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

Journal of School Psychology 51 (2013) 517–533

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Student–teacher relationship quality and academic adjustment in upper elementary school: The role of student personality Marjolein Zee, Helma M.Y. Koomen ⁎, Ineke Van der Veen University of Amsterdam, The Netherlands

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Article history: Received 13 April 2012 Received in revised form 4 May 2013 Accepted 8 May 2013 Keywords: Student–teacher relationships Upper elementary school Big Five Personality Motivation Academic adjustment

a b s t r a c t This study tested a theoretical model considering students' personality traits as predictors of student–teacher relationship quality (closeness, conflict, and dependency), the effects of student–teacher relationship quality on students' math and reading achievement, and the mediating role of students' motivational beliefs on the association between student–teacher relationship quality and achievement in upper elementary school. Surveys and tests were conducted among a nationally representative Dutch sample of 8545 sixth-grade students and their teachers in 395 schools. Structural equation models were used to test direct and indirect effects. Support was found for a model that identified conscientiousness and agreeableness as predictors of close, nonconflictual relationships, and neuroticism as a predictor of dependent and conflictual relationships. Extraversion was associated with higher levels of closeness and conflict, and autonomy was only associated with lower levels of dependency. Students' motivational beliefs mediated the effects of dependency and student-reported closeness on reading and math achievement. © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

1. Introduction The upper elementary school years bring many 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 more independent from their teacher (Ang, Chong, Huan, Quek, & Yeo, 2008; Lynch & Cicchetti, 1997), establish a sense of personal identity 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 school may cause the onset of a downturn in competence-related behaviors and motivation that may prevent them from succeeding academically (Fredricks & Eccles, 2002). Recent research suggests that the quality of student–teacher relationships may play a crucial role in helping students to navigate 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 construct reflecting the level of closeness (encompassing warmth, support, and open communication), conflict (including discordance and negativity), and dependency (including possessiveness and overreliance on the teacher) in the student–teacher relationship (see Pianta, 1994; Pianta, Steinberg, & Rollins, 1995). When there are high levels of closeness and low levels of conflict and dependency, students are more likely to be motivated to succeed, to feel successful in educational pursuits and, consequently, to perform 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 ⁎ Corresponding author at:v Research Institute of Child Development and Education, Universiteit van Amsterdam, PO Box 94208, NL-1090 Amsterdam, The Netherlands. Tel.: +31 20 5251524; fax: +31 20 5251200. E-mail address: [email protected] (H.M.Y. Koomen). ACTION EDITOR: Kathy Moritz Rudasill. 0022-4405/$ – see front matter © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jsp.2013.05.003

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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 this critical 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 between student–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 the potential effects of these negative relationship patterns on students' academic success. This lack of research is unfortunate, given that the quality of student–teacher relationships seems to be deteriorating by the time students reach the upper elementary grades (e.g., Baker, 2006; Furrer & Skinner, 2003; Lynch & Cicchetti, 1997; Spilt, Hughes, Wu, & Kwok, 2012). To address this lack of 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 their motivational 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 quality on students' academic adjustment. The host of those, including transactional, developmental-systems, and self-determination theories, share the assumption that neither individual nor environmental factors exclusively determine students' developmental outcomes. Rather, these outcomes are assumed to be the product of bidirectional interactions between students and their social environment (Pianta, Hamre, & Stuhlman, 2003; Ryan & Deci, 2002; Sameroff & Fiese, 2000). In examining processes that affect students' academic adjustment, this assertion is fundamental, as it points to the potential significance of the role that student features play in modifying the social context, which, in turn, may also adjust students' behavior. Some of these student features are known to be 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 social forces in the classroom, such as the student–teacher relationship. In concert, these student resources may promote or restrain students' active engagement and academic adjustment in the classroom. A comprehensive examination of such inherent and internalized resources may thus advance understanding of how specific student characteristics interface with supports provided by the 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 on the effects of students' inner resources (i.e., personality) on social forces in the classroom (i.e., student–teacher relationship quality), on the one hand, are combined with research on the association between student–teacher relationship quality and internalized student resources (i.e., motivational beliefs) and achievement, on the other (see Fig. 1). Theoretical and empirical justification 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 subsequent motivation and academic functioning is fairly well-established in prior research (Hamre & Pianta, 2001; Ladd, Birch, & Buhs, 1999; Roorda et al., 2011). Studies show that a sense of relatedness between students and teachers may provide students with internalized resources that enable them to 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-quality student–teacher relationships have been connected to a range of positive outcomes that underlie students' motivation to learn, such as 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 of conflict 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, both students and teachers tend to report gradual increases in conflict, and decreases in closeness by the time students reach the upper elementary grades (Baker, 2006; Jerome, Hamre, & Pianta, 2009; Spilt, Hughes et al., 2012; Spilt, Koomen, & Jak, 2012). A small number of studies make it clear, however, that high-quality student–teacher relationships continue to play an important part in older 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-Teacher Relationship Quality

Motivational Beliefs

Academic Achievement

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

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teacher support may diminish feelings of stress associated with increasing school complexity, changes in learning goals, and social comparison (Roeser et al., 1996; Wang & Holcombe, 2010). Students who feel that their efforts and skills are recognized by the teacher 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 with greater effort, to set numerous goals for themselves, and to exhibit greater compliance with teachers' behavioral and academic expectations (e.g., Furrer & Skinner, 2003; Wang & Holcombe, 2010; Wentzel, 2002; Wolters, Yu, & Pintrich, 1996). In a study by Goodenow (1993), over one third of the variance in sixth-to eighth-grade students' interest in and expectations of their academic work was explained by teacher support. A small but growing number of studies have included tests of the hypothesis that students' motivation-related attitudes and beliefs 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, and McGregor (2006) revealed that the emotional quality of students' involvement in middle school mediated the association between student–teacher relationship quality and their school achievement. Similar results were reported by Hughes, Wu, Kwok, Villarreal, and Johnson (2012) in a longitudinal study of children from third through fifth grades. These researchers found that teacher-rated behavioral engagement and students' math competence beliefs mediated the effect of students' perceptions of closeness on math achievement. In addition, teacher-rated engagement was also found to function 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 internalize positive motivational beliefs about their schoolwork. These internalized resources, in turn, are expected to lead to greater academic 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 in part, by (parents' and teachers' perceptions of) students' inner dispositions and behaviors in the classroom, such as temperament and 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 elementary grades, 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) noted that conflictual student–teacher relationships are more determined by student features than any other fluctuating aspects of teachers or the school environment. These findings call attention to the need for further exploration of fairly stable resources that students 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 with teachers 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 in personality differences in relation to student–teacher relationship quality and academic adjustment have described these differences according 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, by tradition, been distinguished from temperamental aspects, as personality traits are assumed to be rooted in temperamental variations in emotion, motor reactivity, and attention that are already present from birth onward (De Pauw & Mervielde, 2010; Mervielde, De Clercq, De Fruyt, & Van Leeuwen, 2005; Rothbart, 2007). Temperamental variations are biologically-based, and largely determine children's reaction to the environment and the processes that regulate them (Rothbart, 2007). The dynamic interplay between children's temperament and their environmental experiences forms the basis of children's personality. Compared to temperament, personality is considered wider in scope, focusing more on comprehensive, higher-order traits that account for behavioral variations among children and adolescents (Mervielde et al., 2005). In addition, whereas temperament mainly comprises a child's emotional and attentional capacities, personality also involves cognitive and motivational 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 coping strategies” (Rothbart, 2007, p. 207). Following this line of reasoning, it may thus be assumed that students' personality traits not only predispose them to engage in and value student–teacher relationships differently. These inner resources may, in turn, also have an influence on differences in students' internalized motivational beliefs. Despite the apparent contrast between temperament and personality, there is accumulating evidence that the common tenets of 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 as key dimensions in personality research, both for youngsters and for grown-ups. To our knowledge, comprehensive research on factors of the Big Five model has not yet been integrated with studies conducted on student–teacher relationships. A simultaneous exploration of all Big Five personality traits in relation to student–teacher relationship quality may help to integrate

