Reading SEM and HLM 2: Synthezing ES in HLM

New Developments and Techniques in Structural Equation Modeling (eBook) by Marcoulides, George A.; Schumacker, Randall E. Publication: Mahwah, N.J. Lawrence Erlbaum Associates, Inc., 2001. Chapter 4 Multilevel Modeling With SEM

Ronald H. Heck University of Hawaii at Manoa The author introduces the strength of multilevel modeling when it was used to analyze cluster data. Many references are listed which can put interested researchers up-to-date on how multilevel modeling developed. Specifically these references can illuminate why and how a family of multilevel models can flourish, in several fields of study in two decades, through powerfully handling the analysis units, providing coefficient estimation and variance component at individual level and organization level, in which individuals nested. This is helpful to students who is studying HLM, which is a very popular multilevel modeling method in social sciences, such as, education and psychology. [Multilevel modeling process may imply some clue for effect size Start model: Null-indicator ANOVA Following: one-factor ANOVA (indicator-added model) Random-effect added model . . . Final model Every two tandem models provide variance change and coefficient change. The inference test of coefficients and test of variance component may be linked to effect size computation for a interested factor. (How? Mixed model, i.e., combined equation , may not be a good way to go. Specifically, in each level, indicators could have corresponding individual effect sizes. . How to defined a effect size index in HLM? When synthesize effect sizes for an indicator across studies, what kind of rules should we follow? Strict rule: Model structure should be identical in previous studies. Loose rule: the target indicator should come same level in previous studies. ) ]