Likelihood ratio tests (LRTs) are a popular tool for comparing statistical models. However,
missing data are also common in empirical research, and multiple imputation (MI) is often …

In structural equation modeling, when multiple imputation is used for handling missing data,
model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under …

ABSTRACT In Ordinary Least Square regression, researchers often are interested in
knowing whether a set of parameters is different from zero. With complete data, this could be …

Background Multiple imputation is a recommended method to handle missing data. For
significance testing after multiple imputation, Rubin's Rules (RR) are easily applied to pool …

Multiple imputation (MI) inference handles missing data by imputing the missing values m
times, and then combining the results from the m complete-data analyses. However, the …

Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation
(MI) is a well‐known likelihood‐based method that has optimal properties in terms of …

Multilevel data are often incomplete, for example, when participants refuse to answer some
items in a questionnaire or drop out of a study that involves multiple measurement …

Missing data are common in psychology research and can lead to bias and reduced power if
not properly handled. Multiple imputation is a state-of-the-art missing data method …

Multiple imputation is a popular method for addressing data that are presumed to be missing
at random. To obtain accurate results, one's imputation model must be congenial to …

Multivariate extensions of well-known linear mixed-effects models have been increasingly
utilized in inference by multiple imputation in the analysis of multilevel incomplete data. The …