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Introductory Statistics for the Behavioral Sciences
of: Joan Welkowitz, Barry H. Cohen, R. Brooke Lea
Wiley, 2011
ISBN: 9781118149713 , 576 Pages
7. Edition
Format: PDF, Read online
Copy protection: DRM
Introductory Statistics for the Behavioral Sciences
3
Contents
9
Preface
17
Acknowledgments
21
Glossary of Symbols
23
Part I: Descriptive Statistics
29
Chapter 1 Introduction
31
Why Study Statistics?
32
Descriptive and Inferential Statistics
33
Populations, Samples, Parameters, and Statistics
34
Measurement Scales
35
Independent and Dependent Variables
38
Summation Notation
40
Ihno’s Study
44
Summary
46
Exercises
47
Thought Questions
51
Computer Exercises
51
Bridge to SPSS
52
Chapter 2 Frequency Distributions and Graphs
54
The Purpose of Descriptive Statistics
55
Regular Frequency Distributions
56
Cumulative Frequency Distributions
58
Grouped Frequency Distributions
59
Real and Apparent Limits
61
Interpreting a Raw Score
62
Definition of Percentile Rank and Percentile
62
Computational Procedures
63
Deciles, Quartiles, and the Median
66
Graphic Representations
67
Shapes of Frequency Distributions
71
Summary
73
Exercises
75
Thought Questions
77
Computer Exercises
77
Bridge to SPSS
78
Chapter 3 Measures of Central Tendency and Variability
81
Introduction
82
The Mode
84
The Median
84
The Mean
86
The Concept of Variability
90
The Range
93
The Standard Deviation and Variance
94
Summary
101
Exercises
103
Thought Questions
104
Computer Exercises
105
Bridge to SPSS
106
Chapter 4 Standardized Scores and the Normal Distribution
109
Interpreting a Raw Score Revisited
110
Rules for Changing ? and ?
112
Standard Scores (z Scores)
113
T Scores, SAT Scores, and IQ Scores
116
The Normal Distribution
118
Table of the Standard Normal Distribution
121
Illustrative Examples
123
Summary
129
Exercises
131
Thought Questions
133
Computer Exercises
134
Bridge to SPSS
134
Part II: Basic Inferential Statistics
137
Chapter 5 Introduction to Statistical Inference
139
Introduction
141
The Goals of Inferential Statistics
142
Sampling Distributions
142
The Standard Error of the Mean
147
The z Score for Sample Means
150
Null Hypothesis Testing
152
Assumptions Required by the Statistical Test for the Mean of a Single Population
160
Summary
161
Exercises
163
Thought Questions
165
Computer Exercises
166
Bridge to SPSS
166
Appendix: The Null Hypothesis Testing Controversy
167
Chapter 6 The One-Sample t Test and Interval Estimation
170
Introduction
171
The Statistical Test for the Mean of a Single Population When ? Is Not Known: The t Distributions
172
Interval Estimation
176
The Standard Error of a Proportion
180
Summary
183
Exercises
184
Thought Questions
185
Computer Exercises
186
Bridge to SPSS
186
Chapter 7 Testing Hypotheses About the Difference Between the Means of Two Populations
188
The Standard Error of the Difference
190
Estimating the Standard Error of the Difference
194
The t Test for Two Sample Means
195
Confidence Intervals for ?1 ? ?2
200
The Assumptions Underlying the Proper Use of the t Test for Two Sample Means
203
Measuring the Size of an Effect
204
The t Test for Matched Samples
206
Summary
213
Exercises
215
Thought Questions
218
Computer Exercises
219
Bridge to SPSS
219
Chapter 8 Nonparametric Tests for the Difference Between Two Means
222
Introduction
223
The Difference Between the Locations of Two Independent Samples: The Rank-Sum Test
227
The Difference Between the Locations of Two Matched Samples: The Wilcoxon Test
233
Summary
238
Exercises
240
Thought Questions
243
Computer Exercises
244
Bridge to SPSS
244
Chapter 9 Linear Correlation
246
Introduction
247
Describing the Linear Relationship Between Two Variables
250
Interpreting the Magnitude of a Pearson r
257
When Is It Important That Pearson’s r Be Large?
