Contributions by Karl G Jöreskog

1. Why are t-values for Error Variances Equal? A Paradox in Path Models for Observed Variables
2. How Large Can a Standardized Coefficient be?
3. What is the interpretation of R²?
4. Formulas for skewness and kurtosis
5. Interpretation of R2 revisited
6. Latent Variable Scores and their uses
7. Analysis of Ordinal Variables Part 1: Preliminary Analysis
8. Analysis of Ordinal Variables Part 2: Cross-Sectional Data
9. Analysis of Ordinal Variables Part 3: Longitudinal Data
10. Analysis of Ordinal Variables Part 4: Multiple Groups
11. Analysis of Ordinal Variables Part 5: Covariates
12. Censored Variables and Censored Regression
13. Factor Analysis by MINRES
14. On Chi-Squares for the Independence Model and Fit Measures in LISREL
15. Multivariate Censored Regression

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References

Aigner, D.J., & Goldberger, A. S., Latent variables in socio-economic models, Amsterdam: North-Holland, 1977.

Arminger, G., Clogg, C. C., & Sobel, M. E., Handbook of statistical modeling for the social and behavioral sciences, Academic Press, 1994.

Bagozzi, R.P., Causal models in marketing, Wiley, 1979.

Bartholomew, D.J., Latent variable models and factor analysis, Oxford University Press, 1987.

Bollen, K.A., Structural equations with latent variables, Wiley, 1989.

Bollen, K.A. & Long, J.S., Testing structural equation models, Sage, 1993.

Byrne, B.M., Structural equation modeling with LISREL, PRELIS and SIMPLIS: basic concepts, applications and programming, Erlbaum, 1998.

Duncan, O.D., Introduction to structural equation models, Academic Press, 1975.

Everitt, B., An introduction to latent variable models, Chapman & Hall, 1984.

Goldberger, A.S., & Duncan, O.D., Structural equation models in the social sciences, New York: Seminar Press/Harcourt Brace, 1973.

Hayduk, L.A., Structural equation modeling with LISREL, John Hopkins Press, 1987.

Hayduk, L.A., LISREL issues, debates, and strategies, John Hopkins Press, 1996.

Heinen, T., Discrete latent variable models, Tilburg, Netherlands: Tilburg University Press, 1993.

Hoyle, R.H., Structural equation modeling: concepts, issues and applications, Sage, 1995.

Jöreskog, K.G., & Sörbom, D., Advances in factor analysis and structural equation models,, [ed. Jay Magidson], Cambridge, Mass.: Abt Books, 1979.

Jöreskog, K.G., & Wold, H., Systems under indirect observation: causality, structure, prediction, North-Holland, 1982.

Kaplan, D., Structural equation modeling: foundations and extensions, Sage, 2000.

Kelloway, E.K., Using LISREL for structural equation modeling: a researcher's guide, Sage,1998.

Kenny, D.A., Correlation and causality, Wiley, 1979.

Kline, R.B., Principles and practice of structural equation modeling, Guilford Press, 1998.

Loehlin, J.C., Latent variable models: an introduction to factor, path, and structural analysis, 3rd ed., Erlbaum, 1998.

Marcoulides, G.A., & Schumacker, R.E., Advanced structural equation modeling: issues and techniques, Erlbaum, 1996.

Marcoulides, G.A., & Schumacker, R.E., New developments and techniques in structural equation modeling, Erlbaum, 2001.

Maruyama, G., Basics of structural equation modeling, Sage,1997.

Mueller, R.O., Basic principles of structural equation modeling: an introduction to LISREL and EQS, Springer-Verlag, 1996.

Pearl, J., Causality: models, reasoning, and inference, Cambridge University Press, 2000.

Raykov, T., & Marcoulides, G.A., A first course in structural equation modeling, Erlbaum, 2000.

Schumacker, R.E., & Lomax, R,G., A beginner's guide to structural equation modeling, 2nd ed, Erlbaum, 2004.

Schumacker, R.E., & Marcoulides, G.A., Advanced structural equation modeling: interaction models, Erlbaum, 1998.

Schumacker, R.E., & Marcoulides, G.A., Interaction and non-linear effects in structural equation modeling, Erlbaum, 1998.

Spirtes, P., Glymour, C., & Scheines, R., Causation, prediction, and search, Springer-Verlag, 1993.

Von Eye, A., & Clogg, C.C., Latent variable analysis: applications for developmental research, Sage, 1994.

LISREL Resources

The following links provide access to some Web postings related to the LISREL statistical software package. They are not intended as a comprehensive information resource, but they may be helpful for users or intending users of this software. As they all point to external resources, ITS cannot be responsible for their content or currency.

• SPSS Inc.
• FAQs (notmany)
• Some general information from the University of Wisconsin
• Usage information from Indiana University, Bloomington
• The Robustness of Normal-theory LISREL Models: Tests Using a New Optimizer, the Bootstrap, and Sampling Experiments, with Applications. Mebane, Walter R. Jr., Jasjeet S. Sekhon and Martin T. Wells. University of California, Riverside.
• LISREL and PRELIS Under UNIX - University of Southern California
• UTexas Statistical Services' Lisrel Introducton
• Lisrel/Prelis homepage at Scientific Software International, the U.S. distributor of Lisrel.
• Lisrel/Prelis FAQs at Scientific Software International.
• Frequently Asked Questions about Structural Equation Modeling at Smallwaters, Inc.
• Links to resources about SEMs and Statistics at Smallwaters, Inc.

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Practical Guide to Statistical Packages

Copyright 1997-8 J Zhao This page is made possible by CCT, a wonderful Chinese emTeXware, available from Institute of Computing , Chinese Academy of Science. PDF files are from Adobe distiller with kind help of Dr Dong Pang (regenerated from dvips32 and HGMP on 29/11/00). A PostScript (unzip first)/PDF viewer/printer is necessary to view/print them.

Cover and Table of Contents (PDF)

Preface (PDF)

Part I. Some preparations

Chapter 1. An Overview (PDF)

Chapter 2. A General Introduction to Statistics (PDF)

Chapter 3. Fundamentals of Practical Computing (PDF)

Part II. General-purpose packages

Chapter 4. SAS (PDF)

Chapter 5. SPSS/PC+ (PDF)

Chapter 6. BMDP (PDF)

Chapter 7. Systat (PDF)

Chapter 8. Stata (PDF)

Chapter 9. S-Plus (PDF)

Chapter 10. Minitab (PDF)

Chapter 11. Genstat (PDF)

Part III. Specialized packages

Chapter 12. MicroTSP (PDF)

Chapter 13. GLIM (PDF)

Chapter 14. LISREL (PDF)

Chapter 15. Epi Info (PDF)

Part IV. Data and graphhics management and word processing

Chapter 16. Data Management (PDF)

Chapter 17. High-resolution Graphics (PDF)

Chapter 18. Word Procesing (PDF)

References and Information (PDF)

Download:

1.Interactive LISREL: User’s Guide

http://www.bus.emory.edu/research_computing/Lsrel%20doCs/Contents.pdf

2. Brief Guide to Use of LISREL 8.50 for Confirmatory Factor Analysis

http://www.unc.edu/~rcm/psy236/lisrel.intro.pdf

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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.

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

(doi:10.1207/S15327574IJT0101_4)

Recommend:

Research Paper: The Robustness of LISREL Modeling Revisited

[此贴子已经被作者于2005-2-20 1:23:47编辑过]

就是要这个啊！

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