Course Outline: MULTILEVEL ANALYSIS WITH MLWIN AND LISREL 8.51
Dr Ken Rowe, Principal Research Fellow, Australian Council for Educational Research
PREREQUISITES
Multiple regression, or equivalent experience. Previous participation in an ACSPRI course on Structural Equation Modeling will also be helpful. The creskog & Sourse will assume familiarity with general linear model concepts and model fitting. Since MLwiN and LISREL 8.51 operate under Windows'95/'98/2000 and/or Windows NT, familiarity with Windows-based PC statistical packages is desirable.
COURSE OUTLINE
reskog & SThe course will focus on the rationale, development and use of multilevel models to analyse data from hierarchically structured populations/samples (e.g., voters within electorates, cases within groups within areas, students within classes within schools, etc.), or longitudinal studies (repeated measures clustered within individuals within groups) - typical of those used in applied epidemiological, psychosocial and educational research. The prime focus of the course will be on the use and application of two recent, interactive, multilevel statistical software packages: (1) MLwiN (Rasbash et al., 2000) and (2) LISREL 8.51 (Joreskog & Sorbom, 2001) - to the analysis of: (1) variance components models, (2) multilevel regression models, including the computation of 'value-added' indices, (3) multilevel logistic models, (4) random coefficients regression models, (5) longitudinal and growth-curve models, (6) cross-classified models, (7) multivariate multilevel models. Participants will also be introduced to 'state-of-the-art' multivariate, multilevel, covariance-structure analysis.
Note that the course is designed as a practical introduction to multilevel analysis, providing hands-on computing experience with actual data sets. Detailed notes with worked examples and references will be provided as a basis for both the lecture and hands-on computing aspects of the course. Participants are encouraged to bring their own data sets for analysis during the course (in ASCII or *.txt format format; Excel *.xls files; SPSS *.sav files).
COURSE TEXTS (ESSENTIAL)
Rasbash, J., Browne, W., Goldstein, H., Yang, M., Plewis, I., Healy, M., Woodhouse, G., Draper, D., Langford, I., & Lewis, T. (2000). A user's guide to MLwiN (Version 2.1). Multilevel Models Project, Institute of Education University of London. Note: This can be downloaded in *.pdf format from: http://www.ioe.ac.uk/mlwin/upgrades.html#newfeatures
Joreskog, K.G., Sorbom, D., du Toit, S., & du Toit, M. (1999). LISREL 8: New statistical features. Chicago, IL: Scientific Software International Inc.
du Toit, M., & du Toit, S. (2001). Interactive LISREL: User's Guide. Lincolnwood, IL: Scientific Software International Inc.
RECOMMENDED READING
Bryk, A.S., & Raudenbush, S.W. (1992). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage.
Goldstein, H. (1995). Multilevel statistical models. London: Edward Arnold.
Hill, P.W., & Rowe, K.J. (1998). Modelling student progress in studies of educational effectiveness. School Effectiveness and School Improvement, 9 (3), 310-333.
Kaplan, D. (2000). Structural equation modeling: Foundations and extensions. Advanced Quantitative Techniques in the Social Sciences Series (No. 10). Thousand Oaks, CA: Sage Publications.
Kreft, I.G., & de Leeuw, J. (1998). Introducing multilevel modeling. Thousand Oaks, CA: Sage.
Rowe, K.J. (2001). Estimating interdependent effects among multilevel composite variables in psychosocial research: An annotated example of the application of multilevel structural equation modeling. In N. Duan and S. Reise (Eds.), Multilevel modeling: Methodological advances, issues and applications (Chap 1, pp. 1-28). Hillsdale, NJ: Lawrence Erlbaum & Associates.
Rowe, K.J., & Hill, P.W. (1998). Modeling educational effectiveness in classrooms: The use of multilevel structural equations to model students' progress. Educational Research and Evaluation, 4 (4), 307-347.
Rowe, K.J., & Rowe, K.S. (1999). Investigating the relationship between students' attentive-inattentive behaviors in the classroom and their literacy progress. International Journal of Educational Research, 31 (2), 1-138 (Whole Issue). Elsevier Science, Pergamon Press.
Structural Equation Modeling With AMOS, EQS, and LISREL: Comparative Approaches to Testing for the Factorial Validity of a Measuring Instrument
Barbara M. Byrne
School of Psychology, University of Ottawa
Using a confirmatory factor analytic (CFA) model as a paradigmatic basis for all comparisons, this article reviews and contrasts important features related to 3 of the most widely-used structural equation modeling (SEM) computer programs: AMOS 4.0 (Arbuckle, 1999), EQS 6 (Bentler, 2000), and LISREL 8 (Joreskog & Sorbom, 1996b). Comparisons focus on (a) key aspects of the programs that bear on the specification and testing of CFA models-preliminary analysis of data, and model specification, estimation, assessment, and misspecification; and (b) other important issues that include treatment of incomplete, nonnormally-distributed, or categorically-scaled data. It is expected that this comparative review will provide readers with at least a flavor of the approach taken by each program with respect to both the application of SEM within the framework of a CFA model, and the critically important issues, previously noted, related to data under study
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