Statistics for the Behavioral and Social Sciences: A Brief Course

Statistics for the Behavioral and Social Sciences: A Brief Course

by Arthur Aron
     
 

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

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Overview

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 language—with a large number of practice problems. For those trying to master statistics, as well as reading and understanding research articles.

Product Details

ISBN-13:
9780136153542
Publisher:
Pearson Prentice Hall
Publication date:
12/28/2007

Table of Contents



 1. Displaying the Order in a Group of Numbers.

The Two Branches of Statistical Methods. Some Basic Concepts. Kinds of Variables. Frequency Tables. Histograms. Frequency Graphs. Shapes of Frequency Distributions. Frequency Tables, Histograms, and Frequency Polygons in Research Articles. Summary. Key Terms. Example Worked-Out Problems. Practice Problems.



 2. The Mean, Variance, Standard Deviation, and Z Scores.

The Mean. The Variance and the Standard Deviation. Z Scores. Mean, Variance, Standard Deviation, and Z Scores in Research Articles. Summary. Key Terms. Example Worked-Out Problems. Practice Problems.



 3. Correlation and Prediction.

Graphing Correlations: The Scatter Diagram. Patterns of Correlation. The Degree of Linear Correlation: The Correlation Coefficient. Issues in Interpreting the Correlation Coefficient. Prediction. Multiple Regression and Multiple Correlation. The Correlation Coefficient and the Proportion of Variance Accounted for. Correlation and Prediction in Research Articles. Summary. Key Terms. Exampled Worked-Out Problems. Practice Problems. Chapter Appendix: Hypothesis Tests and Power for the Correlation Coefficient.



 4. Some Key Ingredients for Inferential Statistics: The Normal Curve, Probability, and Population Versus Sample.

The Normal Distribution. Probability. Sample and Population. Normal Curves, Probabilities, Samples, andPopulations in Research Articles. Summary. Key Terms. Example Worked-Out Problems. Practice Problems.



 5. Introduction to Hypothesis Testing.

A Hypothesis-Testing Example. The Core Logic of Hypothesis Testing. The Hypothesis-Testing Process. One-Tailed and Two-Tailed Hypothesis Tests. Hypothesis Tests in Research Articles. Summary. Key Terms. Example Worked-Out Problem. Practice Problems.



 6. Hypothesis Testing and the Means of Samples.

The Distribution of Means. Hypothesis Testing with a Distribution of Means. Estimation and Confidence Intervals. Hypothesis Tests about Means of Samples and Confidence Intervals in Research Articles. Summary. Key Terms. Example Worked-Out Problems. Practice Problems.



 7. Making Sense of Statistical Significance: Decision Errors, Effect Size, and Statistical Power.

Decision Errors. Effect Size. Statistical Power. What Determines the Power of a Study? The Role of Power When Planning a Study. The Role of Power When Interpreting the Results of a Study. Decision Errors, Effect Size, and Power in Research Articles. Summary. Key Terms. Example Worked-Out Problem. Practice Problems.



 8. Introduction to the t Test.

The t Test for a Single Sample. The t Test for Dependent Means. Assumptions of the t Test. Effect Size and Power for the t Test for Dependent Means. t Tests in Research Articles. Summary. Key Terms. Example Worked-Out Problems. Practice Problems.



 9. The t Test for Independent means.

The Distribution of Differences Between Means. Hypothesis Testing with a t Test for Independent Means. Assumptions of the t Test for Independent Means. Effect Size and Power for the t Test for Independent Means. The t Test for Independent Means in Research Articles. Summary. Key Terms. Example Worked-Out Problems. Practice Problems.



10. Introduction to the Analysis of Variance.

Basic Logic of the Analysis of Variance. Carrying Out an Analysis of Variance. Hypothesis Testing with the Analysis of Variance. Assumptions in the Analysis of Variance. Comparing Each Group to Each Other Group. Effect Size and Power for the Analysis of Variance. Factorial Analysis of Variance. Analyses of Variance in Research Articles. Summary. Key Terms. Example Worked-Out Problems. Practice Problems.



11. Chi-Square Tests and Strategies When Population Distributions Are Not Normal.

Chi-Square Tests. The Chi-Square Statistic and the Chi-Square Test for Goodness of Fit. The Chi-Square Test for Independence. Strategies for Hypothesis Testing When Population Distributions Are Not Normal. Data Transformations. Rank-Order Tests. Chi-Square Tests, Data Transformations, and Rank-Order Tests in Research Articles. Summary. Key Terms. Example Worked-Out Problems. Practice Problems.



12. Making Sense of Advanced Statistical Procedures and Research Articles.

Brief Review of Multiple Regression. Hierarchical and Stepwise Multiple Regression. Partial Correlation. Reliability. Factor Analysis. Causal Modeling. Procedures that Compare Groups. Analysis of Covariance (ANCOVA). Multivariate Analysis of Variance (MANOVA) and Multivariate Analysis of Covariance (MANCOVA). Overview of Statistical Techniques. How to Read Results Involving Unfamiliar Statistical Techniques. Summary. Key Terms. Practice Problems.

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