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Statistical Analysis in the Social Sciences by McKee J. McClendon

Statistical Analysis in the Social Sciences

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Statistical Analysis in the Social Sciences by McKee J. McClendon

Statistical Analysis in the Social Sciences imbeds basic descriptive and inferential statistical concepts within a unified framework: the linear regression model. The regression model is used not only for conducting standard regression analyses but also for other statistical analyses that are ordinarily taught as separate and distinct techniques, such as correlation, difference of means, analysis of variance, and certain nonparametric tests. This extensive use of a single statistical model will increase students' depth of understanding and give them a sense of mastery and self-confidence that they might otherwise fail to achieve in their first statistics course.


Preface. 1. Statistics and Social Science. 2. Distributions of Variable Attributes. 3. Central Tendency. 4. Dispersion. 5. Sampling Error. 6. Inferences About the Mean. 7. Associations. 8. Bivariate Regression Analysis. 9. Inferences About the Regression Slope. 10. Multiple Regression. 11. Nominal Independent Variables. 12. Ordinal Dependent Variables. 13. Nominal Dependent Variables. Appendices: Appendix A: Statistical Tables. Appendix B: Answers to Exercises. References. Index.

About the Author

McKee J. McClendon received his Ph.D. in sociology from the University of Kansas in 1972. He is professor emeritus at the University of Akron where he taught statistics, research design, and sociology for 26 years. He is currently a Senior Research Associate, Case Western Reserve University and University Hospitals of Cleveland.

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Book Info

Publication date

26th October 2003


McKee J. McClendon

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Wadsworth Publishing Co Inc an imprint of Cengage Learning, Inc


360 pages


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