Step-by-Step Approach to Using SAS for Univariate and Multivariate Statistics 2nd edition [Pehme köide]

Acknowledgments		xi
Using This Book		xiii
Basic Concepts in Research and DATA Analysis		1	(20)
Introduction: A Common Language for Researchers		2	(1)
Steps to Follow When Conducting Research		2	(3)
Variables, Values, and Observations		5	(2)
Scales of Measurement		7	(2)
Basic Approaches to Research		9	(3)
Descriptive versus Inferential Statistical Analysis		12	(1)
Hypothesis Testing		13	(6)
Conclusion		19	(2)
Introduction to SAS Programs, SAS Logs, and SAS Output		21	(8)
Introduction: What Is SAS?		22	(1)
Three Types of SAS Files		23	(5)
SAS Customer Support Center		28	(1)
Conclusion		28	(1)
Reference		28	(1)
Data Input		29	(28)
Introduction: Inputting Questionnaire Data versus Other Types of Data		30	(1)
Entering Data: An Illustrative Example		31	(4)
Inputting Data Using the Datalines Statement		35	(5)
Additional Guidelines		40	(8)
Inputting a Correlation or Covariance Matrix		48	(5)
Inputting Data Using the Infile Statement Rather than the Datalines Statement		53	(1)
Controlling the Output Size and Log Pages with the Options Statement		54	(1)
Conclusion		55	(1)
Reference		55	(2)
Working with Variables and Observations in SAS Datasets		57	(32)
Introduction: Manipulating, Subsetting, Concatenating, and Merging Data		58	(1)
Placement of Data Manipulation and Data Subsetting Statements		59	(4)
Data Manipulation		63	(11)
Data Subsetting		74	(5)
A More Comprehensive Example		79	(1)
Concatenating and Merging Datasets		80	(7)
Conclusion		87	(2)
Exploring Data with Proc Means, Proc Freq, Proc Print, and Proc Univariate		89	(30)
Introduction: Why Perform Simple Descriptive Analyses?		90	(1)
Example: An Abridged Volunteerism Survey		91	(2)
Computing Descriptive Statistics with PROC MEANS		93	(3)
Creating Frequency Tables with PROC FREQ		96	(2)
Printing Raw Data with PROC PRINT		98	(1)
Testing for Normality with PROC UNIVARIATE		99	(19)
Conclusion		118	(1)
References		118	(1)
Measures of Bivariate Association		119	(36)
Introduction: Significance Tests versus Measures of Association		120	(1)
Choosing the Correct Statistic		121	(4)
Pearson Correlations		125	(15)
Spearman Correlations		140	(2)
The Chi-Square Test of Independence		142	(11)
Conclusion		153	(1)
Assumptions Underlying the Tests		153	(1)
References		154	(1)
Assessing Scale Reliability with Coefficient Alpha		155	(12)
Introduction: The Basics of Scale Reliability		156	(3)
Coefficient Alpha		159	(1)
Assessing Coefficient Alpha with PROC CORR		160	(5)
Summarizing the Results		165	(1)
Conclusion		166	(1)
References		166	(1)
t Tests: Independent Samples and Paired Samples		167	(42)
Introduction: Two Types of t Tests		168	(1)
The Independent-Samples t Test		169	(19)
The Paired-Samples t Test		188	(19)
Conclusion		207	(1)
Assumptions Underlying the t Test		207	(1)
References		208	(1)
One-Way ANOVA with One Between-Subjects Factor		209	(28)
Introduction: The Basics of One-Way ANOVA, Between-Subjects Design		210	(4)
Example with Significant Differences between Experimental Conditions		214	(13)
Example with Nonsignificant Differences between Experimental Conditions		227	(5)
Understanding the Meaning of the F Statistic		232	(1)
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs		233	(2)
Conclusion		235	(1)
Assumptions Underlying One-Way ANOVA with One Between-Subjects Factor		235	(1)
References		235	(2)
Factorial ANOVA with Two Between-Subjects Factors		237	(42)
Introduction to Factorial Designs		238	(3)
Some Possible Results from a Factorial ANOVA		241	(7)
Example with a Nonsignificant Interaction		248	(12)
Example with a Significant Interaction		260	(15)
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs		275	(3)
Conclusion		278	(1)
Assumptions Underlying Factorial ANOVA with Two Between-Subjects Factors		278	(1)
Multivariate Analysis of Variance (MANOVA) with One Between-Subjects Factor		279	(20)
Introduction: The Basics of Multivariate Analysis of Variance		280	(3)
Example with Significant Differences between Experimental Conditions		283	(11)
Example with Nonsignificant Differences between Experimental Conditions		294	(2)
Conclusion		296	(1)
