Please come to http://web.cenet.org.cn/web/Occidental/
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.
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.
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.
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:
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
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.
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).
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
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