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Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences.
Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational large-scale assessments (ELSAs) or social, health, and economic survey and panel studies. The meta-analytic integration of these results offers unique and novel research opportunities to provide strong empirical evidence of the consistency and generalizability of important phenomena and trends.
Using ELSAs as an example, this tutorial offers methodological guidance on how to use the two-stage approach to IPD meta-analysis to account for the statistical challenges of complex survey designs (e.g., sampling weights, clustered and missing IPD), first, to conduct descriptive analyses (Stage 1), and second, to integrate results with three-level meta-analytic and meta-regression models to take into account dependencies among effect sizes (Stage 2).
The two-stage approach is illustrated with IPD on reading achievement from the Programme for International Student Assessment (PISA). We demonstrate how to analyze and integrate standardized mean differences (e.g., gender differences), correlations (e.g., with students' socioeconomic status [SES]), and interactions between individual characteristics at the participant level (e.g., the interaction between gender and SES) across several PISA cycles.
All the datafiles and R scripts we used are available online. Because complex social, health, or economic survey and panel studies share many methodological features with ELSAs, the guidance offered in this tutorial is also helpful for synthesizing research evidence from these studies.
Wages and wage dynamics directly affect individuals' and families' daily lives. In this article, we show how major theoretical branches of research on wages and inequality-that is, cumulative advantage (CA), human capital theory, and the lifespan perspective-can be integrated into a coherent statistical framework and analyzed with multilevel dynamic structural equation modeling (DSEM). This opens up a new way to empirically investigate the mechanisms that drive growing inequality over time. We demonstrate the new approach by making use of longitudinal, representative U.S. data (NLSY-79). Analyses revealed fundamental between-person differences in both initial wages and autoregressive wage growth rates across the lifespan. Only 0.5% of the sample experienced a "strict" CA and unbounded wage growth, whereas most individuals revealed logarithmic wage growth over time. Adolescent intelligence and adult educational levels explained substantial heterogeneity in both parameters. We discuss how DSEM may help researchers study CA processes and related developmental dynamics, and we highlight the extensions and limitations of the DSEM framework.
Several studies have revealed that older students in a year group reach higher achievement scores than younger students in that group. But less is known about how students' relative age in class relates to their self-perception of academic achievement, their social acceptance in class and to how teachers judge their abilities. Therefore, we examined relative age effects within class on students' academic self-concept, peer relations, grades, and teachers' secondary school recommendation. Analyses were based on a sample of N = 18,956 German fourth graders, who had never been retained or accelerated. We applied multilevel regression to control for covariates at the individual and classroom level. There were no substantial relative age effects within class across any of the outcomes, except for a small advantage for the youngest in their reading self-concept. Our findings therefore contradict the common assumption that younger students in class are disadvantaged compared to their older classmates.
Brandenburg startete im Schuljahr 2012/2013 das Pilotprojekt „Inklusive Grundschule“ (PING). 35 dieser Pilot-Grundschulen wurden wissenschaftlich begleitet (vgl. Spörer, Schründer-Lenzen, Vock & Maaz, 2015). In diesem Beitrag berichten wir Befunde zum sozialen Selbstkonzept, wie die Kinder das Klassenklima erleben und wie sie sich von ihrer Lehrkraft angenommen fühlen. Untersucht wurden 1.435 Kinder in 61 inklusiven Klassen der Jahrgangsstufen 2 und 3. Es finden sich keine durchgängigen Nachteile bei Selbstkonzept und erlebtem Klassenklima für Kinder mit sonderpädagogischem Förderbedarf (SPF), jedoch fühlen sich diese weniger von ihren Lehrkräften angenommen.
Students' achievement emotions are critical in their academic development. Therefore, teachers need to create an emotionally positive learning environment. In the light of this, the present study investigated the connection between students' enjoyment, anxiety, boredom and, in the first case, students' academic self-concept and, in the second, teachers' diagnostic skills. The third part of our study examined whether this link would be moderated by students' academic self-concept. Our sample comprised N = 1803 eighth-grade students who reported their achievement emotions and evaluated the diagnostic skills of both their German and mathematics teachers. Hierarchical models indicated that students experience more enjoyment and less anxiety and boredom if teachers exhibit better diagnostic skills. The role of teachers' diagnostic skills in relation to students' emotions was in part moderated by the students' self-concept. These results are discussed in terms of their implications for effective teaching.