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evidence from different lines of research, as well as move the field forward by generating new hypotheses about the yet unclear roles of personality factors such as autonomy and agreeableness. 1.3.1. Contributions of the Big Five to student–teacher relationship quality Given its leading role in psychological and educational research (Costa & McCrae, 1992; De Pauw & Mervielde, 2010), this study focuses on the factors of the Big Five model. Including this model in our study may offer valuable insights into both the variation in the quality 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 of students' 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, and gregarious behavior. Evidence has shown that students high in extraversion are more likely to experience positive affect during interactions with their teacher, are ready to seek help when needed, and engage more actively in joint activities (Bidjerano & Yun Dai, 2007. By spending more time with others, extraverts may actively create opportunities for warm and cooperative student–teacher relationships 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 between shyness and student–teacher relationship quality suggesting that teachers experience their relationship with shy children as less close 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 to initiate interactions from teachers than their more sociable peers. Other research (e.g., Saft & Pianta, 2001; Wentzel, 1991) has also 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 tend to be more cooperative and generally have higher quality interpersonal interactions, as they minimize interpersonal conflict by being 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 in turn initiates higher levels of motivation to work on school-related tasks, and may better prepare them for the academic challenges 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 individuals tend to reflect poor emotional adjustment in the form of 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) and irritable 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 associated with 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 that neurotic, emotionally unstable students are likely to be rated by teachers as difficult to handle, requiring more energy from the teacher to control their behavior and to assist them with engaging in classroom activities. Students scoring high in neuroticism may therefore have lower quality student–teacher relationships in the classroom. Thus, whereas extraversion and agreeableness may have a prominent position in sustaining high-quality student–teacher relationships, neuroticism will probably result in more conflictual and dependent student–teacher relationships. Compared to interpersonal aspects, cognitive aspects of students' personality are most often linked with motivational aspects in 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 relationships quality 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 being more caring and sociable towards others, adapt more easily to implicit and explicit social norms, and invest more in long-term relationships than their less conscientious peers do (Asendorpf & Wilpers, 1998; Noftle & Shaver, 2006). This pattern is likely to result in enhanced self-esteem, motivation, and appreciation by teachers and peers, which may in turn engender reciprocation in the 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, and also with intelligence (McCrae & Costa, 1987; McCrae & John, 1992). This trait has been marked by self-determination theorists as a 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 to initiate positive and conflict-free student–teacher relationships when they have dispositions towards curiosity, classroom exploration, and self-determination. Because persons higher in autonomy seem to be more open to change, and willing to transfer new skills and behaviors learned in one domain to benefit another, they tend to be more creative in developing solutions when

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conflict arises (Wayne, Musisca, & Fleeson, 2004). Conflict in the classroom is thereby likely to be reduced, resulting in better student–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 might initiate 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 relationship quality, 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 quality indirectly 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 the academic year of 2007–2008 in the Netherlands. COOL is a prospective longitudinal research project in which about 38,000 students from kindergarten, grade 3, and grade 6 are tested every three years in language, reading, and mathematics. Extensive information 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—is organized 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 particular marks the beginning of a challenging and important transition period for Dutch students. During this period, students are frequently 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 may serve 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 important decisions about the type and level of secondary education most suited to them. Generally, teachers make such recommendations on the grounds of an aptitude test called the Final Elementary Education Test, developed by the Dutch National Institute for Educational 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 good quality student–teacher relationships even more important in upper elementary classrooms (e.g., Ang et al., 2008). The present study, therefore, made use of a subset of the nationally representative sample, to examine student–teacher relationships with sixth-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 of the study (range = 8.0 to 13.0 years, SD = 0.6 years). Teachers indicated that 20.1% of the students had special educational needs. For 6.9% of the students, the teachers did not indicate whether their students had special needs or not. Information from the school administrators about student ethnicity was available for 96.8% of the children. A total of 78.8% of the students were of Dutch origin, and 18.1% were of non-Dutch origin. Mothers' educational background, in terms of the highest level of education completed, was available for 93.3% of the cases. In total, 9.0% of the mothers had finished primary school, 23.9% finished pre-vocational secondary education, 41.7% finished senior vocational education, and 18.6% finished higher education. 2.2. Measures 2.2.1. Student–teacher relationship quality Both 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 with each of their students were estimated using an authorized Dutch translated and slightly adapted version of the Student–Teacher Relationship Scale (STRS; Koomen, Verschueren, & Pianta, 2007). This instrument was especially developed to assess relationship quality for 3- to 12-year-old students. Like its original, the adapted STRS has shown to be represented by three distinct factors that are 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 Dependency measure negative aspects of student–teacher relationships, which are those in which teachers observe the relationship with students to be overly conflictual, or in which teachers experience the child to show clingy and demanding behavior. Example items 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 factor loadings reported in earlier research (Koomen et al., 2012). All items were rated on a 5-point Likert type scale, ranging from 1

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(definitely does not apply) to 5 (definitely applies). Investigators using the adapted STRS have reported satisfactory reliability and construct 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 study were .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 dyadic construct, its quality was also measured from the perspective of the student. Accordingly, students answered 7 questions concerning well-being with respect to the relationship with their teacher which primarily measure positive aspects of the student–teacher relationship (Peetsma, Wagenaar, & De Kat, 2001). This student-reported Closeness scale was rated on a 5-point Likert-type scale, ranging from 1 (definitively not true) to 5 (definitively true). Items that made up this scale included statements such as “Usually, my teacher knows how I feel” and “I have a good relationship with my teacher.” Cronbach's alpha of this scale was satisfactory (α = .78). 2.2.2. Outcome variables Students' 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 nationally normed achievement tests, designed to screen and determine the current level of reading comprehension and mathematics in grade 6. The reading comprehension test, which consists of 35 multiple-choice items, gives an indication of proficiency in the areas of conceptual reasoning and practical reading ability. The math test, which consists of 32 multiple-choice items, was designed to tap important 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 math and reading comprehension test are part. They concluded that the test content and structure were consistent across different test versions, 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. Using proper tables for students' age, the raw scores were then converted into age-standard ability scores. These ability scores are based on Item Response Theory and take the number and complexity of items of the reading comprehension and math test into account. In the present study, these ability scores were used to indicate students' achievement in both subjects. 2.2.3. Predictor variables Students 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 questionnaire developed to evaluate a person's position on the psycho-lexically based facets of Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Autonomy. Items that made up this measure were rated by students on a five-point Likert-type scale, ranging from 1 (not at all applicable) to 5 (entirely applicable), with higher scores indicating higher values on the five personality dimensions. Some slight adjustments were made to the FFPI to make its items more 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-person singular (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 the FFPI have been demonstrated to be sufficiently suited for use in this specific age group of students. In a sample of 12- to 13-year old students, Hendriks et al. (2008) reported sufficient reliability (the mean α across the five factors was .70), and showed that the structure of the adapted FFPI was construct-valid across gender and educational level. In the present sample, Cronbach's alpha values were as follows: Extraversion, .76; Agreeableness, .80; Conscientiousness, .79; Neuroticism, .74; Autonomy, .65. Composite scores for the five FFPI subscales were used to represent students' placement on the five personality factors. 2.2.4. Mediator variables Students 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 and expectancies were considered as motivators of academic achievement. The Task Motivation Scale (Seegers, Van Putten, & De Brabander, 2002) was used to evaluate students' motivational goals. This instrument is a self-report instrument composed of 5 items, which measure the extent to which students focus on mastering learning tasks and on learning opportunities in the context of school. All items were rated on 5-point Likert scales that range from 1 (definitively not true) to 5 (definitively true). Examples of items are “I feel satisfied when I have learned something in school that makes sense to me,” and “I feel satisfied when I have learned 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 and across gender, ethnicity, and students' socioeconomic status. In the present sample, the Cronbach's alpha of this measure was .75.

