This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze
statistical data. SAS Enterprise Guide is a graphical user (point-and-click) interface to
the main SAS application. Each chapter contains a brief conceptual overview and then
guides the reader through concrete step-by-step examples to complete the analyses.
The 11 sections of the book cover a wide range of statistical procedures, including descriptive
statistics, correlation and simple regression, t tests, one-way chi-squares, data
transformations, multiple regression, analysis of variance, analysis of covariance, multivariate
analysis of variance, factor analysis, and canonical correlation analysis.
Designed to be used as either a stand-alone resource or an accompaniment to a statistics
course, the book offers a detailed path to statistical analysis with SAS Enterprise
Guide for advanced undergraduate and beginning graduate students, as well as professionals
in psychology, education, business, health, social work, sociology, and many
other fields.

Acknowledgments xix
I Introducing SAS Enterprise Guide
1 SAS Enterprise Guide Projects 3
1.1 A brief history of SAS 3
1.2 Opening a project 4
1.3 The contents of projects 5
1.4 Navigating tabs in the Process Flow screen 9
1.5 The main SAS Enterprise Guide menu 10
1.6 Additional resources 12
2 Placing Data into SAS Enterprise Guide Projects 13
2.1 Overview 13
2.2 Entering data directly into SAS Enterprise Guide 13
2.3 Saving a project 19
2.4 Importing data from Excel 19
II Performing Analyses and Viewing Output
3 Performing Statistical Analyses in SAS
Enterprise Guide 25
3.1 Overview 25
3.2 Numerical example 25
3.3 Selecting the procedure 25
3.4 Assigning Task roles 26
3.5 The Variables to assign and Task roles
panels 28
3.6 Other choices in the navigation panel 28
3.7 Performing the analysis 31
4 Managing and Viewing Output 32
4.1 Overview 32
4.2 Numerical example 32
4.3 Specifying the output format 32
4.4 Examining the statistical results 35
4.5 Saving the output as a PDF document 38
III Manipulating Data
5 Sorting Data and Selecting Cases 43
5.1 Overview 43
5.2 Numerical example 43
5.3 Sorting data 43
5.3 Selecting cases 46
6 Recoding Existing Variables 53
6.1 Overview 53
6.2 Numerical example 54
6.3 Performing the recoding 54
7 Computing New Variables 63
7.1 Overview 63
7.2 Numerical example 63
7.3 Computing a new variable from an existent
variable 64
7.4 Computing a new variable by combining several
variables 69
IV Describing Data
8 Descriptive Statistics 77
8.1 Overview 77
8.2 Categories of descriptive statistics 77
8.3 Numerical example 79
8.4 Obtaining basic descriptive statistics
for the quantitative variables 79
8.5 Obtaining skewness and kurtosis statistics 84
8.6 Obtaining frequency counts for the categorical
variables 88
9 Graphing Data 91
9.1 Overview 91
9.2 Numerical example 91
9.3 Constructing bar charts 92
9.4 Constructing line plots 97
10 Standardizing Variables Based on the
Sample Data 104
10.1 Overview 104
10.2 Numerical example 105
10.3 Obtaining standardized scores: z scores 105
10.3 Obtaining standardized scores: linear T scores 108
11 Standardizing Variables Based on Existing Norms 111
11.1 Overview 111
11.2 Numerical example 111
11.3 Setting up the computing process 112
11.4 Obtaining the standardized values 115
V Score Distribution Assumptions
12 Detecting Outliers 119
12.1 Overview 119
12.2 Specifying the boundary for an outlier 119
12.3 Numerical example 120
12.4 The box and whisker plot 121
12.5 Transforming values to z scores 123
12.6 Obtaining extreme values 124
13 Assessing Normality 130
13.1 Overview 130
13.2 The normality tests provided by SAS 130
13.3 Numerical example 131
13.4 Obtaining the normality assessments 131
14 Nonlinearly Transforming Variables in Order
to Meet Underlying Assumptions 135
14.1 Overview 135
14.2 Notes on transformations 135
14.3 Examples of nonlinear transformations 136
14.4 Numerical example 137
14.5 Transformation strategy 138
14.6 Switch to Update mode 139
14.7 Setting up the computing process 140
14.8 Evaluating the effects of our transformations 148
VI Correlation and Prediction
