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…

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…

The standard error of equating quantifies the variability in the estimation of an equating function. Because common items for deriving equated scores are treated as fixed, the only source of variability typically considered arises from the estimation of common-item parameters from responses of samples of examinees. Use of alternative, equally…

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…

Classroom context and climate are inherently classroom-level (L2) constructs, but applied researchers sometimes--inappropriately--represent them by student-level (L1) responses in single-level models rather than more appropriate multilevel models. Here we focus on important conceptual issues (distinctions between climate and contextual variables;…

Research pertaining to the distortion of the squared canonical correlation coefficient has traditionally been limited to the effects of sampling error and associated correction formulas. The purpose of this study was to compare the degree of attenuation of the squared canonical correlation coefficient under varying conditions of score reliability.…

An extensive review of the research literature and a study comparing over 36,000 survey responses with archival true scores indicated that one should expect a minimum of at least three percent random error for the least ambiguous of self-report measures. The Gulliver Effect occurs when a small proportion of error in a sizable subpopulation exerts…

This report examines the differences in impact estimates and standard errors that arise when these are derived using state achievement tests only (as pre-tests and post-tests), study-administered tests only, or some combination of state- and study-administered tests. State tests may yield different evaluation results relative to a test that is…

This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the individual (L1) and group (L2)…

In large-scale assessments, such as state-wide testing programs, national sample-based assessments, and international comparative studies, there are many steps involved in the measurement and reporting of student achievement. There are always sources of inaccuracies in each of the steps. It is of interest to identify the source and magnitude of…

The constraints of conducting evaluations in real-world settings often necessitate the implementation of less than ideal designs. Unfortunately, the standard method for estimating the precision of a result (i.e., confidence intervals [CI]) cannot be used for evaluative conclusions that are derived from multiple indicators, measures, and data…

A simple, rigorous, small-sample statistical technique is described for testing the hypothesis that two sets of measurements differ only because of errors of measurement and because of differing origins and units of measurement. (Author)

A debate concerning specific issues and the general usefulness of the Rasch latent trait test model is continued. Methods of estimation, necessary sample size, and the applicability of the model are discussed. (JKS)

Statements made in a previous article of this journal concerning the Rasch latent trait test model are questioned. Methods of estimation, necessary sample sizes, several formuli, and the general usefulness of the Rasch model are discussed. (JKS)