Statistics for Psychology,9780139140785

Statistics for Psychology

by ;
Edition: 2nd
ISBN13: 9780139140785
ISBN10: 0139140786
Format: Hardcover
Pub. Date: 1998-09-01
Publisher(s): Pearson College Div
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Summary

For the first course in Statistics, the Second Edition of Statistics for Psychology capitalizes on a successful approach which presents statistics through definitional formulas to emphasize conceptual understanding, not rote memorization. Presenting statistical methods as a living, growing field of research, the book highlights recent controversies and developments. Thoroughly revised with new content and many new examples, the text takes the student from basic procedures through analysis of variance (ANOVA), and in a unique concluding chapter introduces the general linear model to integrate all of the statistical procedures presented. Better than any other text, Statistics for Psychology not only teaches statistics, but prepares students to read and understand research articles.

Table of Contents

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)
Frequency Tables
2(1)
Box 1-1: Important Trivia for Poetic Statistics Students
3(2)
How to Make a Frequency Table
5(2)
Grouped Frequency Tables
7(4)
Histograms
11(1)
Box 1-2: Math Anxiety, Statistics Anxiety, and You: A Message for Those of You Who Are Truly Worried About This Course
12(3)
Frequency Polygons
15(3)
Shapes of Frequency Distributions
18(4)
Controversies and Limitations
22(2)
Box 1-3: Gender, Ethnicity, and Math Performance
24(1)
Frequency Tables, Histograms, and Frequency Polygons in Research Articles
25(2)
Summary
27(1)
Key Terms
28(1)
Practice Problems
28(2)
2 The Mean, Variance, Standard Deviation, and Z Scores
30(32)
The Mean
31(4)
Alternative Measures of Central Tendency
35(4)
The Variance and the Standard Deviation
39(8)
Z Scores
47(1)
Box 2-1: 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)
Summary
58(1)
Key Terms
59(1)
Practice Problems
59(1)
Chapter Appendix: Optional Computational Formulas for the Variance and the Standard Deviation
60(2)
3 Correlation
62(38)
Independent or Predictor Variables and Dependent Variables
64(1)
Graphing Correlations: The Scatter Diagram
65(3)
Patterns of Correlation
68(4)
Computing an Index of Degree of Linear Correlation: The Pearson Correlation Coefficient
72(2)
Box 3-1: 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 3-2: Illusory Correlation: When You Know Perfectly Well That If It's Big, It's Fat--and 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)
Summary
92(1)
Key Terms
93(1)
Practice Problems
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)
4 Prediction
100(34)
Terminology of Bivariate Prediction
101(1)
The Bivariate Prediction Model With Z Scores
102(2)
Bivariate Prediction Using Raw Scores
104(2)
The Regression Line
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 4-1: Clinical Versus Statistical Prediction
122(3)
Controversies and Limitations
125(1)
Prediction Models As Described in Research Articles
126(2)
Summary
128(1)
Key Terms
129(1)
Practice Problems
129(5)
5 Some Key Ingredients for Inferential Statistics: The Normal Curve, Probability, and Population Versus Sample
134(26)
The Normal Distribution
135(2)
Box 5-1: DeMoivre, the Eccentric Stranger Who Invented the Normal Curve
137(6)
Probability
143(2)
Box 5-2: Pascal Begins Probability Theory at the Gambling Table, Then Learns to Bet on God
145(2)
Sample and Population
147(3)
Box 5-3: 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)
Summary
156(1)
Key Terms
157(1)
Practice Problems
157(1)
Chapter Appendix: Probability Rules and Conditional Probabilities
158(2)
6 Introduction to Hypothesis Testing
160(24)
A Hypothesis-Testing Example
162(1)
The Core Logic of Hypothesis Testing
163(1)
The Hypothesis-Testing Process
163(7)
Box 6-1: To Be or Not to Be--But Can Not Being Be? The Problem of Whether and When to Accept the Null Hypothesis
170(2)
One-Tailed and Two-Tailed Hypothesis Tests
172(5)
Controversies and Limitations
177(2)
Hypothesis Tests As Reported in Research Articles
179(1)
Summary
180(1)
Key Terms
181(1)
Practice Problems
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 7-1: 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)
Summary
209(1)
Key Terms
210(1)
Practice Problems
210(2)
8 Statistical Power and Effect Size
212(40)
What Is Statistical Power?
