Missing and Nonnormal Data

Missing and Nonnormal Data

Many modeling techniques that appear to be stable with realistic sample sizes in psychology often times make unrealistic assumptions such as multivariate normality or an otherwise correctly specified model. Missing responses are also a common problem in real datasets. Conversely, sometimes researchers employ planned missing data designs to reduce respondent burden, and preemptively reduce the number of careless or random responses. Our research in this area tests and develops methods for analyzing data under such suboptimal conditions. Much of this research is in the realm of multiple regression or structural equation modeling and/or overlaps with that on mediation analysis.

Avatar
Carl F. Falk
Associate Professor of Quantitative Psychology

Publications

When the multivariate normality assumption is violated in structural equation modeling, a leading remedy involves estimation via normal …

A Monte Carlo simulation was conducted to investigate the Type I error rates of several versions of chi-square difference tests for …

This article builds on the work of Savalei and Bentler (2009), who proposed and evaluated a statistically justified two-stage (TS) …

Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an …