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Statistics for Psychology

by ; ;
Edition:
5th
ISBN13:

9780136010579

ISBN10:
0136010571
Format:
Hardcover
Pub. Date:
3/9/2008
Publisher(s):
Pearson
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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 Instructorp. xi
Introduction to the Studentp. xvi
Displaying the Order in a Group of Numbers Using Tables and Graphsp. 1
The Two Branches of Statistical Methodsp. 2
Some Basic Conceptsp. 3
Important Trivia for Poetic Statistics Studentsp. 6
Frequency Tablesp. 7
Histogramsp. 10
Math Anxiety, Statistics Anxiety, and You: A Message for Those of You Who Are Truly Worried About This Coursep. 12
Shapes of Frequency Distributionsp. 15
Controversy: Misleading Graphsp. 19
Frequency Tables and Histograms in Research Articlesp. 21
Summaryp. 23
Key Termsp. 24
Example Worked-Out Problemsp. 24
Practice Problemsp. 25
Using SPSSp. 29
Chapter Notep. 32
Central Tendency and Variabilityp. 33
Central Tendencyp. 34
Variabilityp. 43
The Sheer Joy (Yes, Joy) of Statistical Analysisp. 51
Controversy: The Tyranny of the Meanp. 52
Gender, Ethnicity, and Math Performancep. 53
Central Tendency and Variability in Research Articlesp. 55
Summaryp. 57
Key Termsp. 57
Example Worked-Out Problemsp. 57
Practice Problemsp. 59
Using SPSSp. 62
Chapter Notesp. 65
Some Key Ingredients for Inferential Statistics: Z Scores, the Normal Curve, Sample versus Population, and Probabilityp. 67
Z Scoresp. 68
The Normal Curvep. 73
de Moivre, the Eccentric Stranger Who Invented the Normal Curvep. 74
Sample and Populationp. 83
Surveys, Polls, and 1948's Costly "Free Sample"p. 86
Probabilityp. 88
Pascal Begins Probability Theory at the Gambling Table, Then Learns to Bet on Godp. 89
Controversies: Is the Normal Curve Really So Normal? and Using Nonrandom Samplesp. 93
Z Scores, Normal Curves, Samples and Populations, and Probabilities in Research Articlesp. 95
Advanced Topics: Probability Rules and Conditional Probabilitiesp. 96
Summaryp. 97
Key Termsp. 98
Example Worked-Out Problemsp. 99
Practice Problemsp. 102
Using SPSSp. 105
Chapter Notesp. 106
Introduction to Hypothesis Testingp. 107
A Hypothesis-Testing Examplep. 108
The Core Logic of Hypothesis Testingp. 109
The Hypothesis-Testing Processp. 110
One-Tailed and Two-Tailed Hypothesis Testsp. 119
Controversy: Should Significance Tests Be Banned?p. 124
Jacob Cohen, the Ultimate New Yorker: Funny, Pushy, Brilliant, and Kindp. 126
Hypothesis Tests in Research Articlesp. 127
Summaryp. 128
Key Termsp. 129
Example Worked-Out Problemsp. 129
Practice Problemsp. 131
Chapter Notesp. 136
Hypothesis Tests with Means of Samplesp. 137
The Distribution of Meansp. 138
Hypothesis Testing with a Distribution of Means: The Z Testp. 146
More About Polls: Sampling Errors and Errors in Thinking About Samplesp. 147
Controversy: Marginal Significancep. 153
Hypothesis Tests About Means of Samples (Z Tests) and Standard Errors in Research Articlesp. 154
Advanced Topic: Estimation, Standard Errors, and Confidence Intervalsp. 156
Advanced Topic Controversy: Confidence Intervals versus Significance Testsp. 162
Advanced Topic: Confidence Intervals in Research Articlesp. 163
Summaryp. 163
Key Termsp. 164
Example Worked-Out Problemsp. 164
Practice Problemsp. 167
Chapter Notesp. 173
Making Sense of Statistical Significance: Decision Errors, Effect Size, and Statistical Powerp. 175
Decision Errorsp. 175
Effect Sizep. 179
Effect Sizes for Relaxation and Meditation: A Restful Meta-Analysisp. 184
