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Statistics for Psychology

by ;
Edition:
3rd
ISBN13:

9780130358103

ISBN10:
013035810X
Format:
Hardcover
Pub. Date:
1/1/2003
Publisher(s):
Prentice Hall
List Price: $112.60
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Summary

A book that focuses on the logic behind the concepts of statistics for psychology, using definitional formulas rather than emphasizing rote memorization. Clearly written, each procedure is conveyed both numerically and verbally, with many visual examples to illustrate the text. It takes the reader from basic procedures through analysis of variance (ANOVA), and not only teaches statistics, but also prepares the user to read and understand research articles as well.This book is an introduction to statistics for psychology, covering such topics as order in a group of numbers; mean, variance, standard deviation, and Z scores; correlation; prediction; the normal curve, probability, and population versus sample; hypothesis testing; the t test; analysis of variance; chi-square tests; the general linear model; and making sense of advanced statistical procedures in research articles.For statisticians, psychologists and those involved in psychological research in the behavioral and social sciences.

Table of Contents

Preface to the Instructor xi
Introduction to the Student xix
Displaying the Order in a Group Of Numbers
1(34)
The Two Branches of Statistical Methods
2(1)
Some Basic Concepts
2(4)
Box 1-1: Important Trivia for Poetic Statistics Students
5(1)
Frequency Tables
6(6)
Box 1-2: Math Anxiety, Statistics Anxiety, and Yon: A Message, for-Those of you Who Are Truly Worried about this Course
10(2)
Frequency Graphs
12(6)
Shapes of Frequency Distributions
18(4)
Controversy: Misleading Graphs
22(4)
Box 1-3: Gender, Ethnicity, and Math Performance
24(2)
Frequency Tables, Histograms, and Frequency Polygons in Research Articles
26(1)
Summary
27(1)
Key Terms
28(1)
Example Worked-Out Computational Problems
28(1)
Practice Problems
29(6)
The Mean, Variance, Standard Deviation, and Z Scores
35(34)
The Mean
35(4)
Other Measures of Central Tendency
39(4)
The Variance and the Standard Deviation
43(8)
Z Scores
51(7)
Box 2-1: The Sheer Joy (Yes, Joy) of Statistical Analysis
52(6)
Controversy: The Tyranny of the Mean
58(3)
The Mean and Standard Deviation in Research Articles
61(1)
Summary
62(1)
Key Terms
63(1)
Example Worked-Out Computational Problems
63(2)
Practice Problems
65(4)
Correlation
69(44)
Graphing Correlations: The Scatter Diagram
70(5)
Patterns of Correlation
75(5)
The Degree of Linear Correlation: The Correlation Coefficient
80(8)
Box 3-1: Galton: Gentleman Genius
82(6)
Correlation and Causality
88(4)
Box 3-2: Illusory Correlation: When You Know Perfectly Well that If It's Big, It's Fat-and you are Perfectly Wrong
91(1)
Issues in Interpreting the Correlation Coefficient
92(3)
Controversy: What Is a Large Correlation?
95(1)
Correlation in Research Articles
96(2)
Summary
98(1)
Key Terms
99(1)
Example Worked-Out Computational Problems
99(2)
Practice Problems
101(8)
Chapter Appendix: Hypothesis Tests and Power for the Correlation Coefficient
109(4)
Prediction
113(44)
Predictor and Criterion Variables
114(1)
Prediction Using Z Scores
114(3)
Raw-Score Prediction Using the Z-Score Prediction Model
117(2)
Raw-Score Prediction Using the Direct Raw-Score Prediction Model
119(6)
The Regression Line
125(3)
Error and Proportionate Reduction in Error
128(8)
Multiple Regression
136(2)
Limitations of Regression
138(1)
Controversy: Comparing Predictors
139(1)
Box 4-1: Clinical Versus Statistical Prediction
139(1)
Prediction in Research Articles
140(3)
Summary
143(1)
Key Terms
144(1)
Example Worked-Out Computational Problems
144(3)
Practice Problems
147(10)
Some Key Ingredients for Inferential Statistics: The Normal Curve, Probability, and Population Versus Sample
157(32)
The Normal Distribution
158(11)
Box 5-1: De Moivre, the Eccentric Stranger Who Invented the Normal Curve
159(10)
Probability
169(4)
Box 5-2: Pascal Begins Probability Theory at the Gambling Table, then Learns to Bet on God
170(3)
Sample and Population
173(4)
Box 5-3: Surveys, Polls, and 1948's Costly ``Free Sample''
176(1)
Controversies: Is the Normal Curve Really Normal?, What Does Probability Really Mean?, and Using Nonrandom Samples