213(3)
Alpha, Beta, and Power
216(3)
Calculating Statistical Power
219(4)
Power Tables
223(1)
What Determines the Power of a Study?
223(1)
Effect Size
224(7)
Sample Size
231(3)
Box 8-1: 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)
Meta-Analysis
242(1)
Box 8-2: Effect Sizes for Relaxation and Meditation: A Restful Meta-Analysis
243(1)
Controversies and Limitations: Statistical Significance Controversy Continued--Effect Size Versus Statistical Significance
244(2)
Power and Effect Size As Described in Research Articles
246(2)
Summary
248(1)
Key Terms
249(1)
Practice Problems
249(3)
9 The t Test for Dependent Means
252(36)
Box 9-1: 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 9-2: 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)
Summary
283(1)
Key Terms
284(1)
Practice Problems
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 10-1: 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)
Summary
313(1)
Key Terms
314(1)
Practice Problems
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 11-1: 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)
Summary
344(1)
Key Terms
345(1)
Practice Problems
345(3)
12 The Structural Model in the Analysis of Variance
348(28)
Principles of the Structural Model
350(3)
Box 12-1: 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 Unequal-Sized Groups
356(6)
Summary of Procedures for Computing an Analysis of Variance Using the Structural Model
362(1)
Multiple Comparisons
362(3)
Assumptions in the Analysis of Variance With Unequal Sample Sizes
365(1)
Effect Size and Power
366(2)
Controversies, Limitations, and Recent Developments
368(1)
Structural Model Analysis of Variance and Multiple Comparisons As Described in Research Articles
369(2)
Summary
371(1)
Key Terms
371(1)
Practice Problems
372(3)
Chapter Appendix I: Optional Computational Formulas for the Sums of Squares in a One-Way 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 Two-Way Analysis of Variance
389(2)
Box 13-1: 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)
Summary
416(1)
Key Terms
417(1)
Practice Problems
417(4)
Chapter Appendix I: Optional Computational Formulas for the Two-Way Analysis of Variance
421(2)
Chapter Appendix II: One-Way Repeated-Measures Analysis of Variance
423(3)
14 Chi-Square Tests
426(32)
The Chi-Square Statistic and the Chi-Square Test for Goodness of Fit
428(1)
Box 14-1: Karl Pearson, Inventor of Chi-Square and Center of Controversy
429(8)
Chi-Square Test for Independence
437(10)
Assumptions for Chi-Square Tests
447(1)
Effect Size and Power for Chi-Square Tests for Independence
447(4)
Controversies and Limitations
451(1)
Chi-Square Tests As Reported in Research Articles
452(1)
Summary
453(1)
Key Terms
454(1)
Practice Problems
454(4)
15 Strategies When Population Distributions Are Not Normal: Data Transformations, Rank-Order Tests, and Computer-Intensive Methods
458(30)
Assumptions in the Standard Hypothesis-Testing Procedures
459(2)
Data Transformations
461(9)
Rank-Order Tests
470(4)
Computer-Intensive Methods
474(5)
Comparison of Methods
479(1)
Box 15-1: Where Do Random Numbers Come From?
480(2)
Controversies
482(1)
Procedures Used When Populations Appear Nonnormal As Described in Research Articles
482(2)
Summary
484(1)
Key Terms
485(1)
Practice Problems
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 16-1: 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 16-2: 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)
Summary
514(1)
Key Terms
515(1)
Practice Problems
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)
Partial Correlation
524(2)
Reliability
526(2)
Factor Analysis
528(2)
Causal Modeling
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 17-1: The Forced Marriage of Fisher and Neyman-Pearson
538(1)
Controversy: Should Statistics Be Controversial?
538(2)
How to Read Results in Research Articles Involving Unfamiliar Statistical Techniques
540(2)
Summary
542(1)
Key Terms
543(1)
Practice Problems
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 B-1 Normal Curve Areas: Percentage of the Normal Curve Between the Mean and the Z Scores Shown 563(3)
Table B-2 Cutoff Scores for the t Distribution 566(1)
Table B-3 Cutoff Scores for the F Distribution 567(3)
Table B-4 Cutoff Scores for the Chi-Square Distribution 570(1)
Table B-5 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

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