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

Name: Step-By-Step Approach to Using SAS for Univariate and Multivariate Statistics
Price: 28.05 USD
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ISBN: 9780471469445

ISBN-10: 0471469440

ISBN-13: 9780471469445

Edition: 2nd 2005 (Revised)

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

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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).

Book details

List price: $136.95
Edition: 2nd
Copyright year: 2005
Publisher: John Wiley & Sons, Incorporated
Publication date: 8/12/2005
Binding: Paperback
Pages: 550
Size: 8.20" wide x 10.80" long x 1.30" tall
Weight: 2.970
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