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7350.rar (2.62 MB) 本附件包括：

• Statistics - Lisrel Models - General Structural Equations.pdf
• Statistics - Structural Equation (Lisrel) Models - Intermediary Topics.pdf
• ulb462.pdf
• anvlisrelht04eng.pdf
• Chapter4.pdf
• Contents.pdf
• Interactive Lisrel.User's Guide.pdf
• kub01p.pdf
• Lisrel8.51.pdf
• Lisrel tutorial.exe
• ls8toc.pdf
• ms08.pdf

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关键词：LISREL lisre Users isrel Guide 下载 Guide LISREL Users

New features in LISREL 8.7 for Windows

• Generalized Linear Models (GLIMs) for complex survey data

  The new SurveyGLIM module in LISREL 8.7 allows users to select from the multinomial, Bernoulli, binomial, Poisson, negative binomial, gamma, Gauss, and inverse Gaussian sampling distributions. Various link functions, such as the log, cumulative logit, cumulative probit, complementary log-log, and logit are available.

  SurveyGLIM allows for the analysis of data from a simple random sample or from a complex sample design. In the latter case it is assumed that the population from which the sample is obtained can be stratified into strata. Within each stratum, clusters (primary sample units or PSUs) are drawn and within each stratum-cluster combination, the ultimate sampling units (USUs) are drawn with specified design weights. There is also an option to correct for finite populations, provided that the sampling rates or population sizes are available.

• Implementation of design weights in the LISREL Multilevel modeling module

  There has been a growing interest in recent years in fitting models to data collected from surveys using complex sample designs. LISREL 8.7 features an option for users to include sample design weights for the analysis of hierarchical linear models. This makes it possible to specify weights on levels 1, 2 or 3 of the hierarchy. Correct parameter estimates and robust standard errors are produced under complex sampling designs.

• Implementation of sampling weights for SEM models when data is missing at random

  In previous versions of LISREL, users were able to compute the appropriate covariance and estimated asymptotic covariance matrices for continuous variables via PRELIS given a normalized weight variable. These matrices are only produced in the case of complete data, or using list-wise deletion in situations where missing data values are present.

  In version 8.7, it is possible to use design weights to fit SEM models to continuous data with missing values. The easiest way to do this is to define the weight variable once a PSF file is displayed. A full information maximum likelihood (FIML) method is used to obtain the correct parameter estimates and robust standard errors given the sampling weights.

• 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.
Part D: Ordinal Data and Interaction Models: Some modern extensions of structural modeling theory are explored and expanded in this section. For depth and breadth, Structural Equation Modeling: Present and Future is definitely a worthy addition to the library of anyone who is involved in the field. Overall, it will provide wonderful insight into the progress that has come from the ongoing work of Dr. Jöreskog and countless others in Factor Analysis and Structural Equation Modeling. Copyright 2001 Scientific Software International, Inc. ISBN: 0-89498-049-1

[此贴子已经被作者于2005-2-20 0:16:51编辑过]

Request: Book in Chinese describing LISREL Structural Equation Model and Its Applications by Kit-Tai Hau, Zhonglin Wen, & Zijuan Cheng. published by Educational Science Publishing House

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

[此贴子已经被作者于2005-2-20 0:22:00编辑过]

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.

[此贴子已经被作者于2005-2-20 0:32:05编辑过]

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.

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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)