a good fit to the data, χ2 (787) = 5549.27, p b .001, RMSEA =.037 (90% CI [.037, .038]), CFI = .93, SRMR = .044. In this model, no systematic patterns of misfit were identified, and the factor

3 Calibration (n = 4,308) and validation (n = 4237) samples did not significantly differ in gender, χ2(1) = 3.65, p = .06; age, t(8543) = 1.10, p = .23; SES,t(8543) = 0.30, p = .76; SEN, t(8543) = −0.04, p = .97; and distribution of ethnicity, χ2 (1) = 1.15, p = .28.


loadings, standard errors and interfactor correlations were of the appropriate sign and magnitude. All standardized factorloadings were considered relatively high (>.50), except for one item for Motivational Beliefs (.36). These results provide evidencethat the factors, when specified, correspond to the hypothesized structure and provide support for the internal validity andcommon factor structure of the measures.

3.2. Structural model

3.2.1. Hypothesized modelThe initial structural model tested was the hypothesized model with main effects only. The model provided acceptable overall

fit: χ2 (818) = 6488.62, p b .001, RMSEA = .040 (90% CI [.039–.041]), SRMR = .053, CFI = .92. To diagnose potential sources ofmisfit in the model, the modification indices were inspected. Based on these indices, three theoretically supported modificationswere made and maintained in the model. These were direct paths from Conscientiousness, Neuroticism, and Autonomy tostudents' Motivational Beliefs. The test results demonstrated a satisfactory fit of the final model to the data, χ2 (815) = 5828.54,p b .001, RMSEA = .038 (90% CI [.037–.039]), SRMR = .047, CFI = .93.

3.2.2. Cross-validationThe fit indices suggested that the structural model fitted the validation sample quite well: χ2 (815) = 6093.79, p b .001,

RMSEA = .039 (90% CI [.038, .040]), CFI = .92, SRMR = .049. The results for the multiple group analysis investigating differencesbetween the calibration and validation sample are presented in Table 1. Results showed sufficient equivalence to speak of anadequate attempt of cross-validation, thereby generally supporting the validity of the final model. The final structural model andstandardized regression coefficients are shown in Table 2 and in Fig. 2. Dashed lines represent the three paths added post hoc.

3.3. Predictors of student–teacher relationship quality and motivational beliefs

The structural model largely reflected the hypothesized effects of students' personality traits on the student–teacherrelationship quality. Assessment of path coefficients in the model pointed to significant paths from teacher-rated student–teacherDependency to students' Agreeableness (β = − .05, p = .003), Neuroticism (β = .14, p b .001), and Autonomy (β = − .05, p =.003). Thus, while controlling for other personality traits, a level of Neuroticism one standard deviation above the mean predictsteacher-reported Dependency .15 standard deviation above the mean. This indicates that the magnitude of the positive pathcoefficient between Neuroticism and teacher-reported Dependency is almost three times greater than the negative paths fromAgreeableness and Autonomy to teacher-reported Dependency (Kline, 2011). The hypothesized paths between teacher-reportedDependency and other student personality traits were not supported. In addition, the paths from student Extraversion (β = .11,p b .001), Agreeableness (β = − .16, p b .001), Conscientiousness (β = − .07 p b .01), Neuroticism (β = .08, p b .001), andAutonomy (β = .05, p = .005) to teacher-reported Conflict were statistically significant.

In terms of Closeness, paths from teacher-reported Closeness to Extraversion (β = .09, p b .001), Agreeableness (β = .11,p b .001), and Conscientiousness (β = .06, p = .001), and paths from student-reported Closeness to Extraversion (β = .11,p b .001), (Agreeableness: β = .24, p b .001), Conscientiousness (β = .23, p b .001), and Neuroticism (β = − .07, p = .001)appeared to be statistically significant. Paths from Conscientiousness (β = .28, p b .001), Neuroticism (β = − .21, p b .001), andAutonomy (β = .27, p b .001) to students' Motivational Beliefs were also found to be statistically significant. Overall, students'personality traits accounted for 7.2% of the variance in teacher-reported Dependency, 11.3% of the variance in teacher-reportedConflict, 5.8% of the variance in teacher-reported Closeness, and 16.9% of the variance in student-reported Closeness.

3.4. Covariates of student–teacher relationship quality and school success

Given their potential influence on student–teacher relationship quality and academic adjustment, the effects of studentgender, ethnicity, SEN, and SES were controlled for in the final model. With regard to these background characteristics, inspection

Table 1Fit indices for multiple-group invariance analyses of the validation and calibration sample.

