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

Norm O'Rourke Ph.D. R.Psych., Larry Hatcher Ph.D.

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.

Informations

• Publié par: SAS Institute
• Date de parution: 23 mars 2013
• Nombre de lectures: 0
• EAN13: 9781629592442
• Langue: English
• Poids de l'ouvrage: 4 Mo

Extrait

A Step-by-Step Approach to Using SAS for
Factor Analysis and Structural Equation Modeling
Second Edition
Norm O Rourke and Larry Hatcher

support.sas.com/bookstore
The correct bibliographic citation for this manual is as follows: O Rourke, Norm, and Larry Hatcher. 2013. A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, Second Edition. Cary, NC: SAS Institute Inc .
A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, Second Edition
Copyright 2013, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-62959-244-2
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I dedicate this book to my parents, who worked hard and sacrificed so that I would have the opportunities that they never had.
L.H.
Contents
About This Book
Acknowledgments from the First Edition
Chapter 1: Principal Component Analysis
Introduction: The Basics of Principal Component Analysis
A Variable Reduction Procedure
An Illustration of Variable Redundancy
What Is a Principal Component?
Principal Component Analysis Is Not Factor Analysis
Example: Analysis of the Prosocial Orientation Inventory
Preparing a Multiple-Item Instrument
Number of Items per Component
Minimal Sample Size Requirements
SAS Program and Output
Writing the SAS Program
Results from the 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
The Questionnaire
Writing the Program
Results of the Initial Analysis
Results of the Second Analysis
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
Example: Investment Model Questionnaire
The Common Factor Model: Basic Concepts
Exploratory Factor Analysis versus Principal Component Analysis
How Factor Analysis Differs from Principal Component Analysis
How Factor Analysis Is Similar to Principal Component Analysis
Preparing and Administering the Investment Model Questionnaire
Writing the Questionnaire Items
Number of Items per Factor
Minimal Sample Size Requirements
SAS Program and Exploratory Factor Analysis Results
Writing the SAS Program
Results from the Output
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
The SAS Program
Determining the Number of Factors to Retain
A Two-Factor Solution
A Four-Factor Solution
Conclusion
Appendix: Assumptions Underlying Exploratory Factor Analysis
References
Chapter 3: Assessing Scale Reliability with Coefficient Alpha
Introduction: The Basics of Response Reliability
Example of a Summated Rating Scale
True Scores and Measurement Error
Underlying Constructs versus Observed Variables
Reliability Defined
Test-Retest Reliability
Internal Consistency
Reliability as a Property of Responses to Scales
Coefficient Alpha
Formula
When Will Coefficient Alpha Be High?
Assessing Coefficient Alpha with PROC CORR
General Form
A 4-Item Scale
How Large Must a Reliability Coefficient Be to Be Considered Acceptable?
A 3-Item Scale
Summarizing the Results
Summarizing the Results in a Table
Preparing a Formal Description of the Results for a Paper
Conclusion
Notes
References
Chapter 4: Path Analysis
Introduction: The Basics of Path Analysis
Some Simple Path Diagrams
Important Terms Used in Path Analysis
Why Perform Path Analysis with PROC CALIS versus PROC REG?
Necessary Conditions for Path Analysis
Overview of the Analysis
Sample Size Requirements for Path Analysis
Statistical Power and Sample Size
Effect Sizes
Estimating Sample Size Requirements
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
Overview
The DATA Input Step
The PROC CALIS Statement
The LINEQS Statement
The VARIANCE Statement
The COV Statement
The VAR Statement
Interpreting the Results of the Analysis
Making Sure That the SAS Output File Looks Right
Assessing the Fit between Model and Data
Characteristics of an Ideal Fit
Modifying the Model
Problems Associated with Model Modification
Recommendations for Modifying Models
Modifying the Present Model
Preparing a Formal Description of the Analysis and Results for a Paper
Preparing Figures and Tables
Preparing Text
Example 2: Path Analysis of a Model Predicting Victim Reactions to Sexual Harassment
Comparing Alternative Models
The SAS Program
Results of the Analysis
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
The Manifest Variable Model
The Latent Variable Model
Basic Concepts in Latent Variable Analyses
Latent Variables versus Manifest Variables
Choosing Indicator Variables
The Confirmatory Factor Analytic Approach
The Measurement Model versus the Structural Model
Advantages of Covariance Structure Analyses
Necessary Conditions for Confirmatory Factor Analysis
Sample Size Requirements for Confirmatory A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling
Calculation of Statistical Power
Calculation of Sample Size Requirements
Example: The Investment Model
The Theoretical Model
Research Method and Overview of the Analysis
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 L