An Introduction to Categorical Data Analysis	3
	Contents	7
	Preface	11
	About the Companion Website	15
	1 Introduction	17
	1.1 CATEGORICAL RESPONSE DATA	17
	1.1.1 Response Variable and Explanatory Variables	18
	1.1.2 Binary–Nominal–Ordinal Scale Distinction	18
	1.1.3 Organization of this Book	19
	1.2 PROBABILITY DISTRIBUTIONS FOR CATEGORICAL DATA	19
	1.2.1 Binomial Distribution	19
	1.2.2 Multinomial Distribution	21
	1.3 STATISTICAL INFERENCE FOR A PROPORTION	21
	1.3.1 Likelihood Function and Maximum Likelihood Estimation	21
	1.3.2 Significance Test About a Binomial Parameter	23
	1.3.3 Example: Surveyed Opinions About Legalized Abortion	23
	1.3.4 Confidence Intervals for a Binomial Parameter	24
	1.3.5 Better Confidence Intervals for a Binomial Proportion	24
	1.4 STATISTICAL INFERENCE FOR DISCRETE DATA	26
	1.4.1 Wald, Likelihood-Ratio, and Score Tests	26
	1.4.2 Example: Wald, Score, and Likelihood-Ratio Binomial Tests	27
	1.4.3 Small-Sample Binomial Inference and the Mid P-Value	28
	1.5 BAYESIAN INFERENCE FOR PROPORTIONS	29
	1.5.1 The Bayesian Approach to Statistical Inference	30
	1.5.2 Bayesian Binomial Inference: Beta Prior Distributions	31
	1.5.3 Example: Opinions about Legalized Abortion, Revisited	32
	1.5.4 Other Prior Distributions	32
	1.6 USING R SOFTWARE FOR STATISTICAL INFERENCE ABOUT PROPORTIONS	33
	1.6.1 Reading Data Files and Installing Packages	33
	1.6.2 Using R for Statistical Inference about Proportions	34
	1.6.3 Summary: Choosing an Inference Method	36
	Exercises	37
	2 Analyzing Contingency Tables	41
	2.1 PROBABILITY STRUCTURE FOR CONTINGENCY TABLES	42
	2.1.1 Joint, Marginal, and Conditional Probabilities	42
	2.1.2 Example: Sensitivity and Specificity	42
	2.1.3 Statistical Independence of Two Categorical Variables	44
	2.1.4 Binomial and Multinomial Sampling	44
	2.2 COMPARING PROPORTIONS IN 2×2 CONTINGENCY TABLES	45
	2.2.1 Difference of Proportions	45
	2.2.2 Example: Aspirin and Incidence of Heart Attacks	46
	2.2.3 Ratio of Proportions (Relative Risk)	46
	2.2.4 Using R for Comparing Proportions in 2×2 Tables	47
	2.3 THE ODDS RATIO	47
	2.3.1 Properties of the Odds Ratio	48
	2.3.2 Example: Odds Ratio for Aspirin Use and Heart Attacks	49
	2.3.3 Inference for Odds Ratios and Log Odds Ratios	49
	2.3.4 Relationship Between Odds Ratio and Relative Risk	50
	2.3.5 Example: The Odds Ratio Applies in Case-Control Studies	51
	2.3.6 Types of Studies: Observational Versus Experimental	52
	2.4 CHI-SQUARED TESTS OF INDEPENDENCE	52
	2.4.1 Pearson Statistic and the Chi-Squared Distribution	53
	2.4.2 Likelihood-Ratio Statistic	54
	2.4.3 Testing Independence in Two-Way Contingency Tables	54
	2.4.4 Example: Gender Gap in Political Party Affiliation	55
	2.4.5 Residuals for Cells in a Contingency Table	55
	2.4.6 Partitioning Chi-Squared Statistics	57
	2.4.7 Limitations of Chi-Squared Tests	58
	2.5 TESTING INDEPENDENCE FOR ORDINAL VARIABLES	58
	2.5.1 Linear Trend Alternative to Independence	59
	2.5.2 Example: Alcohol Use and Infant Malformation	59
	2.5.3 Ordinal Tests Usually Have Greater Power	61
	2.5.4 Choice of Scores	61
	2.5.5 Trend Tests for r×2 and 2×c and Nominal–Ordinal Tables	62
	2.6 EXACT FREQUENTIST AND BAYESIAN INFERENCE	62
	2.6.1 Fisher’s Exact Test for 2×2 Tables	62
	2.6.2 Example: Fisher’s Tea Tasting Colleague	63
	2.6.3 Conservatism for Actual (Type I Error)	65
	2.6.4 Small-Sample Confidence Intervals for Odds Ratio	66
	2.6.5 Bayesian Estimation for Association Measures	66
	2.6.6 Example: Bayesian Inference in a Small Clinical Trial	67
