Statistical methods for psychologyPDF电子书下载

Chapter1 Basic Concepts 1

1.1 Important Terms 2

1.2 Descriptive and Inferential Statistics 5

1.3 Measurement Scales 6

1.4 Using Computers 9

1.5 The Plan of the Book 10

Chapter2 Describing and Exploring Data 15

2.1 Plotting Data 17

2.2 Histograms 19

2.3 Stem-and-Leaf Displays 21

2.4 Alternative Methods of Plotting Data 24

2.5 Describing Distributions 28

2.6 Using Computer Programs to Display Data 31

2.7 Notation 33

2.8 Measures of Central Tendency 35

2.9 Measures of Variability 41

2.10 Boxplots：Graphical Representations of Dispersions and Extreme Scores 57

2.11 Obtaining Measures of Dispersion Using Minitab 60

2.12 Percentiles，Quartiles，and Deciles 62

2.13 The Effect of Linear Transformations on Data 62

Chapter3 The Normal Distribution 73

3.1 The Normal Distribution 76

3.2 The Standard Normal Distribution 79

3.3 Using the Tables of the Standard Normal Distribution 82

3.4 Setting Probable Limits on an Observation 85

3.5 Measures Related to z 86

Chapter4 Sampling Distributions and Hypothesis Testing 91

4.1 Two Simple Examples Involving Course Evaluations and Rude Motorists 92

4.2 Sampling Distributions 95

4.3 Hypothesis Testing 96

4.4 The Null Hypothesis 98

4.5 Test Statistics and Their Sampling Distributions 100

4.6 Using the Normal Distribution to Test Hypotheses 101

4.7 Type Ⅰ and Type Ⅱ Errors 104

4.8 One- and Two-Tailed Tests 107

4.9 What Does It Mean to Reject the Null Hypothesis？ 110

4.10 Effect Size 110

4.11 A Final Worked Example 111

4.12 Back to Course Evaluations and Rude Motorists 112

Chapter5 Basic Concepts of Probability 115

5.1 Probability 116

5.2 Basic Terminology and Rules 118

5.3 Discrete versus Continuous Variables 122

5.4 Probability Distributions for Discrete Variables 123

5.5 Probability Distributions for Continuous Variables 124

5.6 Permutations and Combinations 126

5.7 The Binomial Distribution 129

5.8 Using the Binomial Distribution to Test Hypotheses 134

5.9 The Multinomial Distribution 136

Chapter6 Categorical Data and Chi-Square 141

6.1 The Chi-Square Distribution 143

6.2 Statistical Importance of the Chi-Square Distribution 144

6.3 The Chi-Square Goodness-of-Fit Test—One-Way Classifiication 146

6.4 Two Classification Variables：Contingency Table Analysis 149

6.5 Chi-Square for Larger Contingency Tables 152

6.6 Chi-Square for Ordinal Data 159

6.7 Summary of the Assumptions of Chi-Square 159

6.8 One- and Two-Tailed Tests 161

6.9 Likelihood Ratio Tests 162

6.10 Measures of Association 163

Chapter7 Hypothesis Tests Applied to Means 177

7.1 Sampling Distribution of the Mean 178

7.2 Testing Hypotheses about Means—σ Known 181

7.3 Testing a Sample Mean When σ Is Unknown—The One-Sample t Test 183

7.4 Hypothesis Tests Applied to Means—Two Matched Samples 191

7.5 Hypothesis Tests Applied to Means—Two Independent Samples 198

7.6 Confidence Intervals 206

7.7 A Second Worked Example 211

7.8 Heterogeneity of Variance：The Behrens-Fisher Problem 213

Chapter8 Power 223

8.1 Factors Affecting the Power of a Test 225

8.2 Effect Size 227

8.3 Power Calculations for the One-Sample t 229

8.4 Power Calculations for Differences Between Two Independent Means 232

8.5 Power Calculations for Matched-Sample t 235

8.6 Power Considerations in Terms of Sample Size 237

8.7 Post-Hoc Power 238

Chapter9 Correlation and Regression 243

9.1 Scatterplot 245

9.2 The Relationship Between Stress and Health 250

9.3 The Covariance 252

9.4 The Pearson Product-Moment Correlation Coefficient （r） 253

9.5 The Regression Line 255

9.6 The Accuracy of Prediction 260

9.7 Assumptions Underlying Regression and Correlation 267

9.8 Confiidence Limits on Y 268

9.9 A Computer Example Showing the Role of Test-Taking Skills 270

9.10 Hypothesis Testing 273

9.11 The Role of Assumptions in Correlation and Regression 282

9.12 Factors That Affect the Correlation 282

9.13 Power Calculation for Pearson’s r 285

Chapter10 Alternative Correlational Techniques 295

10.1 Point-Biserial Correlation and Phi：Pearson Correlations by Another Name 297

10.2 Biserial and Tetrachoric Correlation：Non-Pearson Correlation Coefficients 305

10.3 Correlation Coeffiicients for Ranked Data 306

10.4 Analysis of Contingency Tables with Ordered Variables 309

10.5 Kendall’s Coefficient of Concordance （W） 312