Model χ2(df) RMSEA (90% CI) CFI SRMR TRd (df) ΔCFI RDR (90% CI) ΔECVI (90% CI)

Baseline model(Equal form)

11,978.49⁎⁎ .038 .92 .048 – – – –

(1658) (.038, .039)Model 1(Equal factor loadings)

11,985.17⁎⁎ .038 .92 .048 23.64 (28) .00 .00 − .003(1686) (.037, .038) (.00, .009) (− .003, − .002)

Model 2(Equal path coefficients)

12,062.75⁎⁎ .037 .92 .048 43.91 (49) .00 .00 − .006(1735) (.037, .038) (.00, .008) (− .006, − .004)

Model 3(Equal residual variances and covariances)

11,969.08⁎⁎ .037 .92 .049 43.04 .001 .005 (.00, .012) − .004(1774) (.036, .037) (39) (− .004, − .002)

Note. RMSEA = root mean square error of approximation; CFI = comparative fit index; SRMR = standardized root mean square residual; TRd = Satorra–Bentlerscaled chi-square difference test; RDR = root deterioration per restriction; ECVI = expected cross-validation index.⁎⁎ p b .001.

Table 2Maximum likelihood estimates for the final structural model of antecedents and consequences of the student–teacher relationship quality.

Dependency Conflict Closenessteacher Closenessstudent Motivationalbeliefs

Reading Math

dir. ind. total dir. ind. total dir. ind. total dir. ind. total dir. ind. total dir. ind. total dir. ind. total

Extrav. .03⁎ – .03⁎ .11 – .11 .09 – .09 .11 – .11 – .04 .04 – .01 .01 – .01 .01Agree. -.05 – -.05 -.16 – -.16 .11 – .11 .24 – .24 – .06 .06 – .02 .02 – .02 .02Consc. .01⁎ – .01⁎ -.07 – -.07 .06 – .06 .23 – .23 .28 .05 .33 – .01 .01 – .02 .02Neur. .14 – .14 .08 – .08 .02⁎ – .02⁎ -.07 – -.07 -.21 -.03 -.24 – .01 .01 – -.01 -.01Auton. -.05 – -.05 .05 – .05 .00⁎ – .00⁎ -.01⁎ – -.01⁎ .27 .01⁎ .28 – .00⁎ .00⁎ – .00⁎ .00⁎

Depend. – – – – – – – – – – – – -.11 – -.11 -.12 -.03 -.15 -.12 -.03 -.15Conflict – – – – – – – – – – – – .09 – .09 – .02 .02 – .03 .03CloseT – – – – – – – – – – – – .02 ⁎ – .02 ⁎ – .01⁎ .01⁎ – .01⁎ .01⁎

CloseS – – – – – – – – – – – – .27 – .27 – .07 .07 – .08 .08Motiv. – – – – – – – – – – – – – – – .26 – .26 .29 – .29Read. – – – – – – – – – – – – – – – – – – – – –

Math – – – – – – – – – – – – – – – – – – – – –

Gender – – – -.14 – -.14 .12 – .12 – – – -.05 – -.05 .11 – .11 -.12 – -.12SEN .18 – .18 .13 – .13 – – – – – – -.12 – -.12 -.24 – -.24 -.27 – -.27Ethnicity – – – .05 – .05 -.06 – -.06 – – – .09 – .09 -.11 – -.11 -.10 – -.10SES -.09 – -.09 -.07 – -.07 – – – .04 – .04 – – – .29 – .29 .20 – .20

Note. Standardized regression coefficients (β) are reported. Direct effects of the alternative mediation models are not presented in the table. Gender, ethnicity,and special educational needs (SEN) were coded as binary variables (0 = boys and 1 = girls; 0 = native Dutch and 1 = non-Dutch; 0 = without SEN and 1 =with SEN). Dir. = direct effects; Ind. = total indirect effects; Total = total effects. *p > .05. For all other standardized regression coefficients, p b .05.


of the path coefficients in the model indeed revealed statistically significant paths from SEN, gender, ethnicity and SES to student–teacher relationship quality and academic adjustment. First, students with SEN had more teacher-reported Conflict (β = .13,p b .001) and teacher-reported Dependency (β = .18, p b .001) and had lower Motivational Beliefs (β = − .12, p b .01), andlower reading and math scores (β = − .24; β = − .27, p b .001) than students without such needs. Teachers generally

Extrav.

