Suzanne Jak

Bio: Suzanne Jak is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Structural equation modeling & Measurement invariance. The author has an hindex of 16, co-authored 50 publications receiving 1115 citations. Previous affiliations of Suzanne Jak include Utrecht University & National University of Singapore.

Affective Teacher–Student Relationships and Students' Engagement and Achievement: A Meta-Analytic Update and Test of the Mediating Role of Engagement

Abstract: The present study took a meta-analytic approach to investigate whether students' engagement acts as a mediator in the association between affective teacher–student relationships and students' achievement. Furthermore, we examined whether results differed for primary and secondary school and whether similar results were found in a longitudinal subsample. Our sample consisted of 189 studies (249,198 students in total) that included students from preschool to high school. A distinction was made between positive relationship aspects (e.g., closeness) and negative relationship aspects (e.g., conflict). Meta-analytic structural equation modeling showed that, overall, the associations between both positive relationships and achievement and negative relationships and achievement were partially mediated by student engagement. Subsequent analyses revealed that mediation is applicable to both primary and secondary school. Only the direct association between positive relationships and engagement was stronger...

A Test for Cluster Bias: Detecting Violations of Measurement Invariance Across Clusters in Multilevel Data

Abstract: We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings are equal to the between-level factor loadings, and whether the between-level residual variances are zero. The test is illustrated with an example from school research. In a simulation study, we show that the cluster bias test has sufficient power, and the proportions of false positives are close to the chosen levels of significance.

Applied Longitudinal Data Analysis Modeling Change And Event Occurrence

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Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods

Abstract: Partial least squares-based structural equation modelling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are used. One of the most fundamental issues in PLS-SEM is that of minimum sample size estimation. The ‘10-times rule’ has been a favourite because of its simplicity of application, even though it tends to yield imprecise estimates. We propose two related methods, based on mathematical equations, as alternatives for minimum sample size estimation in PLS-SEM: the inverse square root method, and the gamma-exponential method. Based on three Monte Carlo experiments, we demonstrate that both methods are fairly accurate. The inverse square root method is particularly attractive in terms of its simplicity of application. © 2016 John Wiley & Sons Ltd

Teacher Wellbeing: The Importance of Teacher–Student Relationships

Abstract: Many studies have examined the importance of teacher–student relationships for the development of children. Much less is known, however, about how these relationships impact the professional and personal lives of teachers. This review considers the importance of teacher–student relationships for the wellbeing of teachers starting from the Transactional Model of Stress and Coping of Lazarus (1991). Based on theories on interpersonal relationships, it is postulated that teachers have a basic need for relatedness with the students in their class. It is discussed that teachers internalize experiences with students in representational models of relationships that guide emotional responses in daily interactions with students and change teacher wellbeing in the long run. In addition, the notion of mental representations of relationships at different levels of generalization could offer a window to understand how individual teacher–student relationships may affect the professional and personal self-esteem of teachers. Lastly, it is argued that the influence of student misbehavior on teacher stress may be more fully understood from a relationship perspective. The review shows that few studies have directly tested these propositions and offers suggestions for future research.

The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education

Abstract: The extent to which teachers adopt technology in their teaching practice has long been in the focus of research. Indeed, a plethora of models exist explaining influential factors and mechanisms of technology use in classrooms, one of which—the Technology Acceptance Model (TAM) and versions thereof—has dominated the field. Although consensus exists about which factors in the TAM might predict teachers’ technology adoption, the current field abounds in some controversies and inconsistent findings. This meta-analysis seeks to clarify some of these issues by combining meta-analysis with structural equation modeling approaches. Specifically, we synthesized 124 correlation matrices from 114 empirical TAM studies (N = 34,357 teachers) and tested the fit of the TAM and its versions. Overall, the TAM explains technology acceptance well; yet, the role of certain key constructs and the importance of external variables contrast some existing beliefs about the TAM. Implications for research and practice are discussed.