The fourth edition of STATISTICS FOR SOCIAL DATA ANALYSIS continues to show students how to apply statistical methods to answer research questions in various fields. Throughout the text, the authors underscore the importance of formulating substantive hypotheses before attempting to analyze quantitative data. An important aspect of this text is its realistic, hands-on approach. Actual datasets are used in most examples, helping students understand and appreciate what goes into the research process. The book focuses on the continuous-discrete distinction in considering the level at which a variable is measured. Rather than dwelling on the four conventional levels-of-measurement distinctions, the authors discuss statistics for analyzing continuous and discrete variables separately and in combination.
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Part One: BASIC CONCEPTS AND MEASURES. 1. Statistics in the Research Process. 2. Describing Variables. Part Two: MAKING STATISTICAL INFERENCES. 3. Inferences About Means. Part Three: ANALYZING BIVARIATE RELATIONSHIPS. 4. Analysis of Variance. 5. Analyzing Categoric Data. 6. Bivariate Regression and Correlation. Part Four: MULTIVARIATE MODELS. 7. The Logic of Multivariate Contingency Analysis. 8. Multiple Regression Analysis. 9. Nonlinear and Logistic Regression. Part Five: ADVANCED TOPICS. 10. Log-Linear Analysis. 11. Causal Models and Path Analysis. 12. Structural Equation Models. Appendices. List of Mathematical and Statistical Symbols. Answers to Problems. Instructor's Manual and Data Disk Available.
The fourth edition of STATISTICS FOR SOCIAL DATA ANALYSIS continues to show students how to apply statistical methods to answer research questions in various fields. Throughout the text, the authors underscore the importance of formulating substantive hypotheses before attempting to analyze quantitative data. An important aspect of this text is its realistic, hands-on approach. Actual datasets are used in most examples, helping students understand and appreciate what goes into the research process. The book focuses on the continuous-discrete distinction in considering the level at which a variable is measured. Rather than dwelling on the four conventional levels-of-measurement distinctions, the authors discuss statistics for analyzing continuous and discrete variables separately and in combination.
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