Updated for SAS 9, this second edition is an easy-to-understand introduction to SAS as well as to univariate and multivariate statistics. Clear explanations and simple language guide you through the research terminology, data input, data manipulation, and types of statistical analysis that are most commonly used in the social and behavioral sciences. Providing practice data inspired by actual studies, this book teaches you how to choose the right statistic, understand the assumptions underlying the procedure, prepare the SAS program for the analysis, interpret the output, and summarize the analysis and results according to the format prescribed in the Publication Manual of the American Psychological Association. Book jacket.

Norm O'Rourke, Ph.D., R.Psych., is a clinical psychologist and assistant professor in the Department of Gerontology at Simon Fraser University in Vancouver, British Columbia Larry Hatcher, Ph.D., is a professor of psychology at Saginaw Valley State University in Saginaw, Michigan Edward J. Stepanski, Ph.D., is the director of the Sleep Disorders Service and Research Center at Rush University Medical Center in Chicago

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

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