15 Bivariate Correlation: Pearson Product–Moment
and Spearman Rho Correlations 155
15.1 Overview 155
15.2 Some history 155
15.3 The two correlation coefficients of interest here 156
15.4 Numerical example 157
15.5 Setting up the correlation analysis 157
15.6 The correlation output 158
16 Simple Linear Regression 162
16.1 Overview 162
16.2 Naming the classes of variables 162
16.3 Numerical example 163
16.4 Setting up the regression solution 164
16.5 The regression output 166
17 Multiple Linear Regression 170
17.1 Overview 170
17.2 Numerical example 170
17.3 Viewing the correlations 171
17.4 Setting up the regression solution 172
17.5 The regression output 175
18 Simple Logistic Regression 177
18.1 Overview 177
18.2 Some differences between linear and logistic
regression 177
18.3 Two notable features of logistic regression 178
18.4 Numerical example 178
18.5 Setting up the logistic regression
solution 179
18.6 The logistic regression output 181
19 Multiple Logistic Regression 185
19.1 Overview 185
19.2 Coding of binary predictor variables 185
19.3 Numerical example 186
19.4 Setting up the logistic regression
solution 186
19.5 The logistic regression output 188
VII Comparing Means: The t Test
20 Independent-Groups t Test 195
20.1 Overview 195
20.2 Some history 195
20.3 Numerical example 196
the effect 205
22 Single-Sample t Test 206
22.1 Overview 206
22.2 The general approach 206
22.3 Numerical example 207
22.4 Setting up the analysis 207
22.5 The t-test output 207
VIII Comparing Means: ANOVA
23 One-Way Between-Subjects ANOVA 213
23.1 Overview 213
23.2 Naming of ANOVA designs 213
23.3 Some history 214
23.4 Numerical example 215
23.5 Setting up the analysis 216
23.6 The ANOVA output 219
24 Two-Way Between-Subjects Design 223
24.1 Overview 223
24.2 Omnibus and simple effects analysis 224
24.3 Numerical example 224
24.4 Setting up the analysis 225
24.5 The ANOVA output
25 One-Way Within-Subjects ANOVA 238
25.1 Overview 238
25.2 Numerical example 238
25.3 The structure of the data set 239
25.4 Setting up the analysis 240
25.5 Output for the analysis 250
26 Two-Way Mixed ANOVA Design 253
26.1 Overview 253
26.2 The partitioning of the variance in a mixed design 253
26.3 Numerical example 254
26.4 Setting up the analysis 254
26.5 The ANOVA output 263
IX Nonparametric Procedures
27 One-Way Chi-Square 269
27.1 Overview 269
27.2 Numerical example 270
27.3 Setting up the analysis 271
27.4 The chi-square output 272
27.5 Comparing the two most preferred categories:
analysis setup 274
27.6 Comparing the two most preferred categories:
chi-square output 277
28 Two-Way Chi-Square 279
28.1 Overview 279
28.2 The issue of small frequency counts 280
28.3 Numerical example 282
28.4 Setting up the analysis 282
28.5 The chi-square output 284
29 Nonparametric Between-Subjects
One-Way ANOVA 291
29.1 Overview 291
29.2 The nonparametric analogues to One-Way ANOVA 291
29.3 Numerical example 292
29.4 Setting up the analysis 292
29.5 Output of the analyses 293
X Advanced ANOVA Techniques
30 One-Way Between-Subjects Analysis
of Covariance 299
30.1 Overview 299
30.2 Assumptions of ANCOVA 300
30.3 Numerical example 300
30.4 Evaluating the assumptions of ANCOVA 301
30.5 Setting up the ANCOVA 308
30.6 The ANCOVA output 310
31 One-Way Between-Subjects Multivariate
Analysis of Variance 313
31.1 Overview 313
31.2 Univariate and multivariate ANOVA 313
31.3 Numerical example 314
31.4 Setting up the MANOVA 315
31.5 The MANOVA output 316
31.6 Follow-up analyses: setup 319
31.7 Follow-up analyses: output 322
XI Analysis of Structure
32 Factor Analysis 327
32.1 Overview 327
32.2 Some history 327
32.3 The basis of factor analysis 328
32.4 The extraction phase 328
32.5 The rotation phase 330
32.6 Numerical example 331
32.7 Setting up the factor analysis 333
32.8 The factor analysis output 337
33 Canonical Correlation Analysis 345
33.1 Overview 345
33.2 Canonical and linear regression 346
33.3 Number of canonical functions 346
33.4 Canonical and factor analysis 347
33.5 Numerical example 347
33.6 Setting up the Canonical Correlation Analysis 349
33.7 Output for Canonical Correlation Analysis 352
References 365
Author Index 371
Subject Index 373