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Statistical Methods for the Social Sciences Alan Agresti

Statistical Methods for the Social Sciences By Alan Agresti

Statistical Methods for the Social Sciences by Alan Agresti

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Statistical Methods for the Social Sciences Summary

Statistical Methods for the Social Sciences by Alan Agresti

The book presents an introduction to statistical methods for students majoring in social science disciplines. No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra). The book contains sufficient material for a two-semester sequence of courses. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.

Statistical Methods for the Social Sciences Reviews

"This text is readable, understandable, and well-organized. It provides good examples with SPSS output." (Robert Wilson, University of Delaware). "Overall, [Agresti/ Finlay] is a good book for introductory statistics that targets general covers most topics you want to cover and allows the instructor to choose which topics to include." (Youqin Huang, State University of New York, Albany) "I originally started using the Agresti/ Finlay book based on its reputation as "the class of the market", in terms of being unfailingly statistically correct and having a "modern" perspective. By "modern", I mean that it is model rather than test oriented, that it gives heavy emphasis to confidence intervals and p-values rather than using arbitrary levels of significance, and that it eschews computational formulae. It has met those expectations..." (Michael Lacey, Colorado State University) "..the book has been a good and helpful resource for me in preparing the class notes and assigning homework qustions. The main concepts to be understood by students are sampling distribution, confidence interval, p-value, linear regression. The book helps in this..." (Arne Bathke, University of Kentucky)

About Alan Agresti

Alan Agresti is Distinguished Professor in the Department of Statistics at the University of Florida. He has been teaching statistics there for 30 years, including the development of three courses in statistical methods for social science students and three courses in categorical data analysis. He is author of over 100 refereed article and four texts including "Statistics: The Art and Science of Learning From Data" (with Christine Franklin, Prentice Hall, 2nd edition 2009) and "Categorical Data Analysis" (Wiley, 2nd edition 2002). He is a Fellow of the American Statistical Association and recipient of an Honorary Doctor of Science from De Montfort University in the UK. In 2003 he was named "Statistician of the Year" by the Chicago chapter of the American Statistical Association and in 2004 he was the first honoree of the Herman Callaert Leadership Award in Biostatistical Education and Dissemination awarded by the University of Limburgs, Belgium. He has held visiting positions at Harvard University, Boston University, London School of Economics, and Imperial College and has taught courses or short courses for universities and companies in about 20 countries worldwide. He has also received teaching awards from UF and an excellence in writing award from John Wiley & Sons.

Table of Contents

1.Introduction 1.1 Introduction to statistical methodology 1.2 Descriptive statistics and inferential statistics 1.3 The role of computers in statistics 1.4 Chapter summary 2. Sampling and Measurement 2.1 Variables and their measurement 2.2 Randomization 2.3 Sampling variability and potential bias 2.4 other probability sampling methods * 2.4 Chapter summary 3. Descriptive statistics 3.1 Describing data with tables and graphs 3.2 Describing the center of the data 3.3 Describing variability of the data 3.4 Measure of position 3.5 Bivariate descriptive statistics 3.6 Sample statistics and population parameters 3.7 Chapter summary 4. Probability Distributions 4.1 Introduction to probability 4.2 Probablitity distributions for discrete and continuous variables 4.3 The normal probability distribution 4.4 Sampling distributions describe how statistics vary 4.5 Sampling distributions of sample means 4.6 Review: Probability, sample data, and sampling distributions 4.7 Chapter summary 5. Statistical inference: estimation 5.1 Point and interval estimation 5.2 Confidence interval for a proportion 5.3 Confidence interval for a mean 5.4 Choice of sample size 5.5 Confidence intervals for median and other parameters* 5.6 Chapter summary 6. Statistical Inference: Significance Tests 6.1 Steps of a significance test 6.2 Significance test for a eman 6.3 Significance test for a proportion 6.4 Decisions and types of errors in tests 6.5 Limitations of significance tests 6.6 Calculating P (Type II error)* 6.7 Small-sample test for a proportion: the binomial distribution* 6.8 Chapter summary 7. Comparison of Two Groups 7.1 Preliminaries for comparing groups 7.2 Categorical data: comparing two proportions 7.3 Quantitative data: comparing two means 7.4 Comparing means with dependent samples 7.5 Other methods for comparing means* 7.6 Other methods for comparing proportions* 7.7 Nonparametric statistics for comparing groups 7.8 Chapter summary 8. Analyzing Association between Categorical Variables 8.1 Contingency Tables 8.2 Chi-squared test of independence 8.3 Residuals: Detecting the pattern of association 8.4 Measuring association in contingency tables 8.5 Association between ordinal variables* 8.6 Inference for ordinal associations* 8.7 Chapter summary 9. Linear Regression and Correlation 9.1 Linear relationships 9.2 Least squares prediction equation 9.3 The linear regression model 9.4 Measuring linear association - the correlation 9.5 Inference for the slope and correlation 9.6 Model assumptions and violations 9.7 Chapter summary 10. Introduction to multivariate Relationships 10.1 Association and causality 10.2 Controlling for other variables 10.3 Types of multivariate relationships 10.4 Inferenential issus in statistical control 10.5 Chapter summary 11. Multiple Regression and Correlation 11.1 Multiple regression model 11.2 Example with multiple regression computer output 11.3 Multiple correlation and R-squared 11.4 Inference for multiple regression and coefficients 11.5 Interaction between predictors in their effects 11.6 Comparing regression models 11.7 Partial correlation* 11.8 Standardized regression coefficients* 11.9 Chapter summary 12. Comparing groups: Analysis of Variance (ANOVA) methods 12.1 Comparing several means: One way analysis of variance 12.2 Multiple comparisons of means 12.3 Performing ANOVA by regression modeling 12.4 Two-way analysis of variance 12.5 Two way ANOVA and regression 12.6 Repeated measures analysis of variance* 12.7 Two-way ANOVA with repeated measures on one factor* 12.8 Effects of violations of ANOVA assumptions 12.9 Chapter summary 13. Combining regression and ANOVA: Quantitative and Categorical Predictors 13.1 Comparing means and comparing regression lines 13.2 Regression with quantitative and categorical predictors 13.3 Permitting interaction between quantitative and categorical predictors 13.4 Inference for regression with quantitative and categorical predictors 13.5 Adjusted means* 13.6 Chapter summary 14. Model Building with Multiple Regression 14.1 Model selection procedures 14.2 Regression diagnostics 14.3 Effects of multicollinearity 14.4 Generalized linear models 14.5 Nonlinearity: polynomial regression 14.6 Exponential regression and log transforms* 14.7 Chapter summary 15. Logistic Regression: Modeling Categorical Responses 15.1 Logistic regression 15.2 Multiple logistic regression 15.3 Inference for logistic regression models 15.4 Logistic regression models for ordinal variables* 15.5 Logistic models for nominal responses* 15.6 Loglinear models for categorical variables* 15.7 Model goodness of fit tests for contingency tables* 15.9 Chapter summary 16. Introduction to Advanced Topics 16.1 Longitudinal data analysis* 16.2 Multilevel (hierarchical) models* 16.3 Event history analysis* 16.4 Path analysis* 16.5 Factor analysis* 16.6 Structural equation models* 16.7 Markov chains* Appendix: SAS and SPSS for Statistical Analyses Tables Answers to selected odd-numbered problems Index

Additional information

Statistical Methods for the Social Sciences by Alan Agresti
Used - Good
Pearson Education (US)
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in good condition, but if you are not entirely satisfied please get in touch with us

Customer Reviews - Statistical Methods for the Social Sciences