Packed with concrete examples, Larry Hatcher's Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling provides an introduction to more advanced statistical procedures and includes handy appendixes that give basic instruction in using SAS. Novice SAS users will find all they need in this one volume to master SAS basics and to move into advanced statistical analyses. 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 topics of note include "LISREL-type" analyses with the user-friendly PROC CALIS and solving problems encountered in real-world social science research.
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This is just a brief note to let you know how useful I have found your text, A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling. Yours is the only book I have encountered that deals so directly and simply with the points of confusion that arise in factor analysis. The words, phraseology, and illustrations that you use will become part of my vocabulary when I write up my analysis or explain my work to clients and coworkers who do not have a statistical background. The book is truly step-by-step and will make it easy for me to guide the work of others should the need arise. --Bob Gerzoff, Centers for Disease Control, Atlanta
About the Author
Larry Hatcher, Ph.D.
Larry Hatcher, associate professor of psychology at Winthrop University and a SAS user for nearly 15 years, offers you the benefit of his experience as both a researcher and a teacher of statistics using the SAS System.
Larry Hatcher, associate professor of psychology at Winthrop University and a SAS user for nearly 15 years, offers you the benefit of his experience as both a researcher and a teacher of statistics using the SAS System.
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Most Helpful Customer Reviews
16 of 16 people found the following review helpful:
5.0 out of 5 stars
An excellent tutorial for the rusty structural modeler!,
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This review is from: A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling (Paperback)
This book does an outstanding job of taking someone that is somewhat rusty in advanced multivariate techniques and turning them into a conversant and competent modeler. Larry Hatcher uses plain English to explain what a technique may be used for, what the required SAS code is, what the output should look like, and how the output can be interpreted. I'd strongly recommend this book for anyone taking a structural equation modeling course or wishing to apply this technique in research.
4 of 4 people found the following review helpful:
5.0 out of 5 stars
Thank goodness for this book,
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This review is from: A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling (Paperback)
I really needed this for my dissertation. As a computer scientist who is using statistically-based data mining techniques in my PhD dissertation, I needed a good "Quick Course" in factor analysis. More important than just "what is factor analysis", I needed to know how to implement my ideas in SAS. This book did the trick. Now let's hope my advisors think so. :)
2 of 2 people found the following review helpful:
5.0 out of 5 stars
Excellent book,
By A Customer
This review is from: A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling (Paperback)
This outstanding book was one of the mainstays for my Ph.D. dissertation. It explain factor analysis and SEM techniques in a clear and effective way, with an hands-on approach that guides the reader during all the data analysis and result interpretation phases.
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Inside This Book
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First Sentence:
Principal component analysis is appropriate when you have obtained measures on a number of observed variables and wish to develop a smaller number of artificial variables (called principal components) that will account for most of the variance in the observed variables. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
path analysis with latent variables, investment model study, revised measurement model, performing path analysis, manifest exogenous, subsetting statements, relative parsimony ratio, including residual terms, unrotated factor pattern, completed program figure, largest normalized residuals, difference test comparing, large normalized residuals, acceptable measurement model, line number directions, parsimony indices, interpretability criteria, variance extracted estimates, initial measurement model, initial theoretical model, standard measurement model, work place norms, prior communality estimates, estimated factor scores, volunteerism survey Key Phrases - Capitalized Phrases (CAPs): (learn more)
Matrix Entry, Std Err, Manifest Variable Equations, Beverly Hills, Initial Factor Method, Psychological Bulletin, New York, Bentler's Comparative Fit Index, Institute Inc, Schwarz's Bayesian Criterion, Consistent Information Criterion, Correlation Analysis, Normal Theory Reweighted, Parsimonious Index, Root Mean Square Residual, Variances of Endogenous Variables, Z-Test of Wilson, Approx Change of Value, Wald Index, Interpreting the Rotated Solution, John Wiley, Maximum Gradient Element, Average Normalized Residual, Function Calls, Identifying Covariances New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Principal component analysis is appropriate when you have obtained measures on a number of observed variables and wish to develop a smaller number of artificial variables (called principal components) that will account for most of the variance in the observed variables. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
path analysis with latent variables, investment model study, revised measurement model, performing path analysis, manifest exogenous, subsetting statements, relative parsimony ratio, including residual terms, unrotated factor pattern, completed program figure, largest normalized residuals, difference test comparing, large normalized residuals, acceptable measurement model, line number directions, parsimony indices, interpretability criteria, variance extracted estimates, initial measurement model, initial theoretical model, standard measurement model, work place norms, prior communality estimates, estimated factor scores, volunteerism survey Key Phrases - Capitalized Phrases (CAPs): (learn more)
Matrix Entry, Std Err, Manifest Variable Equations, Beverly Hills, Initial Factor Method, Psychological Bulletin, New York, Bentler's Comparative Fit Index, Institute Inc, Schwarz's Bayesian Criterion, Consistent Information Criterion, Correlation Analysis, Normal Theory Reweighted, Parsimonious Index, Root Mean Square Residual, Variances of Endogenous Variables, Z-Test of Wilson, Approx Change of Value, Wald Index, Interpreting the Rotated Solution, John Wiley, Maximum Gradient Element, Average Normalized Residual, Function Calls, Identifying Covariances New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
Citations (learn more)
This book cites 17
books:
See all 17 books this book cites
1
book
cites this book:
- Marketing Research: An Applied Orientation with SPSS (4th Edition) by Naresh Malhotra on page 341, and page 436
- Multiple-Plot Displays: Simplified with Macros (Art Carpenter's SAS Software) by Perry Watts in Back Matter
- Cartoon Guide to Statistics by Larry Gonick in Back Matter
- Basic Business Statistics: A Casebook by Dean P. Foster in Back Matter
- Business Analysis Using Regression: A Casebook by Robert A. Stine in Back Matter
See all 17 books this book cites
- SAS Programming for Researchers and Social Scientists by Paul E. (Elliot) Spector in Back Matter
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