By Alan Agresti
Descripción: For courses in Statistical Methods for the Social Sciences.
Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The
5th Edition uses examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics—often a stumbling block for students—while avoiding reliance on an overly simplistic recipe-based approach to statistics.
Contenido:
Material: 1 Introduction
1.1 Introduction to Statistical Methodology
1.2 Descriptive Statistics and Inferential Statistics
1.3 The Role of Computers and Software 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*
2.5 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 Measures 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 Probability 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: Population, 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 Estimation Methods: Maximum Likelihood and the Bootstrap*
5.6 Chapter Summary
6 Statistical Inference: Significance Tests
6.1 The Five Parts of a Significance Test
6.2 Significance Test for a Mean
6.3 Significance Test for a Proportion
6.4 Decisions and Types of Errors in Tests
6.5 Limitations of Significance Tests
6.6 Finding 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 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 Inferences 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 Inferential Issues in Statistical Control
10.5 Chapter Summary
11 Multiple Regression and Correlation
11.1 The Multiple Regression Model
11.2 Multiple Correlation and R2
11.3 Inferences for Multiple Regression Coefficients
11.4 Modeling Interaction Effects
11.5 Comparing Regression Models
11.6 Partial Correlation*
11.7 Standardized Regression Coefficients*
11.8 Chapter Summary
12 Regression with Categorical Predictors: Analysis of Variance Methods
12.1 Regression Modeling with Dummy Variables for Categories
12.2 Multiple Comparisons of Means
12.3 Comparing Several Means: Analysis of Variance
12.4 Two-Way ANOVA and Regression Modeling
12.5 Repeated-Measures Analysis of Variance*
12.6 Two-Way ANOVA with Repeated Measures on a Factor*
12.7 Chapter Summary
13 Multiple Regression with Quantitative and Categorical Predictors
13.1 Models with Quantitative and Categorical Explanatory Variables
13.2 Inference for Regression with Quantitative and Categorical Predictors
13.3 Case Studies: Using Multiple Regression in Research
13.4 Adjusted Means*
13.5 The Linear Mixed Model*
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 Nonlinear Relationships: Polynomial Regression
14.6 Exponential Regression and Log Transforms*
14.7 Robust Variances and Nonparametric Regression*
14.8 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*
15.8 Chapter Summary
Appendix: R, Stata, SPSS, and SAS for Statistical Analyses
Bibliography
Credits
Index