Statistics for Psychology

by Aron, Arthur Ph.D.; Aron, Elaine N., Ph.D.; Coups, Elliot Ph.D.

• ISBN13: 9780136010579
• ISBN10: 0136010571
• Edition: 5th
• Format: Hardcover
• Copyright: 2008-03-09
• Publisher: Pearson

Summary

The top-selling Statistics for Psychology, Fifth Edition, emphasizes meaning and concepts, not just symbols and numbers. Everything is explained in direct, simple language. Definitional formulas are used throughout to provide a concise symbolic summary of the logic of each particular procedure. Each procedure is taught both verbally and numerically-an important step in permanently establishing a concept in a student's mind. Thoroughly up to date and well written, Statistics for Psychology engages the reader and helps students understand statistics. Book jacket.

Table of Contents

Preface to the Instructor	p. xi
Introduction to the Student	p. xvi
Displaying the Order in a Group of Numbers Using Tables and Graphs	p. 1
The Two Branches of Statistical Methods	p. 2
Some Basic Concepts	p. 3
Important Trivia for Poetic Statistics Students	p. 6
Frequency Tables	p. 7
Histograms	p. 10
Math Anxiety, Statistics Anxiety, and You: A Message for Those of You Who Are Truly Worried About This Course	p. 12
Shapes of Frequency Distributions	p. 15
Controversy: Misleading Graphs	p. 19
Frequency Tables and Histograms in Research Articles	p. 21
Summary	p. 23
Key Terms	p. 24
Example Worked-Out Problems	p. 24
Practice Problems	p. 25
Using SPSS	p. 29
Chapter Note	p. 32
Central Tendency and Variability	p. 33
Central Tendency	p. 34
Variability	p. 43
The Sheer Joy (Yes, Joy) of Statistical Analysis	p. 51
Controversy: The Tyranny of the Mean	p. 52
Gender, Ethnicity, and Math Performance	p. 53
Central Tendency and Variability in Research Articles	p. 55
Summary	p. 57
Key Terms	p. 57
Example Worked-Out Problems	p. 57
Practice Problems	p. 59
Using SPSS	p. 62
Chapter Notes	p. 65
Some Key Ingredients for Inferential Statistics: Z Scores, the Normal Curve, Sample versus Population, and Probability	p. 67
Z Scores	p. 68
The Normal Curve	p. 73
de Moivre, the Eccentric Stranger Who Invented the Normal Curve	p. 74
Sample and Population	p. 83
Surveys, Polls, and 1948's Costly "Free Sample"	p. 86
Probability	p. 88
Pascal Begins Probability Theory at the Gambling Table, Then Learns to Bet on God	p. 89
Controversies: Is the Normal Curve Really So Normal? and Using Nonrandom Samples	p. 93
Z Scores, Normal Curves, Samples and Populations, and Probabilities in Research Articles	p. 95
Advanced Topics: Probability Rules and Conditional Probabilities	p. 96
Summary	p. 97
Key Terms	p. 98
Example Worked-Out Problems	p. 99
Practice Problems	p. 102
Using SPSS	p. 105
Chapter Notes	p. 106
Introduction to Hypothesis Testing	p. 107
A Hypothesis-Testing Example	p. 108
The Core Logic of Hypothesis Testing	p. 109
The Hypothesis-Testing Process	p. 110
One-Tailed and Two-Tailed Hypothesis Tests	p. 119
Controversy: Should Significance Tests Be Banned?	p. 124
Jacob Cohen, the Ultimate New Yorker: Funny, Pushy, Brilliant, and Kind	p. 126
Hypothesis Tests in Research Articles	p. 127
Summary	p. 128
Key Terms	p. 129
Example Worked-Out Problems	p. 129
Practice Problems	p. 131
Chapter Notes	p. 136
Hypothesis Tests with Means of Samples	p. 137
The Distribution of Means	p. 138
Hypothesis Testing with a Distribution of Means: The Z Test	p. 146
More About Polls: Sampling Errors and Errors in Thinking About Samples	p. 147
Controversy: Marginal Significance	p. 153
Hypothesis Tests About Means of Samples (Z Tests) and Standard Errors in Research Articles	p. 154
Advanced Topic: Estimation, Standard Errors, and Confidence Intervals	p. 156
Advanced Topic Controversy: Confidence Intervals versus Significance Tests	p. 162