Statistical Powerp. 187
What Determines the Power of a Study?p. 191
The Power of Typical Psychology Experimentsp. 199
The Role of Power When Planning a Studyp. 203
The Role of Power When Interpreting the Results of a Studyp. 205
Controversy: Statistical Significance versus Effect Sizep. 208
Decision Errors, Effect Size, and Power in Research Articlesp. 210
Advanced Topic: Figuring Statistical Powerp. 212
Summaryp. 214
Key Termsp. 215
Example Worked-Out Problemsp. 215
Practice Problemsp. 217
Chapter Notep. 221
Introduction to t Tests: Single Sample and Dependent Meansp. 222
The t Test for a Single Samplep. 223
William S. Gosset, Alias "Student": Not a Mathematician, But a Practical Manp. 224
The t Test for Dependent Meansp. 236
Assumptions of the t Test for a Single Sample and the t Test for Dependent Meansp. 247
Effect Size and Power for the t Test for Dependent Meansp. 247
Controversy: Advantages and Disadvantages of Repeated-Measures Designsp. 250
The Power of Studies Using Difference Scores: How the Lanarkshire Milk Experiment Could Have Been Milked for Morep. 251
Single Sample t Tests and Dependent Means t Tests in Research Articlesp. 252
Summaryp. 253
Key Termsp. 254
Example Worked-Out Problemsp. 254
Practice Problemsp. 258
Using SPSSp. 265
Chapter Notesp. 268
The t Test for Independent Meansp. 270
The Distribution of Differences Between Meansp. 271
Hypothesis Testing with a t Test for Independent Meansp. 278
Assumptions of the t Test for Independent Meansp. 286
Monte Carlo Methods: When Mathematics Becomes Just an Experiment, and Statistics Depend on a Game of Chancep. 286
Effect Size and Power for the t Test for Independent Meansp. 288
Review and Comparison of the Three Kinds of t Testsp. 290
Controversy: The Problem of Too Many t Testsp. 291
The t Test for Independent Means in Research Articlesp. 292
Advanced Topic: Power for the t Test for Independent Means When Sample Sizes Are Not Equalp. 293
Summaryp. 294
Key Termsp. 295
Example Worked-Out Problemsp. 295
Practice Problemsp. 298
Using SPSSp. 305
Chapter Notesp. 309
Introduction to the Analysis of Variancep. 310
Basic Logic of the Analysis of Variancep. 311
Sir Ronald Fisher, Caustic Genius of Statisticsp. 317
Carrying Out an Analysis of Variancep. 319
Hypothesis Testing with the Analysis of Variancep. 327
Assumptions in the Analysis of Variancep. 331
Planned Contrastsp. 334
Post Hoc Comparisonsp. 337
Effect Size and Power for the Analysis of Variancep. 339
Controversy: Omnibus Tests versus Planned Contrastsp. 343
Analyses of Variance in Research Articlesp. 344
Advanced Topic: The Structural Model in the Analysis of Variancep. 345
Principles of the Structural Modelp. 345
Summaryp. 351
Key Termsp. 352
Example Worked-Out Problemsp. 353
Practice Problemsp. 357
Using SPSSp. 364
Chapter Notesp. 368
Factorial Analysis of Variancep. 370
Basic Logic of Factorial Designs and Interaction Effectsp. 371
Recognizing and Interpreting Interaction Effectsp. 376
Basic Logic of the Two-Way Analysis of Variancep. 386
Personality and Situational Influences on Behavior: An Interaction Effectp. 387
Assumptions in the Factorial Analysis of Variancep. 389
Extensions and Special Cases of the Analysis of Variancep. 389
Controversy: Dichotomizing Numeric Variablesp. 391
Factorial Analysis of Variance in Research Articlesp. 393
Advanced Topic: Figuring a Two-Way Analysis of Variancep. 395
Advanced Topic: Power and Effect Size in the Factorial Analysis of Variancep. 406
Summaryp. 410
