Statistical methods for psychologyPDF|Epub|txt|kindle电子书版本下载

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Chapter1 Basic Concepts1

1.1 Important Terms2

1.2 Descriptive and Inferential Statistics5

1.3 Measurement Scales6

1.4 Using Computers9

1.5 The Plan of the Book10

Chapter2 Describing and Exploring Data15

2.1 Plotting Data17

2.2 Histograms19

2.3 Stem-and-Leaf Displays21

2.4 Alternative Methods of Plotting Data24

2.5 Describing Distributions28

2.6 Using Computer Programs to Display Data31

2.7 Notation33

2.8 Measures of Central Tendency35

2.9 Measures of Variability41

2.10 Boxplots：Graphical Representations of Dispersions and Extreme Scores57

2.11 Obtaining Measures of Dispersion Using Minitab60

2.12 Percentiles，Quartiles，and Deciles62

2.13 The Effect of Linear Transformations on Data62

Chapter3 The Normal Distribution73

3.1 The Normal Distribution76

3.2 The Standard Normal Distribution79

3.3 Using the Tables of the Standard Normal Distribution82

3.4 Setting Probable Limits on an Observation85

3.5 Measures Related to z86

Chapter4 Sampling Distributions and Hypothesis Testing91

4.1 Two Simple Examples Involving Course Evaluations and Rude Motorists92

4.2 Sampling Distributions95

4.3 Hypothesis Testing96

4.4 The Null Hypothesis98

4.5 Test Statistics and Their Sampling Distributions100

4.6 Using the Normal Distribution to Test Hypotheses101

4.7 Type Ⅰ and Type Ⅱ Errors104

4.8 One- and Two-Tailed Tests107

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

4.10 Effect Size110

4.11 A Final Worked Example111

4.12 Back to Course Evaluations and Rude Motorists112

Chapter5 Basic Concepts of Probability115

5.1 Probability116

5.2 Basic Terminology and Rules118

5.3 Discrete versus Continuous Variables122

5.4 Probability Distributions for Discrete Variables123

5.5 Probability Distributions for Continuous Variables124

5.6 Permutations and Combinations126

5.7 The Binomial Distribution129

5.8 Using the Binomial Distribution to Test Hypotheses134

5.9 The Multinomial Distribution136

Chapter6 Categorical Data and Chi-Square141

6.1 The Chi-Square Distribution143

6.2 Statistical Importance of the Chi-Square Distribution144

6.3 The Chi-Square Goodness-of-Fit Test—One-Way Classifiication146

6.4 Two Classification Variables：Contingency Table Analysis149

6.5 Chi-Square for Larger Contingency Tables152

6.6 Chi-Square for Ordinal Data159

6.7 Summary of the Assumptions of Chi-Square159

6.8 One- and Two-Tailed Tests161

6.9 Likelihood Ratio Tests162

6.10 Measures of Association163

Chapter7 Hypothesis Tests Applied to Means177

7.1 Sampling Distribution of the Mean178

7.2 Testing Hypotheses about Means—σ Known181

7.3 Testing a Sample Mean When σ Is Unknown—The One-Sample t Test183

7.4 Hypothesis Tests Applied to Means—Two Matched Samples191

7.5 Hypothesis Tests Applied to Means—Two Independent Samples198

7.6 Confidence Intervals206

7.7 A Second Worked Example211

7.8 Heterogeneity of Variance：The Behrens-Fisher Problem213

Chapter8 Power223

8.1 Factors Affecting the Power of a Test225

8.2 Effect Size227

8.3 Power Calculations for the One-Sample t229

8.4 Power Calculations for Differences Between Two Independent Means232

8.5 Power Calculations for Matched-Sample t235

8.6 Power Considerations in Terms of Sample Size237

8.7 Post-Hoc Power238

Chapter9 Correlation and Regression243

9.1 Scatterplot245

9.2 The Relationship Between Stress and Health250

9.3 The Covariance252

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

9.5 The Regression Line255

9.6 The Accuracy of Prediction260

9.7 Assumptions Underlying Regression and Correlation267

9.8 Confiidence Limits on Y268

9.9 A Computer Example Showing the Role of Test-Taking Skills270

9.10 Hypothesis Testing273

9.11 The Role of Assumptions in Correlation and Regression282

9.12 Factors That Affect the Correlation282

9.13 Power Calculation for Pearson’s r285

Chapter10 Alternative Correlational Techniques295

10.1 Point-Biserial Correlation and Phi：Pearson Correlations by Another Name297

10.2 Biserial and Tetrachoric Correlation：Non-Pearson Correlation Coefficients305

10.3 Correlation Coeffiicients for Ranked Data306

10.4 Analysis of Contingency Tables with Ordered Variables309