	2.7 ASSOCIATION IN THREE-WAY TABLES	68
	2.7.1 Partial Tables	69
	2.7.2 Example: Death Penalty Verdicts and Race	69
	2.7.3 Simpson’s Paradox	70
	2.7.4 Conditional and Marginal Odds Ratios	71
	2.7.5 Homogeneous Association	72
	Exercises	72
	3 Generalized Linear Models	81
	3.1 COMPONENTS OF A GENERALIZED LINEAR MODEL	82
	3.1.1 Random Component	82
	3.1.2 Linear Predictor	82
	3.1.3 Link Function	82
	3.1.4 Ordinary Linear Model: GLM with Normal Random Component	83
	GENERALIZED LINEAR MODELS FOR BINARY DATA	84
	3.2.1 Linear Probability Model	84
	3.2.2 Logistic Regression Model	84
	3.2.3 Example: Snoring and Heart Disease	85
	3.2.4 Using R to Fit Generalized Linear Models for Binary Data	87
	3.2.5 Data Files: Ungrouped or Grouped Binary Data	88
	3.3 GENERALIZED LINEAR MODELS FOR COUNTS AND RATES	88
	3.3.1 Poisson Distribution for Counts	88
	3.3.2 Poisson Loglinear Model	89
	3.3.3 Example: Female Horseshoe Crabs and their Satellites	89
	3.3.4 Overdispersion: Greater Variability than Expected	91
	3.4 STATISTICAL INFERENCE AND MODEL CHECKING	92
	3.4.1 Wald, Likelihood-Ratio, and Score Inference Use the Likelihood Function	93
	3.4.2 Example: Political Ideology and Belief in Evolution	94
	3.4.3 The Deviance of a GLM	96
	3.4.4 Model Comparison Using the Deviance	96
	3.4.5 Residuals Comparing Observations to the Model Fit	97
	3.5 FITTING GENERALIZED LINEAR MODELS	98
	3.5.1 The Fisher Scoring Algorithm Fits GLMs	98
	3.5.2 Bayesian Methods for Generalized Linear Models	99
	3.5.3 GLMs: A Unified Approach to Statistical Analysis	99
	Exercises	100
	4 Logistic Regression	105
	4.1 THE LOGISTIC REGRESSION MODEL	105
	4.1.1 The Logistic Regression Model	106
	4.1.2 Odds Ratio and Linear Approximation Interpretations	106
	4.1.3 Example: Whether a Female Horseshoe Crab Has Satellites	107
	4.1.4 Logistic Regression with Retrospective Studies	109
	4.1.5 Normally Distributed X Implies Logistic Regression for Y	110
	4.2 STATISTICAL INFERENCE FOR LOGISTIC REGRESSION	110
	4.2.1 Confidence Intervals for Effects	110
	4.2.2 Significance Testing	111
	4.2.3 Fitted Values and Confidence Intervals for Probabilities	112
	4.2.4 Why Use a Model to Estimate Probabilities?	113
	4.3 LOGISTIC REGRESSION WITH CATEGORICAL PREDICTORS	114
	4.3.1 Indicator Variables Represent Categories of Predictors	114
	4.3.2 Example: Survey about Marijuana Use	115
	4.3.3 ANOVA-Type Model Representation of Factors	116
	4.3.4 Tests of Conditional Independence and of Homogeneity for Three-Way Contingency Tables	117
	4.4 MULTIPLE LOGISTIC REGRESSION	118
	4.4.1 Example: Horseshoe Crabs with Color and Width Predictors	118
	4.4.2 Model Comparison to Check Whether a Term is Needed	120
	4.4.3 Example: Treating Color as Quantitative or Binary	120
	4.4.4 Allowing Interaction between Explanatory Variables	122
	4.4.5 Effects Depend on Other Explanatory Variables in Model	123
	4.5 SUMMARIZING EFFECTS IN LOGISTIC REGRESSION	123
	4.5.1 Probability-Based Interpretations	123
	4.5.2 Marginal Effects and Their Average	124
	4.5.3 Standardized Interpretations	125
	4.6 SUMMARIZING PREDICTIVE POWER: CLASSIFICATION TABLES, ROC CURVES, AND MULTIPLE CORRELATION	126
	4.6.1 Summarizing Predictive Power: Classification Tables	126
	4.6.2 Summarizing Predictive Power: ROC Curves	127
	4.6.3 Summarizing Predictive Power: Multiple Correlation	128
	EXERCISES	129
	5 Building and Applying Logistic Regression Models	139
	5.1 STRATEGIES IN MODEL SELECTION	139
	5.1.1 How Many Explanatory Variables Can the Model Handle?	140
