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Preface | p. xi |
Basic Concepts and Measures | |
Statistics in the Research Process | p. 3 |
Ideas into Research Projects | p. 5 |
Concepts into Propositions | p. 9 |
Variables into Hypotheses | p. 11 |
Observations into Records | p. 14 |
Data into Numbers | p. 16 |
Statistical Analysis | p. 18 |
The General Linear Model | p. 22 |
Describing Variables | p. 29 |
Frequency Distributions for Discrete and Continuous Variables | p. 29 |
Grouped and Cumulative Distributions | p. 34 |
Graphing Frequency Distributions | p. 37 |
Measures of Central Tendency | p. 40 |
Measures of Dispersion | p. 46 |
Percentiles and Quantiles | p. 53 |
Standardized Scores (Z Scores) | p. 56 |
Exploratory Data Analysis Methods for Displaying Continuous Data | p. 60 |
Statistical Inference | |
Making Statistical Inferences | p. 69 |
Drawing Inferences About Populations from Samples | p. 69 |
Some Basic Probability Concepts | p. 70 |
Chebycheff's Inequality Theorem | p. 73 |
The Normal Distribution | p. 75 |
The Central Limit Theorem | p. 80 |
Sample Point Estimates and Confidence Intervals | p. 81 |
The t Distribution | p. 85 |
Hypothesis Testing | p. 88 |
Testing Hypotheses About Single Means | p. 91 |
Properties of Estimators | p. 101 |
The Chi-Square and F Distributions | p. 102 |
Analyzing Bivariate Relationships | |
Analysis of Variance | p. 111 |
The Logic of ANOVA | p. 111 |
ANOVA Tables: Sums of Squares, Mean Squares, F Ratio | p. 114 |
Tests for Two Means | p. 121 |
The Correlation Ratio: Eta-Squared | p. 131 |
Testing Differences Among Means (Post Hoc) | p. 132 |
Analyzing Categoric Data | p. 139 |
Bivariate Crosstabulation | p. 139 |
Using Chi-Square to Test Significance | p. 142 |
Measuring Association: Q, Phi, Gamma, Tau c, Somer's d[subscript yx] | p. 147 |
Odds and Odds Ratios | p. 159 |
Bivariate Regression and Correlation | p. 169 |
Scatterplots and Regression Lines | p. 169 |
Estimating a Linear Regression Equation | p. 174 |
R-Square and Correlation | p. 182 |
Significance Tests for Regression Parameters | p. 187 |
Standardizing Regression Coefficients | p. 194 |
Comparing Two Regression Equations | p. 198 |
Multivariate Models | |
The Logic of Multivariate Contingency Analysis | p. 207 |
Controlling Additional Variables | p. 208 |
Controlling for a Third Variable in 2 x 2 Tables | p. 213 |
The Partial Correlation Coefficient | p. 223 |
Multiple Regression Analysis | p. 235 |
An Example of a Three-Variable Regression Problem | p. 236 |
The Three-Variable Regression Model | p. 240 |
Multiple Regression with K Independent Variables | p. 255 |
Significance Tests for Parameters | p. 263 |
Comparing Nested Equations | p. 270 |
Dummy Variable Regression: ANCOVA with Interactions | p. 271 |
Comparisons Across Populations | p. 278 |
Nonlinear and Logistic Regression | p. 287 |
Nonlinear Regression | p. 288 |
Dichotomous Dependent Variables | p. 297 |
The Logistic Transformation and Its Properties | p. 299 |
Estimating and Testing Logistic Regression Equations | p. 307 |
The Multinomial-Logit Model | p. 314 |
Advanced Topics | |
Log-Linear Analysis | p. 327 |
Log-Linear Models for 2 x 2 Tables | p. 328 |
Log-Linear Models for Three-Variable Tables | p. 337 |
More Complex Models | p. 349 |
Special Topics in Log-Linear Analysis | p. 355 |
Causal Models and Path Analysis | p. 371 |
Causal Assumptions | p. 371 |
Causal Diagrams | p. 374 |
Path Analysis | p. 377 |
Structural Equation Models | p. 405 |
Review of Correlation and Covariance | p. 406 |
Reliability and Validity in Measurement Theory | p. 408 |
Factor Analysis | p. 414 |
Multiple-Indicator Casual Models | p. 426 |
Models with Ordered Indicators | p. 434 |
Appendices | |
The Use of Summations | p. 449 |
Critical Values of Chi Square (table) | p. 457 |
Areas Under the Normal Curve (table) | p. 459 |
Student's t Distribution (table) | p. 465 |
F Distribution (table) | p. 467 |
Fisher's r-to-Z Transformation (table) | p. 471 |
Glossary of Terms | p. 473 |
List of Mathematical and Statistical Symbols | p. 487 |
Answers to Problems | p. 495 |
Index | p. 523 |
Table of Contents provided by Syndetics. All Rights Reserved. |
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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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