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Statistics for the Behavioral and Social Sciences,9780131505087
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Statistics for the Behavioral and Social Sciences

by ; ;
Edition:
4th
ISBN13:

9780131505087

ISBN10:
0131505084
Format:
Paperback
Pub. Date:
1/1/2008
Publisher(s):
Prentice Hall

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Summary

This unique book capitalizes on a successful approach of using definitional formulas to emphasize concepts of statistics, rather than rote memorization. This conceptual approach constantly reminds readers of the logic behind what they are learning. Procedures are taught verbally, numerically, and visually, which appeals to a variety of users with different learning styles.Focusing on understanding, the book emphasizes the intuitive, de-emphasizes the mathematical, and explains everything in clear, simple languagewith a large number of practice problems.For those trying to master statistics, as well as reading and understanding research articles.

Table of Contents

Preface to the Instructor xi
Introduction to the Student xix
Displaying the Order in a Group of Numbers
1(28)
The Two Branches of Statistical Methods
2(1)
Some Basic Concepts
2(1)
Kinds of Variables
3(1)
Frequency Tables
4(4)
Frequency Graphs
8(5)
Shapes of Frequency Distributions
13(7)
Math Anxiety, Statistics Anxiety, and You: A Message for Those of You Who Are Truly Worried About This Course
14(4)
Gender, Ethnicity, and Math Performance
18(2)
Frequency Tables, Histograms, and Frequency Polygons in Research Articles
20(1)
Summary
21(1)
Key Terms
22(1)
Example Worked-Out Problems
22(1)
Practice Problems
23(6)
The Mean, Variance, Standard Deviation, and Z Scores
29(28)
The Mean
30(6)
The Variance and the Standard Deviation
36(6)
Z Scores
42(7)
The Psychology of Statistics and the Tyranny of the Mean
43(6)
Mean, Variance, Standard Deviation, and Z Scores in Research Articles
49(2)
Summary
51(1)
Key Terms
51(1)
Example Worked-Out Problems
51(2)
Practice Problems
53(4)
Correlation and Prediction
57(50)
Graphing Correlations: The Scatter Diagram
59(1)
Patterns of Correlation
60(7)
The Degree of Linear Correlation: The Correlation Coefficient
67(8)
Galton: Gentleman Genius
71(4)
Issues in Interpreting the Correlation Coefficient
75(3)
Prediction
78(5)
Multiple Regression and Multiple Correlation
83(3)
The Correlation Coefficient and the Proportion of Variance Accounted For
86(1)
Correlation and Prediction in Research Articles
86(3)
Summary
89(2)
Key Terms
91(1)
Exampled Worked-Out Problems
91(3)
Practice Problems
94(10)
Chapter Appendix: Hypothesis Tests and Power for the Correlation Coefficient
104(3)
Some Key Ingredients for Inferential Statistics: The Normal Curve, Probability, and Population Versus Sample
107(25)
The Normal Distribution
107(10)
Probability
117(3)
Sample and Population
120(5)
Surveys, Polls, and 1948's Costly ``Free Sample''
124(1)
Normal Curves, Probabilities, Samples, and Populations in Research Articles
125(1)
Summary
125(1)
Key Terms
126(1)
Example Worked-Out Problems
126(2)
Practice Problems
128(4)
Introduction to Hypothesis Testing
132(22)
A Hypothesis-Testing Example
133(1)
The Core Logic of Hypothesis Testing
134(1)
The Hypothesis-Testing Process
135(5)
One-Tailed and Two-Tailed Hypothesis Tests
140(6)
To Be or Not to Be---But Can Not Being Be? The Problem of Whether and When to Accept the Null Hypothesis
143(3)
Hypothesis Tests in Research Articles
146(1)
Summary
147(1)
Key Terms
147(1)
Example Worked-Out Problem
147(2)
Practice Problems
149(5)
Hypothesis Testing and the Means of Samples
154(31)
The Distribution of Means
154(10)
Hypothesis Testing with a Distribution of Means
164(4)
Estimation and Confidence Intervals
168(4)
Hypothesis Tests about Means of Samples and Confidence Intervals in Research Articles
172(3)
More about Polls: Sampling Errors and Errors in Thinking about Samples
173(2)
Summary
175(1)
Key Terms
176(1)
Example Worked-Out Problems
176(1)
Practice Problems
177(8)
Making Sense of Statistical Significance: Decision Errors, Effect Size, and Statistical Power
185(32)
Decision Errors
185(4)
Effect Size
189(5)
Statistical Power
194(5)
Effect Sizes for Relaxation and Meditation: A Restful Meta-Analysis
195(4)
What Determines the Power of a Study?
199(7)
The Role of Power When Planning a Study
206(2)
The Role of Power When Interpreting the Results of a Study
208(3)
Decision Errors, Effect Size, and Power in Research Articles
211(1)
Summary
212(1)
Key Terms
213(1)
Example Worked-Out Problem
213(1)
Practice Problems
213(4)
Introduction to the t Test
217(39)
The t Test for a Single Sample
218(10)
William S. Gosset, alias ``Student'': Not a Mathematician, but a Practical Man
219(9)
The t Test for Dependent Means
228(11)
Assumptions of the t Test
239(1)
Effect Size and Power for the t Test for Dependent Means
240(3)
t Test in Research Articles
243(2)
Summary
245(1)
Key Terms
245(1)
Example Worked-Out Problems
245(4)
Practice Problems
249(7)
The t Test for Independent Means
256(33)
The Distribution of Differences Between Means
257(6)
Hypothesis Testing with a t Test for Independent Means
263(9)
Assumptions of the t Test for Independent Means
272(1)
Effect Size and Power for the t Test for Independent Means
272(1)
The t Test for Independent Means in Research Articles
272(5)
Two Women Make a Point about Gender and Statistics
274(3)
Summary
277(1)
Key Terms
278(1)
Example Worked-Out Problems
278(3)
Practice Problems
281(8)
Introduction to the Analysis of Variance
289(44)
Basic Logic of the Analysis of Variance
290(7)
Sir Ronald Fisher, Caustic Genius at Statistics
296(1)
Carrying Out an Analysis of Variance
297(8)
Hypothesis Testing with the Analysis of Variance
305(1)
Assumptions in the Analysis of Variance
306(1)
Comparing Each Group to Each Other Group
306(3)
Effect Size and Power for the Analysis of Variance
309(3)
Factorial Analysis of Variance
312(3)
Recognizing and Interpreting Interaction Effects
315(6)
Analysis of Variance in Research Articles
321(1)
Summary
321(1)
Key Terms
322(1)
Example Worked-Out Problems
323(1)
Practice Problems
324(9)
Chi-Square Tests and Strategies When Population Distribution Are Not Normal
333(44)
Chi-Square Tests
333(2)
The Chi-Square Statistic and the Chi-Square Test for Goodness of Fit
335(6)
Karl Pearson: Inventor of Chi-Square and Center of Controversy
336(5)
The Chi-Square Test for Independence
341(8)
Assumptions for the Chi-Square Tests
349(1)
Effect Size and Power for the Chi-Square Tests for Independence
350(3)
Strategies for Hypothesis Testing When Population Distributions Are Not Normal
353(3)
Data Transformations
356(4)
Rank-Order Tests
360(4)
Chi-Square Tests, Data Transformations, and Rank-Order Tests in Research Articles
364(2)
Summary
366(1)
Key Terms
367(1)
Example Worked-Out Problems
367(4)
Practice Problems
371(6)
Making Sense of Advanced Statistical Procedures in Research Articles
377(36)
Brief Review of Multiple Regression
378(1)
Hierarchical and Stepwise Multiple Regression
378(4)
Partial Correlation
382(1)
Reliability
383(1)
Factor Analysis
384(3)
Causal Modeling
387(4)
Procedures that Compare Groups
391(1)
Analysis of Covariance (ANCOVA)
392(1)
Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA)
393(1)
Overview of Statistical Techniques
394(1)
The Golden Age of Statistics: Four Guys around London
395(1)
How to Read Results Involving Unfamiliar Statistical Techniques
395(1)
Summary
396(1)
Key Terms
397(1)
Practice Problems
397(16)
Appendix: Tables 413(8)
Answers to Set I Practice Problems 421(21)
Glossary 442(7)
Glossary of Symbols 449(2)
References 451(6)
Index 457