Advanced Topic: Confidence Intervals in Research Articles	p. 163
Summary	p. 163
Key Terms	p. 164
Example Worked-Out Problems	p. 164
Practice Problems	p. 167
Chapter Notes	p. 173
Making Sense of Statistical Significance: Decision Errors, Effect Size, and Statistical Power	p. 175
Decision Errors	p. 175
Effect Size	p. 179
Effect Sizes for Relaxation and Meditation: A Restful Meta-Analysis	p. 184
Statistical Power	p. 187
What Determines the Power of a Study?	p. 191
The Power of Typical Psychology Experiments	p. 199
The Role of Power When Planning a Study	p. 203
The Role of Power When Interpreting the Results of a Study	p. 205
Controversy: Statistical Significance versus Effect Size	p. 208
Decision Errors, Effect Size, and Power in Research Articles	p. 210
Advanced Topic: Figuring Statistical Power	p. 212
Summary	p. 214
Key Terms	p. 215
Example Worked-Out Problems	p. 215
Practice Problems	p. 217
Chapter Note	p. 221
Introduction to t Tests: Single Sample and Dependent Means	p. 222
The t Test for a Single Sample	p. 223
William S. Gosset, Alias "Student": Not a Mathematician, But a Practical Man	p. 224
The t Test for Dependent Means	p. 236
Assumptions of the t Test for a Single Sample and the t Test for Dependent Means	p. 247
Effect Size and Power for the t Test for Dependent Means	p. 247
Controversy: Advantages and Disadvantages of Repeated-Measures Designs	p. 250
The Power of Studies Using Difference Scores: How the Lanarkshire Milk Experiment Could Have Been Milked for More	p. 251
Single Sample t Tests and Dependent Means t Tests in Research Articles	p. 252
Summary	p. 253
Key Terms	p. 254
Example Worked-Out Problems	p. 254
Practice Problems	p. 258
Using SPSS	p. 265
Chapter Notes	p. 268
The t Test for Independent Means	p. 270
The Distribution of Differences Between Means	p. 271
Hypothesis Testing with a t Test for Independent Means	p. 278
Assumptions of the t Test for Independent Means	p. 286
Monte Carlo Methods: When Mathematics Becomes Just an Experiment, and Statistics Depend on a Game of Chance	p. 286
Effect Size and Power for the t Test for Independent Means	p. 288
Review and Comparison of the Three Kinds of t Tests	p. 290
Controversy: The Problem of Too Many t Tests	p. 291
The t Test for Independent Means in Research Articles	p. 292
Advanced Topic: Power for the t Test for Independent Means When Sample Sizes Are Not Equal	p. 293
Summary	p. 294
Key Terms	p. 295
Example Worked-Out Problems	p. 295
Practice Problems	p. 298
Using SPSS	p. 305
Chapter Notes	p. 309
Introduction to the Analysis of Variance	p. 310
Basic Logic of the Analysis of Variance	p. 311
Sir Ronald Fisher, Caustic Genius of Statistics	p. 317
Carrying Out an Analysis of Variance	p. 319
Hypothesis Testing with the Analysis of Variance	p. 327
Assumptions in the Analysis of Variance	p. 331
Planned Contrasts	p. 334
Post Hoc Comparisons	p. 337
Effect Size and Power for the Analysis of Variance	p. 339
Controversy: Omnibus Tests versus Planned Contrasts	p. 343
Analyses of Variance in Research Articles	p. 344
Advanced Topic: The Structural Model in the Analysis of Variance	p. 345
Principles of the Structural Model	p. 345
Summary	p. 351
Key Terms	p. 352
Example Worked-Out Problems	p. 353
Practice Problems	p. 357
Using SPSS	p. 364
Chapter Notes	p. 368
Factorial Analysis of Variance	p. 370
Basic Logic of Factorial Designs and Interaction Effects	p. 371
Recognizing and Interpreting Interaction Effects	p. 376
Basic Logic of the Two-Way Analysis of Variance	p. 386
Personality and Situational Influences on Behavior: An Interaction Effect	p. 387
Assumptions in the Factorial Analysis of Variance	p. 389
Extensions and Special Cases of the Analysis of Variance	p. 389
Controversy: Dichotomizing Numeric Variables	p. 391
Factorial Analysis of Variance in Research Articles	p. 393
