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ERIC Number: ED465802
Record Type: Non-Journal
Publication Date: 2002-Mar
Pages: 26
Abstractor: N/A
Reference Count: N/A
Latent Variable Modeling in the Hierarchical Modeling Framework: Exploring Initial Status X Treatment Interactions in Longitudinal Studies. CSE Technical Report.
Seltzer, Michael; Choi, Kilchan; Thum, Yeow Meng
In intervention studies, it is important to assess whether one program might be more effective for individuals with extreme initial difficulties, while another might be more effective for individuals with less extreme initial difficulties. In setting in which time-series data are obtained for each person, this entails examining interactions between treatment and initial status on rate of change. This report illustrates a fully Bayesian approach to studying interactions of this kind in the Hierarchical Modeling (HM) framework. This approach provides data analysis with a number of important advantages, including the ability to handle situations in which the number and spacing of time-series observations vary substantially across individuals and the ability to obtain robust estimates of parameters of interest. Various extensions of the approach are discussed in detail. (Contains 4 figures, 3 tables, and 22 references.) (Author/SLD)
Center for the Study of Evaluation, National Center for Research on Evaluation, Standards, and Student Testing, Graduate School of Education & Information Studies, University of California at Los Angeles, 300 Charles E. Young Dr. North, Los Angeles, CA 90095-1522. Tel: 310-266-1532. For full text:
Publication Type: Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Office of Educational Research and Improvement (ED), Washington, DC.
Authoring Institution: National Center for Research on Evaluation, Standards, and Student Testing, Los Angeles, CA.; California Univ., Los Angeles. Center for the Study of Evaluation.