Statistical methods for categorical data analysis /
Statistical Methods for Categorical Data Analysis is designed as an accessible reference work and textbook about categorical data (that is, data arising from counts instead of measurement). Examples include data about birth, death, marriage, and so forth. It integrates statistical and econometric ap...
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| Format: | Book |
| Language: | English |
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San Diego :
Academic Press,
c2000
San Diego : Academic, ©2000 San Diego : c2000 San Diego ; London : [2000], ©2000 San Diego ; London : c2000 San Diego ; London : [2000] |
| Subjects: |
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| 100 | 1 | |a Powers, Daniel A |1 http://viaf.org/viaf/43797779 | |
| 100 | 1 | |a Powers, Daniel A | |
| 245 | 1 | 0 | |a Statistical methods for categorical data analysis / |c Daniel A. Powers, Yu Xie |
| 260 | |a San Diego : |b Academic Press, |c c2000 | ||
| 260 | |a San Diego : |b Academic Press, |c ©2000 | ||
| 260 | |a San Diego : |b Academic, |c c2000 | ||
| 260 | |a San Diego ; |a London : |b Academic, |c [2000], ©2000 | ||
| 260 | |a San Diego ; |a London : |b Academic, |c c2000 | ||
| 264 | 1 | |a San Diego ; |a London : |b Academic, |c [2000] | |
| 264 | 4 | |c ©2000 | |
| 300 | |a xv, 305 p. : |b ill. ; |c 24 cm | ||
| 300 | |a xv, 305 p. ; |c 24 cm | ||
| 300 | |a xv, 305 pages : |b illustrations ; |c 24 cm | ||
| 300 | |a xv, 305 pages ; |c 24 cm | ||
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| 500 | |a This WorldCat-derived record is shareable under Open Data Commons ODC-BY, with attribution to OCLC |5 CTY | ||
| 504 | |a Includes bibliographical references (p. [285]-295) and index | ||
| 504 | |a Includes bibliographical references (pages 285-295) and index | ||
| 504 | |a Includes bibliographical references and index | ||
| 505 | 0 | 0 | |g 1 |t Introduction -- |g 2. |t Review of Linear Regression Models -- |g 3. |t Logit and Probit Models for Binary Data -- |g 4. |t Loglinear Models for Contingency Tables -- |g 5. |t Statistical Models for Rates -- |g 6. |t Models for Ordinal Dependent Variables -- |g 7. |t Models for Unordered Dependent Variables -- |g A. |t Matrix Approach to Regression -- |g B. |t Maximum Likelihood Estimation. |
| 505 | 0 | 0 | |g 1 |t Introduction -- |g 2. |t Review of Linear Regression Models -- |g 3. |t Logit and Probit Models for Binary Data -- |g 4. |t Loglinear Models for Contingency Tables -- |g 5. |t Statistical Models for Rates -- |g 6. |t Models for Ordinal Dependent Variables -- |g 7. |t Models for Unordered Dependent Variables -- |g A. |t The Matrix Approach to Regression -- |g B. |t Maximum Likelihood Estimation. |
