Modeling Incomplete Data in Exercise Behavior Research Using Structural Equation Methodology

in Journal of Sport and Exercise Psychology
View More View Less
  • 1 Oregon Research Institute
  • 2 Stanford University
  • 3 Oregon Research Institute
Restricted access

Purchase article

USD  $24.95

Student 1 year online subscription

USD  $85.00

1 year online subscription

USD  $114.00

Student 2 year online subscription

USD  $162.00

2 year online subscription

USD  $216.00

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 concerning the missing data mechanism. Model estimation with distinct missing data patterns can, in many cases, be carried out utilizing existing structural equation modeling software that allow for the simultaneous analysis of mean and covariance structures for multiple groups. Findings are discussed in relation to the utility of latent variable structural equation modeling techniques for analysis with incomplete data in the study of social-psychological determinants of exercise behavior.

T.E. Duncan is with the Oregon Research Institute, 1899 Willamette St., Eugene, OR 97401. R. Oman is with the Stanford Center for Research and Disease Prevention at Stanford University, Stanford, CA 94305. S.C. Duncan is with the Oregon Research Institute, 1715 Franklin Blvd., Eugene, OR 97403.

All Time Past Year Past 30 Days
Abstract Views 425 403 56
Full Text Views 2 2 0
PDF Downloads 0 0 0