Preface to the Instructor 

xiii  (8) 
Introduction to the Student 

xxi  

1 Displaying the Order in a Group of Numbers 


1  (29) 

The Two Branches of Statistical Methods 


2  (1) 


2  (1) 

Box 11: Important Trivia for Poetic Statistics Students 


3  (2) 

How to Make a Frequency Table 


5  (2) 


7  (4) 


11  (1) 

Box 12: Math Anxiety, Statistics Anxiety, and You: A Message for Those of You Who Are Truly Worried About This Course 


12  (3) 


15  (3) 

Shapes of Frequency Distributions 


18  (4) 

Controversies and Limitations 


22  (2) 

Box 13: Gender, Ethnicity, and Math Performance 


24  (1) 

Frequency Tables, Histograms, and Frequency Polygons in Research Articles 


25  (2) 


27  (1) 


28  (1) 


28  (2) 

2 The Mean, Variance, Standard Deviation, and Z Scores 


30  (32) 


31  (4) 

Alternative Measures of Central Tendency 


35  (4) 

The Variance and the Standard Deviation 


39  (8) 


47  (1) 

Box 21: The Sheer Joy (Yes, Joy) of Statistical Analysis 


48  (6) 

Controversies and Limitations: The Tyranny of the Mean 


54  (2) 

The Mean and Standard Deviation As Described in Research Articles 


56  (2) 


58  (1) 


59  (1) 


59  (1) 

Chapter Appendix: Optional Computational Formulas for the Variance and the Standard Deviation 


60  (2) 


62  (38) 

Independent or Predictor Variables and Dependent Variables 


64  (1) 

Graphing Correlations: The Scatter Diagram 


65  (3) 


68  (4) 

Computing an Index of Degree of Linear Correlation: The Pearson Correlation Coefficient 


72  (2) 

Box 31: Galton: Gentleman Genius 


74  (5) 

Integrating the Steps and Additional Examples 


79  (5) 

Testing the Statistical Significance of the Correlation of the Correlation Coefficient 


84  (1) 

Box 32: Illusory Correlation: When You Know Perfectly Well That If It's Big, It's Fatand You Are Perfectly Wrong 


85  (1) 

Issues in Interpreting the Correlation Coefficient 


86  (2) 

Controversies and Recent Developments: What Is a Large Correlation? 


88  (3) 

Correlation Coefficients As Described in Research Articles 


91  (1) 


92  (1) 


93  (1) 


94  (2) 

Chapter Appendix I: Optional Computational Formula for the Correlation Coefficient 


96  (2) 

Chapter Appendix II: Hypothesis Tests and Power for the Correlation Coefficient 


98  (2) 


100  (34) 

Terminology of Bivariate Prediction 


101  (1) 

The Bivariate Prediction Model With Z Scores 


102  (2) 

Bivariate Prediction Using Raw Scores 


104  (2) 


106  (2) 

Error and Proportionate Reduction in Error 


108  (6) 

Another Example of Bivariate Prediction 


114  (3) 

Extension to Multiple Regression and Correlation 


117  (5) 

Box 41: Clinical Versus Statistical Prediction 


122  (3) 

Controversies and Limitations 


125  (1) 

Prediction Models As Described in Research Articles 


126  (2) 


128  (1) 


129  (1) 


129  (5) 

5 Some Key Ingredients for Inferential Statistics: The Normal Curve, Probability, and Population Versus Sample 


134  (26) 


135  (2) 

Box 51: DeMoivre, the Eccentric Stranger Who Invented the Normal Curve 


137  (6) 


143  (2) 

Box 52: Pascal Begins Probability Theory at the Gambling Table, Then Learns to Bet on God 


145  (2) 


147  (3) 

Box 53: Surveys, Polls, and 1948's Costly "Free Sample" 


150  (1) 

Relation of Normal Curve, Probability, and Sample Versus Population 


151  (1) 

Controversies and Limitations 


152  (3) 

Normal Curves, Probabilities, Samples, and Populations in Research Articles 


155  (1) 


156  (1) 


157  (1) 


157  (1) 

Chapter Appendix: Probability Rules and Conditional Probabilities 


158  (2) 

6 Introduction to Hypothesis Testing 


160  (24) 

A HypothesisTesting Example 


162  (1) 

The Core Logic of Hypothesis Testing 


163  (1) 

The HypothesisTesting Process 


163  (7) 

Box 61: To Be or Not to BeBut Can Not Being Be? The Problem of Whether and When to Accept the Null Hypothesis 


170  (2) 

OneTailed and TwoTailed Hypothesis Tests 


172  (5) 

Controversies and Limitations 


177  (2) 

Hypothesis Tests As Reported in Research Articles 


179  (1) 


180  (1) 


181  (1) 


181  (3) 

7 Hypothesis Tests With Means of Samples 


184  (28) 

The Distribution of Means 


185  (1) 

Constructing a Distribution of Means 


186  (2) 

Characteristics of a Distribution of Means 


188  (6) 

Hypothesis Testing Involving a Distribution of Means 


194  (1) 

Box 71: More About Polls: Sampling Errors and Errors in Thinking About Samples 


195  (6) 

Estimation and Confidence Intervals 


201  (4) 

Controversies and Limitations: Confidence Intervals or Significance Tests? 


