Statistical Methods for the Social Sciences

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  • Edition: 4TH
  • Format: Hardcover
  • Copyright: 2007-12-28
  • Publisher: Pearson
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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.

Author Biography

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

Introduction to statistical methodology
Descriptive statistics and inferential statistics
The role of computers in statistics
Chapter summary
Sampling and Measurement
Variables and their measurement
Sampling variability and potential bias
other probability sampling methods
Chapter summary
Descriptive statistics
Describing data with tables and graphs
Describing the center of the data
Describing variability of the data
Measure of position
Bivariate descriptive statistics
Sample statistics and population parameters
Chapter summary
Probability Distributions
Introduction to probability
Probablitity distributions for discrete and continuous variables
The normal probability distribution
Sampling distributions describe how statistics vary
Sampling distributions of sample means
Review: Probability, sample data, and sampling distributions
Chapter summary
Statistical inference: estimation
Point and interval estimation
Confidence interval for a proportion
Confidence interval for a mean
Choice of sample size
Confidence intervals for median and other parameters
Chapter summary
Statistical Inference: Significance Tests
Steps of a significance test
Significance test for a eman
Significance test for a proportion
Decisions and types of errors in tests
Limitations of significance tests
Calculating P (Type II error)
Small-sample test for a proportion: the binomial distribution
Chapter summary
Comparison of Two Groups
Preliminaries for comparing groups
Categorical data: comparing two proportions
Quantitative data: comparing two means
Comparing means with dependent samples
Other methods for comparing means
Other methods for comparing proportions
Nonparametric statistics for comparing groups
Chapter summary
Analyzing Association between Categorical Variables
Contingency Tables
Chi-squared test of independence
Residuals: Detecting the pattern of association
Measuring association in contingency tables
Association between ordinal variables
Inference for ordinal associations
Chapter summary
Linear Regression and Correlation
Linear relationships
Least squares prediction equation
The linear regression model
Measuring linear association - the correlation
Inference for the slope and correlation
Model assumptions and violations
Chapter summary
Introduction to multivariate Relationships
Association and causality
Controlling for other variables
Types of multivariate relationships
Inferenential issus in statistical control
Chapter summary
Multiple Regression and Correlation
Multiple regression model
Example with multiple regression computer output
Multiple correlation and R-squared
Inference for multiple regression and coefficients
Interaction between predictors in their effects
Comparing regression models
Partial correlation
Standardized regression coefficients
Chapter summary
Comparing groups: Analysis of Variance (ANOVA) methods
Comparing several means: One way analysis of variance
Multiple comparisons of means
Performing ANOVA by regression modeling
Two-way analysis
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