Although modern lines for dealing with missing data are well established from the 1970s,
today there is a challenge when researchers encounter this problem in multilevel models …

Social science research often utilizes measurement instruments that generate ordinal data
(eg, Likert scales). Many empirical studies also face the challenge of missing data, which …

In many applied situations, the questionnaire items in measurement instruments do not
approximate continuous, normally distributed variables but instead are ordinal. Properties of …

Although missing data methods have advanced in recent years, methodologists have
devoted less attention to multilevel data structures where observations at level-1 are nested …

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 …

A variety of indices are commonly used to assess model fit in structural equation modeling.
However, fit indices obtained from the normal theory maximum likelihood fit function are …

The performance of five simple multiple imputation methods for dealing with missing data
were compared. In addition, random imputation and multivariate normal imputation were …

A new test of model misspecification is proposed, based on the ratio of in-sample and out-of-
sample likelihoods. The test is broadly applicable and, in simple problems, approximates …

The purpose of this paper is to assess the impact of misspecification on the estimation,
testing, and improvement of structural equation models. A population study is conducted …

This study focused on developing and examining an effect-size index for evaluation of
model-data fit in structural equation modeling. Based on MacCallum and his colleagues' …