Chapter11 Simple Analysis of Variance 319

11.1 An Example 320

11.2 The Underlying Model 321

11.3 The Logic of the Analysis of Variance 324

11.4 Calculations in the Analysis of Variance 326

11.5 Computer Solutions 333

11.6 Derivation of the Analysis of Variance 336

11.7 Unequal Sample Sizes 338

11.8 Violations of Assumptions 340

11.9 Transformations 342

11.10 Fixed versus Random Models 350

11.11 Magnitude of Experimental Effect 350

11.12 Power 354

11.13 Computer Analyses 360

Chapter12 Multiple Comparisons Among Treatment Means 369

12.1 Error Rates 370

12.2 Multiple Comparisons in a Simple Experiment on Morphine Tolerance 373

12.3 A Priori Comparisons 375

12.4 Post Hoc Comparisons 391

12.5 Tukey’s Test 398

12.6 The Ryan Procedure （REGWQ） 399

12.7 The Scheffe Test 400

12.8 Dunnett’s Test for Comparing All Treatments with a Control 401

12.9 Comparison of Dunnett’s Test and the Bonferroni t 402

12.10 Comparison of the Alternative Procedures 402

12.11 Which Test？ 404

12.12 Computer Solution 404

12.13 Trend Analysis 408

Chapter13 Factorial Analysis of Variance 421

13.1 An Extension of the Eysenck Study 424

13.2 Structural Models and Expected Mean Squares 429

13.3 Interactions 430

13.4 Simple Effects 432

13.5 Analysis of Variance Applied to the Effects of Smoking 436

13.6 Multiple Comparisons 438

13.7 Power Analysis for Factorial Experiments 440

13.8 Expected Mean Squares 442

13.9 Magnitude of Experimental Effects 446

13.10 Unequal Sample Sizes 449

13.11 Analysis for Unequal Sample Sizes Using SAS 455

13.12 Higher-Order Factorial Designs 456

13.13 A Computer Example 464

Chapter14 Repeated-Measures Designs 471

14.1 The Structural Model 474

14.2 F Ratios 475

14.3 The Covariance Matrix 476

14.4 Analysis of Variance Applied to Relaxation Therapy 477

14.5 One Between-Subjects Variable and One Within-Subjects Variable 480

14.6 Two Within-Subjects Variables 494

14.7 Two Between-Subjects Variables and One Within-Subjects Variable 494

14.8 Two Within-Subjects Variables and One Between-Subjects Variable 500

14.9 Three Within-Subjects Variables 508

14.10 Intraclass Correlation 512

14.11 Other Considerations 515

14.12 A Computer Analysis Using a Traditional Approach 516

14.13 Multivariate Analysis of Variance for Repeated-Measures Designs 519

Chapter15 Multiple Regression 533

15.1 Multiple Linear Regression 534

15.2 Standard Errors and Tests of Regression Coeffiicients 543

15.3 Residual Variance 544

15.4 Distribution Assumptions 545

15.5 The Multiple Correlation Coeffiicient 546

15.6 Geometric Representation of Multiple Regression 548

15.7 Partial and Semipartial Correlation 552

15.8 Suppressor Variables 557

15.9 Regression Diagnostics 558

15.10 Constructing a Regression Equation 563

15.11 The “Importance” of Individual Variables 571

15.12 Using Approximate Regression Coeffiicients 573

15.13 Mediating and Moderating Relationships 574

15.14 Logistic Regression 583

Chapter16 Analyses of Variance and Covariance as General Linear Models 603

16.1 The General Linear Model 604

16.2 One-Way Analysis of Variance 607

16.3 Factorial Designs 610

16.4 Analysis of Variance with Unequal Sample Sizes 618

16.5 The One-Way Analysis of Covariance 625

16.6 Interpreting an Analysis of Covariance 636

16.7 The Factorial Analysis of Covariance 638

16.8 Using Multiple Covariates 647

16.9 Alternative Experimental Designs 648

Chapter17 Log-Linear Analysis 655

17.1 Two-Way Contingency Tables 658

17.2 Model Specifiication 662

17.3 Testing Models 665

17.4 Odds and Odds Ratios 669

17.5 Treatment Effects （Lambda） 669

17.6 Three-Way Tables 671

17.7 Deriving Models 678

17.8 Treatment Effects 682

Chapter18 Resampling and Nonparametric Approaches to Data 691

18.1 Bootstrapping as a General Approach 694

18.2 Bootstrapping with One Sample 696

18.3 Resampling with Two Paired Samples 699

18.4 Resampling with Two Independent Samples 702

18.5 Bootstrapping Confiidence Limits on a Correlation Coeffiicient 704

18.6 Wilcoxon’s Rank-Sum Test 707

18.7 Wilcoxon’s Matched-Pairs Signed-Ranks Test 713

18.8 The Sign Test 717

18.9 Kruskal-Wallis One-Way Analysis of Variance 719

18.10 Friedman’s Rank Test for k Correlated Samples 720

Appendices 727

References 763

Answers to Selected Exercises 773

Index 791