Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

There has been conspicuously little research concerning missing data problems in the
applied psychology literature. Fortunately, other fields have begun to investigate this issue …

With detailed examples, this book demonstrates the use of the computer program LISREL
and how it can be applied to the analysis of interactions in regression frameworks. The …

The use of the Gibbs sampler with fully conditionally specified models, where the distribution
of each variable given the other variables is the starting point, has become a popular …

The effects of using two methods (item mean and person mean) for replacing missing data in
Likert scales were studied. The results showed that both methods were good …

The authors propose that a reciprocal resource gain spiral forms between coworker-based
perceived social support and trust, which leads to coworkers investing personal resources …

A Monte Carlo simulation examined the performance of a recently available full information
maximum likelihood (FIML) estimator in a multiple regression model with missing data. The …

For organizational research on individual change, missing data can greatly reduce
longitudinal sample size and potentially bias parameter estimates. Within the structural …

This handbook presents many methodological advances and the latest applications of
missing data methods in empirical research. It outlines a general taxonomy of missing data …

The effect of a number of factors, such as the choice of analytical method, the handling
method for missing data, sample size, and proportion of missing data, were examined to …