205  (2) 

Standard Deviation of the Distribution of Sample Means, Hypothesis Tests About Means of Samples, and Confidence Intervals As Described in Research Articles 


207  (2) 


209  (1) 


210  (1) 


210  (2) 

8 Statistical Power and Effect Size 


212  (40) 

What Is Statistical Power? 


213  (3) 


216  (3) 

Calculating Statistical Power 


219  (4) 


223  (1) 

What Determines the Power of a Study? 


223  (1) 


224  (7) 


231  (3) 

Box 81: The Power of Typical Psychology Experiments 


234  (1) 

Other Influences on Power 


235  (1) 

Role of Power When Designing an Experiment 


236  (4) 

Importance of Power in Evaluating the Results of a Study 


240  (2) 

Power, Effect Size, and Confidence Intervals 


242  (1) 


242  (1) 

Box 82: Effect Sizes for Relaxation and Meditation: A Restful MetaAnalysis 


243  (1) 

Controversies and Limitations: Statistical Significance Controversy ContinuedEffect Size Versus Statistical Significance 


244  (2) 

Power and Effect Size As Described in Research Articles 


246  (2) 


248  (1) 


249  (1) 


249  (3) 

9 The t Test for Dependent Means 


252  (36) 

Box 91: William S. Gosset, Alias "Student": Not a Mathematician, but a "Practical Man" 


254  (1) 

Introduction to the t Test: The t Test for a Single Sample 


255  (11) 

The t Test for Dependent Means 


266  (10) 

Assumptions of the t Test 


276  (1) 

Effect Size and Power for the t Test for Dependent Means 


276  (4) 

Box 92: The Power of Studies Using Difference Scores: How the Lanarkshire Milk Experiment Could Have Been Milked for More 


280  (1) 

Controversies and Limitations 


280  (1) 

t Tests As Described in Research Articles 


281  (2) 


283  (1) 


284  (1) 


284  (3) 

Chapter Appendix: Optional Computational Formulas for the t Test for Dependent Means 


287  (1) 

10 The t Test for Independent Means 


288  (30) 

Basic Strategy of the t Test for Independent Means: The Distribution of Differences Between Means 


289  (6) 

Steps of Hypothesis Testing With a t Test for Independent Means 


295  (9) 

Assumptions of the t Test for Independent Means 


304  (1) 

Effect Size and Power for the t Test for Independent Means 


305  (1) 

Box 101: Monte Carlo Methods, or When Mathematics Becomes Just an Experiment and Statistics Depend on a Game of Chance 


306  (3) 

Controversies and Limitations 


309  (1) 

The t Test for Independent Means As Described in Research Articles 


310  (3) 


313  (1) 


314  (1) 


314  (2) 

Chapter Appendix: Optional Computational Formulas for the t Test for Independent Means 


316  (2) 

11 Introduction to the Analysis of Variance 


318  (30) 

Basic Logic of the Analysis of Variance 


320  (6) 

Box 111: Sir Ronald Fisher, Caustic Genius of Statistics 


326  (2) 

Carrying Out an Analysis of Variance 


328  (7) 

Hypothesis Testing With the Analysis of Variance 


335  (2) 

Assumptions in the Analysis of Variance 


337  (1) 

Effect Size and Power for the Analysis of Variance 


338  (4) 

Controversies and Limitations: Random Assignment Versus Systematic Selection 


342  (1) 

Analyses of Variance As Described in Research Articles 


343  (1) 


344  (1) 


345  (1) 


345  (3) 

12 The Structural Model in the Analysis of Variance 


348  (28) 

Principles of the Structural Model 


350  (3) 

Box 121: Analysis of Variance As a Way of Thinking About the World 


353  (1) 

Using the Structural Model to Conduct an Analysis of Variance 


354  (1) 

Analysis of Variance Tables 


355  (1) 

Analysis of Variance With UnequalSized Groups 


356  (6) 

Summary of Procedures for Computing an Analysis of Variance Using the Structural Model 


362  (1) 


362  (3) 

Assumptions in the Analysis of Variance With Unequal Sample Sizes 


365  (1) 


366  (2) 

Controversies, Limitations, and Recent Developments 


368  (1) 

Structural Model Analysis of Variance and Multiple Comparisons As Described in Research Articles 


369  (2) 


371  (1) 


371  (1) 


372  (3) 

Chapter Appendix I: Optional Computational Formulas for the Sums of Squares in a OneWay Analysis of Variance 


375  (1) 

13 Factorial Analysis of Variance 


376  (50) 

