search for books and compare prices
Tables of Contents for Data Analysis and Interpretation in the Behavioral Sciences
Chapter/Section Title
Page #
Page Count
Preface to the Instructor
xvii
 
Preface to the Student
xxi
 
Introduction to the I-D-E-A Model of Data Analysis and Interpretation
1
20
Introduction
2
1
What Is/Are Data?
2
1
Why (Specifically) and How (Generally) Do Scientists Do Research?
3
1
What Is an Experiment?
4
3
How Are Behavior and Events Measured?
7
2
What Is the Role of Statistics in Behavioral Science Research?
9
1
How Do I Get a Sample of Behavior?
10
2
What Question Are You Asking?
12
1
How Confident Can I Be of My Answer?
12
1
An I-D-E-A for Data Analysis and Interpretation
13
2
What You Have Learned and the Next Step
15
1
Key Concepts
16
1
Answers to Your Turn Questions
16
1
Analyzing and Interpreting Data: Problems and Exercises
17
4
PART 1 INSPECTING AND DESCRIBING DATA FROM ONE GROUP
21
84
Inspecting Data Point by Point
22
21
Introduction
23
1
Cleaning Data
24
1
How to Spot Suspicious Data Points
25
3
Klinkers
25
1
Missing Data
25
1
Outliers
26
2
A Hypothetical Data Set
28
3
Using Tabular Inspection Methods
31
9
Stem-and-Leaf Display
33
2
Frequency Distribution
35
2
Decisions 1 and 2
37
1
Decision 3
37
3
What You Have Learned and the Next Step
40
1
Key Concepts
40
1
Answers to Your Turn Questions
41
1
Analyzing and Interpreting Data: Problems and Exercises
42
1
Inspecting Distributions of Data
43
32
Introduction
44
1
Using Histograms to Inspect Distributions
45
8
Skewness
49
2
Kurtosis
51
2
Frequency Polygons
53
2
Graphing Nominal Data
55
1
Transforming Data
56
3
What to Do About Skewed Distributions
59
4
Dealing with Positive Skewness
60
3
Dealing with Negative Skewness
63
1
Discarding Data
63
3
What You Have Learned and the Next Step
66
2
Key Concepts
68
1
Answers to Your Turn Questions
69
1
Analyzing and Interpreting Data: Problems and Exercises
69
6
Describing Data From One Group
75
30
Introduction
76
1
How Do We Describe Data?
77
1
What Type of Data Are We Seeking to Describe?
77
2
Measures of Central Tendency
79
3
How Is Variability (Dispersion) Measured?
82
4
The Standard Deviation and Standard Scores
86
2
Data Description and the Normal Curve
88
4
How Do We Use a Normal Distribution to Describe the Relative Positions of Scores?
92
2
Comparing Apples and Oranges Again (or IQ and Height)
94
5
What You Have Learned and the Next Step
99
1
Key Concepts
100
1
Answers to Your Turn Questions
100
1
Analyzing and Interpreting Data: Problems and Exercises
101
4
PART 2 I-D-E-A FOR A STUDY INVOLVING A SINGLE MEAN
105
36
Estimating Confidence in a Mean
106
16
Introduction
107
1
Point Estimates and Interval Estimates
108
1
What Is Sampling Variability?
108
1
The Sampling Distribution of the Mean
109
2
Probability and Normal Distributions
111
2
Probability and the Sampling Distribution of the Mean
113
2
How Do We Use a Sampling Distribution to Estimate Confidence in Our Finding?