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Students rated their expectancies about their capability to perform academic tasks in the classroom using a translated version of the Academic Efficacy subscale from the Patterns of Adaptive Learning Survey (PALS; Midgley et al., 2000). The 6 items of this self-report measure were scored on a 5-point Likert scale, ranging from 1 (definitively not true) to 5 (definitively true). Statements such as “I'm certain I can figure out how to do the most difficult class work” and “I can do almost all the work in class if I don't give up” were included in this scale. The Academic Efficacy subscale from PALS has been widely applied and there are some studies to suggest that the Academic Efficacy subscale from PALS shows adequate construct validity. For example, the negative association between Academic Efficacy and personal performance-avoidance goals found in a study of Middleton and Midgley (1997) implies that the Academic Efficacy subscale is related in expected ways to other measures from PALS. Moreover, in previous studies (e.g., Midgley et al., 2000), the Academic Efficacy subscale displayed sufficient reliability (α = .78). In the present sample, the Cronbach's alpha of this measure was also .78, and therefore sufficient as well. 2.2.5. Covariates The 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). Because maternal education has previously been demonstrated to be a good indicator of a number of school-related outcomes (e.g., Magnuson, 2007; Saft & Pianta, 2001), this variable was used as a proxy of students' SES. Maternal education comprised four categories: no more than primary education, secondary prevocational education, senior secondary vocational education, and higher education. Information about ethnicity was collected from the school administrators, and based 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 students with 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 operationalized as those who received some sort of special education based on an individual education plan (IEP). Eligibility criteria for IEPs were visual 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 was admitted 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 a formal letter of invitation to take part in the COOL cohort-study. Of these schools, 550 were ultimately recruited. After gaining the school's agreement to research participation, informed consent was obtained from the parents by providing them with a written account of the study's purposes and a permission form in their native language that could be returned to the student's school. In the second phase (September 2007), extensive data about students' background characteristics were obtained from the school administrators. In the third and last phase (January–April 2008), students' scores on mathematics and reading comprehension were gathered by e-mail from the teachers. During that same period, questionnaires regarding personality, student–teacher relationship quality, and motivational beliefs were administered to teachers and students by research assistants. Both students and teachers completed these questionnaires in their own classrooms. The response rate of the student-completed questionnaires was 94.9% and completed teacher-reported questionnaires were available for 94.3% of the sample. Nonparticipation of students and teachers was due to absences or sickness at the time of data collection. 2.4. Statistical analyses The data were not independent, as they were nested within classrooms and corresponding teachers. 1 To avoid underestimation of standard errors, structural equation modeling (SEM) procedures for complex survey data were warranted in examining the hypothesized theoretical model (Muthén & Muthén, 2007). Unlike traditional linear modeling techniques, SEM procedures for complex survey data are quite flexible in that they allow for the simultaneous estimation of direct and indirect influences in hierarchically clustered data, and the adjustment of measurement errors by using latent constructs (Kline, 2011; Preacher, Zyphur, & Zhang, 2010). Model fitting was performed in Mplus, version 6.11, using maximum likelihood estimation with robust standard errors, and a mean-adjusted chi-square statistic test (MLR; Muthén & Muthén, 2007). A check of the data for statistical assumptions showed no problems.2 Missing data on the continuous variables (b6%) were handled by use of the EM algorithm, after finding that Little's MCAR 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 the range 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 present study. 2 Tests of skewness and kurtosis were nonsignificant for all variables included in the study. Data were found to meet assumptions regarding multivariate normality and linearity.

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Given the relatively large sample size and the exploratory nature of the study, models were fitted using a cross-validation procedure, involving randomly selecting calibration and validation samples, 3 estimating the hypothesized model with the calibration sample and then refitting the model to the validation sample. In order to determine whether the hypothesized model holds 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 model Prior 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, a model 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 evidence was found supporting the factor structure for the latent constructs, the remaining single indicator variables (i.e., students' personality traits, their reading and math scores, and covariates) were included in the model. To achieve model identification, the first unstandardized factor loading of each construct was fixed to equal 1.0, and all latent variable variances and covariances were allowed to be 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 model The hypothesized structural model was evaluated in three steps. The first step involved fitting the hypothesized structural model with main effects only. Covariates and additional parameters were added stepwise. The second step entailed a cross-validation of this model. Cross-validation of complex structural models is acknowledged to be essential, given that model respecification in the initial sample might have been capitalized on chance aspects of the data (e.g., Yuan et al., 2002). Multiple group analyses were performed to test the correspondence between the two samples. Equality constraints were gradually imposed on both the measurement and regression coefficients. In the third step, two alternative models were estimated to test for mediation. First, a direct effects model was fitted, in which the effects of the student–teacher relationship quality on math and reading comprehension were freely estimated and the effects of the mediators constrained to equal zero. Second, a partial mediation model was fitted, in which the Motivational Beliefs factor was inserted back in the model. For ease of interpretation, reading and math achievement were centered around their grand mean, and their error variances were allowed to covary. Because aspects of the student–teacher relationship were assumed to be indicators of a similar construct (i.e., student–teacher relationship quality) and were generally reported by the same source, their factor covariances were freely estimated as well. 2.4.3. Model goodness-of-fit The overall goodness-of-fit of the models was evaluated by the mean-adjusted χ2 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 expected and the observed model may lead to rejection of the model (Chen, 2007). Therefore, additional fit indices were calculated, including the 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 the modification indices, residual correlations, and their associated summary statistic SRMR (standardized root mean square residual). Values ≤.08 indicate relatively good 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 being indicative 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 essentially equivalent fit. ECVI-differences between two hierarchically nested models are considered equal when their 90% confidence interval 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, χ 2 (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 correlations between 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: χ 2 (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 single indicators as well as the latent constructs, the model also yielded a good fit to the data, χ 2 (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.

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loadings, standard errors and interfactor correlations were of the appropriate sign and magnitude. All standardized factor loadings were considered relatively high (>.50), except for one item for Motivational Beliefs (.36). These results provide evidence that the factors, when specified, correspond to the hypothesized structure and provide support for the internal validity and common factor structure of the measures. 3.2. Structural model 3.2.1. Hypothesized model The initial structural model tested was the hypothesized model with main effects only. The model provided acceptable overall fit: χ 2 (818) = 6488.62, p b .001, RMSEA = .040 (90% CI [.039–.041]), SRMR = .053, CFI = .92. To diagnose potential sources of misfit in the model, the modification indices were inspected. Based on these indices, three theoretically supported modifications were made and maintained in the model. These were direct paths from Conscientiousness, Neuroticism, and Autonomy to students' Motivational Beliefs. The test results demonstrated a satisfactory fit of the final model to the data, χ 2 (815) = 5828.54, p b .001, RMSEA = .038 (90% CI [.037–.039]), SRMR = .047, CFI = .93. 3.2.2. Cross-validation The fit indices suggested that the structural model fitted the validation sample quite well: χ 2 (815) = 6093.79, p b .001, RMSEA = .039 (90% CI [.038, .040]), CFI = .92, SRMR = .049. The results for the multiple group analysis investigating differences between the calibration and validation sample are presented in Table 1. Results showed sufficient equivalence to speak of an adequate attempt of cross-validation, thereby generally supporting the validity of the final model. The final structural model and standardized 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–teacher relationship quality. Assessment of path coefficients in the model pointed to significant paths from teacher-rated student–teacher Dependency 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 predicts teacher-reported Dependency .15 standard deviation above the mean. This indicates that the magnitude of the positive path coefficient between Neuroticism and teacher-reported Dependency is almost three times greater than the negative paths from Agreeableness and Autonomy to teacher-reported Dependency (Kline, 2011). The hypothesized paths between teacher-reported Dependency 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), and Autonomy (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), and Autonomy (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-reported Conflict, 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 student gender, ethnicity, SEN, and SES were controlled for in the final model. With regard to these background characteristics, inspection

Table 1 Fit 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) Model 1 (Equal factor loadings) Model 2 (Equal path coefficients) Model 3 (Equal residual variances and covariances)

11,978.49⁎⁎ (1658) 11,985.17⁎⁎

.038 (.038, .038 (.037, .037 (.037, .037 (.036,

.92

.048

–

–

–

–

.92

.048

23.64 (28)

.00

.92

.048

43.91 (49)