Consc.

Auton.

Neurot.

Agree.

D

D

D

D

D

Close T

C2 C3 C4 C5C1

Close S

C3 C4 C5 C6C2C1 C7

Depend.

D2 D3 D4 D5D1

Confl.

C2 C3 C4 C5C1

Motiv.

M2 M3 M4 M5M1

E4E3E1 E2 E5 E6

Math

Read

D

D

Note. Dashed lines represent paths added post hoc. For reasons of parsimony, the freely estimated factor covariances are not displayed. Extrav. = Extraversion; Agree. = Agreeableness; Neurot. = Neuroticism; Auton. = Autonomy; Close T = Teacher-reported Closeness; Confl. = Conflict; Depend. = Dependency; Close S = Student-reported Closeness; Motiv. = Motivational Beliefs; Math = Math test; Read = Reading Comprehension test.

ε ε ε ε ε ε ε

ε ε ε ε ε

ε ε

εεεεεεεεεε

ε ε ε ε ε

ε ε ε ε

Fig. 2. Final structural model.


experienced less Conflict (β = − .14, p b .001) and more Closeness (β = .12, p b .001) in the relationship with girl students.Moreover, girls appeared to have lower Motivational Beliefs (β = − .05, p = .001) and math scores (β = − .12, p b .001) thanboys, but they had better reading comprehension skills (β = .11, p b .001). Dutch students and students from higher-SESbackgrounds performed better on reading (β = .11; β = .29, p b .001) and math (β = .10; β = .20, p b .001) than didnon-Dutch students and students from lower-SES backgrounds. Ethnic minority students had higher Motivational Beliefs (β =.09, p b .001) and more teacher-reported Conflict (β = .05, p b .001) and less teacher-reported Closeness (β = − .06, p b .001)in relationships with teachers than their Dutch peers. Jointly, students' background characteristics, their personality traits, and thestudent–teacher relationship quality accounted for 38.4% of the variance in students' Motivational Beliefs, 27.1% of the variance inmath achievement, and 27.4% of the variance in reading comprehension.

3.5. Consequences of student–teacher relationship quality and motivational beliefs

Model results for the quality of student–teacher relationships and students' Motivational Beliefs were not all according toexpectations. First, as expected, positive coefficients were found for the path from student-reported Closeness to students'Motivational beliefs (β = .27, p b .001), and negative coefficients were found for the path from teacher-reported Dependency tostudents' Motivational Beliefs (β = − .11, p b .001). With other aspects of the student–teacher relationship in the model, thepath coefficient between teacher-reported Closeness and Motivational Beliefs was not statistically significant (β = .02).

Most unexpectedly, a significant positive path from teacher-reported Conflict to Motivational Beliefs (β = .09, p b .001) wasrevealed in the model. It seems as if teacher-reported Conflict functioned as a suppressor variable for the effects of other aspectsof the relationship quality on Motivational Beliefs. Specifically, teacher-reported Conflict correlated substantially withteacher-reported Dependency (r = .54), whereas it had only modest negative correlations with students' Motivational Beliefs(r = − .10). In the structural model, however, the coefficient of the path from teacher-reported Conflict to Motivational Beliefswas opposite in sign, and the coefficient of the path from Dependency to Motivational Beliefs became somewhat stronger. Whenentered alone, the path coefficient from teacher-reported Dependency to Motivational Beliefs decreased significantly (β = − .06,p b .001) and the path from teacher-reported Conflict to Motivational Beliefs reached zero (β = .02). This pattern may indicatethat teacher-reported Conflict has much more in common with teacher-reported Dependency, than with the variance of students'Motivational Beliefs. Thus, by controlling for irrelevant variance that is shared with teacher-reported Dependency, but not withMotivational beliefs, teacher-reported Conflict improves Dependency as a predictor of Motivational Beliefs. The effects ofteacher-reported Conflict and Dependency are therefore better interpreted in combination with each other, rather thanseparately (Maassen & Bakker, 2001; Pedhazur, 1982).

