Suzanne Jak

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

Although research consistently points to poorer teacher-student relationships for boys than girls, there are no studies that take into account the effects of teacher gender and control for possible measurement non-invariance across student and teacher gender. This study addressed both issues. The sample included 649 primary school teachers (182 men) and 1493 students (685 boys). Teachers completed a slightly adapted version of the Student-Teacher Relationship Scale. The results indicated limited measurement non-invariance in teacher reports. Female teachers reported better (i.e., more close, less conflictual, and less dependent) relationships with students than male teachers. In addition, both male and female teachers reported more conflictual relationships with boys than with girls, and female teachers also reported less close relationships with boys than with girls. The findings challenge societys presumption that male teachers have better relationships with boys than women teachers.

A test for cluster bias: Detecting violations of measurement invariance across clusters in multilevel data

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.

Measurement Bias in Multilevel Data

Measurement bias can be detected using structural equation modeling (SEM), by testing measurement invariance with multigroup factor analysis (Jöreskog, 1971;Meredith, 1993;Sörbom, 1974) MIMIC modeling (Muthén, 1989) or restricted factor analysis (Oort, 1992,1998). In educational research, data often have a nested, multilevel structure, for example when data are collected from children in classrooms. Multilevel structures might complicate measurement bias research. In 2-level data, the potentially “biasing trait” or “violator” can be a Level 1 variable (e.g., pupil sex), or a Level 2 variable (e.g., teacher sex). One can also test measurement invariance with respect to the clustering variable (e.g., classroom). This article provides a stepwise approach for the detection of measurement bias with respect to these 3 types of violators. This approach works from Level 1 upward, so the final model accounts for all bias and substantive findings at both levels. The 5 proposed steps are illustrated with data of teacher–child relationships.

Meta-Analytic Structural Equation Modelling

Meta-analysis is a prominent statistical tool in many research disciplines. It is a statistical method to combine the effect sizes of separate independent studies, in order to draw overall conclusions based on the pooled results. Structural equation modeling is a multivariate technique to fit path models, factor models, and combinations of these to data. By combining meta-analysis and structural equation modeling, information from multiple studies can be used to test a single model that explains the relationships between a set of variables or to compare several models that are supported by different studies or theories. This chapter provides a short introduction to meta-analysis and structural equation modeling.

The long reach of childhood. Childhood experiences influence close relationships and loneliness across life

This paper intends to gain insight into the role of childhood relationships and experiences within the parental home for the formation and meaning of later family relationships and loneliness. Particularly, childhood attachment to mother and father and stressful childhood experiences were studied in their association with satisfaction in the romantic relationship, the quality of adult family ties and the perceived quality of the social network, i.e. loneliness in adulthood. Based on data from the Netherlands Kinship Panel Study (N=3980) structural equation models were estimated to predict adult relationships and loneliness with childhood experiences. Positive attachment experiences with parents, such as reliability, closeness and supportiveness during childhood were associated with greater satisfaction in the romantic relationship, stronger family ties and less loneliness, whereas stressful childhood experiences, such as conflicts and violence negatively predicted the quality of adult relationships. Life span theoretical perspectives, such as attachment theory are discussed as useful unifying framework to study social relationships, their interconnectedness and association with outcome during all phases of life.

Research self-efficacy of lecturers in non-university higher education

During the last decade, the relationship between university and non-university higher education institutions has changed. As a contribution to the knowledge economy, non-university higher education institutions are expected to educate their students in research activities. Previously, teaching was the main responsibility of lecturers in non-university higher education, while research hardly played a role. This paper is about the belief of lecturers in non-university higher education in their own research ability (research self-efficacy). In a survey study conducted among Dutch lecturers (N = 790), the research self-efficacy has been measured. A structural equation model shows the effects of personal aspects, mastery experience and organisational context on the research self-efficacy of lecturers. Research self-efficacy is also modelled in relation to lecturers’ need to work on professional development in research skills. Results show that research self-efficacy is mostly affected by aspects of mastery experience, in which the context is similar to the given task. Implications are discussed.

