Statistical Methods for the Social Sciences

ISBN-10: 0205632491

ISBN-13: 9780205632497

Edition: 4th 2009

List price: $198.00
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Assuming no prior knowledge of statistics, these authors combine their varied backgrounds-one a statistician, the other a social scientist-to introduce statistical methods with a high degree of statistical accuracy and a wealth of examples that are interesting and relevant to social scientists.The FourthEditionhas been both updated and improved to integrate real-world data into examples and exercises, and make coverage more accessible throughout.
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Book details

List price: $198.00
Edition: 4th
Copyright year: 2009
Publisher: Allyn & Bacon, Incorporated
Publication date: 5/23/2008
Binding: Mixed Media
Pages: 624
Size: 8.25" wide x 10.75" long x 1.25" tall
Weight: 3.190
Language: English

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
Probability 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 issues 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 of variance
Two way ANOVA and regression
Repeated measures analysis of variance
Two-way ANOVA with repeated measures on one factor
Effects of violations of ANOVA assumptions
Chapter summary
Combining regression and ANOVA: Quantitative and Categorical Predictors
Comparing means and comparing regression lines
Regression with quantitative and categorical predictors
Permitting interaction between quantitative and categorical predictors
Inference for regression with quantitative and categorical predictors
Adjusted means
Chapter summary
Model Building with Multiple Regression
Model selection procedures
Regression diagnostics
Effects of multicollinearity
Generalized linear models
Nonlinearity: polynomial regression
Exponential regression and log transforms
Chapter summary
Logistic Regression: Modeling Categorical Responses
Logistic regression
Multiple logistic regression
Inference for logistic regression models
Logistic regression models for ordinal variables
Logistic models for nominal responses
Loglinear models for categorical variables
Model goodness of fit tests for contingency tables
Chapter summary
Introduction to Advanced Topics
Longitudinal data analysis
Multilevel (hierarchical) models
Event history analysis
Path analysis
Factor analysis
Structural equation models
Markov chains
Appendix: SAS and SPSS for Statistical Analyses
Answers to selected odd-numbered problems
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