3.5.1. Mediation effectsThe positive paths between students' Motivational Beliefs and their academic achievement suggest that, after controlling for

students' background characteristics, Motivational Beliefs did positively add to both their reading (β = .26, p b .001) and mathachievement (β = .29, p b .001). To investigate whether Motivational Beliefs also functioned as a mediator between the student–teacher relationship quality and academic achievement, a series of alternative models was tested (but not reported in Fig. 2). First, adirect effects model was fitted in which the effects of the student–teacher relationship quality factors on math and readingachievement were freely estimated and the mediator constrained to equal zero. This model fitted the data relatively well: χ2

(811) = 5994.39, p b .001, RMSEA = .039 (90% CI [.038–.039]), SRMR = .055, CFI = .92. When eliminating Motivational Beliefsfrom the model, the paths from teacher-reported Dependency to math (β = − .14, p b .001) and reading achievement (β = − .14,p b .001) appeared to be negative and statistically significant. The direct paths from teacher-reported Conflict and teacher-reportedCloseness to the twomeasures of achievementwere not supported by themodel. The combined predictors accounted for 21.6% of thevariance in math and 23.6% of the variance in reading comprehension.

Secondly, a partially mediated model was fitted, in which students' Motivational Beliefs were inserted back into the model.The resulting model (not reported in Fig. 2) had a satisfactory fit to the data: χ2 (807) = 5718.83, p b .001, RMSEA = .038 (90%CI [.037–.039]), SRMR = .044, CFI = .93. The presence of Motivational Beliefs in the model did not considerably reduce thecoefficient of the direct paths from teacher-reported Dependency to students' reading (β = − .12, p b .001 and mathachievement (β = − .12, p b .001). Indirect effects were estimated using the Delta method (see MacIntosh & Hashim, 2003).Results showed that the indirect paths from teacher-reported Dependency to math (β = − .03, p b .001) and reading (β = − .03,p b .001) and from student-reported Closeness to math (β = .08, p b .001) and reading (β = .07 p b .001), though very small,were statistically significant. The indirect paths from teacher-reported Conflict and student-reported Closeness to theachievement scores were not statistically significant. These results suggest that teacher-reported Dependency is not only directlyrelated to students' academic achievement, but also indirectly, through their Motivational Beliefs. The relationship betweenstudent-reported Closeness and students' reading and math achievement, in addition, was fully mediated by their MotivationalBeliefs.

4. Discussion

In this study, a theoretical model was tested hypothesizing that upper elementary school students' personality traits predict thequality of student–teacher relationships, and that student–teacher relationship quality indirectly affects students' achievement via


the direct effect on their motivational beliefs. In general, the findings provided only modest support for the study's propositions, butthey contributed to the existing literature on student–teacher relationships in several ways.

4.1. Predictors of student–teacher relationship quality in upper elementary school

Students' personality traits appeared to be associated with the degree of closeness, conflict, and dependency in the student–teacher relationship. Notably, interpersonal aspects of students' personality (i.e., extraversion, agreeableness, and neuroticism)seemed to contribute more than cognitive aspects (i.e., conscientiousness and autonomy) to explaining the student–teacherrelationship quality. Students who reported higher levels of agreeableness and lower levels of neuroticism were more likely tohave closer and less dependent or conflictual relationships with their teachers. These findings are largely consonant with those ofMartin, Watson, and Wan (2000), who identified a lower-order Cynical Cognition trait that represents a combination of neuroticand disagreeable characteristics. Persons scoring high on this trait seem to be more likely to mistrust others, and to feelmistreated and insecure in social relationships (Crick & Dodge, 1994). Other work (e.g., Birch & Ladd, 1998; Howes et al., 2000;Ladd & Burgess, 1999; Rudasill & Rimm-Kaufman, 2009) has also pointed to the association between low levels of student–teacher relationship quality and aspects of temperament, such as a lack of effortful control and anger.

Students with dispositions towards neuroticism were also less likely to display positive motivational beliefs. Typically,emotionally unstable students are described as vulnerable to stress and lacking in confidence, and seem to focus more on theiremotional state than on their school work (De Raad & Schouwenburg, 1996). Such traits are likely to produce students whobelieve that they are not fully able to cope with academic tasks and responsibilities (Judge, Erez, Bono, & Thoresen, 2002). Thus,for highly neurotic students who are already less likely to profit from close relationships with teachers, poorer motivationalbeliefs may place them at additional risk for performance decrements in upper elementary school (Fredricks & Eccles, 2002).

