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Tables of Contents for Step-by-step Basic Statistics Using Sas
Chapter/Section Title
Page #
Page Count
Acknowledgments
ix
Using This Student Guide
1
12
Introduction
3
1
Introduction to the SAS System
4
2
Contents of This Student Guide
6
5
Conclusion
11
2
Terms and Concepts Used in This Guide
13
34
Introduction
15
1
Research Hypotheses and Statistical Hypotheses
16
5
Data, Variables, Values, and Observations
21
3
Classifying Variables According to Their Scales of Measurement
24
3
Classifying Variables According to the Number of Values They Display
27
2
Basic Approaches to Research
29
3
Using Type-of-Variable Figures to Represent Dependent and Independent Variables
32
5
The Three Types of SAS Files
37
8
Conclusion
45
2
Tutorial: Writing and Submitting SAS Programs
47
64
Introduction
48
2
Tutorial Part I: Basics of Using the SAS Windowing Environment
50
25
Tutorial Part II: Opening and Editing an Existing SAS Program
75
19
Tutorial Part III: Submitting a Program with an Error
94
8
Tutorial Part IV: Practicing What You Have Learned
102
3
Summary of Steps for Frequently Performed Activities
105
4
Controlling the Size of the Output Page with the OPTIONS Statement
109
1
For More Information
110
1
Conclusion
110
1
Data Input
111
34
Introduction
113
4
Example 4.1: Creating a Simple SAS Data Set
117
5
Example 4.2: A More Complex Data Set
122
9
Using Proc Means and Proc Freq to Identify Obvious Problems with the Data Set
131
8
Using Proc Print to Create a Printout of Raw Data
139
3
The Complete SAS Program
142
2
Conclusion
144
1
Creating Frequency Tables
145
14
Introduction
146
1
Example 5.1: A Political Donation Study
147
5
Using Proc Freq to Create a Frequency Table
152
3
Examples of Questions That Can Be Answered by Interpreting a Frequency Table
155
2
Conclusion
157
2
Creating Graphs
159
20
Introduction
160
1
Reprise of Example 5.1: the Political Donation Study
161
1
Using Proc Chart to Create a Frequency Bar Chart
162
12
Using Proc Chart to Plot Means for Subgroups
174
3
Conclusion
177
2
Measures of Central Tendency and Variability
179
36
Introduction
181
1
Reprise of Example 5.1: The Political Donation Study
181
2
Measures of Central Tendency: The Mode, Median, and Mean
183
4
Interpreting a Stem-and-Leaf Plot Created by Proc Univariate
187
3
Using Proc Univariate to Determine the Shape of Distributions
190
10
Simple Measures of Variability: The Range, the Interquartile Range, and the Semi-Interquartile Range
200
4
More Complex Measures of Central Tendency: The Variance and Standard Deviation
204
3
Variance and Standard Deviation: Three Formulas
207
3
Using Proc Means to Compute the Variance and Standard Deviation
210
4
Conclusion
214
1
Creating and Modifying Variables and Data Sets
215
46
Introduction
217
1
Example 8.1: An Achievement Motivation Study
218
4
Using Proc Print to Create a Printout of Raw Data
222
3
Where to Place Data Manipulation and Data Subsetting Statements
225
3
Basic Data Manipulation
228
7
Recoding a Reversed Item and Creating a New Variable for the Achievement Motivation Study
235
4
Using If-Then Control Statements
239
9
Data Subsetting
248
8
Combining a Large Number of Data Manipulation and Data Subsetting Statements in a Single Program
256
4
Conclusion
260
1
z Scores
261
26
Introduction
262
4
Example 9.1: Comparing Mid-Term Test Scores for Two Courses
266
2
Converting a Single Raw-Score Variable into a z-Score Variables
268
10
Converting Two Raw-Score Variables into z-Score Variables
278
7
Standardizing Variables with Proc Standard
285
1
Conclusion
286
1
Bivariate Correlation
287
52
Introduction
290
1
Situations Appropriate for the Pearson Correlation Coefficient
290
3
Interpreting the Sign and Size of a Correlation Coefficient
293
4
Interpreting the Statistical Significance of a Correlation Coefficient
297
2
Problems with Using Correlations to Investigate Causal Relationships
299
4
Example 10.1: Correlating Weight Loss with a Variety of Predictor Variables
303
4
Using Proc Plot to Create a Scattergram
307
6
Using Proc Corr to Compute the Pearson Correlation between Two Variables