262
Testing the Significance of the Correlation Coefficient
264
The Relationship Between Two Ranked Variables: The Spearman Rank-Order Correlation Coefficient
267
Summary
270
Exercises
272
Thought Questions
275
Computer Exercises
276
Bridge to SPSS
276
Appendix: Equivalence of the Various Formulas for r
279
Chapter 10 Prediction and Linear Regression
281
Introduction
282
Using Linear Regression to Make Predictions
282
Measuring Prediction Error: The Standard Error of Estimate
291
The Connection Between Correlation and the t Test
293
Estimating the Proportion of Variance Accounted for in the Population
299
Summary
301
Exercises
303
Thought Questions
305
Computer Exercises
305
Bridge to SPSS
306
Chapter 11 Introduction to Power Analysis
309
Introduction
310
Concepts of Power Analysis
311
The Significance Test of the Mean of a Single Population
313
The Significance Test of the Proportion of a Single Population
318
The Significance Test of a Pearson r
320
Testing the Difference Between Independent Means
321
Testing the Difference Between the Means of Two Matched Populations
325
Choosing a Value for d for a Power Analysis Involving Independent Means
327
Using Power Analysis Concepts to Interpret the Results of Null Hypothesis Tests
329
Summary
332
Exercises
334
Thought Questions
336
Computer Exercises
337
Bridge to SPSS
338
Part III: Analysis of Variance Methods
341
Chapter 12 One-Way Analysis of Variance
343
Introduction
345
The General Logic of ANOVA
346
Computational Procedures
349
Testing the F Ratio for Statistical Significance
354
Calculating the One-Way ANOVA From Means and Standard Deviations
356
Comparing the One-Way ANOVA With the t Test
357
A Simplified ANOVA Formula for Equal Sample Sizes
358
Effect Size for the One-Way ANOVA
359
Some Comments on the Use of ANOVA
361
A Nonparametric Alternative to the One-Way ANOVA: The Kruskal-Wallis H Test
364
Summary
367
Exercises
371
Thought Questions
374
Computer Exercises
374
Bridge to SPSS
374
Appendix: Proof That the Total Sum of Squares Is Equal to the Sum of the Between-Group and the Within-Group Sum of Squares
376
Chapter 13 Multiple Comparisons
377
Introduction
378
Fisher’s Protected t Tests and the Least Significant Difference (LSD)
379
Tukey’s Honestly Significant Difference (HSD)
383
Other Multiple Comparison Procedures
388
Planned and Complex Comparisons
390
Nonparametric Multiple Comparisons: The Protected Rank-Sum Test
393
Summary
394
Exercises
396
Thought Questions
397
Computer Exercises
398
Bridge to SPSS
398
Chapter 14 Introduction to Factorial Design: Two-Way Analysis of Variance
400
Introduction
401
Computational Procedures
402
The Meaning of Interaction
412
Following Up a Significant Interaction
415
Measuring Effect Size in a Factorial ANOVA
418
Summary
420
Exercises
423
Thought Questions
426
Computer Exercises
427
Bridge to SPSS
427
Chapter 15 Repeated-Measures ANOVA
430
Introduction
431
Calculating the One-Way RM ANOVA
431
Rationale for the RM ANOVA Error Term
436
Assumptions and Other Considerations Involving the RM ANOVA
436
The RM Versus RB Design: An Introduction to the Issues of Experimental Design
439
The Two-Way Mixed Design
443
Summary
451
Exercises
456
Thought Questions
458
Computer Exercises
458
Bridge to SPSS
459
Part IV: Nonparametric Statistics for Categorical Data
463
Chapter 16 Probability of Discrete Events and the Binomial Distribution
465
Introduction
466
Probability
467
The Binomial Distribution
470
The Sign Test for Matched Samples
476
Summary
478
Exercises
479
Thought Questions
481
Computer Exercises
481
Bridge to SPSS
482
Chapter 17 Chi-Square Tests
485
Chi Square and the Goodness of Fit: One-Variable Problems
486
Chi Square as a Test of Independence: Two-Variable Problems
492
Measures of Strength of Association in Two-Variable Tables
498
Summary
500
Exercises
502
Thought Questions
504
Computer Exercises
505
Bridge to SPSS
506
Appendix
509
Statistical Tables
511
Answers to Odd-Numbered Exercises
527
Data From Ihno’s Experiment
539
Glossary of Terms
543
References
553
Index
555
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