On Latent Change Model Choice in Longitudinal Studies

Tenko Raykov
1. Juni 2016 bei GESIS in Mannheim (Konferenzraum B2,8 rechts) 13:45 Uhr


Abstract This talk is concerned with two helpful aids in the process of choosing between models of change in repeated measure investigations in the behavioral and social sciences: (i) interval estimates of proportions explained variance in longitudinally followed variables, and (ii) individual case residuals associated with these measures. The discussed method allows obtaining confidence intervals for the R-squared indices of repeatedly administered measures, as well as subject-specific discrepancies between model predictions and raw data on the observed variables. In addition to facilitating evaluation of local model fit, the approach is useful for the purpose of differentiating between plausible models stipulating different patterns of change over time. This feature of the described method becomes particularly helpful in empirical situations characterized by (very) large samples and high statistical power, which are becoming increasingly more frequent in complex sample design studies in the behavioral, health, marketing, and social sciences. The approach is similarly applicable in cross-sectional investigations, as well as with general structural equation models, and extends the set of means available to substantive researchers and methodologists for model fit evaluation beyond the traditionally used overall goodness of fit indexes. The discussed method is illustrated using data from a nationally representative study of older adults.


Zur Person Tenko Raykov is Professor of Measurement and Quantitative Methods at Michigan State University, East Lansing, USA, and specializes in a several applied statistical and measurement related areas. These include latent variable and structural equation modeling, reliability and validity evaluation, missing data, longitudinal modeling, scale construction and development, multilevel modeling, survival analysis, and multivariate statistics. He has authored or co-authored more than 100 papers in leading quantitative behavioral science journals, including “Structural Equation Modeling”, “British Journal of Mathematical and Statistical Psychology”, “Journal of Educational and Behavioral Statistics”, “Multivariate Behavioral Research”, “Applied Psychological Measurement”, “Educational and Psychological Measurement”. He is on the editorial boards of most of these journals”, and has co-authored several textbooks in applied statistics and measurement in the behavioral and social sciences.