.00

.92

.049

43.04 (39)

.001

.00 (.00, .009) .00 (.00, .008) .005 (.00, .012)

−.003 (−.003, −.002) −.006 (−.006, −.004) −.004 (−.004, −.002)

(1686) 12,062.75⁎⁎ (1735) 11,969.08⁎⁎ (1774)

.039) .038) .038) .037)

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

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

Extrav. Agree. Consc. Neur. Auton. Depend. Conflict CloseT CloseS Motiv. Read. Math Gender SEN Ethnicity SES

Conflict

Closenessteacher

Closenessstudent

Motivational beliefs

Reading

Math

dir.

ind.

total

dir.

ind.

total

dir.

ind.

total

dir.

ind.

total

dir.

ind.

total

dir.

ind.

total

dir.

ind.

total

.03⁎ -.05 .01⁎ .14 -.05 – – – – – – – – .18 – -.09

– – – – – – – – – – – – – – – –

.03⁎ -.05 .01⁎ .14 -.05 – – – – – – – – .18 – -.09

.11 -.16 -.07 .08 .05 – – – – – – – -.14 .13 .05 -.07

– – – – – – – – – – – – – – – –

.11 -.16 -.07 .08 .05 – – – – – – – -.14 .13 .05 -.07

.09 .11 .06 .02⁎ .00⁎

– – – – – – – – – – – – – – – –

.09 .11 .06 .02⁎ .00⁎

.11 .24 .23 -.07 -.01⁎

– – .28 -.21 .27 -.11 .09 .02 ⁎

.04 .06 .33 -.24 .28 -.11 .09 .02 ⁎

– – – – – -.12 – – – .26 – – .11 -.24 -.11 .29

.01 .02 .01 .01 .00⁎ -.03 .02 .01⁎

.01 .02 .01 .01 .00⁎ -.15 .02 .01⁎

.01 .02 .02 -.01 .00⁎

-.03 .03 .01⁎

-.15 .03 .01⁎

.07 – – – – – – –

.07 .26 – – .11 -.24 -.11 .29

– – – – – -.12 – – – .29 – – -.12 -.27 -.10 .20

.01 .02 .02 -.01 .00⁎

– – – – – – – – – – .04

.11 .24 .23 -.07 -.01⁎ – – – – – – – – – – .04

.04 .06 .05 -.03 .01⁎

– – – – – – – .12 – -.06 –

– – – – – – – – – – – – – – – –

.08 – – – – – – –

.08 .29 – – -.12 -.27 -.10 .20

– – – – – – – .12 – -.06 –

– – – – – – – – – – –

.27 – – – -.05 -.12 .09 –

.27 – – – -.05 -.12 .09 –

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), and lower reading and math scores (β = − .24; β = − .27, p b .001) than students without such needs. Teachers generally ε

ε

ε

ε

ε

C1

C2

C3

C4

C5

D

Confl.

Extrav. D Consc.

ε

ε

ε

ε

ε

D1

D2

D3

D4

D5

ε

ε

ε

ε

ε

M1

M2

M3

M4

M5

D

D

Depend.

M ath

Agree. Motiv.

D

Close T

Read

Neurot.

D

D

C1

C2

C3

C4

C5

ε

ε

ε

ε

ε

Auton.

E1

E2

E3

E4

E5

E6

ε

ε

ε

ε

ε

ε

Close S

C1

C2

C3

C4

C5

C6

C7

ε

ε

ε

ε

ε

ε

ε

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.

Fig. 2. Final structural model.

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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) than boys, but they had better reading comprehension skills (β = .11, p b .001). Dutch students and students from higher-SES backgrounds performed better on reading (β = .11; β = .29, p b .001) and math (β = .10; β = .20, p b .001) than did non-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 the student–teacher relationship quality accounted for 38.4% of the variance in students' Motivational Beliefs, 27.1% of the variance in math 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 to expectations. 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 to students' Motivational Beliefs (β = − .11, p b .001). With other aspects of the student–teacher relationship in the model, the path 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) was revealed in the model. It seems as if teacher-reported Conflict functioned as a suppressor variable for the effects of other aspects of the relationship quality on Motivational Beliefs. Specifically, teacher-reported Conflict correlated substantially with teacher-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 Beliefs was opposite in sign, and the coefficient of the path from Dependency to Motivational Beliefs became somewhat stronger. When entered 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 indicate that 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 with Motivational beliefs, teacher-reported Conflict improves Dependency as a predictor of Motivational Beliefs. The effects of teacher-reported Conflict and Dependency are therefore better interpreted in combination with each other, rather than separately (Maassen & Bakker, 2001; Pedhazur, 1982). 3.5.1. Mediation effects The 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 math achievement (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, a direct effects model was fitted in which the effects of the student–teacher relationship quality factors on math and reading achievement 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 Beliefs from 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-reported Closeness to the two measures of achievement were not supported by the model. The combined predictors accounted for 21.6% of the variance 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: χ 2 (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 the coefficient of the direct paths from teacher-reported Dependency to students' reading (β = − .12, p b .001 and math achievement (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 the achievement scores were not statistically significant. These results suggest that teacher-reported Dependency is not only directly related to students' academic achievement, but also indirectly, through their Motivational Beliefs. The relationship between student-reported Closeness and students' reading and math achievement, in addition, was fully mediated by their Motivational Beliefs. 4. Discussion In this study, a theoretical model was tested hypothesizing that upper elementary school students' personality traits predict the quality of student–teacher relationships, and that student–teacher relationship quality indirectly affects students' achievement via

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the direct effect on their motivational beliefs. In general, the findings provided only modest support for the study's propositions, but they 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–teacher relationship quality. Students who reported higher levels of agreeableness and lower levels of neuroticism were more likely to have closer and less dependent or conflictual relationships with their teachers. These findings are largely consonant with those of Martin, Watson, and Wan (2000), who identified a lower-order Cynical Cognition trait that represents a combination of neurotic and disagreeable characteristics. Persons scoring high on this trait seem to be more likely to mistrust others, and to feel mistreated 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 their emotional state than on their school work (De Raad & Schouwenburg, 1996). Such traits are likely to produce students who believe 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 motivational beliefs 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 of conflict between teachers and students. Thus, despite the fact that extraverted students are likely to display behavior that increases the opportunity for student–teacher closeness, such as assertive and gregarious behavior, being extraverted may also lead students to engage in more conflictual relationships. These results substantiate those of other studies in that extraverted students may be more likely to experience simultaneously high conflict and closeness with teachers, whereas inhibited dispositions 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 was found that withdrawn-oriented, introvert students typically display quiet and obedient behaviors and are therefore more unlikely to engage in disrupting behaviors in the classroom. In line with previous studies (e.g., Hair & Graziano, 2003), cognitive aspects of students' personality appeared to be more strongly associated with students' academic adjustment than with student–teacher relationship quality. Both highly conscientious and highly autonomous students were more likely to be motivated to succeed, compared with less autonomous or less conscientious peers. This finding 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 than autonomy. Consistent with expectations, results suggest that higher conscientiousness prevented unfavorable relationships and promoted warmth and security between teachers and students. Unlike conscientiousness, autonomy did not seem to be associated with the degree of warmth and security experienced by teachers and students. A possible explanation is that highly autonomous 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 distance between 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 own show no sign of having any—positive or negative—relationship with their teacher at all. Thus, autonomous students may just not be 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 how well 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 between student–teacher relationship quality and students' academic achievement. Regarding positive relationships, students' motivational beliefs fully mediated the associations between student-reported closeness but not the associations between teacher-reported closeness and math and reading achievement. One possible explanation for this difference is that some of the variance that is shared between students' perceptions of closeness and their motivational beliefs might be attributed to source effects. This explanation is in line with research indicating that student reports typically account for the largest proportion of method variance and that teacher reports 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 academic achievement was partially mediated by students' motivational beliefs. Students having overly dependent relationships with