	5.1.2 Example: Horseshoe Crab Satellites Revisited	140
	5.1.3 Stepwise Variable Selection Algorithms	141
	5.1.4 Purposeful Selection of Explanatory Variables	142
	5.1.5 Example: Variable Selection for Horseshoe Crabs	143
	5.1.6 AIC and the Bias/Variance Tradeoff	144
	5.2 MODEL CHECKING	146
	5.2.1 Goodness of Fit: Model Comparison Using the Deviance	146
	5.2.2 Example: Goodness of Fit for Marijuana Use Survey	147
	5.2.3 Goodness of Fit: Grouped versus Ungrouped Data and Continuous Predictors	147
	5.2.4 Residuals for Logistic Models with Categorical Predictors	148
	5.2.5 Example: Graduate Admissions at University of Florida	148
	5.2.6 Standardized versus Pearson and Deviance Residuals	150
	5.2.7 Influence Diagnostics for Logistic Regression	150
	5.2.8 Example: Heart Disease and Blood Pressure	151
	5.3 INFINITE ESTIMATES IN LOGISTIC REGRESSION	152
	5.3.1 Complete and Quasi-Complete Separation: Perfect Discrimination	152
	5.3.2 Example: Infinite Estimate for Toy Example	153
	5.3.3 Sparse Data and Infinite Effects with Categorical Predictors	154
	5.3.4 Example: Risk Factors for Endometrial Cancer Grade	155
	5.4 BAYESIAN INFERENCE, PENALIZED LIKELIHOOD, AND CONDITIONAL LIKELIHOOD FOR LOGISTIC REGRESSION	156
	5.4.1 Bayesian Modeling: Specification of Prior Distributions	157
	5.4.2 Example: Risk Factors for Endometrial Cancer Revisited	157
	5.4.3 Penalized Likelihood Reduces Bias in Logistic Regression	159
	5.4.4 Example: Risk Factors for Endometrial Cancer Revisited	160
	5.4.5 Conditional Likelihood and Conditional Logistic Regression	160
	5.4.6 Conditional Logistic Regression and Exact Tests for Contingency Tables	161
	5.5 ALTERNATIVE LINK FUNCTIONS: LINEAR PROBABILITY AND PROBIT MODELS	161
	5.5.1 Linear Probability Model	162
	5.5.2 Example: Political Ideology and Belief in Evolution	162
	5.5.3 Probit Model and Normal Latent Variable Model	163
	5.5.4 Example: Snoring and Heart Disease Revisited	164
	5.5.5 Latent Variable Models Imply Binary Regression Models	165
	5.5.6 CDFs and Shapes of Curves for Binary Regression Models	165
	5.6 SAMPLE SIZE AND POWER FOR LOGISTIC REGRESSION	166
	5.6.1 Sample Size for Comparing Two Proportions	166
	5.6.2 Sample Size in Logistic Regression Modeling	166
	5.6.3 Example: Modeling the Probability of Heart Disease	167
	Exercises	167
	6 Multicategory Logit Models	175
	6.1 BASELINE-CATEGORY LOGIT MODELS FOR NOMINAL RESPONSES	175
	6.1.1 Baseline-Category Logits	176
	6.1.2 Example: What Do Alligators Eat?	176
	6.1.3 Estimating Response Probabilities	179
	6.1.4 Checking Multinomial Model Goodness of Fit	180
	6.1.5 Example: Belief in Afterlife	180
	6.1.6 Discrete Choice Models	182
	6.1.7 Example: Shopping Destination Choice	183
	6.2 CUMULATIVE LOGIT MODELS FOR ORDINAL RESPONSES	183
	6.2.1 Cumulative Logit Models with Proportional Odds	184
	6.2.2 Example: Political Ideology and Political Party Affiliation	185
	6.2.3 Inference about Cumulative Logit Model Parameters	187
	6.2.4 Increased Power for Ordinal Analyses	188
	6.2.5 Example: Happiness and Family Income	188
	6.2.6 Latent Variable Linear Models Imply Cumulative Link Models	190
	6.2.7 Invariance to Choice of Response Categories	191
	6.3 CUMULATIVE LINK MODELS: MODEL CHECKING AND EXTENSIONS	192
	6.3.1 Checking Ordinal Model Goodness of Fit	192
	6.3.2 Cumulative Logit Model without Proportional Odds	192
	6.3.3 Simpler Interpretations Use Probabilities	194
	6.3.4 Example: Modeling Mental Impairment	194
	6.3.5 A Latent Variable Probability Comparison of Groups	196
