title: Missing data techniques for structural equation modeling.
abstract: As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods.
authors.affiliation: Sociology Department, University of Pennsylvania, Philadelphia, PA 19104-6299, USA. allison@ssc.upenn.edu
authors.forename: Paul D
authors.initials: PD
authors.lastname: Allison
mesh_terms: D000328:Adult; D000987:Antisocial Personality Disorder / Q000453:epidemiology; D015982:Bias; D002648:Child; D006801:Humans; D016013:Likelihood Functions; D008432:Mathematical Computing; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D011594:Psychometrics / Q000706:statistics & numerical data*; D011599:Psychopathology / Q000706:statistics & numerical data*