177(3)
Normal Curves, Probabilities, Samples, and Populations in Research Articles
180(1)
Summary
181(1)
Key Terms
182(1)
Example Worked-Out Computational Problems
182(2)
Practice Problems
184(3)
Chapter Appendix: Probability Rules and Conditional Probabilities
187(2)
Introduction to Hypothesis Testing
189(28)
A Hypothesis-Testing Example
190(1)
The Core Logic of Hypothesis Testing
191(1)
The Hypothesis-Testing Process
191(8)
One-Tailed and Two-Tailed Hypothesis Tests
199(5)
Controversy: Should Significance Tests Be Banned?
204(3)
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
206(1)
Hypothesis Tests in Research Articles
207(1)
Summary
208(1)
Key Terms
209(1)
Example Worked-Out Computational Problems
209(1)
Practice Problems
210(7)
Hypothesis Tests With Means of Samples
217(36)
The Distribution of Means
217(9)
Hypothesis Testing with a Distribution of Means
226(7)
Box 7-1: More about Polls: Sampling Errors and Errors in Thinking about Samples
227(6)
Estimation, Standard Errors, and Confidence Intervals
233(5)
Controversy: Confidence Intervals or Significance Tests?
238(2)
Hypothesis Tests about Means of Samples, Standard Errors, and Confidence Intervals in Research Articles
240(2)
Summary
242(1)
Key Terms
243(1)
Example Worked-Out Computational Problems
244(1)
Practice Problems
245(8)
Making Sense of Statistical Significance: Effect Size, Decision Error, and Statistical Power
253(46)
Effect Size
254(7)
Box 8-1: Effect Sizes for Relaxation and Meditation: A Restful Meta-Analysis
259(2)
Decision Errors
261(3)
Statistical Power
264(7)
What Determines the Power of a Study?
271(12)
Box 8-2: The Power of Typical Psychology Experiments
279(4)
The Role of Power When Planning a Study
283(1)
The Importance of Power When Evaluating the Results of a Study
284(3)
Controversy: Statistical Significance Controversy Continued-Effect Size Versus Statistical Significance
287(2)
Effect Size, Decision Errors, and Power in Research Articles
289(2)
Summary
291(1)
Key Terms
292(1)
Example Worked-Out Computational Problems
292(1)
Practice Problems
293(6)
Introduction to the t Test
299(42)
The t Test For a Single Sample
300(12)
Box 9-1: William S. Gosset, Alias ``Student'': Not a Mathematician, but a Practical Man
301(11)
The t Test for Dependent Means
312(10)
Assumptions
322(1)
Effect Size and Power for the t Test for Dependent Means
323(3)
Controversy: Advantages and Disadvantages of Repeated-Measures Designs
326(1)
Box 9-2: The Power of Studies Using Difference Scores: How the Lanarkshire Milk Experiment Could Have Been Milked for More
327(1)
t Tests in Research Articles
327(2)
Summary
329(1)
Key Terms
329(1)
Example Worked-Out Computational Problems
329(2)
Practice Problems
331(10)
The t Test For Independent Means
341(36)
The Distribution of Differences between Means
342(7)
Hypothesis Testing With a t Test for Independent Means
349(7)
Assumptions of the t Test for Independent Means
356(3)
Box 10-1: Monte Carlo Methods: When Mathematics Becomes Just an Experiment and Statistics Depend on a Game of Chance
357(2)
Effect Size and Power for the t Test for Independent Means
359(3)
Controversy: The Problem of Too Many t Tests
362(2)
The t Test for Independent Means in Research Articles
364(2)
Summary
366(1)
Key Terms
366(1)
Example Worked-Out Computational Problems
366(3)
Practice Problems
369(8)
Introduction to the Analysis Of Variance
377(40)
Basic Logic of the Analysis of Variance
378(8)
Box 11-1: Sir Ronald Fisher, Caustic Genius of Statistics
384(2)
Carrying Out an Analysis of Variance
386(8)
Hypothesis Testing with the Analysis of Variance
394(4)
Assumptions in the Analysis of Variance
398(2)
Planned Comparisons
400(4)
Controversy: Omnibus Tests versus Planned Comparisons
404(1)
Analyses of Variance in Research Articles
405(1)
Summary
406(1)
Key Terms
407(1)
Example Worked-Out Computational Problems
407(2)
Practice Problems
409(8)
The Structural Model in the Analysis Of Variance
417(34)
Principles of the Structural Model
418(5)
Box 12-1: Analysis of Variance as a Way of Thinking About the World
421(2)
Using the Structural Model to Figure an Analysis of Variance
423(5)
Assumptions in the Analysis of Variance with Unequal Sample Sizes
428(3)
Post-Hoc Comparisons
