A step-by-step approach to using the SAS system for factor analysis and structural equation modeling /
Larry Hatcher.
Cary, NC : SAS Institute, c1994
xiv, 588 p. : ill. ; 28 cm.
More Details
Cary, NC : SAS Institute, c1994
catalogue key
Includes bibliographical references and index.
A Look Inside
About the Author
Author Affiliation
Larry Hatcher, assistant professor of psychology at Saginaw Valley State University in Michigan and a SAS user for over 16 years
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Main Description
Using easy-to-comprehend terms and uncomplicated examples, author Larry Hatcher walks you, step-by-step, through this introduction to using SAS software for performing advanced statistical procedures in social science research and interpreting the results. Part one of the book discusses exploratory factor analysis at an easily understood, introductory level. Part two instructs the reader on how to use the CALIS procedure to perform confirmatory factor analysis, path analysis with manifest variables, and path analysis with latent variables. This book includes appendices that give basic instruction in using SAS software. Book jacket.
Unpaid Annotation
Packed with concrete examples, Hatcher's book provides an introduction to more advanced statistical procedures and includes handy appendices that give basic instruction in using SAS. Novice SAS users will find all they need to master SAS basics and to move into advanced statistical analyses in this one book. Featured is a simple, step-by-step approach to testing structural equation models with latent variables using the CALIS procedure. The following topics are explained in easy-to-understand terms: exploratory factor analysis, principal component analysis, and developing measurement models with confirmatory factor analysis. Other covered topics of note include "LISREL-type" analyses with the user-friendly PROC CALIS and solving problems encountered in real-world social science research.
Table of Contents
Acknowledgmentsp. vii
Dedicationp. viii
Using This Bookp. ix
Principal Component Analysisp. 1
Introduction: The Basics of Principal Component Analysisp. 2
Example: Analysis of the Prosocial Orientation Inventoryp. 10
SAS Program and Outputp. 13
Steps in Conducting Principal Component Analysisp. 21
An Example with Three Retained Componentsp. 41
Conclusionp. 55
Assumptions Underlying Principal Component Analysisp. 55
Referencesp. 56
Exploratory Factor Analysisp. 57
Introduction: When Is Exploratory Factor Analysis Appropriate?p. 59
Introduction to the Common Factor Modelp. 60
Exploratory Factor Analysis versus Principal Component Analysisp. 68
Preparing and Administering the Investment Model Questionnairep. 72
SAS Program and Analysis Resultsp. 73
Steps in Conducting Exploratory Factor Analysisp. 79
A More Complex Example: The Job Search Skills Questionnairep. 107
Conclusionp. 125
Assumptions Underlying Exploratory Factor Analysisp. 126
Referencesp. 127
Assessing Scale Reliability with Coefficient Alphap. 129
Introduction: The Basics of Scale Reliabilityp. 130
Coefficient Alphap. 132
Assessing Coefficient Alpha with PROC CORRp. 133
Summarizing the Resultsp. 138
Conclusionp. 140
Referencesp. 140
Path Analysis with Manifest Variablesp. 141
Introduction: The Basics of Path Analysisp. 143
A Path-Analytic Investigation of the Investment Modelp. 150
Overview of the Rules for Performing Path Analysisp. 151
Preparing the Program Figurep. 153
Preparing the SAS Programp. 164
Interpreting the Results of the Analysisp. 181
Modifying the Modelp. 198
Preparing a Formal Description of the Analysis and Results for a Paperp. 225
Path Analysis of a Model Predicting Victim Reactions to Sexual Harassmentp. 234
Conclusion: Learning More about Path Analysisp. 245
Referencesp. 245
Developing Measurement Models with Confirmatory Factor Analysisp. 249
Introduction: A Two-Step Approach to Path Analysis with Latent Variablesp. 250
A Model of the Determinants of Work Performancep. 251
Basic Concepts in Latent-Variable Analysesp. 254
Advantages of Path Analysis with Latent Variablesp. 257
Necessary Conditions for Confirmatory Factor Analysis and Path Analysis with Latent Variablesp. 259
Example: The Investment Modelp. 261
Testing the Fit of the Measurement Model from the Investment Model Studyp. 264
Conclusion: On to Path Analysis with Latent Variables?p. 340
Referencesp. 340
Path Analysis with Latent Variablesp. 343
Recapitulation: Basic Concepts in Path Analysis with Latent Variablesp. 344
Testing the Fit of the Theoretical Model from the Investment Model Studyp. 347
Preparing a Formal Description of Results for a Paperp. 410
Additional Examplesp. 422
Conclusion: Learning More about Latent Variable Modelsp. 436
Referencesp. 436
Introduction to SAS Program, SAS Logs, and SAS Outputp. 439
Introduction: What is the SAS System?p. 439
Three Types of SAS System Filesp. 440
Conclusionp. 447
Data Inputp. 449
Introduction: Inputting Questionnaire Data versus Other Types of Datap. 450
Keying Data: An Illustrative Examplep. 450
Inputting Data Using the CARDS Statementp. 455
Additional Guidelinesp. 460
Inputting a Correlation or Covariance Matrixp. 469
Inputting Data Using the INFILE Statement Rather than the CARDS Statementp. 474
Controlling the Size of the Output and Log Pages with the OPTIONS Statementp. 476
Conclusionp. 477
Referencesp. 477
Working with Variables and Observations in SAS Data Setsp. 479
Introduction: Manipulating, Subsetting, Concatenating, and Merging Datap. 480
Placement of Data Manipulation and Data Subsetting Statementsp. 481
Data Manipulationp. 486
Data Subsettingp. 498
A More Comprehensive Examplep. 504
Concatenating and Merging Data Setsp. 505
Conclusionp. 514
Referencesp. 514
Introduction: Why Perform Simple Descriptive Analyses?p. 516
Example: A Revised Volunteerism Surveyp. 517
Computing Descriptive Statistics with PROC MEANSp. 519
Creating Frequency Tables with PROC FREQp. 523
Printing Raw Data with PROC PRINTp. 526
Testing for Normality with PROC UNIVARIATEp. 527
Conclusionp. 546
Referencesp. 546
Preparing Scattergrams and Computing Correlationsp. 547
Introduction: When Are Pearson Correlations Appropriate?p. 548
Interpreting the Coefficientp. 548
Linear versus Nonlinear Relationshipsp. 550
Producing Scattergrams with PROC PLOTp. 552
Computing Pearson Correlations with PROC CORRp. 557
Assumptions Underlying the Pearson Correlation Coefficientp. 563
Referencep. 564
Data Setsp. 565
Critical Values of the Chi-Square Distributionp. 569
Indexp. 571
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