Analyzing Big Data in Psychology : A Split/Analyze/Meta-Analyze Approach

Big data is a field that has traditionally been dominated by disciplines such as computer science and business, where mainly data-driven analyses have been performed. Psychology, a discipline in which a strong emphasis is placed on behavioral theories and empirical research, has the potential to contribute greatly to the big data movement. However, one challenge to psychologists—and probably the most crucial one—is that most researchers may not have the necessary programming and computational skills to analyze big data. In this study we argue that psychologists can also conduct big data research and that, rather than trying to acquire new programming and computational skills, they should focus on their strengths, such as performing psychometric analyses and testing theories using multivariate analyses to explain phenomena. We propose a split/analyze/meta-analyze approach that allows psychologists to easily analyze big data. Two real datasets are used to demonstrate the proposed procedures in R. A new research agenda related to the analysis of big data in psychology is outlined at the end of the study.

Testing strong factorial invariance using three-level structural equation modeling

Within structural equation modeling, the most prevalent model to investigate measurement bias is the multigroup model. Equal factor loadings and intercepts across groups in a multigroup model represent strong factorial invariance (absence of measurement bias) across groups. Although this approach is possible in principle, it is hardly practical when the number of groups is large or when the group size is relatively small. Jak et al. (2013) showed how strong factorial invariance across large numbers of groups can be tested in a multilevel structural equation modeling framework, by treating group as a random instead of a fixed variable. In the present study, this model is extended for use with three-level data. The proposed method is illustrated with an investigation of strong factorial invariance across 156 school classes and 50 schools in a Dutch dyscalculia test, using three-level structural equation modeling.

Testing and Explaining Differences in Common and Residual Factors Across Many Countries

To make valid comparisons across countries, a measurement instrument needs to be measurement invariant across countries. The present article provides a nontechnical exposition of a recently proposed multilevel factor analysis approach to test measurement invariance across countries. It is explained that strong factorial invariance across countries implies equal factor loadings across levels and zero residual variance at the country level in a two-level factor model. Using two-level factor analysis, the decomposition of the variance at each level can be investigated, measurement invariance can be tested, and country-level variables can be added to explain differences in the common or residual factors. The approach is illustrated using two examples. The first example features data about well-being from the European Social Survey and the second example uses data about mathematical ability from the Programme for International Student Assessment (PISA) study. The input-files and annotated output-files for both examples are provided in the supplementary files.

Functioning of cannabis abuse and dependence criteria across two different countries: the United States and the Netherlands

Background: Cross-national differences could affect the likelihood of endorsement of DSM cannabis abuse and dependence criteria. The present study examines whether cannabis abuse and dependence criteria function differently across U.S. and Dutch cannabis users. Method: Data on lifetime endorsement of DSM-IV cannabis abuse/dependence criteria were utilized from U.S. cannabis users who participated in the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) and from Dutch cannabis users who participated in the Zuid-Holland study. In total, 1,568 cannabis users participated in the NESARC sample, and 359 cannabis users participated in the Zuid-Holland sample. The DSM-IV cannabis abuse/dependence criteria as well as cannabis withdrawal were determined using face-to-face computer-assisted personal interviews. Results: Using Restricted Factor Analysis with Latent Moderated Structures, the cannabis abuse/dependence criteria legal problems (β = −0.43), failed quit attempts (β = −1.09), use despite problems (β = −0.32), and withdrawal (β = −0.53) showed measurement bias, and were more likely to be endorsed by U.S. than by Dutch cannabis users. Also, men were more likely than women to endorse the criteria hazardous use (β = −0.27), legal problems (β = −0.49) and tolerance (β = −0.20). Findings on failed quit attempts and withdrawal were replicated in matched subsamples, while results on legal problems (country and gender) were partly replicated. Conclusions: Several CUD criteria showed measurement bias across two countries and between males and females. Therefore, differences between countries and gender in prevalence rates of CUD should be regarded with caution.