431(2)
Effect Size and Power for the Analysis of Variance
433(4)
Controversy: The Independence Assumption and the Unit of Analysis Question
437(2)
Structural Model Analysis of Variance and Post-Hoc Comparisons in Research Articles
439(1)
Summary
439(2)
Key Terms
441(1)
Example Worked-Out Computational Problems
441(3)
Practice Problems
444(7)
Factorial Analysis of Variance
451(56)
Basic Logic of Factorial Designs and Interaction Effects
452(4)
Recognizing and Interpreting Interaction Effects
456(7)
Basic Logic of the Two-Way Analysis of Variance
463(4)
Box 13-1: Personality and Situational Influences on Behavior: An Interaction Effect
466(1)
Figuring a Two-Way Analysis of Variance
467(13)
Power and Effect Size in the Factorial Analysis of Variance
480(3)
Extensions and Special Cases of the Factorial Analysis of Variance
483(2)
Controversy: Unequal Cell Sizes and Dichotomizing Numeric Variables
485(2)
Factorial Analysis of Variance Results in Research Articles
487(2)
Summary
489(1)
Key Terms
490(1)
Example Worked-Out Computational Problems
490(3)
Practice Problems
493(14)
Chi-Square Tests
507(36)
The Chi-Square Statistic and the Chi-Square Test for Goodness of Fit
509(8)
Box 14-1: Karl Pearson, Inventor of Chi-Square and Center of Controversy
510(7)
The Chi-Square Test for Independence
517(9)
Assumptions for Chi-Square Tests
526(1)
Effect Size and Power for Chi-Square Tests for Independence
526(4)
Controversy: The Minimum Expected Frequency
530(1)
Chi-Square Tests in Research Articles
531(1)
Summary
532(1)
Key Terms
532(1)
Example Worked-Out Computational Problems
533(3)
Practice Problems
536(7)
Strategies When Population Distributions Are Not Normal: Data Transformations and Rank-Order Tests
543(26)
Assumptions in the Standard Hypothesis-Testing Procedures
544(1)
Data Transformations
545(5)
Rank-Order Tests
550(5)
Comparison of Methods
555(1)
Controversy: Computer Intensive Methods
556(3)
Data Transformations and Rank-Order Tests in Research Articles
559(2)
Box 15-1: Where Do Random Numbers Come Front?
560(1)
Summary
561(1)
Key Terms
561(1)
Example Worked-Out Computational Problems
562(1)
Practice Problems
562(7)
Integrating What You Have Learned: The General Linear Model
569(30)
The Relationships Between Major Statistical Methods
569(1)
Review of the Principles of Multiple Regression
570(1)
The General Linear Model
571(2)
The General Linear Model and Multiple Regression
573(1)
Bivariate Regression and Correlation as Special Cases of Multiple Regression
573(1)
The t Test as a Special Case of the Analysis of Variance
574(4)
Box 76-1: The Golden Age of Statistics: Four Guys around London
575(3)
The t Test as a Special Case of the Significance Test for the Correlation Coefficient
578(5)
The Analysis of Variance as a Special Case of the Significance Test of the Multiple Regression
583(5)
Choice of Statistical Tests
588(3)
Box 16-2: Two Women Make a Point about Gender and Statistics
589(2)
Controversy: Whay is Causality?
591(1)
Summary
592(1)
Key Terms
593(1)
Practice Problems
594(5)
Making Sense of Advanced Statistical Procedures in Research Articles
599(40)
Brief Review of Multiple Regression
600(1)
Hierarchial and Stepwise Multiple Regression
600(5)
Partial Correlation
605(1)
Reliability
606(2)
Factor Analysis
608(2)
Causal Modeling
610(5)
Procedures that Compare Groups and Independent and Dependent Variables
615(1)
Analysis of Covariance (ANCOVA)
616(1)
Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA)
617(1)
Overview of Statistical Techniques
618(1)
Controversy: Should Statistics Be Controversial?
619(3)
Box 17-1: The Forced Partnership of Fisher and Pearson
620(2)
How to Read Results Using Unfamiliar Statistical Techniques
622(1)
Summary
623(1)
Key Terms
624(1)
Practice Problems
624(15)
Appendix A Tables 639(8)
Table A-1 Normal Curve Areas: Percentage of the Normal Curve between the Mean and the Z Scores Shown
639(3)
Table A-2 Cutoff Scores for the t Distribution
642(1)
Table A-3 Cutoff Scores for the F Distribution
643(3)
Table A-4 Cutoff Scores for the Chi-Square Distribution
646(1)
Table A-5 Index to Power Tables and Tables Giving Number of Participants Needed for 80% Power
646(1)
Answers To Set I Practice Problems 647(30)
Glossary 677(8)
Glossary of Symbols 685(2)
References 687(10)
Index 697