In addition, extraversion was not only associated with teacher- and student-reported closeness, but also with higher levels ofconflict between teachers and students. Thus, despite the fact that extraverted students are likely to display behavior thatincreases the opportunity for student–teacher closeness, such as assertive and gregarious behavior, being extraverted may alsolead students to engage in more conflictual relationships. These results substantiate those of other studies in that extravertedstudents may be more likely to experience simultaneously high conflict and closeness with teachers, whereas inhibiteddispositions such as shyness may function as a buffer against conflict in the classroom (e.g., Rudasill & Rimm-Kaufman, 2009;Rydell, Bohlin, & Thorell, 2005). It may be that, because extraverted individuals frequently seek and enjoy the attention of others,they occasionally exert behaviors that push teachers away from them. Indeed, in a study of Rudasill and Konold (2008), it wasfound that withdrawn-oriented, introvert students typically display quiet and obedient behaviors and are therefore more unlikelyto engage in disrupting behaviors in the classroom.

In line with previous studies (e.g., Hair & Graziano, 2003), cognitive aspects of students' personality appeared to bemore stronglyassociated with students' academic adjustment than with student–teacher relationship quality. Both highly conscientious and highlyautonomous students were more likely to bemotivated to succeed, compared with less autonomous or less conscientious peers. Thisfinding is consistent with motivational research indicating that when students' needs for academic support and autonomy are met,their task motivation, self-efficacy beliefs, and locus of responsibility for their own learning are likely to be increased (Patrick,Mantzicopoulos, Samarapungavan, & French, 2008; Reeve et al., 1999; Ryan & Powelson, 1991).

Of the cognitive Big-Five dimensions, conscientiousness was a better predictor of student–teacher relationship quality thanautonomy. Consistent with expectations, results suggest that higher conscientiousness prevented unfavorable relationships andpromoted warmth and security between teachers and students. Unlike conscientiousness, autonomy did not seem to beassociated with the degree of warmth and security experienced by teachers and students. A possible explanation is that highlyautonomous students, and older children in particular, may have a tendency to meet their own emotional needs (cf. Ang, 2005).By doing so, they are likely to make fewer emotional demands on their teacher and, in turn, increase the psychological distancebetween themselves and the teacher, possibly resulting in more negative student–teacher relationships (Ang et al., 2008).Findings from Coplan, Prakash, O'Neil and Armer (2004) furthermore suggest that children who mainly operate on their ownshow no sign of having any—positive or negative—relationship with their teacher at all. Thus, autonomous students may just notbe interested in social contact with their teacher, and, in turn, may be less demanding, and perfectly content to work alone.Overall, the present results suggest that aspects of students' personality are associated with howwell they relate to their teachers,and perform academically.

4.2. Student–teacher relationship quality and academic adjustment in upper elementary school

Mediation models suggested that students' motivational beliefs, at least in part, acted as a mediator of the association betweenstudent–teacher relationship quality and students' academic achievement. Regarding positive relationships, students' motivationalbeliefs fully mediated the associations between student-reported closeness but not the associations between teacher-reportedcloseness and math and reading achievement. One possible explanation for this difference is that some of the variance that is sharedbetween students' perceptions of closeness and their motivational beliefs might be attributed to source effects. This explanation is inline with research indicating that student reports typically account for the largest proportion of method variance and that teacherreports account for the largest proportion of trait variance (Li, Hughes, Hsu, & Kwok, 2011).

With respect to negative relationships, results demonstrated that the association between dependency and academicachievement was partially mediated by students' motivational beliefs. Students having overly dependent relationships with


teachers were more likely to feel less confident in their ability to achieve academically, and these beliefs, in turn, were likely to beassociated with lower academic performance. Moreover, beyond this indirect association, there was a direct negative associationbetween dependency and students' math and reading achievement. It is noteworthy that conflict in the student–teacherrelationship did not independently predict variance in students' academic adjustment. Although conflict usually has the strongestassociations with school outcomes (e.g., Hamre & Pianta, 2001), in this study, it merely seemed to function as a correction factorfor predicting the negative association between dependency and students' academic success. Thus, contrary to previous beliefs(e.g., Ang, 2005), inappropriate degrees of overreliance on the teacher may take a more important position than high levels ofconflict in predicting older elementary students' adjustment.

4.3. Limitations and future directions

Several limitations of this study that call for further research need to be considered. First, student–teacher relationships,motivational beliefs, and student's academic achievement are part of complex processes that may not be fully captured by thedesign of the current study. The direction of influences is difficult to establish, and there also may be reciprocal relations betweenthe quality of student–teacher relationships and the outcome variables in this study. Longitudinal designs could deepen theunderstanding of how the quality of student–teacher relationships affects students' academic adjustment, especially for studentsat risk of adjustment problems. To disentangle these effects, it is recommended to employ data from the forthcoming waves of theCOOL cohort-study in future studies.

