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SOC 681: INTRODUCTION TO LINEAR STRUCTURAL …

5 SOC 681: INTRODUCTION TO LINEAR STRUCTURAL EQUATION MODELS (LISREL) Spring 2000 SRI Laboratory Tu,Th 4:30-5:45 PM Professor James G. Anderson (Stone 353, 494-4703) The course will introduce participants to STRUCTURAL Equation Models (SEMs) using LISREL, one of the most widely available computer programs for STRUCTURAL equation modeling in social, behavioral, and economic research. SEMs simultaneously model the measurement and conceptual structure of social phenomena and thus combine the strengths of factor analysis, path analysis, and simultaneous equation models. The course will be taught in the Social Research Institute Laboratory. Participants will be assisted in constructing, estimating and interpreting SEMs based on their own data.

6 TEXTS Anderson, J.G. Introduction to Linear Structural Equation Models. Schumacker, R.E. and R.G. Lomax. A Beginner's Guide to Structural Equation Modeling.

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Transcription of SOC 681: INTRODUCTION TO LINEAR STRUCTURAL …

1 5 SOC 681: INTRODUCTION TO LINEAR STRUCTURAL EQUATION MODELS (LISREL) Spring 2000 SRI Laboratory Tu,Th 4:30-5:45 PM Professor James G. Anderson (Stone 353, 494-4703) The course will introduce participants to STRUCTURAL Equation Models (SEMs) using LISREL, one of the most widely available computer programs for STRUCTURAL equation modeling in social, behavioral, and economic research. SEMs simultaneously model the measurement and conceptual structure of social phenomena and thus combine the strengths of factor analysis, path analysis, and simultaneous equation models. The course will be taught in the Social Research Institute Laboratory. Participants will be assisted in constructing, estimating and interpreting SEMs based on their own data.

2 They will be expected to make an oral presentation based on their research and to prepare a manuscript that may be submitted to a professional journal in their field. Topics will include: 1. Basics: Causality and Causal Models 2. Models with Directly Observed Variables 3. Measurement Models: Confirmatory Factor Analysis 4. STRUCTURAL Equation Models with Latent Variables 5. Model Building: Testing Goodness of Fit 6. Analysis of Longitudinal Data 7. Multiple Groups Analysis 8. Analysis of Experimental Data COURSE DESCRIPTION Throughout the course, participants will use data analysis exercises to illustrate the various topics covered in class. Participants will be expected to construct, critique, and estimate STRUCTURAL equation models using LISREL.

3 Exercises will be due each week. They will be graded and returned for you to make corrections. You will also be expected to complete a term project involving the construction, the estimation, and testing of a STRUCTURAL equation model involving measurement error and latent variables. The project is to be written in article format and you are encouraged to submit it to a journal for possible publication. 6 TEXTS Anderson, INTRODUCTION to LINEAR STRUCTURAL Equation Models. Schumacker, and Lomax. A Beginner's Guide to STRUCTURAL Equation Modeling. Hillsdale, NJ: Lawrence Earlbaurn Associates, 1996. Joreskog, K. and D, Sorbom. LISREL 8: STRUCTURAL Equation Modeling with the SIMPLIS Command Language. Hillsdale, NJ: Lawrence Earlbaurn Associates, 1993.

4 Kline, Principles and Practices of STRUCTURAL Equation Modeling. New York: The Guildford Press, 1998. Schedule: Date Topic Assignment Jan 11, 18 INTRODUCTION to STRUCTURAL Equation Models Chapt. 1 Data Preparation Using PRELIS Exercise 1: Setting up a LISREL Program Jan 25 Causal Models with Directly Observed Variables Chapt. 2 Exercise 2: Causal models with Directly observed Variables Feb 1 Confirmatory Factor Analysis Chapt. 3 Exercise 3: Confirmatory Factor Analysis Feb 3 Preliminary Outline of Research Project Due Feb.

5 8, 15 STRUCTURAL Equation Models with Latent Variables Chapt. 4 Exercise 4: STRUCTURAL Equation Models with Latent Variables Feb 17 Preliminary Analysis Due Feb 22 Model Building Chapt. 5 Exercise 5: Testing Goodness of Fit Feb 29 Model Building Chapt. 5 Exercise 6: Model Building Alternative Models Mar 7 Analysis of Longitudinal Data Chapt.

6 6 Exercise 7: STRUCTURAL Equation Models with Longitudinal Data 7 Mar 14 Spring Break Mar 21 Multiple Groups Analysis Chapt. 7 Exercise 8: Multiple Groups Analysis Mar 28 STRUCTURAL Equation Models in Experimental Research Chapt. 8 Exercise 9: Analysis of Experimental Data April 4 Writing about SEMs Chapt. 9 April 11 Working on Individual Research Projects April 13 Preliminary Research Reports Due April 18, 25 Presentation of Research Projects May 2 Final Research Reports Due Chapter 1 INTRODUCTION to STRUCTURAL Equation Models Causal Theories Variables Manifest and Latent Relationships Covariation Causal Relationships Formulation of Causal Theories Data preparation Using PRELIS Class Exercise 1 Data Preparation with PRELIS Reading Assignments: A Beginner s Guide to SEM, Chapts.

7 1-2 PRELIS Users Guide and Reference Principles and Practices of SEM, Chapts. 1-4 Chapter 2 - Causal Models with Directly Observed Variables 1. Setting up a LISREL program with the SIMPLIS command language. 2. Multiple regression and path analysis 3. Class Exercise 2 Setting up a LISREL program. 84. Interpreting the results. A. Examining values of the parameters B. Testing parameter estimates for significance 5. Exercise 2: Causal Models with Directly Observed Variables Reading Assignments: A Beginner's Guide to SEM, Chapt. 3 LISREL 8, Chapt. Principles and Practices of SEM, Chapts. 5-6 Chapter 3- Confirmatory Factor Analysis 1. Manifest and Latent Variables. 2. Confirmatory Versus Exploratory Factor Analysis.

8 3. STRUCTURAL Relations Among the Factors. 4. Specification of the Confirmatory Factor Model: Example 3. 5. Identification of the Confirmatory Factor Model. A. Conditions for Identification B. Scale Indeterminacy/Setting a Metric 6. Estimation of the Confirmatory Factor Model. 7. Exercise 3: Confirmatory Factor Analysis. Reading Assignments: A Beginner's Guide to SEM, Chapt. 3 LISREL 8, Chapt. Principles and Practices of SEM, Chapt. 7 Chapter 4 - STRUCTURAL Equation Models with Latent Variables 1 . Steps in STRUCTURAL Equation Modeling A. Model Specification B. Identification C.

9 Estimation D. Testing Fit E. Respecification 2. The Models. A. The Measurement Model (1). Specification of the Measurement Model (2). The Covariance Structure. B. The STRUCTURAL Model (1) Specification of the STRUCTURAL Model 9(2) The Covariance Structure (3) Types of STRUCTURAL Equation Models 3. Class Exercise 4: STRUCTURAL Equation Models with Latent Variables. 4. Standardized Solutions 5. Total, Direct and Indirect Effects 6. Path Diagrams 7.

10 LISREL output 8. Exercise 3: STRUCTURAL Equation Models with Latent Variables Reading Assignments: A Beginner's Guide to SEM, Chapts. 4-5 LISREL 8, Chapts ,3,5 Principles and Practices of SEM, Chapt. 8 Chapter 5 - Model Building 1 . The Model Building Process A. Verbal Theory B. Specification of a Theoretical Model C. Data Collection D. Model Specification E. Identification F. Parameter Estimation G. Testing Model's Goodness of Fit H.


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