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 …

Missing data are a prevalent problem in psychological research that can reduce statistical
power and bias parameter estimates. These problems can be mostly resolved with multiple …

The performance of multiple imputation in questionnaire data has been studied in various
simulation studies. However, in practice, questionnaire data are usually more complex than …

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 …

Analyses of multivariate data are frequently hampered by missing values. Until recently, the
only missing-data methods available to most data analysts have been relatively ad1 hoc …

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 …

Social scientists typically devote considerable effort to mitigating measurement error during
data collection but then ignore the issue during data analysis. Although many statistical …

Adequately addressing missing data is a pervasive issue in the social sciences. Failure to
correctly address missing data can lead to biased or inefficient estimation of parameters …

Missing data frequently complicates data analysis for scientific investigations. The
development of statistical methods to address missing data has been an active area of …

Multiple Imputation (MI) is the most efficient way of estimating missing values for non-normal
categorical variables (Enders, 2017). A challenge related to MI is the choice of appropriate …