Assumptions Underlying Multivariate ANOVA with One Between-Subjects Factor		296	(1)
References		297	(2)
One-Way ANOVA with One Repeated-Measures Factor		299	(26)
Introduction: What Is a Repeated-Measures Design?		300	(2)
Example: Significant Differences in Investment Size across Time		302	(13)
Further Notes on Repeated-Measures Analyses		315	(7)
Conclusion		322	(1)
Assumptions Underlying the One-Way ANOVA with One Repeated-Measures Factor		322	(2)
References		324	(1)
Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors		325	(42)
Introduction: The Basics of Mixed-Design ANOVA		326	(5)
Some Possible Results from a Two-Way Mixed-Design ANOVA		331	(5)
Problems with the Mixed-Design ANOVA		336	(1)
Example with a Nonsignificant Interaction		336	(13)
Example with a Significant Inteaction		349	(15)
Use of Other Post-Hoc Tests with the Repeated-Measures Variable		364	(1)
Conclusion		364	(1)
Assumptions Underlying Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors		364	(2)
References		366	(1)
Multiple Regression		367	(62)
Introduction: Answering Questions with Multiple Regression		368	(5)
Background: Predicting a Criterion Variable from Multiple Predictors		373	(8)
The Results of a Multiple Regression Analysis		381	(19)
Example: A Test of the Investment Model		400	(1)
Overview of the Analysis		401	(1)
Gathering and Entering Data		402	(4)
Computing Bivariate Correlations with PROC CORR		406	(3)
Estimating the Full Multiple Regression Equation with PROC REG		409	(6)
Computing Uniqueness Indices with PROC REG		415	(8)
Summarizing the Results in Tables		423	(1)
Getting the Big Picture		424	(1)
Formal Description of Results for a Paper		425	(1)
Conclusion: Learning More about Multiple Regression		426	(1)
Assumptions Underlying Multiple Regression		427	(1)
References		428	(1)
Principal Component Analysis		429	(54)
Introduction: The Basics of Principal Component Analysis		430	(8)
Example: Analysis of the Prosocial Orientation Inventory		438	(3)
SAS Program and Output		441	(8)
Steps in Conducting Principal Component Analysis		449	(19)
An Example with Three Retained Components		468	(13)
Conclusion		481	(1)
Assumptions Underlying Principal Component Analysis		481	(1)
References		481	(2)
Appendix A Choosing the Correct Statistic		483	(8)
Introduction: Thinking about the Number and Scale of Your Variables		484	(2)
Guidelines for Choosing the Correct Statistic		486	(4)
Conclusion		490	(1)
Reference		490	(1)
Appendix B Datasets		491	(4)
Dataset from Chapter 7: Assessing Scale Reliability with Coefficient Alpha		492	(1)
Dataset from Chapter 14: Multiple Regression		493	(1)
Dataset from Chapter 15: Principal Component Analysis		494	(1)
Appendix C Critical Values of the F Distribution		495	(4)
Index		499

Norm O' Rourke,?Ph.D., R. Psych., is a clinical psychologist and assistant professor in the Department of Geontology at Simon Fraser University in Vancouver, Bristish Columbia, and an associate member of the SFU Department of Psychology. Dr. O'Rourke's areas of research interest include mental illness and well-being, marriage in later life, and test construction and validation. He has lived and worked in Canada, the United States, Europe, and Israel. Larry Hatcher, Ph.D., is a professor of psychology at Saginaw Valley State University in Saginaw, Michigan, where he teaches courses in general psychology, industrial psychology, statistics, and computer applications in data analysis. The author of several books dealing with statistics and data analysis, Dr. Hatcher has taught at the college level since 1984, after earning his doctorate in industrial and organizational psychology from Bowling Green State University.

Edward J. Stepanski, Ph.D., is the director of the Sleep Disorders Service and Research Center at Rush University Medical Center in Chicago and as associate professor of psychology and medicine at Rush Medical College. He uses data warehousing and computer analysis for a variety of research projects aimed at understanding sleep disorders and quantifying sleep-related daytime impairment. Dr. Stepanski earned his doctorate in clinical psychology from Bowling Green State University in 1985 and has written more than 50 papers and book chapters on sleep disorders medicine.