115
2
What You Have Learned and the Next Step
117
1
Key Concepts
118
1
Answers to Your Turn Questions
118
1
Analyzing and Interpreting Data: Problems and Exercises
119
3
Constructing a Confidence Interval and Announcing Results
122
19
Introduction
123
1
The t Distribution
124
4
Establishing a Confidence Interval for the Population Mean Based on the t Distribution
128
1
Interpreting Confidence Intervals
129
2
Increasing Precision and Confidence in Our Estimate
131
2
A Slight Variation When There Is a Hypothesized Population Mean
133
2
Announcing Results Based on a Single-Sample Mean
135
1
What You Have Learned and the Next Step
136
1
Key Concepts
137
1
Answers to Your Turn Questions
137
1
Analyzing and Interpreting Data: Problems and Exercises
138
3
PART 3 I-D-E-A WHEN THERE ARE TWO MEANS
141
98
Inspecting and Describing Data From Two Groups
142
23
Introduction
143
1
Getting Two Sets of Data to Compare
144
1
Inspecting Two Distributions
145
6
Using a Stem-and-Leaf Plot to Inspect Data from Two Small Samples
147
1
Using Stem-and-Leaf Plots to Inspect Data from Large Samples
148
3
Describing Two Distributions
151
1
Describing the Difference Between Two Samples
152
3
Repeated Measures Designs
155
5
Inspecting Difference Scores
156
1
Describing Differences
156
2
Matched Groups
158
2
What You Have Learned and the Next Step
160
1
Key Concepts
161
1
Answers to Your Turn Questions
161
1
Analyzing and Interpreting Data: Problems and Exercises
162
3
Estimating Using Confidence Intervals
165
24
Introduction
166
1
Constructing Confidence Intervals for the Difference Between Two Means
167
5
What Makes Confidence Intervals Wide or Narrow?
172
1
Interpreting Differences Between Means
173
6
Standardizing Comparisons
174
5
What Does the Magnitude of the Effect Size Mean?
179
1
Overwhelmed?
180
1
Confidence Intervals for Difference Scores
180
1
Effect Sizes for Difference Scores
181
1
What You Have Learned and the Next Step
182
1
Key Concepts
183
1
Answers to Your Turn Questions
183
1
Analyzing and Interpreting Data: Problems and Exercises
184
5
Estimating Using Null Hypothesis Significance Testing
189
25
Introduction
190
1
Testing Hypotheses
191
1
Rejection Criteria
192
6
Rejection Criteria with One Sample
193
1
Rejection Criteria When Comparing Two Groups
194
2
An Illustrative Analysis
196
2
The t Test for Independent Groups
198
3
Step 1: If the Null Hypothesis Were True, What Would the Sampling Distribution of the Difference between Two Means Be?
198
1
Step 2: What Is the Standard Deviation of the Sampling Distribution if the Null Hypothesis Is True?
199
1
Step 3: Where Is the Actual Difference Between the Two Group Means Located in the Sampling Distribution Based on the Null Hypothesis?
199
1
Step 4: How Do We Calculate the t of M1 - M2 with Respect to the Sampling Distribution Based on the Null Hypothesis?
200
1
Assumptions Underlying t Tests
201
7
Directional and Nondirectional t Tests
202
2
Effect of Sample Size
204
2
The t Test for Repeated Measurements and Matched Groups Designs
206
1
The Contribution of the t Test
207
1
What You Have Learned and the Next Step
208
1
Key Concepts
209
1
Answers to Your Turn Questions
209
1
Analyzing and Interpreting Data: Problems and Exercises
209
5
Interpreting and Announcing Results
214
25
Introduction
215
1
Correctly Interpreting Null Hypothesis Significance Testing
216
6
Statistically Significant Does Not Mean Causal
216
1
Statistically Significant Does Not Mean Important
217
1
Statistically Significant Does Not Mean the Results Can Be Generalized
218
1
Statistically Significant Does Not Mean Replication Is Assured