10.5 Kendall’s Coefficient of Concordance （W）312

Chapter11 Simple Analysis of Variance319

11.1 An Example320

11.2 The Underlying Model321

11.3 The Logic of the Analysis of Variance324

11.4 Calculations in the Analysis of Variance326

11.5 Computer Solutions333

11.6 Derivation of the Analysis of Variance336

11.7 Unequal Sample Sizes338

11.8 Violations of Assumptions340

11.9 Transformations342

11.10 Fixed versus Random Models350

11.11 Magnitude of Experimental Effect350

11.12 Power354

11.13 Computer Analyses360

Chapter12 Multiple Comparisons Among Treatment Means369

12.1 Error Rates370

12.2 Multiple Comparisons in a Simple Experiment on Morphine Tolerance373

12.3 A Priori Comparisons375

12.4 Post Hoc Comparisons391

12.5 Tukey’s Test398

12.6 The Ryan Procedure （REGWQ）399

12.7 The Scheffe Test400

12.8 Dunnett’s Test for Comparing All Treatments with a Control401

12.9 Comparison of Dunnett’s Test and the Bonferroni t402

12.10 Comparison of the Alternative Procedures402

12.11 Which Test？404

12.12 Computer Solution404

12.13 Trend Analysis408

Chapter13 Factorial Analysis of Variance421

13.1 An Extension of the Eysenck Study424

13.2 Structural Models and Expected Mean Squares429

13.3 Interactions430

13.4 Simple Effects432

13.5 Analysis of Variance Applied to the Effects of Smoking436

13.6 Multiple Comparisons438

13.7 Power Analysis for Factorial Experiments440

13.8 Expected Mean Squares442

13.9 Magnitude of Experimental Effects446

13.10 Unequal Sample Sizes449

13.11 Analysis for Unequal Sample Sizes Using SAS455

13.12 Higher-Order Factorial Designs456

13.13 A Computer Example464

Chapter14 Repeated-Measures Designs471

14.1 The Structural Model474

14.2 F Ratios475

14.3 The Covariance Matrix476

14.4 Analysis of Variance Applied to Relaxation Therapy477

14.5 One Between-Subjects Variable and One Within-Subjects Variable480

14.6 Two Within-Subjects Variables494

14.7 Two Between-Subjects Variables and One Within-Subjects Variable494

14.8 Two Within-Subjects Variables and One Between-Subjects Variable500

14.9 Three Within-Subjects Variables508

14.10 Intraclass Correlation512

14.11 Other Considerations515

14.12 A Computer Analysis Using a Traditional Approach516

14.13 Multivariate Analysis of Variance for Repeated-Measures Designs519

Chapter15 Multiple Regression533

15.1 Multiple Linear Regression534

15.2 Standard Errors and Tests of Regression Coeffiicients543

15.3 Residual Variance544

15.4 Distribution Assumptions545

15.5 The Multiple Correlation Coeffiicient546

15.6 Geometric Representation of Multiple Regression548

15.7 Partial and Semipartial Correlation552

15.8 Suppressor Variables557

15.9 Regression Diagnostics558

15.10 Constructing a Regression Equation563

15.11 The “Importance” of Individual Variables571

15.12 Using Approximate Regression Coeffiicients573

15.13 Mediating and Moderating Relationships574

15.14 Logistic Regression583

Chapter16 Analyses of Variance and Covariance as General Linear Models603

16.1 The General Linear Model604

16.2 One-Way Analysis of Variance607

16.3 Factorial Designs610

16.4 Analysis of Variance with Unequal Sample Sizes618

16.5 The One-Way Analysis of Covariance625

16.6 Interpreting an Analysis of Covariance636

16.7 The Factorial Analysis of Covariance638

16.8 Using Multiple Covariates647

16.9 Alternative Experimental Designs648

Chapter17 Log-Linear Analysis655

17.1 Two-Way Contingency Tables658

17.2 Model Specifiication662

17.3 Testing Models665

17.4 Odds and Odds Ratios669

17.5 Treatment Effects （Lambda）669

17.6 Three-Way Tables671

17.7 Deriving Models678

17.8 Treatment Effects682

Chapter18 Resampling and Nonparametric Approaches to Data691

18.1 Bootstrapping as a General Approach694

18.2 Bootstrapping with One Sample696

18.3 Resampling with Two Paired Samples699

18.4 Resampling with Two Independent Samples702

18.5 Bootstrapping Confiidence Limits on a Correlation Coeffiicient704

18.6 Wilcoxon’s Rank-Sum Test707

18.7 Wilcoxon’s Matched-Pairs Signed-Ranks Test713

18.8 The Sign Test717

18.9 Kruskal-Wallis One-Way Analysis of Variance719

18.10 Friedman’s Rank Test for k Correlated Samples720

Appendices727

References763

Answers to Selected Exercises773

Index791