313
7
Using PROC CORR to Compute All Possible Correlations for a Group of Variables
320
4
Summarizing Results Involving a Nonsignificant Correlation
324
5
Using the VAR and WITH Statements to Suppress the Printing of Some Correlations
329
3
Computing the Spearman Rank-Order Correlation Coefficient for Ordinal-Level Variables
332
1
Some Options Available with Proc Corr
333
2
Problems with Seeking Significant Results
335
3
Conclusion
338
1
Bivariate Regression
339
46
Introduction
341
1
Choosing between the Terms Predictor Variable, Criterion Variable, Independent Variable, and Dependent Variable
341
3
Situations Appropriate for Bivariate Linear Regression
344
2
Example 11.1: Predicting Weight Loss from a Variety of Predictor Variables
346
4
Using Proc Reg: Example with a Significant Positive Regression Coefficient
350
21
Using Proc Reg: Example with a Significant Negative Regression Coefficient
371
8
Using Proc Reg: Example with a Nonsignificant Regression Coefficient
379
4
Conclusion
383
2
Single-Sample t Test
385
28
Introduction
387
1
Situations Appropriate for the Single-Sample t Test
387
1
Results Produced in a Single-Sample t Test
388
5
Example 12.1: Assessing Spatial Recall in a Reading Comprehension Task (Significant Results)
393
13
One-Tailed Tests versus Two-Tailed Tests
406
1
Example 12.2: An Illustration of Nonsignificant Results
407
5
Conclusion
412
1
Independent-Samples t Test
413
38
Introduction
415
2
Situations Appropriate for the Independent-Samples t Test
417
3
Results Produced in an Independent-Samples t Test
420
8
Example 13.1: Observed Consequences for Modeled Aggression: Effects on Subsequent Subject Aggression (Significant Differences)
428
18
Example 13.2: An Illustration of Results Showing Nonsignificant Differences
446
4
Conclusion
450
1
Paired-Samples t Test
451
38
Introduction
453
1
Situations Appropriate for the Paired-Samples t Test
453
4
Similarities between the Paired-Samples t Test and the Single-Sample t Test
457
4
Results Produced in a Paired-Samples t Test
461
2
Example 14.1: Women's Responses to Emotional versus Sexual Infidelity
463
20
Example 14.2: An Illustration of Results Showing Nonsignificant Differences
483
4
Conclusion
487
2
One-Way Anova with One Between-Subjects Factor
489
50
Introduction
491
1
Situations Appropriate for One-Way Anova with One Between-Subjects Factor
491
3
A Study Investigating Aggression
494
3
Treatment Effects, Multiple Comparison Procedures, and a New Index of Effect Size
497
3
Some Possible Results from a One-Way Anova
500
5
Example 15.1: One-Way Anova Revealing a Significant Treatment Effect
505
24
Example 15.2: One-Way Anova Revealing a Nonsignificant Treatment Effect
529
8
Conclusion
537
2
Factorial Anova with Two Between-Subjects Factors
539
90
Introduction
542
1
Situations Appropriate for Factorial Anova with Two Between-Subjects Factors
542
4
Using Factorial Designs in Research
546
1
A Different Study Investigating Aggression
546
4
Understanding Figures That Illustrate the Results of a Factorial Anova
550
3
Some Possible Results from a Factorial Anova
553
12
Example of a Factorial Anova Revealing Two Significant Main Effects and a Nonsignificant Interaction
565
42
Example of a Factorial Anova Revealing Nonsignificant Main Effects and a Nonsignificant Interaction
607
10
Example of a Factorial Anova Revealing a Significant Interaction
617
8
Using the Lsmeans Statement to Analyze Data from Unbalanced Designs
625
2
Learning More about Using SAS for Factorial Anova
627
1
Conclusion
628
1
Chi-Square Test of Independence
629
44
Introduction
631
1
Situations That Are Appropriate for the Chi-Square Test of Independence
631
3
Using Two-Way Classification Tables
634
3
Results Produced in a Chi-Square Test of Independence
637
3
A Study Investigating Computer Preferences
640
2
Computing Chi-Square from Raw Data versus Tabular Data
642
1
Example of a Chi-Square Test That Reveals a Significant Relationship
643
18
Example of a Chi-Square Test That Reveals a Nonsignificant Relationship
661
7
Computing Chi-Square from Raw Data
668
3
Conclusion
671
2
References
673
2
Index
675