MODELING INCOMPLETE DATA IN EXERCISE BEHAVIOR RESEARCH USING STRUCTURAL EQUATION METHODOLOGY

Exercise behavior research typically suffers from attrition and other forms of missing data. In studies that suffer from this common malady, several researchers have demonstrated that correct maximum likelihood estimation with missing data can be obtained under mild assumptions c oncerning the missing data mechanism. Model estimation with distinct m issing data patterns can, in many cases, be carried out utilizing exis ting structural equation modeling software that allow for the simultan eous analysis of mean and covariance structures for multiple groups. F indings are discussed in relation to the utility of latent variable st ructural equation modeling techniques for analysis with incomplete dat a in the study of social-psychological determinants of exercise behavi or.