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teachers were more likely to feel less confident in their ability to achieve academically, and these beliefs, in turn, were likely to be associated with lower academic performance. Moreover, beyond this indirect association, there was a direct negative association between dependency and students' math and reading achievement. It is noteworthy that conflict in the student–teacher relationship did not independently predict variance in students' academic adjustment. Although conflict usually has the strongest associations with school outcomes (e.g., Hamre & Pianta, 2001), in this study, it merely seemed to function as a correction factor for 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 of conflict 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 the design of the current study. The direction of influences is difficult to establish, and there also may be reciprocal relations between the quality of student–teacher relationships and the outcome variables in this study. Longitudinal designs could deepen the understanding of how the quality of student–teacher relationships affects students' academic adjustment, especially for students at risk of adjustment problems. To disentangle these effects, it is recommended to employ data from the forthcoming waves of the COOL 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 the fact that the number of classes per school was only 1.9 on average. This suggests that the hypothesized relationships between the predictors and outcome variables in this study did not vary across schools. Despite this, the clustering of classrooms within schools 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 potentially limited 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 and academic 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, might have affected the meaningfulness of the results. There is some evidence to suggest that the inherently abstract nature of some of the 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 is generally high (Hendriks et al., 2008; Laidra et al., 2007). Although the FFPI has been shown to be psychometrically suited for upper elementary students' self-perceptions of their personality (Hendriks et al., 2008) and cross-validation of the data was employed, 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 not exclusively rely on teacher-reports for characterizing the quality of student–teacher relationships, no student-reported measures of conflict and dependency or observational data about students were available. Additionally, this study included only characteristics of the 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 ethnic match between students and teachers have as well been found to have an impact on the extent to which ethnicity 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 and student characteristics, and the possible match between them, and use multiple data sources to elucidate the complexities of the student–teacher relationship. Lastly, it should be noted that the measure of SEN was based on a dichotomous single indicator. Although this item is consistent with measures used in research on learning problems (e.g., Hamre & Pianta, 2005), it does not differentiate between the 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 developing students 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 or hamper the quality of relationships between teachers and upper elementary students. Students' personality traits can hardly be altered. However, teachers' awareness of students' character may help them to shape a supportive learning environment that is more 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 least partially) associated with upper elementary students' achievement through their internalized resources (i.e., motivational beliefs). The finding that dependency rather than conflict was the most relevant negative predictor of academic adjustment is

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particularly important, given that previous issues of reliability and validity have prevented researchers to establish and verify its significance (e.g., Doumen et al., 2009; Ewing & Taylor, 2009; Kesner, 2000). Future research could use the adapted and psychometrically sound dependency scale (Koomen et al., 2007, 2012) to further investigate the ways in which conflict and dependency 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). References Ang, R. P. (2005). Development and validation of the teacher–student relationship inventory using exploratory and confirmatory factor analysis. The Journal of Experimental Education, 74, 55–74. Ang, R. P., Chong, W. H., Huan, V. S., Quek, C. L., & Yeo, L. S. (2008). Teacher–student relationship inventory: Testing for invariance across upper elementary and junior high samples. Journal of Psychoeducational Assessment, 26, 339–349, http://dx.doi.org/10.1177/0734282908315132. Arbeau, K. A., Coplan, R. J., & Weeks, M. (2010). Shyness, teacher–child relationships, and socio-emotional adjustment in grade 1. International Journal of Behavioral Development, 34, 259–269, http://dx.doi.org/10.1177/0165025409350959. Asendorpf, J. B., & Wilpers, S. (1998). Personality effects on social relationships. Journal of Personality and Social Psychology, 74, 1531–1544. Baker, J. A. (1999). Teacher–student interaction in urban at-risk classrooms: Differential behavior, relationship quality, and student satisfaction with school. The Elementary School Journal, 100, 57–70 (doi: 0013-5984/100/10001-0004$02.00). Baker, J. A. (2006). Contributions of teacher–child relationships to positive school adjustment during elementary school. Journal of School Psychology, 44, 211–229, http://dx.doi.org/10.1016/j.jsp.2006.02.002. Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44, 1–26, http: //dx.doi.org/10.1111/j.1744-6570.1991.tb00688.x. Barrick, M. R., Stewart, G. L., & Piotrowski, M. (2002). Personality and job performance: Test of the mediating effects of motivation among sales representatives. Journal of Applied Psychology, 87, 43–51, http://dx.doi.org/10.1037/0021-9010.87.1.43. Bentler, P. M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological Bulletin, 112, 400–404, http://dx.doi.org/10.1037/ 0033-2909.112.3.400. Bidjerano, T., & Yun Dai, D. (2007). The relationship between the Big-Five model of personality and self-regulated learning strategies. Learning and Individual Differences, 17, 69–81, http://dx.doi.org/10.1016/j.lindif.2007.02.001. Birch, S. H., & Ladd, G. W. (1998). Children's interpersonal behaviors and the teacher–child relationship. Developmental Psychology, 34, 934–946, http: //dx.doi.org/10.1037/0012-1649.34.5.934. Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage. Chamorro-Premuzic, T., & Furnham, A. (2003). Personality predicts academic performance: Evidence from two longitudinal university samples. Journal of Research in Personality, 37, 319–338, http://dx.doi.org/10.1016/S0092-6566(02)00578-0. Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14, 464–504, http://dx.doi.org/10.1080/10705510701301834. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255, http: //dx.doi.org/10.1207/S15328007SEM0902_5. CITO (2008). Omzetting Cijfer naar Standaardscore. Retrieved from. http://www.cito.nl Colwell, M. J., & Lindsey, E. W. (2003). Teacher–child interactions and preschool children's perceptions of self and peers. Early Child Development and Care, 173, 249–258, http://dx.doi.org/10.1080/0300443031000071888. Coplan, R. J., Prakash, K., O'Neil, K., & Armer, M. (2004). Do you “want” to play? Distinguishing between conflicted shyness and social disinterest in early childhood. Developmental Pyschology, 2, 244–258, http://dx.doi.org/10.1037/0012-1649.40.2.244. Costa, P. T., & McCrae, R. R. (1992). The five-factor model of personality and its relevance to personality disorders. Journal of Personality Disorders, 6, 343–359. Crick, N. R., & Dodge, K. A. (1994). A review and reformulation of social information-processing mechanisms in children's social adjustment. Psychological Bulletin, 115, 74–101. De Pauw, S. W. S., & Mervielde, I. (2010). Temperament, personality and developmental psychopathology: A review based on the conceptual dimensions underlying childhood traits. Child Psychiatry and Human Development, 41, 313–329, http://dx.doi.org/10.1007/s10578-009-0171-8. De Raad, B., & Schouwenburg, H. C. (1996). Personality in learning and education: A review. European Journal of Personality, 10, 303–336, http: //dx.doi.org/10.1002/(SICI)1099-0984(199612)10:5b303::AID-PER262>3.0.CO;2-2. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227–268, http://dx.doi.org/10.1207/S15327965PLI1104_01. Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology, 49, 182–185, http://dx.doi.org/10.1037/a0012801. Diener, E., Larsen, R. J., & Emmons, R. A. (1984). Person x situation interactions: Choice of situations and congruence response models. Journal of Personality and Social Psychology, 47, 580–592, http://dx.doi.org/10.1037/0022-3514.47.3.580. DiLalla, L. F., Marcus, J. L., & Wright-Phillips, M. V. (2004). Longitudinal effects of preschool behavioral styles on early adolescent school performance. Journal of School Psychology, 42, 385–401, http://dx.doi.org/10.1016/j.jsp.2004.05.002. Doumen, S., Koomen, H. M. Y., Buyse, E., Wouters, S., & Verschueren, K. (2012). Teacher and observer views on student–teacher relationships: Convergence across kindergarten and relations with student engagement. Journal of School Psychology, 50, 61–76, http://dx.doi.org/10.1016/j.jsp.2011.08.004. Doumen, S., Verschueren, K., Buyse, E., De Munter, S., Max, K., & Moens, L. (2009). Further examination of the convergent and discriminant validity of the student– teacher relationship scale. Infant and Child Development, 18, 502–520, http://dx.doi.org/10.1002/icd.635. Driessen, G., Mulder, L., Ledoux, G., Roeleveld, J., & Van der Veen, I. (2007). Cohortonderzoek COOL 5-18. Basisrapport basisonderwijs, eerste meting 2007/2008. Nijmegen: ITS. Dudgeon, P. (2003). NIESEM: A computer program for calculating noncentral interval estimates (andpower analysis) for structural equation modeling. Melbourne: University of Melbourne, Department of Psychology. Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., et al. (1993). The impact of stageenvironment fit on young adolescents' experiences of school and in families. American Psychologist, 48, 90–101. Ewing, A. R., & Taylor, A. R. (2009). The role of child gender and ethnicity in teacher–child relationship quality and children's behavioral adjustment in preschool. Early Childhood Research Quarterly, 24, 92–105, http://dx.doi.org/10.1016/j.ecresq.2008.09.002. Fredricks, J. A., & Eccles, J. S. (2002). Children's competence and value beliefs from childhood through adolescence: Growth trajectories in two male sex-typed domains. Developmental Psychology, 38, 519–533, http://dx.doi.org/10.1037//0012-1649.38.4.519.