Excerpts

The heart of this book was written over a summer in a small apartment near the Place Saint Ferdinand, having been outlined in nearby cafes and on walks in the Bois de Boulogne. It is based on our 35 years of experience teaching, researching, and writing. We believe that the book we wrote is as different from the conventional lot of statistics texts as Paris is from Calcutta, yet still comfortable and stimulating to the long-suffering community of statistics instructors. Our approach was developed over three decades of successful teaching--successful not only in the sense that students have consistently rated the course (a statistics course, remember) as a highlight of their major, but also in the sense that students come back to us later saying, "I was light-years ahead of my fellow graduate students because of your course," or "Even though I don't do research, your course has really helped me read the journals in my field." The response to the first and second edition has been overwhelming. We have received hundreds of thank-you e-mails and letters from instructors (and from students themselves!) from all over the English-speaking world. Of course, we were also delighted by the enthusiastic review inContemporary Psychology(Bourgeois, 1997). In this third edition we have tried to maintain those things that have been especially appreciated, while reworking the book to take into account the feedback we have received, our own experiences, and advances and changes in the field. We have also added new pedagogical features to make the book even more accessible for students. However, before turning to the third edition, we want to reiterate what we said in the first edition about how this book from the beginning has been quite different from other statistics texts. A BRIEF HISTORY OF THE STATISTICS TEXT GENRE In the 1950s and 1960s statistics texts were dry, daunting, mathematical tomes that quickly left most students behind. In the 1970s, there was a revolution--in swept the intuitive approach, with much less emphasis on derivations, proofs, and mathematical foundations. The approach worked. Students became less afraid of statistics courses and found the material more accessible, even if not quite clear. The intuitive trend continued in the 1980s, adding in the 1990s some nicely straightforward writing. A few texts have now also begun to encourage students to use the computer to do statistical analyses. However, discussions of intuitive understandings are becoming briefer and briefer. The standard is a cursory overview of the key idea and sometimes the associated definitional formula for each technique. Then come the procedures and examples for actually doing the computation, using another "computational" formula. Even with all this streamlining, or perhaps because of it, at the end of the course most students cannot give a clear explanation of the logic behind the techniques they have learned. A few months later they can rarely carry out the procedures either. Most important, the three main purposes of the introductory statistics course ark, not accomplished: Students are not able to make sense of the results of psychology research articles, they are poorly prepared for further courses in statistics (where instructors must inevitably spend half the semester reteaching the introductory course), and the exposure to deep thinking that is supposed to justify the course's meeting general education requirements in the quantitative area has not occurred. WHAT WE HAVE DONE DIFFERENTLY We continue to do what the best of the newer books are already doing well: emphasizing the intuitive, de-emphasizing the mathematical, and explaining everything in direct, simple language. But what we have done differs from these other books in 11 key respects. 1.The definitional formulas are brought to center stagebecause they provide a con


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