Basic statistics for behavioral science research

목차 (Table of Contents)

• CONTENTS
• Preface = xv
• 1 Introduction = 1
• Why Study Statistics? = 1
• Statistics and Research = 1
• What Are the Uses of Statistics? = 5
• Descriptive Versus Inferential Statistics = 6
• Populations Versus Samples = 7
• Parameters Versus Statistics = 9
• Summary of Uses of Statistics = 10
• What Are the Characteristics of Variables? = 11
• Constants Versus Variables = 11
• Qualitative Versus Quantitative Variables = 11
• Scales or Levels of Measurement = 14
• Relationships Among the Levels of Measurement = 16
• Why Does Level of Measurement Matter? = 17
• What Are Parametric and Nonparametric Tests? = 18
• Disadvantages and Advantages of Parametric Tests = 19
• What Symbols and Procedures Are Used in Statistics? = 20
• Symbols for Scores = 20
• Symbols for Mathematical Procedures = 21
• Other Mathematical Procedures = 25
• Formulas and Equations = 29
• What Is the Role of Calculators and Computers? = 30
• What Is the Best Way to Study Statistics? = 31
• Chapter Summary = 33
• Chapter Review = 34
• Multiple-Choice Questions = 34
• Problems = 35
• Study Questions = 37
• 2 Introduction to Research Design = 38
• What Is Scientific Research? = 38
• Role of Observation = 39
• Role of Hypotheses = 39
• What Are Some Approaches to Research? = 41
• Types of Research = 41
• Purposes of Research = 50
• Reserach Setting = 51
• How Are Extraneous Variables Controlled? = 52
• Methods of Control = 52
• Internal and External Validity = 56
• What Are Some Guidelines for Using Statistics? = 57
• Consider the Meaning of the Numbers = 57
• Consider Whether Assumptions Are Met = 58
• Estimate the Results That Would Be Reasonable = 58
• Chapter Summary = 59
• Chapter Review = 60
• Multiple-Choice Questions = 60
• Problems = 61
• Study Questions = 63
• 3 Describing Data : Tables and Graphs = 65
• How Should Scores Be Organized? = 65
• How Are Tables of Frequency Distributions Used? = 66
• Simple Frequency Distribution = 66
• Grouped Frequency Distributions = 67
• Stem-and-Leaf Displays = 69
• Cumulative Frequency Distributions = 70
• Relative Frequency Distributions = 71
• Cummulative Relative Frequency Distributions = 72
• What Are Percentiles? = 73
• Percentile Ranks = 73
• Guidelines for Computing Percentile Ranks = 74
• How Are Graphs of Frequency Distribution Used? = 75
• Ordinate and Abscissa = 75
• Graphing Qualitative Data = 76
• Graphing Quantitative Data = 77
• Shapes of Frequency Distributions = 80
• Graphs Showing the Relationship Between Variables = 81
• What Is the Best Way to Present Data? = 84
• Tables = 84
• Graphs = 86
• Chapter Summary = 87
• Chapter Review = 88
• Multiple-Choice Questions = 88
• Problems = 90
• Study Questions = 92
• 4 Describing Data : Central Tendency, Variability, and Standard Scores = 93
• What Is a Measure of Central Tendency? = 93
• Mode = 93
• Median = 94
• Mean = 96
• Selecting the Most Appropriate Measure of Central Tendency = 99
• Estimating Measures of Central Tendency from Grouped Data = 101
• Reporting Measures of Central Tendency in APA Style = 101
• What Is a Measure of Variability? = 101
• Range = 102
• Mean Absolute Deviation = 104
• Standard Deviation = 104
• Variance = 111
• Selecting the Most Appropriate Measure of Variability = 113
• Estimating Variability from Grouped Data = 114
• Reporting Measures of Variability in APA Style = 115
• What Are Standard Scores? = 115
• z-Scores = 115
• Other Standard Scores = 118
• When Should You Use the Techniques? = 120
• Chapter Summary = 121
• Chapter Review = 122
• Multiple-Choice Questions = 122
• Problems = 124
• Study Questions = 125
• 5 Probability and the Normal Curve = 126
• What Are Empirical and Theoretical Distributions? = 126
• Types of Theoretical Distributions = 126
• What Is Probability? = 127
• Combining Probabilities = 128