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

531

Furrer, C., & Skinner, E. (2003). Sense of relatedness as a factor in children's academic engagement and performance. Journal of Educational Psychology, 95, 148–162, http://dx.doi.org/10.1037/0022-0663.95.1.148. Goldberg, L. R. (2001). Analyses of Digman's child–personality data: Derivation of Big-Five factor scores from each of six samples. Journal of Personality, 69, 709–743, http://dx.doi.org/10.1111/1467-6494.695161. Goodenow, C. (1993). Classroom belonging among early adolescent students: Relationship to motivation and achievement. Journal of Early Adolescence, 13, 21–43, http://dx.doi.org/10.1177/0272431693013001002. Graziano, W. G., Jensen-Campbell, L. A., & Hair, E. C. (1996). Perceiving interpersonal conflict and reacting to it: The case for agreeableness. Journal of Personality and Social Psychology, 70, 820–835, http://dx.doi.org/10.1037/0022-3514.70.4.820. Graziano, P. A., Reavis, R. D., Keane, S. P., & Calkins, S. D. (2007). Do personality and learning climate predict competence for learning? An investigation in a Greek academic setting. Journal of School Psychology, 45, 3–19, http://dx.doi.org/10.1016/j.jsp.2006.09.002. Hair, E. C., & Graziano, W. G. (2003). Self-esteem, personality and achievement in high school: A prospective longitudinal study in Texas. Journal of Personality, 71, 971–994, http://dx.doi.org/10.1111/1467-6494.7106004. Hamre, B. K., & Pianta, R. C. (2001). Early teacher–child relationships and the trajectory of children's school outcomes through eighth grade. Child Development, 72, 625–638, http://dx.doi.org/10.1111/1467-8624.00301. Hamre, B. K., & Pianta, R. C. (2005). Can instructional and emotional support in the first-grade classroom make a difference for children at risk of school failure? Child Development, 76, 949–967, http://dx.doi.org/10.1111/j. 1467-8624.2005. 00889. x. Hendriks, A. A. J., Hofstee, W. K. B., & De Raad, B. (1999). The Five-Factor Personality Inventory (FFPI). Personality and Individual Differences, 27, 307–325, http: //dx.doi.org/10.1016/S0191-8869(98)00245-1. Hendriks, A. A. J., Kuyper, H., Offringa, G. J., & Van der Werf, M. P. C. (2008). Assessing young adolescents' personality with the five-factor personality inventory. Assessment, 15, 304–316, http://dx.doi.org/10.1177/1073191107313761. Herrero, J., Estevez, E., & Musitu, G. (2006). The relationships of adolescent school-related deviant behaviour and victimization with psychological distress: Testing a general model of the mediational role of parents and teachers across groups of gender and age. Journal of Adolescence, 29, 671–690, http: //dx.doi.org/10.1016/j.adolescence.2005.08.015. Hornstra, L., Van der Veen, I., Peetsma, T., & Volman, M. (2013). Developments in motivation and achievement during primary school: A longitudinal study on group-specific differences. Learning and Individual Differences, 23, 195–204, http://dx.doi.org/10.1016/j.lindif.2012.09.004. Howes, C., Phillipsen, L. C., & Peisner Feinberg, E. (2000). The consistency of perceived teacher–child relationships between preschool and kindergarten. Journal of School Psychology, 38, 113–132, http://dx.doi.org/10.1016/S0022-4405(99)00044-8. Hughes, J. N., Wu, J. Y., Kwok, I., Villarreal, V., & Johnson, A. Y. (2012). Indirect effects of child reports of teacher–student relationship on achievement. Journal of Educational Psychology, 104, 350–365, http://dx.doi.org/10.1037/a0026339. Jerome, E. M., Hamre, B. K., & Pianta, R. C. (2009). Teacher–child relationships from kindergarten to sixth grade: Early childhood predictors of teacher-perceived conflict and closeness. Social Development, 18, 915–945, http://dx.doi.org/10.1111/j.1467-9507.2008.00508.x. Judge, T. A., Erez, A., Bono, J. E., & Thoresen, C. J. (2002). Are measures of self-esteem, neuroticism, locus of control, and generalized self-efficacy indicators of a common core construct? Journal of Personality and Social Psychology, 83, 693–710, http://dx.doi.org/10.1037//0022-3514.83.3.693. Justice, L. M., Cottone, E. A., Mashburn, A., & Rimm-Kaufman, S. E. (2008). Relationships between teachers and preschoolers who are at risk: Contribution of children's language skills, temperamentally based attributes, and gender. Early Education and Development, 19, 600–621, http://dx.doi.org/10.1080/ 10409280802231021. Kesner, J. E. (2000). Teacher characteristics and the quality of child–teacher relationships. Journal of School Psychology, 28, 133–149, http://dx.doi.org/10.1016/ S0022-4405(99)00043-6. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.)New York, NY: Guilford. Koenig, J. L., Barry, R. A., & Kochanska, G. (2010). Rearing difficult children: Parents' personality and children's proneness to anger as predictors of future parenting. Parenting: Science and Practice, 10, 258–273, http://dx.doi.org/10.1080/15295192.2010.492038. Koomen, H. M. Y., Verschueren, K., & Pianta, R. C. (2007). Leerling Leerkracht Relatie Vragenlijst. Handleiding. [Student Teacher Relationship Scale: Manual]. Houten: Bohn Stafleu van Loghum. Koomen, H. M. Y., Verschueren, K., van Schooten, E., Jak, S., & Pianta, R. C. (2012). Validating the student–teacher relationship scale: Testing factor structure and measurement invariance across child gender and age in a Dutch sample. Journal of School Psychology, 50, 215–234, http://dx.doi.org/10.1016/ j.jsp.2011.09.001. Ladd, G. W., Birch, S. H., & Buhs, E. S. (1999). Children's social and scholastic lives in kindergarten: Related spheres of influence? Child Development, 70, 1373–1400, http://dx.doi.org/10.1111/1467-8624.00101. Ladd, G. W., & Burgess, K. B. (1999). Relationship trajectories of aggressive, withdrawn, aggressive/withdrawn children during school. Child Development, 70, 910–929, http://dx.doi.org/10.1111/1467-8624.00066. Laidra, K., Pullmann, H., & Allik, J. (2007). Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school. Personality and Individual Differences, 42, 441–451, http://dx.doi.org/10.1016/j.paid.2006.08.001. LePine, J. A., & Van Dyne, L. (2001). Voice and cooperative behavior as contrasting forms of contextual performance: Evidence of differential relationships with Big Five personality characteristics and cognitive ability. Journal of Applied Psychology, 86, 326–336, http://dx.doi.org/10.1037//0021-9010.86.2.326. Li, Y., Hughes, J. N., Hsu, H. Y., & Kwok, O. (2011). Evidence of convergent and divergent validity of child, teacher, and peer reports of teacher–student relationship support. Psychological Assessment, 24, 54–65, http://dx.doi.org/10.1037/a0024481. Little, E., & Hudson, A. (1998). Conduct problems and treatment across home and school: A review of the literature. Behaviour Change, 15, 213–227. Lynch, M., & Cicchetti, D. (1997). Children's relationships with adults and peers: An examination of elementary and junior high school students. Journal of School Psychology, 35, 81–99. Maassen, G. H., & Bakker, A. B. (2001). Suppressor variables in path models: Definitions and interpretations. Sociological Methods & Research, 30, 241–270, http: //dx.doi.org/10.1177/0049124101030002004. MacIntosh, R., & Hashim, S. (2003). Variance estimation for converting MIMIC model parameters to IRT parameters in DIF analysis. Applied Psychological Measurement, 27, 372–379, http://dx.doi.org/10.1177/0146621603256021. Magnuson, K. (2007). Maternal education and children's academic achievement during middle childhood. Developmental Psychology, 43, 1497–1512, http: //dx.doi.org/10.1037/0012-1649.43.6.1497. Malecki, C. K., & Demaray, M. K. (2006). Social support as a buffer in the relationship between socioeconomic status and academic performance. School Psychology Quarterly, 21, 375–395. Mantzicopoulos, P. (2005). Conflictual relationships between kindergarten children and their teachers: Associations with child and classroom context variables. Journal of School Psychology, 43, 425–442, http://dx.doi.org/10.1016/j.jsp.2005.09.004. Martin, R., Watson, D., & Wan, C. K. (2000). A three-factor model of trait anger: Dimensions of affect, behavior, and cognition. Journal of Personality, 68, 869–897. Mashburn, A. J., & Henry, G. T. (2004). Assessing school readiness: Validity and bias in preschool and kindergarten teachers' ratings. Educational Measurement: Issues and Practice, 23, 16–30, http://dx.doi.org/10.1111/j.1745-3992.2004.tb00165.x. McCrae, R. R., & Costa, P. T. (1987). Validation of the five-factor model of personality across instruments and observers. Journal of Personality and Social Psychology, 52, 81–90, http://dx.doi.org/10.1037/0022-3514.52.1.81. McCrae, R. R., & John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60, 175–215, http://dx.doi.org/10.1111/ j.14676494.1992. tb00970.x. McWilliam, R. A., Scarborough, A. A., & Kim, H. (2003). Adult interactions and child engagement. Early Education and Development, 14, 7–27, http://dx.doi.org/ 10.1207/s15566935eed1401_2. Mervielde, I., Buyst, V., & De Fruyt, F. (1995). The validity of the Big-five as a model for teachers' ratings of individual differences among children aged 4–12 years. Personality and Individual Differences, 18, 525–534, http://dx.doi.org/10.1016/0191-8869(94)00175-R.