| 505 | 0 | 0 | |g 1.1 |t Why Categorical Data Analysis? |g 1 -- |g 1.1.1 |t Defining Categorical Variables |g 2 -- |g 1.1.2 |t Dependent and Independent Variables |g 3 -- |g 1.1.3 |t Categorical Dependent Variables |g 4 -- |g 1.1.4 |t Types of Measurement |g 5 -- |g 1.2 |t Two Philosophies of Categorical Data |g 7 -- |g 1.2.1 |t The Transformational Approach |g 8 -- |g 1.2.2 |t The Latent Variable Approach |g 9 -- |g 1.3 |t An Historical Note |g 11 -- |g 1.4 |t Approach of This Book |g 12 -- |g 1.4.1 |t Organization of the Book |g 13 -- |g 2 |t Review of Linear Regression Models -- |g 2.1 |t Regression Models |g 15 -- |g 2.1.1 |t Three Conceptualizations of Regression |g 16 -- |g 2.1.2 |t Anatomy of Linear Regression |g 18 -- |g 2.1.3 |t Basics of Statistical Inference |g 20 -- |g 2.1.4 |t Tension between Accuracy and Parsimony |g 22 -- |g 2.2 |t Linear Regression Models Revisited |g 24 -- |g 2.2.1 |t Least Squares Estimation |g 24 -- |g 2.2.2 |t Maximum Likelihood Estimation |g 25 -- |g 2.2.3 |t Assumptions for Least Squares Regression |g 29 -- |g 2.2.4 |t Comparisons of Conditional Means |g 30 -- |g 2.2.5 |t Linear Models with Weaker Assumptions |g 32 -- |g 2.3 |t Categorical and Continuous Dependent Variables |g 37 -- |g 2.3.1 |t A Working Typology |g 38 -- |g 3 |t Logit and Probit Models for Binary Data -- |g 3.1 |t Introduction to Binary Data |g 41 -- |g 3.2 |t The Transformational Approach |g 43 -- |g 3.2.1 |t The Linear Probability Model |g 43 -- |g 3.2.2 |t The Logit Model |g 49 -- |g 3.2.3 |t The Probit Model |g 52 -- |g 3.2.4 |t An Application Using Grouped Data |g 53 -- |g 3.3 |t Justification of Logit and Probit Models |g 55 -- |g 3.3.1 |t The Latent Variable Approach |g 56 -- |g 3.3.2 |t Extending the Latent Variable Approach |g 59 -- |g 3.3.3 |t Estimation of Binary Response Models |g 61 -- |g 3.3.4 |t Goodness-of-Fit and Model Selection |g 63 -- |g 3.3.5 |t Hypothesis Testing and Statistical Inference |g 71 -- |g 3.4 |t Interpreting Estimates |g 75 -- |g 3.4.1 |t The Odds-Ratio |g 75 -- |g 3.4.2 |t Marginal Effects |g 76 -- |g 3.4.3 |t An Application Using Individual-Level Data |g 80 -- |g 3.5 |t Alternative Probability Models |g 83 -- |g 3.5.1 |t The Complementary Log-Log Model |g 83 -- |g 3.5.2 |t Programming Binomial Response Models |g 85 -- |g 4 |t Loglinear Models for Contingency Tables -- |g 4.1 |t Contingency Tables |g 87 -- |g 4.1.1 |t Types of Contingency Tables |g 88 -- |g 4.1.2 |t An Example and Notation |g 88 -- |g 4.1.3 |t Independence and the Pearson x[superscript 2] Statistic |g 90 -- |g 4.2 |t Measures of Association |g 93 -- |g 4.2.1 |t Homogeneous Proportions |g 93 -- |g 4.2.2 |t Relative Risks |g 94 -- |g 4.2.3 |t Odds-Ratios |g 95 -- |g 4.2.4 |t The Invariance Property of Odds-Ratios |g 97 -- |g 4.3 |t Estimation and Goodness-of-Fit |g 99 -- |g 4.3.1 |t Simple Models and the Pearson x[superscript 2] Statistic |g 100 -- |g 4.3.2 |t Sampling Models and Maximum Likelihood Estimation |g 102 -- |g 4.3.3 |t The Likelihood-Ratio Chi-Squared Statistic |g 104 -- |g 4.3.4 |t Bayesian Information Criterion |g 106 -- |g 4.4 |t Models for Two-Way Tables |g 107 -- |g 4.4.1 |t The General Setup |g 107 -- |g 4.4.2 |t Normalization |g 108 -- |g 4.4.3 |t Interpretation of Parameters |g 110 -- |g 4.4.4 |t Topological Model |g 111 -- |g 4.4.5 |t Quasi-independence Model |g 114 -- |g 4.4.6 |t Symmetry and Quasi-symmetry |g 116 -- |g 4.4.7 |t Crossings Model |g 117 -- |g 4.5 |t Models for Ordinal Variables |g 