Book description
This easytounderstand guide makes SEM accessible to all users. This second edition contains new material on samplesize estimation for path analysis and structural equation modeling. In a single userfriendly 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
 About This Book
 Acknowledgments from the First Edition

Chapter 1: Principal Component Analysis
 Introduction: The Basics of Principal Component Analysis
 Example: Analysis of the Prosocial Orientation Inventory
 SAS Program and Output

Steps in Conducting Principal Component Analysis
 Step 1: Initial Extraction of the Components
 Step 2: Determining the Number of “Meaningful” Components to Retain
 Step 3: Rotation to a Final Solution
 Step 4: Interpreting the Rotated Solution
 Step 5: Creating Factor Scores or FactorBased Scores
 Step 6: Summarizing the Results in a Table
 Step 7: Preparing a Formal Description of the Results for a Paper
 An Example with Three Retained Components
 Conclusion
 Appendix: Assumptions Underlying Principal Component Analysis
 References

Chapter 2: Exploratory Factor Analysis
 Introduction: When Is Exploratory Factor Analysis Appropriate?
 Introduction to the Common Factor Model
 Exploratory Factor Analysis versus Principal Component Analysis
 Preparing and Administering the Investment Model Questionnaire
 SAS Program and Exploratory Factor Analysis Results

Steps in Conducting Exploratory Factor Analysis
 Step 1: Initial Extraction of the Factors
 Step 2: Determining the Number of “Meaningful” Factors to Retain
 Step 3: Rotation to a Final Solution
 Step 4: Interpreting the Rotated Solution
 Step 5: Creating Factor Scores or FactorBased Scores
 Step 6: Summarizing the Results in a Table
 Step 7: Preparing a Formal Description of the Results for a Paper
 A More Complex Example: The Job Search Skills Questionnaire
 Conclusion
 Appendix: Assumptions Underlying Exploratory Factor Analysis
 References
 Chapter 3: Assessing Scale Reliability with Coefficient Alpha

Chapter 4: Path Analysis
 Introduction: The Basics of Path Analysis
 Sample Size Requirements for Path Analysis
 Example 1: A PathAnalytic Investigation of the Investment Model
 Overview of the Rules for Performing Path Analysis

Preparing the Program Figure
 Step 1: Drawing the Basic Model
 Step 2: Assigning Short Variable Names to Manifest Variables
 Step 3: Identifying Covariances among Exogenous Variables
 Step 4: Identifying Residual Terms for Endogenous Variables
 Step 5: Identifying Variances to Be Estimated
 Step 6: Identifying Covariances to Be Estimated
 Step 7: Identifying the Path Coefficients to Be Estimated
 Step 8: Verifying that the Model Is Overidentified
 Preparing the SAS Program
 Interpreting the Results of the Analysis
 Modifying the Model
 Preparing a Formal Description of the Analysis and Results for a Paper
 Example 2: Path Analysis of a Model Predicting Victim Reactions to Sexual Harassment
 Conclusion: How to Learn More about Path Analysis
 Note
 References

Chapter 5: Developing Measurement Models with Confirmatory Factor Analysis
 Introduction: A TwoStep Approach to Analyses with Latent Variables
 A Model of the Determinants of Work Performance
 Basic Concepts in Latent Variable Analyses
 Advantages of Covariance Structure Analyses
 Necessary Conditions for Confirmatory Factor Analysis
 Sample Size Requirements for Confirmatory Factor Analysis and Structural Equation Modeling
 Example: The Investment Model

Testing the Fit of the Measurement Model from the Investment Model Study
 Preparing the Program Figure
 Preparing the SAS Program
 Making Sure That the SAS Log and Output Files “Look Right”
 Assessing the Fit between Model and Data
 Modifying the Measurement Model
 Estimating the Revised Measurement Model
 Assessing Reliability and Validity of Constructs and Indicators
 Characteristics of an “Ideal Fit” for the Measurement Model
 Conclusion: On to Covariance Analyses with Latent Variables?
 References

Chapter 6: Structural Equation Modeling
 Basic Concepts in Covariance Analyses with Latent Variables
 Testing the Fit of the Theoretical Model from the Investment Model Study
 Preparing a Formal Description of Results for a Paper
 Additional Example: A SEM Predicting Victim Reactions to Sexual Harassment
 Conclusion: To Learn More about Latent Variable Models
 References
 Appendix A.1: Introduction to SAS Programs, SAS Logs, and SAS Output
 What Is SAS?
 Three Types of SAS Files
 SAS Customer Support
 Conclusion
 Reference
 Appendix A.2: Data Input
 Introduction: Inputting Questionnaire Data versus Other Types of Data
 Entering Data: An Illustrative Example
 Inputting Data Using the DATALINES Statement
 Additional Guidelines
 Inputting a Correlation or Covariance Matrix
 Inputting Data Using the INFILE Statement Rather Than the DATALINES Statement
 Conclusion
 References
 Appendix A.3: Working with Variables and Observations in SAS Datasets
 Introduction: Manipulating, Subsetting, Concatenating, and Merging Data
 Placement of DataManipulation and DataSubsetting Statements

Data Manipulation
 Creating Duplicate Variables with New Variable Names
 Duplicating Variables versus Renaming Variables
 Creating New Variables from Existing Variables
 Priority of Operators in Compound Expressions
 Recoding Reversed Variables
 Using IFTHEN Control Statements
 Using ELSE Statements
 Using the Conditional Statements AND and OR
 Working with Character Variables
 Using the IN Operator
 Data Subsetting
 A More Comprehensive Example
 Concatenating and Merging Datasets
 Conclusion
 Reference
 Appendix A.4: Exploring Data with PROC MEANS, PROC FREQ, PROC PRINT, and PROC UNIVARIATE
 Introduction: Why Perform Simple Descriptive Analyses?
 Example: An Abridged Volunteerism Survey
 Computing Descriptive Statistics with PROC MEANS
 Creating Frequency Tables with PROC FREQ
 Printing Raw Data with PROC PRINT
 Testing for Normality with PROC UNIVARIATE
 Conclusion
 Reference
 Appendix A.5: Preparing Scattergrams and Computing Correlations
 Introduction: When Are Pearson Correlations Appropriate?
 Interpreting the Coefficient
 Linear versus Nonlinear Relationships
 Producing Scattergrams with PROC PLOT
 Computing Pearson Correlations with PROC CORR
 Appendix: Assumptions Underlying the Pearson Correlation Coefficient
 References
 Appendix B: Datasets
 Dataset from Chapter 1: Principal Component Analysis
 Datasets from Chapter 2: Exploratory Factor Analysis
 Dataset from Chapter 3: Assessing Scale Reliability with Coefficient Alpha
 Appendix C: Critical Values for the ChiSquare Distribution
 Index
Product information
 Title: A StepbyStep Approach to Using SAS for Factor Analysis and Structural Equation Modeling, Second Edition, 2nd Edition
 Author(s):
 Release date: March 2013
 Publisher(s): SAS Institute
 ISBN: 9781629592442
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