A step-by-step approach to using SAS for univariate and multivariate statistics

Title:

Personal Author:

O'Rourke, Norm

Edition:

2nd ed.

Publication Information:

New York, NY : Wiley-Interscience, 2005

ISBN:

9780471469445

Subject Term:

Multivariate analysis

Added Author:

Hatcher, Larry

Stepanski, Edward J.

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000004727644	QA278 O76 2005	Open Access Book	Book	Searching... Unknown

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).

Author Notes

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	p. xi
Using This Book	p. xiii
Chapter 1 Basic Concepts in Research and DATA Analysis	p. 1
Introduction: A Common Language for Researchers	p. 2
Steps to Follow When Conducting Research	p. 2
Variables, Values, and Observations	p. 5
Scales of Measurement	p. 7
Basic Approaches to Research	p. 9
Descriptive versus Inferential Statistical Analysis	p. 12
Hypothesis Testing	p. 13
Conclusion	p. 19
Chapter 2 Introduction to SAS Programs, SAS Logs, and SAS Output	p. 21
Introduction: What Is SAS?	p. 22
Three Types of SAS Files	p. 23
SAS Customer Support Center	p. 28
Conclusion	p. 28
Reference	p. 28
Chapter 3 Data Input	p. 29
Introduction: Inputting Questionnaire Data versus Other Types of Data	p. 30
Entering Data: An Illustrative Example	p. 31
Inputting Data Using the DATALINES Statement	p. 35
Additional Guidelines	p. 40
Inputting a Correlation or Covariance Matrix	p. 48
Inputting Data Using the INFILE Statement Rather than the DATALINES Statement	p. 53
Controlling the Output Size and Log Pages with the OPTIONS Statement	p. 54
Conclusion	p. 55
Reference	p. 55
Chapter 4 Working with Variables and Obsrvations in SAS Datasets	p. 57
Introduction: Manipulating, Subsetting, Concatenating, and Merging Data	p. 58
Placement of Data Manipulation and Data Subsetting Statements	p. 59
Data Manipulation	p. 63
Data Subsetting	p. 74
A More Comprehensive Example	p. 79
Concatenating and Merging Datasets	p. 80
Conclusion	p. 87
Chapter 5 Exploring Data with PROC MEANS, PROC FREQ, PROC PRINT, and PROC UNIVARIATE	p. 89
Introduction: Why Perform Simple Descriptive Analyses?	p. 90
Example: An Abridged Volunteerism Survey	p. 91
Computing Descriptive Statistics with PROC MEANS	p. 93
Creating Frequency Tables with PROC FREQ	p. 96
Printing Raw Data with PROC PRINT	p. 98
Testing for Normality with PROC UNIVARIATE	p. 99
Conclusion	p. 118
References	p. 118
Chapter 6 Measures of Bivariate Association	p. 119
Introduction: Significance Tests versus Measures of Association	p. 120
Choosing the Correct Statistic	p. 121
Pearson Correlations	p. 125
Spearman Correlations	p. 140
The Chi-Square Test of Independence	p. 142
Conclusion	p. 153
Assumptions Underlying the Tests	p. 153
References	p. 154
Chapter 7 Assessing Scale Reliability with Coefficient Alpha	p. 155
Introduction: The Basics of Scale Reliability	p. 156
Coefficient Alpha	p. 159
Assessing Coefficient Alpha with PROC CORR	p. 160
Summarizing the Results	p. 165
Conclusion	p. 166
References	p. 166
Chapter 8 T Tests: Independent Samples and Paired Samples	p. 167
Introduction: Two Types of t Tests	p. 168
The Independent-Samples t Test	p. 169
The Paired-Samples t Test	p. 188
Conclusion	p. 207
Assumptions Underlying the t Test	p. 207
References	p. 208
Chapter 9 One-Way ANOVA with One Between-Subjects Factor	p. 209
Introduction: The Basics of One-Way ANOVA, Between-Subjects Design	p. 210
Example with Significant Differences between Experimental Conditions	p. 214
Example with Nonsignificant Differences between Experimental Conditions	p. 227
Understanding the Meaning of the F Statistic	p. 232
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs	p. 233