119 -- |g 4.5.1 |t Linear-by-Linear Association |g 119 -- |g 4.5.2 |t Uniform Association |g 120 -- |g 4.5.3 |t Row-Effect and Column-Effect Models |g 122 -- |g 4.5.4 |t Goodman's RC Model |g 124 -- |g 4.6 |t Models for Multiway Tables |g 129 -- |g 4.6.1 |t Three-Way Tables |g 130 -- |g 4.6.2 |t The Saturated Model for Three-Way Tables |g 132 -- |g 4.6.3 |t Collapsibility |g 133 -- |g 4.6.4 |t Classes of Models for Three-Way Tables |g 135 -- |g 4.6.5 |t Analysis of Variation in Association |g 140 -- |g 4.6.6 |t Model Selection |g 145 -- |g 5 |t Statistical Models for Rates -- |g 5.2 |t Log-Rate Models |g 148 -- |g 5.2.1 |t The Role of Exposure |g 149 -- |g 5.2.2 |t Estimating Log-Rate Models |g 154 -- |g 5.2.4 |t Interpretation |g 159 -- |g 5.3 |t Discrete-Time Hazard Models |g 160 -- |g 5.3.1 |t Data Structure |g 161 -- |g 5.3.2 |t Estimation |g 162 -- |g 5.4 |t Semiparametric Rate Models |g 168 -- |g 5.4.1 |t The Piecewise Constant Exponential Model |g 169 -- |g 5.4.2 |t The Cox Model |g 174 -- |g 5.5 |t Models for Panel Data |g 177 -- |g 5.5.1 |t Fixed Effects Models for Binary Data |g 179 -- |g 5.5.2 |t Random Effects Models for Binary Data |g 183 -- |g 5.6 |t Unobserved Heterogeneity in Event-History Models |g 188 -- |g 5.6.1 |t The Gamma Mixture Model |g 190 -- |g 6 |t Models for Ordinal Dependent Variables -- |g 6.2 |t Scoring Methods |g 202 -- |g 6.2.1 |t Integer Scoring |g 202 -- |g 6.2.2 |t Midpoint Scoring |g 203 -- |g 6.2.3 |t Normal Score Transformation |g 204 -- |g 6.2.4 |t Scaling with Additional Information |g 205 -- |g 6.3 |t Logit Models for Grouped Data |g 206 -- |g 6.3.1 |t Baseline, Adjacent, and Cumulative Logits |g 206 -- |g 6.3.2 |t Adjacent Category Logit Model |g 207 -- |g 6.3.3 |t Adjacent Category Logit Models and Loglinear Models |g 209 -- |g 6.4 |t Ordered Logit and Probit Models |g 210 -- |g 6.4.1 |t Cumulative Logits and Probits |g 211 -- |g 6.4.2 |t The Ordered Logit Model |g 212 -- |g 6.4.3 |t The Ordered Probit Model |g 214 -- |g 6.4.4 |t The Latent Variable Approach |g 215 -- |g 6.4.5 |t Estimation |g 217 -- |g 6.4.6 |t Marginal Effects |g 220 -- |g 7 |t Models for Unordered Dependent Variables -- |g 7.2 |t Multinomial Logit Models |g 224 -- |g 7.2.1 |t Review of the Binary Logit Model |g 224 -- |g 7.2.2 |t General Setup for the Multinomial Logit Model |g 225 -- |g 7.3 |t The Standard Multinomial Logit Model |g 227 -- |g 7.3.1 |t Estimation |g 229 -- |g 7.3.2 |t Interpreting Results from Multinomial Logit Models |g 230 -- |g 7.4 |t Loglinear Models for Grouped Data |g 234 -- |g 7.4.1 |t Two-Way Tables |g 234 -- |g 7.4.2 |t Three-and Higher-Way Tables |g 235 -- |g 7.5 |t The Latent Variable Approach |g 238 -- |g 7.6 |t The Conditional Logit Model |g 239 -- |g 7.6.1 |t Interpretation |g 240 -- |g 7.6.2 |t The Mixed Model |g 242 -- |g 7.7 |t Specification Issues |g 245 -- |g 7.7.1 |t Independence of Irrelevant Alternatives: The IIA Assumption |g 245 -- |g 7.7.2 |t Sequential Logit Models |g 249 -- |g A |t The Matrix Approach to Regression -- |g A.2 |t Matrix Algebra |g 253 -- |g A.2.1 |t The Matrix Approach to Regression |g 254 -- |g