Quantitative psychology research:the 78th Annual Meeting of the Psychometric Society

The 78th Annual Meeting of the Psychometric Society (IMPS) builds on the Psychometric Society's mission to share quantitative methods relevant to psychology. The chapters of this volume present cutting-edge work in the field. Topics include studies of item response theory, computerized adaptive testing, cognitive diagnostic modeling, and psychological scaling. Additional psychometric topics relate to structural equation modeling, factor analysis, causal modeling, mediation, missing data methods, and longitudinal data analysis, among others. The papers in this volume will be especially useful for researchers in the social sciences who use quantitative methods. Prior knowledge of statistical methods is recommended. The 78th annual meeting took place in Arnhem, The Netherlands between July 22nd and 26th, 2013. The previous volume to showcase work from the Psychometric Society’s Meeting is New Developments in Quantitative Psychology: Presentations from the 77th Annual Psychometric Society Meeting (Springer, 2014)

A test for ordinal measurement invariance

One problem with the analysis of measurement invariance is the reliance of the analysis on having a parametric model that accurately describes the data. In this paper an ordinal version of the property of measurement invariance is proposed, which relies only on nonparametric restrictions. This property of ordinal measurement invariance provides a coarse (initial) indication of measurement invariance, based on the sum scores. A small example is given to illustrate the procedure for testing the property of ordinal measurement invariance

Comparing estimation methods for categorical marginal models

Categorical marginal models are flexible models for modelling dependent or clustered categorical data which do not involve any specific assumptions about the nature of the dependencies. Categorical marginal models are used for different purposes, including hypothesis testing, assessing model fit, and regression problems. Two different estimation methods are used to estimate marginal models: maximum likelihood (ML) and generalized estimating equations (GEE). We explored three different cases to find out to what extent the two types of estimation methods are appropriate for investigating different types of research questions. The results suggest that ML may be preferred for assessing model fit because GEE has limited fit indices, whereas both methods can be used to assess the effect of independent factors in regression. Moreover, ML is asymptotically efficient, while GEE loses efficiency when the working correlation matrix is not correctly specified. However, for parameter estimation in regression GEE is easier to apply from a computational perspective

Invariance in Measurement and Prediction Revisited

measurement invariance, predictive invariance, factorial invariance, test bias, selection accuracy,