Estimating interaction effects with incomplete predictor variables.

ABSTRACT: The existing missing data literature does not provide a clear prescription for estimating interaction effects with missing data, particularly when the interaction involves a pair of continuous variables. In this article, we describe maximum likelihood and multiple imputation procedures for this common analysis problem. We outline 3 latent variable model specifications for interaction analyses with missing data. These models apply procedures from the latent variable interaction literature to analyses with a single indicator per construct (e.g., a regression analysis with scale scores). We also discuss multiple imputation for interaction effects, emphasizing an approach that applies standard imputation procedures to the product of 2 raw score predictors. We thoroughly describe the process of

SUBMITTER: Enders CK 

PROVIDER: S-EPMC4826075 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature