A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, 2nd Edition Front Cover

A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, 2nd Edition

  • Length: 412 pages
  • Edition: 2
  • Publisher:
  • Publication Date: 2013-04-08
  • ISBN-10: 1599942305
  • ISBN-13: 9781599942308
  • Sales Rank: #445538 (See Top 100 Books)
Description

Structural equation modeling (SEM) has become one of the most important statistical procedures in the social and behavioral sciences. This easy-to-understand guide makes SEM accessible to all users—even those whose training in statistics is limited or who have never used SAS. It gently guides users through the basics of using SAS and shows how to perform some of the most sophisticated data-analysis procedures used by researchers: exploratory factor analysis, path analysis, confirmatory factor analysis, and structural equation modeling. It shows how to perform analyses with user-friendly PROC CALIS, and offers solutions for problems often encountered in real-world research.

This second edition contains new material on sample-size estimation for path analysis and structural equation modeling. In a single user-friendly volume, students and researchers will find all the information they need in order to master SAS basics before moving on to factor analysis, path analysis, and other advanced statistical procedures.

Table of Contents

Chapter 1: Principal Component Analysis
Chapter 2: Exploratory Factor Analysis
Chapter 3: Assessing Scale Reliability with Coefficient Alpha
Chapter 4: Path Analysis
Chapter 5: Developing Measurement Models with Confirmatory Factor Analysis
Chapter 6: Structural Equation Modeling

Appendix A.1: Introduction to SAS Programs, SAS Logs, and SAS Output
Appendix A.2: Data Input
Appendix A.3: Working with Variables and Observations in SAS Datasets
Appendix A.4: Exploring Data with PROC MEANS, PROC FREQ, PROC PRINT, and PROC UNIVARIATE
Appendix A.5: Preparing Scattergrams and Computing Correlations
Appendix B: Datasets
Appendix C: Critical Values for the Chi-Square Distribution

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