218
3
Not Significant Does Not Mean Zero Difference
221
1
Type I and Type II Errors
222
3
Pulling It All Together and Announcing Results
225
7
A Two-Group Experiment on Therapy
225
1
Inspecting
226
1
Describing
226
2
Estimating
228
3
Announcing
231
1
Presenting Exact Probabilities
232
1
What You Have Learned and the Next Step
232
1
Key Concepts
233
1
Answers to Your Turn Questions
233
1
Analyzing and Interpreting Data: Problems and Exercises
234
5
PART 4 I-D-E-A WHEN THERE ARE MORE THAN TWO MEANS
239
86
Inspecting, Describing, and Estimating Using Confidence Intervals
240
23
Introduction
241
1
Inspecting Data from an Independent Groups Design with One Independent Variable That Has Three or More Levels
242
2
Describing the Data: Measures of Central Tendency and Variability
244
1
Looking for Covariation
245
1
Constructing Confidence Intervals for an Independent Groups Experiment
245
7
Error Bars versus Confidence Intervals
252
2
Obtaining a Measure of Effect Size for an Independent Groups Experiment with One Independent Variable
254
1
Decisions About Differences Between Two Means in a Single-Factor Experiment
254
2
What You Have Learned and the Next Step
256
2
Key Concepts
258
1
Answers to Your Turn Questions
258
1
Analyzing and Interpreting Data: Problems and Exercises
259
4
Estimating Confidence Using Null Hypothesis Significance Testing and Announcing Results
263
28
Introduction
264
1
The Role of NHST in an Independent Groups Experiment with One Independent Variable (the E in I-D-E-A)
265
1
The Logic of ANOVA
266
8
An Illustration of ANOVA: Does Type of Presentation Affect Recall?
274
3
Measures of Strength of Association for Independent Groups Designs
277
2
Two-Group Comparisons in a Multi-Group Experiment
279
4
Two-Group Comparisons Following a Significant Omnibus F
279
3
Planned Two-Group Comparisons in a Multi-Group Experiment
282
1
Assessing Power in an Independent Groups Experiment
283
1
Announcing Results (the A in I-D-E-A)
284
1
What You Have Learned and the Next Step
285
1
Key Concepts
286
1
Answers to Your Turn Questions
287
1
Analyzing and Interpreting Data: Problems and Exercises
288
3
I-D-E-A For Complex Designs
291
34
Introduction
292
1
Complex (Factorial) Designs
293
1
Inspecting Data from a Complex (Factorial) Design
294
1
Describing Results of a Complex Design: Cell Means, Main Effects, and Interaction
294
5
Cell Means
294
1
Main Effects
295
2
Interaction
297
2
Constructing Confidence Intervals for Means in a Complex Design
299
3
Beyond 2 X 2
302
2
ANOVA for a Complex Design
304
9
Hypotheses
305
2
Computation
307
2
ANOVA Summary Table
309
2
Analytical Comparisons
311
1
Analysis of Simple Main Effects
311
2
Effect Size Measures for Complex Designs
313
1
Announcing Results of a Complex Design
314
1
What You Have Learned and the Next Step
315
2
Key Concepts
317
1
Answers to Your Turn Questions
317
2
Analyzing and Interpreting Data: Problems and Exercises
319
6
PART 5 I-D-E-A WHEN EXAMINING THE RELATIONSHIP BETWEEN TWO VARIABLES
325
122
Inspecting and Describing Correlational Data
326
41
Introduction
327
1
The Analysis Problem
328
1
Constructing Scatterplots
328
7
Some Things to Look for in a Scatterplot
331
4
Describing Relationships Quantitatively
335
5
The Original Correlation Formula
340
4
Changing Scales
344
1
What We Have Done So Far
344
1
Inspecting the Relationships Between Two Variables
344
8
The Big Picture
346
2
Impossible Combinations
348
1
Two-Dimensional Outliers
349
3
Limitations of Correlational Analyses
352
6
Third Variables
354
1
Restriction of Range
355
3
What Questions Do We Ask that Involve Two Variables?
