Fundamental Statistics for Behavioral Sciences / Edition 8by Robert B. McCall
Pub. Date: 08/03/2000
Publisher: Cengage Learning
This eighth edition of McCall's well-respected book continues to present concepts in a way that students can easily understand. The new edition has been updated throughout and now includes recommendations by the APA Task Force on Statistical Inference. As in previous editions, McCall helps students see the many real applications of statistics to research in the
This eighth edition of McCall's well-respected book continues to present concepts in a way that students can easily understand. The new edition has been updated throughout and now includes recommendations by the APA Task Force on Statistical Inference. As in previous editions, McCall helps students see the many real applications of statistics to research in the behavioral sciences. Taking a traditional approach to teaching the basic statistical concepts and methods used in behavioral research. McCall emphasizes building an understanding of the logic of statistics rather than stressing the mechanics. In this exciting revision, McCall continues to keep the data for the computational problems simple, so your students can focus on the rationale and outcome of techniques rather on the calculations themselves. Using clear discussion, a wide variety of end-of-chapter exercises, and examples drawn from actual studies, McCall helps students learn how to choose appropriate statistical methods and correctly interpret the results. Also retained in this edition are the author's step-by-step explanations for each proof and his clear definitions of symbols-the essential vocabulary of statistics-that have been so successful in helping students master the material.
Table of Contents
Part I: Descriptive Statistics. 1. The Study Of Statistics. Why Study Statistics? Descriptive and Inferential Statistics. Measurement. Summation sign. Summary. 2. Frequency Distributions And Graphing. Types of Frequency Distributions. Constructing Frequency Distributions with Class Intervals. Graphs of Frequency Distributions. How Distributions Differ. Summary. 3. Characteristics Of Distributions. Indicators of Central Tendency. Indicators of Variability. Populations and Samples. A Note on Calculators and Computers. Summary. 4. Elements Of Exploratory Data Analysis. Stem and Leaf Displays. Resistant Indicators. Summary. 5. Indicators Of Relative Standing. Percentiles. Changing the Properties of Scales. Standard Scores and the Normal Distribution. Summary. 6. Regression. Linear Relationships. Regression Constants and the Regression Line. Standard Error of Estimate. Summary. 7. Correlation. The Pearson Product-Moment Correlation Coefficient. Properties of the Correlation Coefficient. Sampling Factors that Change the Correlation Coefficient. Causality and Correlation. Summary. Part II: Inferential Statistics. 8. Sampling, Sampling Distributions, And Probability. Methods of Sampling. Sampling Distributions and Sampling Error. Probability and its Application to Hypothesis Testing. Estimation. Summary. 9. Introduction To Hypothesis Testing: Terminology And Theory. Statistical Terminology. Hypothesis Testing When Alpha X is Estimated by Sigma X. Summary. 10. Elementary Techniques Of Hypothesis Testing. Inferences About the Difference Between Means. Inferences About Correlation Coefficients. A Comparison of the Difference Between Means and Correlation. Statistics in the Journals. Summary. 11. Beyond Hypothesis Testing: Effect Size And Interval Estimation. Beyond Hypothesis Testing. Indices of Size. Interval Estimation. Summary. Part III: Special Topics. 12. Introduction To Research Design. Scientific Questions. Operationalizing. Data Collection and Data Analysis. Conclusions and Interpretations. The Research Report. Ethical Considerations. Summary. 13. Topics On Probability. Set Theory. Simple Classical Probability. Probability of Complex Events. Methods of Counting. Summary. 14. Simple Analysis Of Variance. Logic of the Analysis of Variance. Computational Procedures. Comparisons Between Specific Means. Size of Relationship. Summary. 15. Two-Factor Analysis Of Variance. Two-Factor Classification. Logic of Two-Factor Analysis of Variance. Computational Procedures. Summary. 16. Nonparametric Techniques. Parametric and Nonparametric Tests. Tests on Independent Samples. Tests on Correlated Samples. Rank-Order Correlation. Summary. Appendix 1: Math Review. Appendix 2: Tables. Appendix 3: Symbols. Appendix 4: Terms. Appendix 5: Answers. Index.
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