A.2.2 |t Basic Matrix Operations |g 255 -- |g A.2.3 |t Numerical Example |g 259 -- |g B |t Maximum Likelihood Estimation -- |g B.2.1 |t Example 1: Binomial Proportion |g 262 -- |g B.2.2 |t Example 2: Normal Mean and Variance |g 264 -- |g B.2.3 |t Example 3: Binary Logit Model |g 266 -- |g B.2.4 |t Example 4: Loglinear Model |g 272 -- |g B.2.5 |t Iteratively Reweighted Least Squares |g 275 -- |g B.2.6 |t Generalized Linear Models |g 277 -- |g B.2.7 |t Minimum x[superscript 2] Estimation |g 281 |
| 505 | 0 | 0 | |t Why Categorical Data Analysis? -- |t Defining Categorical Variables -- |t Dependent and Independent Variables -- |t Categorical Dependent Variables -- |t Types of Measurement -- |t Two Philosophies of Categorical Data -- |t The Transformational Approach -- |t The Latent Variable Approach -- |t An Historical Note -- |t Approach of This Book -- |t Organization of the Book -- |t Review of Linear Regression Models -- |t Regression Models -- |t Three Conceptualizations of Regression -- |t Anatomy of Linear Regression -- |t Basics of Statistical Inference -- |t Tension between Accuracy and Parsimony -- |t Linear Regression Models Revisited -- |t Least Squares Estimation -- |t Maximum Likelihood Estimation -- |t Assumptions for Least Squares Regression -- |t Comparisons of Conditional Means -- |t Linear Models with Weaker Assumptions -- |t Categorical and Continuous Dependent Variables -- |t A Working Typology -- |t Logit and Probit Models for Binary Data -- |t Introduction to Binary Data -- |t The Transformational Approach -- |t The Linear Probability Model -- |t The Logit Model -- |t The Probit Model -- |t An Application Using Grouped Data -- |t Justification of Logit and Probit Models -- |t The Latent Variable Approach -- |t Extending the Latent Variable Approach -- |t Estimation of Binary Response Models -- |t Goodness-of-Fit and Model Selection -- |t Hypothesis Testing and Statistical Inference -- |t Interpreting Estimates -- |t The Odds-Ratio -- |t Marginal Effects -- |t An Application Using Individual-Level Data -- |t Alternative Probability Models -- |t The Complementary Log-Log Model -- |t Programming Binomial Response Models |
| 520 | |a Statistical Methods for Categorical Data Analysis is designed as an accessible reference work and textbook about categorical data (that is, data arising from counts instead of measurement). Examples include data about birth, death, marriage, and so forth. It integrates statistical and econometric approaches to the analysis of limited and categorical dependent variables, thereby offering a practical, mathematically uncomplicated approach to the topics of modern data analysis. The volume offers a comprehensive presentation of many different models in a one-volume format, with Web site | ||
| 650 | 0 | |a Multivariate analysis | |
| 650 | 0 | |a Social sciences |x Statistical methods | |
| 650 | 2 | |a Multivariate Analysis | |
| 650 | 4 | |a Multivariate analysis | |
| 650 | 4 | |a Social sciences |x Statistical methods | |
| 650 | 7 | |a Multivariate analysis |2 fast | |
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