• What Is the Binomial Distribution? = 132
• What Is the Normal Distribution? = 114
• Graphing a Normal Distribution : The Normal Curve = 135
• Relationship Between the Normal Curve and Probability = 135
• Dividing the Area Under the Normal Curve = 136
• What Is a Normal Curve Table? = 137
• Probability and the Normal Curve Table = 138
• Different Types of Normal Curve Problems = 140
• Interpolation = 150
• What Are Some General Guidelines for Solving Normal Curve Problems? = 152
• Deciding When to Use the Technique = 154
• Chapter Summary = 154
• Chapter Review = 155
• Multiple-Choice Questions = 155
• Problems = 156
• Study Questions = 157
• 6 Correlation = 159
• What Is Correlation? = 159
• Use of Scattergrams = 160
• What Is the Pearson γ Statistic? = 163
• Range of Values of γ = 163
• Assumptions of Pearson γ = 164
• Formulas for Pearson γ = 166
• What Is a Significant Pearson γ? = 172
• Characteristics of a Statistically Significant γ = 173
• Using a Table of the Significance of γ = 173
• What to Do If γ Is Not Significant = 176
• How to Interpret a Significant γ = 177
• Describing a Significant Correlation = 180
• Reporting a Significant Correlation in APA Style = 180
• Are There Other Types of Correlation? = 180
• Spearman Rho = 181
• Partial and Multiple Correlations = 184
• When Are Correlation Techniques Used? = 184
• Nature of the Research = 184
• Characteristics of the Data = 184
• Cautions = 185
• Chapter Summary = 186
• Chapter Review = 186
• Multiple-Choice Questions = 186
• Problems = 188
• Study Questions = 190
• 7 Regression = 191
• What Is Regression? = 191
• Predicted Values = 191
• Relationship of Regression to Correlation = 192
• What Are Formulas for Regression? = 193
• z-Score Formula for Regression = 193
• Computational Formula = 194
• Using the Regression Equations = 197
• How Are Regression Lines Plotted and Used? = 201
• Plotting the Regression Line = 201
• Using the Regression Line = 201
• What Is Regression Toward the Mean? = 203
• Conceptual Examples = 203
• Research Examples = 204
• What Is the Standard Error of Estimate? = 208
• Formulas = 208
• Interpretation = 209
• What Is Multiple Regression? = 211
• When Is Regression Used? = 211
• Chapter Summary = 212
• Chapter Review = 212
• Multiple-Choice Questions = 212
• Problems = 214
• Study Questions = 216
• 8 The Logic of Inferential Statistics = 218
• How Are Samples Used in Inferential Statistics? = 218
• Random Sampling = 219
• Stratified Sampling = 222
• Quota Sampling = 224
• Systematic Sampling = 224
• Cluster Sampling = 225
• Convenience or Haphazard Sampling = 225
• Clearly Biased Sampling = 226
• Generalizing to a Population = 227
• What Are Sampling Distributions and How Are They Used? = 227
• Expected Value and Standard Error of a Sampling Distribution = 228
• Central Limit Theorem = 228
• What Are z-Tests and How Are They Used? = 231
• General Procedure = 231
• Using a z-Test with a Single Sample = 233
• Using a z-Test to Compare Two Sample Means = 234
• What Are the Uses of Inferential Statistics? = 237
• Parameter Estimation = 237
• Hypothesis Testing = 239
• How Are Hypotheses Tested? = 240
• Null Versus Alternative Hypotheses = 240
• Significance Levels = 243
• Types of Errors = 245
• Power = 246
• Effect Size = 247
• One-Tailed Versus Two-Tailed Significance Tests = 248
• Statistical Significance Versus Meaningfulness = 249
• Chapter Summary = 250
• Chapter Review = 252
• Multiple-Choice Questions = 252
• Problems = 254
• Study Questions = 255
• 9 t-Tests and Confidence Intervals About Means = 257
• How Are t-Tests Used to Test Hypotheses About Means? = 257
• z-Tests Versus t-Tests = 257
• Characteristics of t-Tests = 258
• Why Are There Different Types of t-Tests? = 261
• Single-Sample t-Test = 261
• Independent-Samples t-Test = 266
• Dependent-Samples t-Test = 271
• How Are Confidence Intervals Used? = 278