532

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

Mervielde, I., De Clercq, B., De Fruyt, F., & Van Leeuwen, K. (2005). Temperament, personality, and developmental psychopathology as childhood antecendents of personality disorders. Journal of Personality Disorders, 19, 171–201, http://dx.doi.org/10.1521/pedi.19.2.171.62627. Middleton, M., & Midgley, C. (1997). Avoiding the demonstration of lack of ability: An under-explored aspect of goal theory. Journal of Educational Psychology, 89, 710–718. Midgley, C., Meahr, M. L., Hruda, L. Z., Anderman, E., Anderman, L., Freeman, K. E., et al. (2000). Manual for the patterns of adaptive learning scales. Michigan: University of Michigan. Murray, C., Murray, K. M., & Waas, G. A. (2008). Child and teacher reports of teacher–student relationships: Concordance of perspectives and associations with school adjustment in urban kindergarten classrooms. Journal of Applied Developmental Psychology, 29, 49–61, http://dx.doi.org/10.1016/ j.appdev.2007.10.006. Muthén, L. K., & Muthén, B. O. (1998-2007). Mplus user's guide (5th ed.)Los Angeles, CA: Muthén & Muthén. Nezlek, J. B. (2008). An introduction to multilevel modeling for social and personality psychology. Social and Personality Psychology Compass, 2, 842–860. Noftle, E. E., & Shaver, P. R. (2006). Attachment dimensions and the big five personality traits: Associations and comparative ability to predict relationship quality. Journal of Research in Personality, 40, 179–208, http://dx.doi.org/10.1016/j.jrp.2004.11.003. Ntalianis, F. (2010). Do personality and learning climate predict competence for learning? An investigation in a Greek academic setting. Learning and Individual Differences, 20, 664–668, http://dx.doi.org/10.1016/j.lindif.2010.08.003. Oort, F. J. (2009). Three-mode models for multitrait-multimethod data. Methodology, 5, 78–87, http://dx.doi.org/10.1027/1614-2241.5.3.78. Palermo, F., Hanish, L. D., & Martin, C. L. (2007). Preschoolers' academic readiness: What role does the teacher–child relationship play? Early Childhood Research Quarterly, 22, 407–422, http://dx.doi.org/10.1016/j.ecresq.2007.04.002. Patrick, H., Mantzicopoulos, P., Samarapungavan, A., & French, B. F. (2008). Patterns of young children's motivation for science and teacher–child relationships. The Journal of Experimental Education, 76, 121–144. Paunonen, S. V., & Ashton, M. C. (2001). Big Five predictors of academic achievement. Journal of Research in Personality, 35, 78–90, http://dx.doi.org/ 10.1006/jrpe.2000.2309. Pedhazur, E. J. (1982). Multiple regression in behavioral research. New York, NY: Holt, Rinehart & Winston. Peetsma, T. T. D., Wagenaar, E., & De Kat, E. (2001). School motivation, future time perspective and well-being of high school student in segregated and integrated schools in the Netherlands and the role of ethnic self-description. In J. K. Koppen, I. Lunt, & C. Wulf (Eds.), Education in Europe, cultures, values, institutions in transition, Vol. 14. (pp. 54–74)Münster/New York, NY: Waxmann. Pianta, R. C. (1994). Patterns of relationships between children and kindergarten teachers. Journal of School Psychology, 32, 15–31, http://dx.doi.org/10.1016/0022-4405(94) 90026-4. Pianta, R. C., Hamre, B., & Stuhlman, M. (2003). Relationships between teachers and children. In W. M. Reynolds, & G. E. Miller (Eds.), Handbook of psychology: Educational psychology, Vol. 7. (pp. 199–234)Hoboken, NJ: John Wiley & Sons. Pianta, R. C., La Paro, K. M., Payne, C., Cox, M. J., & Bradley, R. (2002). The relation of kindergarten classroom environment to teacher, family, and school characteristics and child outcomes. The Elementary School Journal, 102, 225–238. Pianta, R. C., Steinberg, M. S., & Rollins, K. (1995). The first two years of school: Teacher–child relationships and deflections in children's classroom adjustment. Development and Psychopathology, 7, 295–312, http://dx.doi.org/10.1017/S0954579400006519. Pijl, S. J., Frostad, P., & Flem, A. (2008). The social position of pupils with special needs in regular schools. Scandinavian Journal of Educational Research, 52, 387–405, http://dx.doi.org/10.1080/00313830802184558. Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15, 209–233, http: //dx.doi.org/10.1037/a0020141. Reeve, J., Bolt, E., & Cai, Y. (1999). Autonomy supportive teachers: How they teach and motivate students. Journal of Educational Psychology, 91, 537–548. Resnick, M. D., Bearman, P. S., Blum, R. W., Bauman, K. E., Harris, K. M., Jones, J., et al. (1997). Protecting adolescents from harm: Findings from the national longitudinal study on adolescent health. Journal of the American Medical Association, 278, 823–832, http://dx.doi.org/10.1001/jama.1997.03550100049038. Rhodes, J. E., Grossman, J. B., & Resch, N. R. (2000). Agents of change: Pathways through which mentoring relationships influence adolescents' academic adjustment. Child Development, 71, 1662–1671, http://dx.doi.org/10.1111/1467-8624.00256. Roeser, R. W., & Eccles, J. S. (1998). Adolescents' perceptions of middle school: Relation to longitudinal changes in academic and psychological adjustment. Journal of Research on Adolescence, 8, 123–158, http://dx.doi.org/10.1207/s15327795jra0801_6. Roeser, R. W., Midgley, C., & Urdan, T. C. (1996). Perceptions of the school psychological environment and early adolescents' psychological and behavioral functioning in school: The mediating role of goals and belonging. Journal of Educational Psychology, 88, 408–422, http://dx.doi.org/10.1037/0022-0663.88.3.408. Roorda, D. L., Koomen, H. M. Y., Spilt, J. L., & Oort, F. J. (2011). The influence of affective teacher–student relationships on students' school engagement and achievement: A meta-analytic approach. Review of Educational Research, 81, 493–529. Rothbart, M. K. (2007). Temperament, development, and personality. Current Directions in Psychological Science, 16, 207–212, http://dx.doi.org/10.1111/ j.1467-8721.2007.00505.x. Rudasill, K. M., & Konold, T. R. (2008). Contributions of children's temperament to teachers' judgments of social competence from kindergarten through second grade. Early Education and Development, 19, 643–666, http://dx.doi.org/10.1080/104092802231096. Rudasill, K. M., & Rimm-Kaufman, S. E. (2009). Teacher–child relationship quality: The roles of child temperament and teacher–child interactions. Early Childhood Research Quarterly, 24, 107–120, http://dx.doi.org/10.1016/j.ecresq.2008.12.003. Rudasill, K. M., Rimm-Kaufman, S. E., Justice, L. M., & Pence, K. (2006). Temperament and language skills as predictors of teacher–child relationships quality in preschool. Early Education and Development, 17, 271–291, http://dx.doi.org/10.1207/s15566935eed1702_4. Ryan, R., & Deci, E. (2002). Overview of self-determination theory: An organismic dialectical perspective. In E. Deci, & R. Ryan (Eds.), Handbook of self-determination research (pp. 3–33). Rochester, NY: University of Rochester Press. Ryan, R. M., & Powelson, C. L. (1991). Autonomy and relatedness as fundamental to motivation and education. The Journal of Experimental Education, 60, 49–66. Rydell, A. M., Bohlin, G., & Thorell, L. B. (2005). Representations of attachment to parents and shyness as predictors of children's relationships with teachers and peer competence in preschool. Attachment & Human Development, 7, 187–204, http://dx.doi.org/10.1080/14616730500134282. Saft, E. W., & Pianta, R. C. (2001). Teachers' perceptions of their relationships with students: Effects of child age, gender, and ethnicity of teachers and children. School Psychology Quarterly, 16, 125–141, http://dx.doi.org/10.1521/scpq.16.2.125.18698. Sameroff, A. J., & Fiese, B. H. (2000). Models of development and developmental risk. In C. H. ZeanahJr. (Ed.), Handbook of infant mental health (pp. 3–19) (2nd ed.). New York, NY: Guilford Press. Satorra, A. (2000). Scaled and adjusted restricted tests in multi-sample analysis of moment structures. In R. D. H. Heijmans, D. S. G. Pollock, & A. Satorra (Eds.), Innovations in multivariate statistical analysis. A Festschrift for Heinz Neudecker (pp. 233–247). London, UK: Kluwer Academic Publishers. Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75, 243–248, http://dx.doi.org/10.1007/ s11336-009-9135-y. Seegers, G., Van Putten, C. M., & De Brabander, C. J. (2002). Goal orientation, perceived task outcome and task demands in mathematical tasks: Effects on students' attitude in actual task settings. British Journal of Educational Psychology, 72, 365–384, http://dx.doi.org/10.1348/000709902320634366. Shiner, R. L., & Caspi, A. (2003). Personality differences in childhood and adolescence: measurement, development, and consequences. Journal of Child Psychology and Psychiatry, 44, 2–32, http://dx.doi.org/10.1111/1469-7610.00101. Spilt, J. L., Hughes, J. N., Wu, J. Y., & Kwok, O. M. (2012a). Dynamics of teacher–student relationships: Stability and change across elementary school and the influence on children's academic success. Child Development, 83, 1180–1195 (10.111/j.1467-8624.2012.01761.x). Spilt, J. L., Koomen, H. M. Y., & Jak, S. (2012b). Are boys better off with male and girls with female teachers? A multilevel investigation of measurement invariance and gender match in teacher–student relationship quality. Journal of School Psychology, 50, 363–378, http://dx.doi.org/10.1016/j.jsp.2011.12.002. Steinmayr, R., & Spinath, B. (2008). Sex differences in school achievement: what are the roles of personality and achievement motivation? European Journal of Personality, 22, 185–209, http://dx.doi.org/10.1002/per.676.

