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ERIC Number: ED354273
Record Type: Non-Journal
Publication Date: 1992-Nov
Pages: 20
Abstractor: N/A
Reference Count: N/A
Advances in Multi-Level Psychometric Models: Latent Variable Modeling of Growth with Missing Data and Multilevel Data. Project 2.6: Analytic Models To Monitor Status and Progress of Learning and Performance and Their Antecedents.
Muthen, Bengt O.
Three important methods areas of multivariate analysis that are not always thought of in terms of latent variable constructs, but for which latent variable modeling can be used to great advantage, are discussed. These methods are: (1) random coefficients describing individual differences in growth; (2) unobserved variables corresponding to missing data; and (3) variance components describing data from cluster sampling. An educational achievement dataset of longitudinal observations on secondary mathematics achievement (the National Longitudinal Study of American Youth) is described as a motivating example. It is shown that all three topics can be simply expressed in terms of latent variable modeling that fits into existing and generally available structural modeling software. This approach makes possible a connection between psychometricians and other methodologists interested in latent variable modeling. Interesting extensions of these statistical analyses are discussed. One table presents missing data patterns.
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Inst. on Alcohol Abuse and Alcoholism (DHHS), Rockville, MD.; Office of Educational Research and Improvement (ED), Washington, DC.
Authoring Institution: National Center for Research on Evaluation, Standards, and Student Testing, Los Angeles, CA.