Second, in this study a two-level model was tested, thereby ignoring the potential clustering of classrooms within schools(Nezlek, 2008). In this study, however, it was found that only a very small proportion of variance (ICCs ranged from .00 to .02)was associated with the third level of hierarchy (i.e., the school level). This small proportion of variance might be explained by thefact that the number of classes per school was only 1.9 on average. This suggests that the hypothesized relationships between thepredictors and outcome variables in this study did not vary across schools. Despite this, the clustering of classrooms withinschools may warrant consideration in future studies, especially when the number of classes per school is generally large.

Third, the occurrence of a suppressor variable made the interpretation of some of the results difficult, and it has potentiallylimited the generalizability of the findings (Maassen & Bakker, 2001). The complex set of associations between the student–teacher relationship variables suggests that, to better understand the links between student–teacher relationship quality andacademic adjustment, it may be advisable for future researchers to investigate the relationship qualities separately.

Fourth, the low reliability estimate of one of the Big Five personality subscales used in this study, measuring autonomy, mighthave affected the meaningfulness of the results. There is some evidence to suggest that the inherently abstract nature of some ofthe items of this scale may lead upper elementary students to misinterpret these items more frequently (e.g., Hendriks et al.,2008). In relation to this, young students' self-reports of autonomy appear to be more reliable when their cognitive ability level isgenerally high (Hendriks et al., 2008; Laidra et al., 2007). Although the FFPI has been shown to be psychometrically suited forupper elementary students' self-perceptions of their personality (Hendriks et al., 2008) and cross-validation of the data wasemployed, replication is needed to ensure the consistency of the results found in this study.

Fifth, the methods of data collection used in this study may also have influenced the findings. Although this study did notexclusively rely on teacher-reports for characterizing the quality of student–teacher relationships, no student-reported measures ofconflict and dependency or observational data about students were available. Additionally, this study included only characteristics ofthe students, whereas the quality of student–teacher relationships has also been found to be driven by characteristics of the teachers,such as ethnicity, gender, and educational level (Kesner, 2000; Mashburn & Henry, 2004; Saft & Pianta, 2001; Spilt, Koomen et al.,2012). Factors such as ethnicmatch between students and teachers have aswell been found to have an impact on the extent towhichethnicity affects student–teacher relationships (e.g., Murray, Murray, & Waas, 2008; Saft & Pianta, 2001; Thijs, Westhof, & Koomen,2012). In any attempt to replicate the results, it is recommended that future researchers should take account of both teacher andstudent characteristics, and the possible match between them, and use multiple data sources to elucidate the complexities of thestudent–teacher relationship.

Lastly, it should be noted that the measure of SEN was based on a dichotomous single indicator. Although this item isconsistent with measures used in research on learning problems (e.g., Hamre & Pianta, 2005), it does not differentiate betweenthe various types of special needs that students have in the classroom. The use of a more comprehensive measure of students'special educational needs is warranted to more specifically address the source of the differences between typically developingstudents and students with special needs found in this study.

5. Conclusion

Despite its limitations, the current study contributes to the literature on student–teacher relationships in several ways. First,this study has shown that students' inner resources, especially interpersonal aspects of personality, predict and may promote orhamper the quality of relationships between teachers and upper elementary students. Students' personality traits can hardly bealtered. However, teachers' awareness of students' character may help them to shape a supportive learning environment that ismore in tune with students' social and academic needs in upper elementary school.

Second, both student perceptions of closeness and teacher perceptions of dependency in the relationship appear to be (at leastpartially) associated with upper elementary students' achievement through their internalized resources (i.e., motivationalbeliefs). The finding that dependency rather than conflict was the most relevant negative predictor of academic adjustment is


particularly important, given that previous issues of reliability and validity have prevented researchers to establish and verify itssignificance (e.g., Doumen et al., 2009; Ewing & Taylor, 2009; Kesner, 2000). Future research could use the adapted andpsychometrically sound dependency scale (Koomen et al., 2007, 2012) to further investigate the ways in which conflict anddependency uniquely and in combination affect elementary students' academic adjustment.

Acknowledgments

The collection of the data used for this research was funded by the Netherlands Organization for Scientific Research(NWO-PROO).

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