358
1
What You Have Learned and the Next Step
359
1
Key Concepts
360
1
Answers to Your Turn Questions
360
1
Analyzing and Interpreting Data: Problems and Exercises
361
6
Estimating Confidence Using Confidence Intervals
367
23
Introduction
368
1
Confidence Intervals for Correlation Coefficients
369
4
Interpreting Confidence Intervals of Correlation Coefficients
373
2
Effect Sizes of Correlation Coefficients
375
4
Effect Sizes When Comparing Two Means: A Review
375
1
r Is an Effect Size
376
1
Comparing r to Other Effect Size Statistics
376
3
Interpreting the Effect Size of Correlations
379
1
Avoiding Common Misunderstandings of Correlations
379
6
Don't Underestimate the Meaning of r2
381
2
Don't Do at Test When a Correlation Would Be Better
383
2
What You Have Learned and the Next Step
385
1
Key Concepts
385
1
Answers to Your Turn Questions
386
1
Analyzing and Interpreting Data: Problems and Exercises
386
4
Estimating Confidence Using Null Hypothesis Significance Testing and Announcing Results
390
25
Introduction
391
1
Null Hypotheses Involving Correlation Coefficients
392
1
Testing Whether r is Different from .00
392
3
Testing Whether r is Greater than .00
395
2
Using a Table Instead of the t Formula
397
1
Testing Whether r Differs from a Known p
398
3
Testing Whether Two Independent Correlations Differ from Each Other
401
1
Pulling It All Together
402
8
What You Have Learned and the Next Step
410
1
Key Concepts
411
1
Answers to Your Turn Questions
411
1
Analyzing and Interpreting Data: Problems and Exercises
411
4
Making Predictions
415
32
Introduction
416
1
Graphing Linear Equations
417
3
Graphing Variables Used in the Behavioral Sciences
420
1
Calculating a Regression Equation
421
8
Inspect Data
421
1
Find the Regression Equation
421
5
The Relationship Between rxy and byx
426
1
Plotting Regression Equations
427
2
Using Regression Predictions
429
5
How Certain Can We Be of These Predictions?
430
1
Error in Predictions
431
3
An Important Additional Detail About the Precision of Predictions
434
1
Announcing the Results of a Regression Analysis
435
1
Cautions in Using Regression Equations to Make Predictions
436
5
Don't Predict Beyond the Range of the Derivation Sample
436
1
Don't Try to Predict the Independent Variable Using the Dependent Variable
436
1
Remain on Guard for Restriction of Range
437
2
Make Predictions Only When the Derivation Sample Reflects the Population for Whom Predictions Are Being Made
439
1
Verify That Predictions Would Be Equally Precise Throughout the Range of X
439
2
What You Have Learned and the Next Step
441
1
Key Concepts
442
1
Answers to Your Turn Questions
442
1
Analyzing and Interpreting Data: Problems and Exercises
443
4
PART 6 I-D-E-A FOR STUDIES WITH NOMINAL DATA
447
26
I-D-E-A with Nominal Data
448
25
Introduction
449
1
What Question Are You Asking?
450
1
The I-D-E-A Model for a Proportion from a Single (Large) Sample
451
3
Inspection
452
1
Description
452
1
Estimation
452
2
Announcing Results
454
1
NHST with Nominal Data
454
2
Chi-square (χ2) Goodness-of-Fit Test
456
7
Inspection and Description
456
1
Estimation
456
3
Announcement
459
4
Chi-square (χ2) Test of Independence
463
3
Inspection and Description
463
1
Estimation
464
2
Calculating an Effect Size for a Chi-Square Test of Independence
466
1
Announcing Results of a Chi-Square Test of Independence
467
1
What You Have Learned and the Next Step
468
1
Key Concepts
469
1
Answers to Your Turn Questions
469
1
Analyzing and Interpreting Data: Problems and Exercises
470
3
Appendix A
473
40
A.1 Proportions of Area Under the Standard Normal Curve
474
3
A.2 Critical Values of t
477
1
A.3 Critical Values of F
478
5
A.4 Transformation of r to Zr
483
2
A.5 Critical Values of r
485
2
A.6 Critical Values of Chi-Square (χ2)
487
2
Appendix B
A Brief Introduction to Power Analysis
489
24
References
513
4
Index
517