Advanced Topic: Figuring a Two-Way Analysis of Variance	p. 395
Advanced Topic: Power and Effect Size in the Factorial Analysis of Variance	p. 406
Summary	p. 410
Key Terms	p. 411
Example Worked-Out Problems	p. 412
Practice Problems	p. 415
Using SPSS	p. 426
Chapter Notes	p. 431
Correlation	p. 432
Graphing Correlations: The Scatter Diagram	p. 434
Patterns of Correlation	p. 437
The Correlation Coefficient	p. 443
Galton: Gentleman Genius	p. 446
Significance of a Correlation Coefficient	p. 452
Correlation and Causality	p. 456
Issues in Interpreting the Correlation Coefficient	p. 458
Illusory Correlation: When You Know Perfectly Well That If It's Big, It's Fat-and You Are Perfectly Wrong	p. 460
Effect Size and Power for the Correlation Coefficient	p. 464
Controversy: What Is a Large Correlation?	p. 466
Correlation in Research Articles	p. 467
Summary	p. 469
Key Terms	p. 471
Example Worked-Out Problems	p. 471
Practice Problems	p. 474
Using SPSS	p. 482
Chapter Notes	p. 485
Prediction	p. 487
Predictor (X) and Criterion (Y) Variables	p. 488
The Linear Prediction Rule	p. 488
The Regression Line	p. 492
Finding the Best Linear Prediction Rule	p. 496
The Least Squared Error Principle	p. 498
Issues in Prediction	p. 503
Multiple Regression	p. 506
Limitations of Prediction	p. 508
Controversy: Unstandardized and Standardized Regression Coefficients; Comparing Predictors	p. 509
Clinical versus Statistical Prediction	p. 510
Prediction in Research Articles	p. 511
Advanced Topic: Error and Proportionate Reduction in Error	p. 514
Summary	p. 518
Key Terms	p. 519
Example Worked-Out Problems	p. 519
Practice Problems	p. 524
Using SPSS	p. 532
Chapter Notes	p. 535
Chi-Square Tests	p. 536
Karl Pearson, Inventor of Chi-Square and Center of Controversy	p. 537
The Chi-Square Statistic and the Chi-Square Test for Goodness of Fit	p. 538
The Chi-Square Test for Independence	p. 546
Assumptions for Chi-Square Tests	p. 554
Effect Size and Power for Chi-Square Tests for Independence	p. 554
Controversy: The Minimum Expected Frequency	p. 558
Chi-Square Tests in Research Articles	p. 559
Summary	p. 560
Key Terms	p. 561
Example Worked-Out Problems	p. 561
Practice Problems	p. 565
Using SPSS	p. 572
Chapter Notes	p. 576
Strategies When Population Distributions Are Not Normal: Data Transformations and Rank-Order Tests	p. 577
Assumptions in the Standard Hypothesis-Testing Procedures	p. 578
Data Transformations	p. 580
Rank-Order Tests	p. 585
Comparison of Methods	p. 589
Controversy: Computer-Intensive Methods	p. 591
Where Do Random Numbers Come From?	p. 594
Data Transformations and Rank-Order Tests in Research Articles	p. 595
Summary	p. 596
Key Terms	p. 597
Example Worked-Out Problems	p. 597
Practice Problems	p. 597
Using SPSS	p. 602
Chapter Notes	p. 609
The General Linear Model and Making Sense of Advanced Statistical Procedures in Research Articles	p. 611
The General Linear Model	p. 612
Two Women Make a Point About Gender and Statistics	p. 616
Partial Correlation	p. 617
Reliability	p. 618
Multilevel Modeling	p. 620
Factor Analysis	p. 622
Causal Modeling	p. 625
The Golden Age of Statistics: Four Guys Around London	p. 627
Procedures That Compare Groups	p. 634
Analysis of Covariance (ANCOVA)	p. 634
Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA)	p. 635
Overview of Statistical Techniques	p. 636
Controversy: Should Statistics Be Controversial?	p. 637
The Forced Partnership of Fisher and Pearson	p. 638
How to Read Results Using Unfamiliar Statistical Techniques	p. 639
Summary	p. 641
Key Terms	p. 642
Practice Problems	p. 642
Using SPSS	p. 654
Chapter Notes	p. 662
Tables	p. 664
Answers to Set I Practice Problems	p. 673
Glossary	p. 701
Glossary of Symbols	p. 708
References	p. 710
Index	p. 719
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