Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE…

Data harmonization is an emerging approach to strategically combining data from multiple independent studies, enabling addressing new research questions that are not answerable by a single contributing study. A fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across…

When practitioners use modern measurement models to evaluate rating quality, they commonly examine rater fit statistics that summarize how well each rater's ratings fit the expectations of the measurement model. Essentially, this approach involves examining the unexpected ratings that each misfitting rater assigned (i.e., carrying out analyses of…

The aim of this study was to compare, by simulation, the accuracy of mapping a cut-score from one test to another by expert judgement (using the Angoff method) versus the accuracy with a small-sample equating method (chained linear equating). As expected, the standard-setting method resulted in more accurate equating when we assumed a higher level…

Modeling the distribution of site level effects is an important problem, but it is also an incredibly difficult one. Current methods rely on distributional assumptions in multilevel models for estimation. There it is hoped that the partial pooling of site level estimates with overall estimates, designed to take into account individual variation as…

Measurement invariance (MI) is a property of measurement that is often implicitly assumed, but in many cases, not tested. When the assumption of MI is tested, it generally involves determining if the measurement holds longitudinally or cross-culturally. A growing literature shows that other groupings can, and should, be considered as well.…

Trend estimation in international comparative large-scale assessments relies on measurement invariance between countries. However, cross-national differential item functioning (DIF) has been repeatedly documented. We ran a simulation study using national item parameters, which required trends to be computed separately for each country, to compare…

Personnel selection shows an enduring need for short stand-alone tests consisting of, say, 5 to 15 items. Despite their efficiency, short tests are more vulnerable to measurement error than longer test versions. Consequently, the question arises to what extent reducing test length deteriorates decision quality due to increased impact of…

Assessing measurement equivalence in the framework of the common factor linear models (CFL) is known as factorial invariance. This methodology is used to evaluate the equivalence among the parameters of a measurement model among different groups. However, when dichotomous, Likert, or ordered responses are used, one of the assumptions of the CFL is…

Growth models are used extensively in the context of educational accountability to evaluate student-, class-, and school-level growth. However, when error-prone test scores are used as independent variables or right-hand-side controls, the estimation of such growth models can be substantially biased. This article introduces a…

Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators…

Test equating is a statistical procedure for adjusting for test form differences in difficulty in a standardized assessment. Equating results are supposed to hold for a specified target population (Kolen & Brennan, 2004; von Davier, Holland, & Thayer, 2004) and to be (relatively) independent of the subpopulations from the target population (see…

In multilevel modeling, group-level variables (L2) for assessing contextual effects are frequently generated by aggregating variables from a lower level (L1). A major problem of contextual analyses in the social sciences is that there is no error-free measurement of constructs. In the present article, 2 types of error occurring in multilevel data…

We develop a Bayesian hierarchical model for the analysis of ordinal data from multirater ranking studies. The model for a rater's score includes four latent factors: one is a latent item trait determining the true order of items and the other three are the rater's performance characteristics, including bias, discrimination, and measurement error…

This dissertation comprises three papers that propose, discuss, and illustrate models to make improved inferences about research questions regarding student achievement in education. Addressing the types of questions common in educational research today requires three different "extensions" to traditional educational assessment: (1)…