Key Termsp. 411
Example Worked-Out Problemsp. 412
Practice Problemsp. 415
Using SPSSp. 426
Chapter Notesp. 431
Correlationp. 432
Graphing Correlations: The Scatter Diagramp. 434
Patterns of Correlationp. 437
The Correlation Coefficientp. 443
Galton: Gentleman Geniusp. 446
Significance of a Correlation Coefficientp. 452
Correlation and Causalityp. 456
Issues in Interpreting the Correlation Coefficientp. 458
Illusory Correlation: When You Know Perfectly Well That If It's Big, It's Fat-and You Are Perfectly Wrongp. 460
Effect Size and Power for the Correlation Coefficientp. 464
Controversy: What Is a Large Correlation?p. 466
Correlation in Research Articlesp. 467
Summaryp. 469
Key Termsp. 471
Example Worked-Out Problemsp. 471
Practice Problemsp. 474
Using SPSSp. 482
Chapter Notesp. 485
Predictionp. 487
Predictor (X) and Criterion (Y) Variablesp. 488
The Linear Prediction Rulep. 488
The Regression Linep. 492
Finding the Best Linear Prediction Rulep. 496
The Least Squared Error Principlep. 498
Issues in Predictionp. 503
Multiple Regressionp. 506
Limitations of Predictionp. 508
Controversy: Unstandardized and Standardized Regression Coefficients; Comparing Predictorsp. 509
Clinical versus Statistical Predictionp. 510
Prediction in Research Articlesp. 511
Advanced Topic: Error and Proportionate Reduction in Errorp. 514
Summaryp. 518
Key Termsp. 519
Example Worked-Out Problemsp. 519
Practice Problemsp. 524
Using SPSSp. 532
Chapter Notesp. 535
Chi-Square Testsp. 536
Karl Pearson, Inventor of Chi-Square and Center of Controversyp. 537
The Chi-Square Statistic and the Chi-Square Test for Goodness of Fitp. 538
The Chi-Square Test for Independencep. 546
Assumptions for Chi-Square Testsp. 554
Effect Size and Power for Chi-Square Tests for Independencep. 554
Controversy: The Minimum Expected Frequencyp. 558
Chi-Square Tests in Research Articlesp. 559
Summaryp. 560
Key Termsp. 561
Example Worked-Out Problemsp. 561
Practice Problemsp. 565
Using SPSSp. 572
Chapter Notesp. 576
Strategies When Population Distributions Are Not Normal: Data Transformations and Rank-Order Testsp. 577
Assumptions in the Standard Hypothesis-Testing Proceduresp. 578
Data Transformationsp. 580
Rank-Order Testsp. 585
Comparison of Methodsp. 589
Controversy: Computer-Intensive Methodsp. 591
Where Do Random Numbers Come From?p. 594
Data Transformations and Rank-Order Tests in Research Articlesp. 595
Summaryp. 596
Key Termsp. 597
Example Worked-Out Problemsp. 597
Practice Problemsp. 597
Using SPSSp. 602
Chapter Notesp. 609
The General Linear Model and Making Sense of Advanced Statistical Procedures in Research Articlesp. 611
The General Linear Modelp. 612
Two Women Make a Point About Gender and Statisticsp. 616
Partial Correlationp. 617
Reliabilityp. 618
Multilevel Modelingp. 620
Factor Analysisp. 622
Causal Modelingp. 625
The Golden Age of Statistics: Four Guys Around Londonp. 627
Procedures That Compare Groupsp. 634
Analysis of Covariance (ANCOVA)p. 634
Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA)p. 635
Overview of Statistical Techniquesp. 636
Controversy: Should Statistics Be Controversial?p. 637
The Forced Partnership of Fisher and Pearsonp. 638
How to Read Results Using Unfamiliar Statistical Techniquesp. 639
Summaryp. 641
Key Termsp. 642
Practice Problemsp. 642
Using SPSSp. 654
Chapter Notesp. 662
Tablesp. 664
Answers to Set I Practice Problemsp. 673
Glossaryp. 701
Glossary of Symbolsp. 708
Referencesp. 710
Indexp. 719
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