Conclusion	p. 235
Assumptions Underlying One-Way ANOVA with One Between-Subjects Factor	p. 235
References	p. 235
Chapter 10 Factorial ANOVA with Two Between-Subjects Factors	p. 237
Introduction to Factorial Designs	p. 238
Some Possible Results from a Factorial ANOVA	p. 241
Example with a Nonsignificant Interaction	p. 248
Example with a Significant Interaction	p. 260
Using the LSMEANS Statement to Analyze Data from Unbalanced Designs	p. 275
Conclusion	p. 278
Assumptions Underlying Factorial ANOVA with Two Between-Subjects Factors	p. 278
Chapter 11 Multivariate Analysis of Variance (MANOVA) with One Between-Subjects Factor	p. 279
Introduction: The Basics of Multivariate Analysis of Variance	p. 280
Example with Significant Differences between Experimental Conditions	p. 283
Example with Nonsignificant Differences between Experimental Conditions	p. 294
Conclusion	p. 296
Assumptions Underlying Multivariate ANOVA with One Between-Subjects Factor	p. 296
References	p. 297
Chapter 12 One-Way ANOVA with One Repeated-Measures Factor	p. 299
Introduction: What Is a Repeated-Measures Design?	p. 300
Example: Significant Differences in Investment Size across Time	p. 302
Further Notes on Repeated-Measures Analyses	p. 315
Conclusion	p. 322
Assumptions Underlying the One-Way ANOVA with One Repeated-Measures Factor	p. 322
References	p. 324
Chapter 13 Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors	p. 325
Introduction: The Basics of Mixed-Design ANOVA	p. 326
Some Possible Results from a Two-Way Mixed-Design ANOVA	p. 331
Problems with the Mixed-Design ANOVA	p. 336
Example with a Nonsignificant Interaction	p. 336
Example with a Significant Inteaction	p. 349
Use of Other Post-Hoc Tests with the Repeated-Measures Variable	p. 364
Conclusion	p. 364
Assumptions Underlying Factorial ANOVA with Repeated-Measures Factors and Between-Subjects Factors	p. 364
References	p. 366
Chapter 14 Multiple Regression	p. 367
Introduction: Answering Questions with Multiple Regression	p. 368
Background: Predicting a Criterion Variable from Multiple Predictors	p. 373
The Results of a Multiple Regression Analysis	p. 381
Example: A Test of the Investment Model	p. 400
Overview of the Analysis	p. 401
Gathering and Entering Data	p. 402
Computing Bivariate Correlations with PROC CORR	p. 406
Estimating the Full Multiple Regression Equation with PROC REG	p. 409
Computing Uniqueness Indices with PROC REG	p. 415
Summarizing the Results in Tables	p. 423
Getting the Big Picture	p. 424
Formal Description of Results for a Paper	p. 425
Conclusion: Learning More about Multiple Regression	p. 426
Assumptions Underlying Multiple Regression	p. 427
References	p. 428
Chapter 15 Principal Component Analysis	p. 429
Introduction: The Basics of Principal Component Analysis	p. 430
Example: Analysis of the Prosocial Orientation Inventory	p. 438
SAS Program and Output	p. 441
Steps in Conducting Principal Component Analysis	p. 449
An Example with Three Retained Components	p. 468
Conclusion	p. 481
Assumptions Underlying Principal Component Analysis	p. 481
References	p. 481
Appendix A Choosing the Correct Statistic	p. 483
Introduction: Thinking about the Number and Scale of Your Variables	p. 484
Guidelines for Choosing the Correct Statistic	p. 486
Conclusion	p. 490
Reference	p. 490
Appendix B Datasets	p. 491
Dataset from Chapter 7: Assessing Scale Reliability with Coefficient Alpha	p. 492
Dataset from Chapter 14: Multiple Regression	p. 493
Dataset from Chapter 15: Principal Component Analysis	p. 494
Appendix C Critical Values of the F Distribution	p. 495
Index	p. 499

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Table of Contents