Statistical Methods for the Social Sciences

by Agresti, Alan; Finlay, Barbara

• ISBN13: 9780130272959
• ISBN10: 0130272957
• Format: Hardcover
• Copyright: 2007-12-28
• Publisher: Pearson

Summary

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

Introduction to statistical methodology

Descriptive statistics and inferential statistics

The role of computers in statistics

Chapter summary

Sampling and Measurement

Variables and their measurement

Randomization

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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