TOC: Multivariate Behav Res
Introduction
Multivariate Behavioral Research, 50(5)
Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences
–Daniel M. McNeish [] []
A Comparison of Imputation Strategies for Ordinal Missing Data on Likert Scale Variables
–Wei Wu, Fan Jia & Craig Enders [] []
Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation
–Gina L. Mazza, Craig K. Enders & Linda S. Ruehlman [] []
How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study
–Patricia Rodríguez De Gil, Aarti P. Bellara, Rheta E. Lanehart, Reginald S. Lee, Eun Sook Kim & Jeffrey D. Kromrey [] []
Correcting Too Much or Too Little? The Performance of Three Chi-Square Corrections
–Njål Foldnes & Ulf Henning Olsson [] []
Models for the Detection of Deviations from the Expected Processing Strategy in Completing the Items of Cognitive Measures
–Karl Schweizer, Michael Altmeyer, Xuezhu Ren & Michael Schreiner [] []
Extending the Debate Between Spearman and Wilson 1929: When do Single Variables Optimally Reproduce the Common Part of the Observed Covariances?
–André Beauducel & Norbert Hilger [] []
