Background: Standard methods for analysing data from large-scale assessments (LSA) cannot merely be adopted if hierarchical (or multilevel) regression modelling should be applied. Currently various approaches exist; they all follow generally a design-based model of estimation using the pseudo maximum likelihood method and adjusted weights for the…

This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and…

We are concerned with the unbiased estimation of a treatment effect in the context of non-experimental studies with grouped or multilevel data. When analyzing such data with this goal, practitioners typically include as many predictors (controls) as possible, in an attempt to satisfy ignorability of the treatment assignment. In the multilevel…

Multiple imputation (MI) can be used to address missing data at Level 2 in multilevel research. In this article, we compare joint modeling (JM) and the fully conditional specification (FCS) of MI as well as different strategies for including auxiliary variables at Level 1 using either their manifest or their latent cluster means. We show with…

The traditional approach to estimating the consistency of school effects across subject areas and the stability of school effects across time is to fit separate value-added multilevel models to each subject or cohort and to correlate the resulting empirical Bayes predictions. We show that this gives biased correlations and these biases cannot be…

The current simulation study examined the effects of Item Parameter Drift (IPD) occurring in a short scale on parameter estimates in multilevel models where scores from a scale were employed as a time-varying predictor to account for outcome scores. Five factors, including three decisions about IPD, were considered for simulation conditions. It…

Fostering peer interaction and shared learning is an important aim of inclusive instruction. However, it has not been established whether it is possible to offer explicit and intensive support for low achievers in inclusive settings. This longitudinal study examined whether a structured program that includes cooperative learning fosters…

The selection of an appropriate booklet design is an important element of large-scale assessments of student achievement. Two design properties that are typically optimized are the "balance" with respect to the positions the items are presented and with respect to the mutual occurrence of pairs of items in the same booklet. The purpose…

This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the…

Longitudinal research in higher education faces several challenges. Appropriate methods of analyzing competence growth of students are needed to deal with those challenges and thereby obtain valid results. In this article, a pretest-posttest-posttest multivariate multilevel IRT model for repeated measures is introduced which is designed to address…

The current study applied the person-fit statistic, l[subscript z], to data from a Canadian provincial achievement test to explore the usefulness of conducting person-fit analysis on large-scale assessments. Item parameter estimates were compared before and after the misfitting student responses, as identified by l[subscript z], were removed. The…

In traditional studies on value-added indicators of educational effectiveness, students are usually treated as belonging to those schools where they took their final examination. However, in practice, students sometimes attend multiple schools and therefore it is questionable whether this assumption of belonging to the last school they attended…

Nonlinear multilevel latent variable modeling has been suggested as an alternative to traditional hierarchical linear modeling to more properly handle measurement error and sampling error issues in contextual effects modeling. However, a nonlinear multilevel latent variable model requires significant computational effort because the estimation…

Maximum likelihood (ML) estimation of generalized linear mixed models (GLMMs) is technically challenging because of the intractable likelihoods that involve high dimensional integrations over random effects. The problem is magnified when the random effects have a crossed design and thus the data cannot be reduced to small independent clusters. A…

In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…