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

533

Stuhlman, M., & Pianta, R. (2002). Teachers' narratives about their relationships with children: Associations with behavior in classrooms. School Psychology Review, 31, 148–163. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.)Boston, MA: Allyn and Bacon. Thijs, J., & Koomen, H. M. Y. (2009). Toward a further understanding of teachers' reports of early teacher–child relationships: Examining the roles of behavior appraisals and attributions. Early Childhood Research Quarterly, 24, 186–197, http://dx.doi.org/10.1016/j.ecresq.2009.03.001. Thijs, J., Westhof, S., & Koomen, H. M. Y. (2012). Ethnic incongruence and the student–teacher relationship: The perspective of ethnic majority teachers. Journal of Shool Psychology, 50, 257–273, http://dx.doi.org/10.1016/j.jsp.2011.09.004. van Boxtel, H., Engelen, R., & de Wijs, A. (2011). Wetenschappelijke verantwoording van de Eindtoets Basisonderwijs 2010. Arnhem: CITO. Verschueren, K., Buyck, P., & Marcoen, A. (2001). Self-representations and socioemotional competence in young children: A 3-year longitudinal study. Developmental Psychology, 37, 126–134, http://dx.doi.org/10.1037/0012-1649.37.1.126. Wang, M. T., & Eccles, J. S. (2012). Social support matters: Longitudinal effects of social support on three dimensions of school engagement from middle to high school. Child Development, 8, 877–895, http://dx.doi.org/10.1111/j.1467-8624.2012.01745.x. Wang, M. T., & Holcombe, R. (2010). Adolescents' perceptions of classroom environment, school engagement, and academic achievement. American Educational Research Journal, 47, 633–662, http://dx.doi.org/10.3102/0002831209361209. Wayne, J. H., Musisca, N., & Fleeson, W. (2004). Considering the role of personality in the work–family experience: Relationships of the big five to work–family conflict and facilitation. Journal of Vocational Behavior, 64, 108–130, http://dx.doi.org/10.1016/S0001-8791(03)00035-6. Wentzel, K. R. (1991). Relations between social competence and academic achievement in early adolescence. Child Development, 62, 1066–1078, http: //dx.doi.org/10.1111/j.1467-8624.1991.tb01589.x. Wentzel, K. R. (1998). Social relationships and motivation in middle school: The role of parents, teachers, and peers. Journal of Educational Psychology, 90, 202–209. Wentzel, K. R. (2002). Are effective teachers like good parents? Teaching styles and student adjustment in early adolescence. Child Development, 73, 287–301. Wolters, C., Yu, S., & Pintrich, P. (1996). The relation between goal orientation and students' motivational beliefs and self-regulated learning. Learning and Individual Differences, 8, 211–238. Woolley, M. E., Kol, K. L., & Bowen, G. L. (2009). The social context of school success for Latino middle school students: Direct and indirect influences of teachers, family, and friends. Journal of Early Adolescence, 29, 43–70, http://dx.doi.org/10.1177/0272431608324478. Yuan, K. H., Marshall, L. L., & Weston, R. (2002). Cross-validation by downweighting influential cases in structural equation modelling. British Journal of Mathematical and Statistical Psychology, 55, 125–143, http://dx.doi.org/10.1348/000711002159734. Zimmer-Gembeck, M. J., Chipuer, H. M., Hanisch, M., Creed, P. A., & McGregor, L. (2006). Relationships at school and stage-environment fit as resources for adolescent engagement and achievement. Journal of Adolescence, 29, 911–933, http://dx.doi.org/10.1016/j.adolescence.2006.04.008.