Book description
This easy-to-understand guide makes SEM accessible to all users. 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
- 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 Factor-Based 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 Factor-Based 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 Path-Analytic 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 Two-Step 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 Data-Manipulation and Data-Subsetting 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 IF-THEN 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 Chi-Square Distribution
- Index
Product information
- Title: A Step-by-Step 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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