[下载] lisrel Users Guide

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Multivariate Censored Regression

Univariate regression for a censored response variable is available since LISREL 8.54. In LISREL 8.7, this method is extended to allow for multivariate censored regression. In addition, the appropriate sample covariance matrix for a set of censored variables may be computed and used to fit structural equation models to censored data.

Goodness-of-fit statistics

Since the release of LISREL 8.52 for Windows, the computation of the chi-square test statistic value for the independence model is based on the normal-theory weighted least squares (NT-WLS) chi-square test statistic value rather than on the minimum fit function chi-square test statistic value. This change implied that the goodness-of-fit statistics, which is based on the chi-square test statistic value for the independence model such as the CFI, NFI, NNFI, IFI, etc., were different and led to numerous inquiries by our LISREL users. As a result, LISREL 8.7 produces an additional file with the file extension 揊TB?that contains a listing of these goodness-of-fit statistics based on all four chi-square test statistic values that LISREL 8.7 reports.

Changes to the windows/menus/dialogs

There are three new options in the Compute dialog box starting with version 8.7 of LISREL. These are: (i) TIME (ii) AUTOLAG/ORDER, (iii) CHISQ(DF)

The first option enables users to create a new variable called TIME, that assumes integer values 1, 2, 3, ? ncases. Functions of TIME, for example TIME**2 can also be computed. The second option allows the user to create new variables that assumes the same values than an existing variable, but with a user-specified lag. These new variables are useful in identifying time series processes and for the calculation of lagged correlation matrices. Lastly, one can generate random deviates from a chi-square distribution with a specified number of degrees of freedom.

Additions/changes to the dialog boxes of the multilevel module include: (i) No-Intercept option (ii) Select weights list box (iii) Print asymptotic covariances checkbox (iv) Print values of within and between covariance matrices checkbox. Note that the specification of a level-1 ID variable is no longer required.

LISREL

LISREL is a software product designed to estimate and test Structural Equation Models (SEMs). Structural Equation Models are statistical models of linear relationships among latent (unobserved) and manifest (observed) variables. You can also use this software to carry out both exploratory and confirmatory factor analysis, as well as path analysis.

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LISREL Table of Contents

Click on a topic to skip to that section.

• Introduction
• Availability
• Getting Started
• Documentation
  • Built-in Help
  • Manuals, Guides, Books
  • Usage Notes (from the Computation Center)
  • Frequently Asked Questions (at UT Austin)
• Information on the Internet
  • Ed Rigdon's SEM pages
  • Other software and Statistical Information Archives
  • USENET News Groups
• Getting Help
  • Getting help in the SMF
  • Getting help from the Applications Consultants
• Training
• For More Information
• LISREL and the year 2000

Lisrel

• Information
  • Lisrel Overview
  • Availability - where to find LISREL
  • Sales - how to buy LISREL
• Support
  • Local Resources
    • Running Lisrel
    • Lisrel Example
  • Web Resources
    • Lisrel at SSICentral - Vendor's Page
• See Also
  • Statistics Sites

Request: Structural Equation Modeling: Present and Future A Festschrift in honor of Karl Jöreskog

Robert Cudeck, Stephen du Toit & Dag Sörbom (Editors)

The text honors Dr. Karl Jöreskog's outstanding academic career through contributions of current researchers in Structural Equation Modeling.

The book contains the following sections:

• Part A: History and Perspectives: This section will be indispensable to educators and students alike who want to explore the roots of factor analysis, including some more personal accounts by two of Dr. Jöreskog's former students.

• Part B: Robustness, Reliability, and Fit Assessment: Six chapters explore the evolution and current execution of the methodology in greater depth.
• Part C: Repeated Measurements, Experimental Design: Investigations and discussion of longitudinal data analysis, including some new approaches to model design.

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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

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

International Journal of Testing2001, Vol. 1, No. 1, Pages 55-86

(doi:10.1207/S15327574IJT0101_4)

Recommend:

Research Paper: The Robustness of LISREL Modeling Revisited

现在好像用AMOS的越来越多了，不过有谁研究过AMOS的算法吗？

就是要这个啊！