• Formulas = 279
• When Should These Techniques Be Used? = 283
• Choosing the Appropriate Technique = 284
• Chapter Summary = 286
• Chapter Review = 286
• Multiple-Choice Questions = 286
• Problems = 289
• Study Questions = 292
• 10 One-Way Analysis of Variance = 293
• What Is the F-Distribution? = 293
• Characteristics of the F-Distribution = 294
• Uses of F = 294
• What Is an Analysis of Variance? = 299
• Relationship of F and t = 300
• Assumptions = 300
• ANOVA Versus Multiple t-Tests = 301
• Formulas for the Analysis of Variance = 302
• Analysis of Variance Summary Table = 307
• Examples of One-Way ANOVAs = 307
• Interpretation of F from ANOVA = 313
• How Do You Decide Which Analysis to Use? = 314
• One-Way ANOVA Versus t-Test = 314
• Other Analysis of Variance Procedures = 314
• ANOVA Versus Multiple Regression = 317
• Chapter Summary = 318
• Chapter Review = 319
• Multiple-Choice Questions = 319
• Problems = 321
• Study Questions = 323
• 11 Comparisons Among Means = 325
• What Is a Comparison? = 325
• Ways of Testing Differences in Means = 325
• Advantages of a Comparison = 326
• A Priori Versus Post Hoc Comparisons = 327
• Weights = 328
• What Is the Scheff$$\acute e$$ Comparison Procedure? = 330
• Formulas = 330
• Computing the Critical Value = 332
• Examples of Scheff$$\acute e$$ Comparisons = 333
• Orthogonality = 337
• Summary of Advantages and Disadvantages of the Scheff$$\acute e$$ Procedure = 340
• What Is the Tukey HSD Procedure? = 342
• Formulas = 342
• Examples of Tukey HSD = 343
• Advantages and Disadvantages of Tukey HSD = 345
• What Are Some Other Comparison Procedures? = 346
• How Do You Select the Appropriate Procedure? = 347
• Questions to Consider = 347
• Other Issues = 348
• Chapter Summary = 349
• Chapter Review = 350
• Multiple-Choice Questions = 350
• Problems = 351
• Study Questions = 354
• 12 Chi-Square = 356
• What Is a Chi-Square Test? = 356
• X² Distribution = 357
• Assumptions = 357
• What Is a X² Goodness-of-Fit Test? = 358
• Formula = 359
• Examples of Goodness-of-Fit Tests = 360
• Deciding on the Hypothesis to Test = 363
• What Is a X² Test of Independence? = 364
• Contingency Tables = 365
• Formulas = 365
• Examples of X² Tests = 368
• How Can You Correct for Small Expected Frequencies? = 374
• Collapse Across Categories = 374
• Yates' Correction = 375
• Fisher's Exact Probability Test = 375
• How Are Results of X² Tests Reported? = 375
• APA Style = 375
• Describing the Finding = 376
• When Should X² Tests Be Used? = 376
• Goodness of Fit Versus Test of Independence = 376
• X² Versus γ or ρ = 377
• X² Versus t or ANOVA = 377
• Chapter Summary = 377
• Chapter Review = 378
• Multiple-Choice Questions = 378
• Problems = 380
• Study Questions = 382
• 13 Selecting Appropriate Statistical Tests = 384
• What Is the Nature of Your Data? = 384
• Scale of Measurement = 384
• Shape, Variance, and Other Characteristics = 385
• What Is Your Purpose? = 386
• Describing Data = 386
• Predicting Scores = 386
• Relating Variables = 387
• Comparing Means = 387
• Comparing Medians = 388
• Comparing Frequencies or Proportions = 389
• Multiple Independent and Dependent Variables = 389
• Multiple Significance Tests = 389
• How Do Your Questions and Hypotheses Influence the Choice of Analysis? = 390
• Questions and Hypotheses = 390
• Examples of Research Situations = 391
• What Are Some Issues in Reporting Research Results? = 394
• What to Omit = 394
• What to Include = 394
• APA Style = 395
• Significance = 395
• What Next? = 395
• Take More Statistics Courses = 396
• Learn About Computers = 396
• Know When and How to Seek Help = 397
• Get Involved in Research = 397
• Chapter Summary = 398
• Chapter Review = 398
• Multiple-Choice Questions = 398
• Problems = 400
• Study Questions = 403
• References = 405
• Appendix Tables = 409
• Glossary = 431
• Answers and Solutions = 439
• Index = 469