Basic Logic of Factorial Designs and Interaction Effects 


377  (12) 

Basic Logic of the TwoWay Analysis of Variance 


389  (2) 

Box 131: Personality and Situational Influences on Behavior: An Interaction Effect 


391  (16) 

Power and Effect Size in the Factorial Analysis of Variance 


407  (4) 

Extensions and Special Cases of the Factorial Analysis of Variance 


411  (1) 

Controversies, Limitations, and Recent Developments 


412  (3) 

Factorial Analysis of Variance Results As Reported in Research Articles 


415  (1) 


416  (1) 


417  (1) 


417  (4) 

Chapter Appendix I: Optional Computational Formulas for the TwoWay Analysis of Variance 


421  (2) 

Chapter Appendix II: OneWay RepeatedMeasures Analysis of Variance 


423  (3) 


426  (32) 

The ChiSquare Statistic and the ChiSquare Test for Goodness of Fit 


428  (1) 

Box 141: Karl Pearson, Inventor of ChiSquare and Center of Controversy 


429  (8) 

ChiSquare Test for Independence 


437  (10) 

Assumptions for ChiSquare Tests 


447  (1) 

Effect Size and Power for ChiSquare Tests for Independence 


447  (4) 

Controversies and Limitations 


451  (1) 

ChiSquare Tests As Reported in Research Articles 


452  (1) 


453  (1) 


454  (1) 


454  (4) 

15 Strategies When Population Distributions Are Not Normal: Data Transformations, RankOrder Tests, and ComputerIntensive Methods 


458  (30) 

Assumptions in the Standard HypothesisTesting Procedures 


459  (2) 


461  (9) 


470  (4) 

ComputerIntensive Methods 


474  (5) 


479  (1) 

Box 151: Where Do Random Numbers Come From? 


480  (2) 


482  (1) 

Procedures Used When Populations Appear Nonnormal As Described in Research Articles 


482  (2) 


484  (1) 


485  (1) 


485  (3) 

16 Integrating What You Have Learned: The General Linear Model 


488  (30) 

The Relationships Among Major Statistical Methods 


489  (1) 

Review of the Principles of Multiple Regression and Correlation 


490  (2) 

Introduction to the General Linear Model 


492  (1) 

The General Linear Model and Multiple Regression/Correlation 


493  (1) 

Bivariate Regression and Correlation As Special Cases of Multiple Regression/Correlation 


493  (1) 

The t Test As a Special Case of the Analysis of Variance 


493  (2) 

Box 161: The Golden Age of Statistics: Four Guys Around London 


495  (3) 

The t Test As a Special Case of the Significance Test for the Correlation Coefficient 


498  (5) 

The Analysis of Variance As a Special Case of the Significance Test of the Multiple Correlation Coefficient 


503  (6) 

Box 162: Two Women Make a Point About Gender and Statistics 


509  (1) 

Choice of Statistical Tests 


510  (2) 

Assumptions and the General Linear Model 


512  (1) 

Controversies and Limitations 


512  (2) 


514  (1) 


515  (1) 


515  (3) 

17 Making Sense of Advanced Statistical Procedures in Research Articles 


518  (33) 

Brief Review of Multiple Regression and Correlation 


520  (1) 

Hierarchial and Stepwise Multiple Regression 


521  (3) 


524  (2) 


526  (2) 


528  (2) 


530  (4) 

Analysis of Covariance (ANCOVA) 


534  (1) 

Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA) 


535  (2) 

Overview of Statistical Techniques 


537  (1) 

Box 171: The Forced Marriage of Fisher and NeymanPearson 


538  (1) 

Controversy: Should Statistics Be Controversial? 


538  (2) 

How to Read Results in Research Articles Involving Unfamiliar Statistical Techniques 


540  (2) 


542  (1) 


543  (1) 


543  (8) 
Appendix A Overview of the Logic and Language of Psychology Research 

551  (12) 
The Traditionally Ideal Research Approach 

552  (1) 
Equivalence of Participants in Experimental and Control Groups 

553  (5) 
Equivalence of Circumstances for Experimental and Control Groups 

558  (1) 
Representativeness of the Sample 

559  (1) 
Measurement 

560  (2) 
Key Terms 

562  (1) 
Appendix B Tables 

563  (8) 
Table B1 Normal Curve Areas: Percentage of the Normal Curve Between the Mean and the Z Scores Shown 

563  (3) 
Table B2 Cutoff Scores for the t Distribution 

566  (1) 
Table B3 Cutoff Scores for the F Distribution 

567  (3) 
Table B4 Cutoff Scores for the ChiSquare Distribution 

570  (1) 
Table B5 Index to Power Tables and Tables Giving Number of Participants Needed for 80% Power 

570  (1) 
Answers to Set I Practice Problems 

571  (40) 
Glossary 

611  (10) 
Glossary of Symbols 

621  (2) 
References 

623  (8) 
Index 

631  