Excerpts

TO THE INSTRUCTOR 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 40 years of experience teaching, researching, and writing. We believe that this book is as different from the conventional lot of statistics books as Paris is from Calcutta, yet still comfortable and stimulating to the long-suffering community of statistics instructors. The approach embodied in this text has been developed during our combined 40 years 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 undergraduate years, 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 understand statistics that I read about in my field." In this third edition of thisBrief Coursewe have tried to maintain those things about the book that have been especially appreciated, while reworking the text 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 some comments. we made in the first edition about how this book from the beginning has been quite different from other statistics texts. WHAT WE HAVE DONE DIFFERENTLY We continue to do what the best of the newer books are already doing well: emphasizing the intuitive, deemphasizing the mathematical, and explaining everything in direct, simple language. But what we have done differs from these other books in 10 key respects. 1.The definitional formulas are brought to center stagebecause they provide a concise symbolic summary of the logic of each particular procedure. All our explanations, examples, practice problems, and test bank items are based on these definitional formulas. (The amount of data to be processed in our practice problems and test items are reduced appropriately to keep computations manageable.) Why this approach? To date, statistics texts have failed to adjust to technologic reality. What is important is not that the students learn to calculate a correlation coefficient with a large data set--computers can do that for them. What is important is that students work problems in a way that they remain constantly aware of the underlying logic of what they are doing. Consider the population variance--the average of the squared deviations from the mean. This concept is immediately clear from the definitional formula (once the student is used to the symbols) Teaching computational formulas today is an anachronism. Researchers do their statistics on computers now. At the same time, the use of statistical software makes the understanding of the basic principles, as they are symbolically expressed in the definitional formula, more important than ever. Students still need to work lots of problems by hand to learn the material. But they need to work them using the definitional formulas that reinforce the concepts, not using the computational formulas that obscure them. Those formulas once made some sense as time-savers for researchers who had to work with large data sets by hand, but they were always poor teaching tools. (Because some instructors may feel naked without them, we still provide the computational formulas, usually in a brief footnote, at the point in the chapter where they would traditionally have been introduced.) 2.Each procedure is taught both verbally and numerically--and usually visually as well. In fact, when we introduceevery


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