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Step-by-Step Approach to Using SAS for Univariate and Multivariate Statistics

ISBN-10: 0471469440

ISBN-13: 9780471469445

Edition: 2nd 2005 (Revised)

Authors: Larry Hatcher, Staff SAS Institute Inc., Norm O'Rourke, Edward J. Stepanski, Inc. SAS Institute

List price: $122.00
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Description:

One in a series of books co-published with SAS, this book provides a user-friendly introduction to both the SAS system and elementary statistical procedures for researchers and students in the Social Sciences. This "Second Edition, updated to cover version 9 of the SAS software, guides readers step by step through the basic concepts of research and data analysis, to data input, and on to ANOVA (analysis of variance) and MANOVA (multivariate analysis of variance).
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Book details

List price: $122.00
Edition: 2nd
Copyright year: 2005
Publisher: John Wiley & Sons, Incorporated
Publication date: 8/12/2005
Binding: Paperback
Pages: 550
Size: 8.25" wide x 11.00" long x 1.25" tall
Weight: 2.948
Language: English

Larry Hatcher, Ph.D., is a professor of psychology at Saginaw Valley State University in Saginaw, Michigan, where he teaches classes in general psychology, industrial psychology, statistics, and computer applications in data analysis. The author of several books dealing with statistics and data analysis, Larry has taught at the college level since 1984 after earning his doctorate in industrial and organizational psychology from Bowling Green State University in 1983.

Dr. Stepanski is currently the Chief Operating Officer of ACORN Research LLC, a company that conducts clinical research in oncology. In this role, he oversees operations for several service areas including a US-based oncology research network, a contract research organization, and a health outcomes unit. He has written over 90 publications on a variety of topics related to clinical research.

Acknowledgments
Using This Book
Basic Concepts in Research and DATA Analysis
Introduction: A Common Language for Researchers
Steps to Follow When Conducting Research
Variables, Values, and Observations
Scales of Measurement
Basic Approaches to Research
Descriptive versus Inferential Statistical Analysis
Hypothesis Testing
Conclusion
Introduction to SAS Programs, SAS Logs, and SAS Output
Introduction: What Is SAS?
Three Types of SAS Files
SAS Customer Support Center
Conclusion
Reference
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
Controlling the Output Size and Log Pages with the OPTIONS Statement
Conclusion
Reference
Working with Variables and Obsrvations in SAS Datasets
Introduction: Manipulating, Subsetting, Concatenating, and Merging Data
Placement of Data Manipulation and Data Subsetting Statements
Data Manipulation
Data Subsetting
A More Comprehensive Example
Concatenating and Merging Datasets
Conclusion
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
References
Measures of Bivariate Association
Introduction: Significance Tests versus Measures of Association
Choosing the Correct Statistic
Pearson Correlations
Spearman Correlations
The Chi-Square Test of Independence
Conclusion
Assumptions Underlying the Tests
References
Assessing Scale Reliability with Coefficient Alpha
Introduction: The Basics of Scale Reliability
Coefficient Alpha
Assessing Coefficient Alpha with PROC CORR
Summarizing the Results
Conclusion
References
T Tests: Independent Samples and Paired Samples
Introduction: Two Types of t Tests
The Independent-Samples t Test
The Paired-Samples t Test
Conclusion
Assumptions Underlying the t Test
References
One-Way ANOVA with One Between-Subjects Factor
Introduction: The Basics of One-Way ANOVA, Between-Subjects Design
Example with Significant Differences between Experimental Conditions
Example with Nonsignificant Differences between Experimental Conditions
Understanding the Meaning of the F Statistic
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
Conclusion
Assumptions Underlying One-Way ANOVA with One Between-Subjects Factor
References
Factorial ANOVA with Two Between-Subjects Factors
Introduction to Factorial Designs
Some Possible Results from a Factorial ANOVA
Example with a Nonsignificant Interaction
Example with a Significant Interaction
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs
Conclusion
Assumptions Underlying Factorial ANOVA with Two Between-Subjects Factors
Multivariate Analysis of Variance (MANOVA) with One Between-Subjects Factor
Introduction: The Basics of Multivariate Analysis of Variance
Example with Significant Differences between Experimental Conditions
Example with Nonsignificant Differences between Experimental Conditions
Conclusion
Assumptions Underlying Multivariate ANOVA with One Between-Subjects Factor
References
One-Way ANOVA with One Repeated-Measures Factor
Introduction: What Is a Repeated-Measures Design?
Example: Significant Differences in Investment Size across Time
Further Notes on Repeated-Measures Analyses
Conclusion
Assumptions Underlying the One-Way ANOVA with One Repeated-Measures Factor
References
Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
Introduction: The Basics of Mixed-Design ANOVA
Some Possible Results from a Two-Way Mixed-Design ANOVA
Problems with the Mixed-Design ANOVA
Example with a Nonsignificant Interaction
Example with a Significant Inteaction
Use of Other Post-Hoc Tests with the Repeated-Measures Variable
Conclusion
Assumptions Underlying Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors
References
Multiple Regression
Introduction: Answering Questions with Multiple Regression
Background: Predicting a Criterion Variable from Multiple Predictors
The Results of a Multiple Regression Analysis
Example: A Test of the Investment Model
Overview of the Analysis
Gathering and Entering Data
Computing Bivariate Correlations with PROC CORR
Estimating the Full Multiple Regression Equation with PROC REG
Computing Uniqueness Indices with PROC REG
Summarizing the Results in Tables
Getting the Big Picture
Formal Description of Results for a Paper
Conclusion: Learning More about Multiple Regression
Assumptions Underlying Multiple Regression
References
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
An Example with Three Retained Components
Conclusion
Assumptions Underlying Principal Component Analysis
References
Choosing the Correct Statistic
Introduction: Thinking about the Number and Scale of Your Variables
Guidelines for Choosing the Correct Statistic
Conclusion
Reference
Datasets
Dataset from Chapter 7: Assessing Scale Reliability with Coefficient Alpha
Dataset from Chapter 14: Multiple Regression
Dataset from Chapter 15: Principal Component Analysis
Critical Values of the F Distribution
Index