	6.3.6 Cumulative Probit Model	197
	6.3.7 R2 Based on the Latent Variable Model	198
	6.3.8 Bayesian Inference for Multinomial Models	199
	6.3.9 Example: Modeling Mental Impairment Revisited	199
	6.4 PAIRED-CATEGORY LOGIT MODELING OF ORDINAL RESPONSES	200
	6.4.1 Adjacent-Categories Logits	200
	6.4.2 Example: Political Ideology Revisited	201
	6.4.3 Sequential Logits	202
	6.4.4 Example: Tonsil Size and Streptococcus	202
	Exercises	203
	7 Loglinear Models for Contingency Tables and Counts	209
	7.1 LOGLINEAR MODELS FOR COUNTS IN CONTINGENCY TABLES	210
	7.1.1 Loglinear Model of Independence for Two-Way Contingency Tables	210
	7.1.2 Interpretation of Parameters in the Independence Model	210
	7.1.3 Example: Happiness and Belief in Heaven	211
	7.1.4 Saturated Model for Two-Way Contingency Tables	212
	7.1.5 Loglinear Models for Three-Way Contingency Tables	213
	7.1.6 Two-Factor Parameters Describe Conditional Associations	213
	7.1.7 Example: Student Alcohol, Cigarette, and Marijuana Use	214
	7.2 STATISTICAL INFERENCE FOR LOGLINEAR MODELS	216
	7.2.1 Chi-Squared Goodness-of-Fit Tests	216
	7.2.2 Cell Standardized Residuals for Loglinear Models	217
	7.2.3 Significance Tests about Conditional Associations	217
	7.2.4 Confidence Intervals for Conditional Odds Ratios	218
	7.2.5 Bayesian Fitting of Loglinear Models	219
	7.2.6 Loglinear Models for Higher-Dimensional Contingency Tables	219
	7.2.7 Example: Automobile Accidents and Seat Belts	220
	7.2.8 Interpreting Three-Factor Interaction Terms	221
	7.2.9 Statistical Versus Practical Significance: Dissimilarity Index	222
	7.3 THE LOGLINEAR – LOGISTIC MODEL CONNECTION	223
	7.3.1 Using Logistic Models to Interpret Loglinear Models	223
	7.3.2 Example: Auto Accident Data Revisited	224
	7.3.3 Condition for Equivalent Loglinear and Logistic Models	225
	7.3.4 Loglinear/Logistic Model Selection Issues	225
	7.4 INDEPENDENCE GRAPHS AND COLLAPSIBILITY	226
	7.4.1 Independence Graphs	226
	7.4.2 Collapsibility Conditions for Contingency Tables	227
	7.4.3 Example: Loglinear Model Building for Student Substance Use	228
	7.4.4 Collapsibility and Logistic Models	229
	7.5 MODELING ORDINAL ASSOCIATIONS IN CONTINGENCY TABLES	230
	7.5.1 Linear-by-Linear Association Model	231
	7.5.2 Example: Linear-by-Linear Association for Sex Opinions	232
	7.5.3 Ordinal Significance Tests of Independence	232
	7.6 LOGLINEAR MODELING OF COUNT RESPONSE VARIABLES	233
	7.6.1 Count Regression Modeling of Rate Data	233
	7.6.2 Example: Death Rates for Lung Cancer Patients	234
	7.6.3 Negative Binomial Regression Models	236
	7.6.4 Example: Female Horseshoe Crab Satellites Revisited	236
	Exercises	237
	8 Models for Matched Pairs	243
	8.1 COMPARING DEPENDENT PROPORTIONS FOR BINARY MATCHED PAIRS	244
	8.1.1 McNemar Test Comparing Marginal Proportions	244
	8.1.2 Estimating the Difference between Dependent Proportions	246
	8.2 MARGINAL MODELS AND SUBJECT-SPECIFIC MODELS FOR MATCHED PAIRS	246
	8.2.1 Marginal Models for Marginal Proportions	246
	8.2.2 Example: Environmental Opinions Revisited	247
	8.2.3 Subject-Specific and Population-Averaged Tables	248
	8.2.4 Conditional Logistic Regression for Matched-Pairs	249
	8.2.5 Logistic Regression for Matched Case-Control Studies	250
	8.3 COMPARING PROPORTIONS FOR NOMINAL MATCHED-PAIRS RESPONSES	251
	8.3.1 Marginal Homogeneity for Baseline-Category Logit Models	251
	8.3.2 Example: Coffee Brand Market Share	251
	8.3.3 Using the Cochran–Mantel–Haenszel Test to Test Marginal Homogeneity	253
	8.3.4 Symmetry and Quasi-Symmetry Models for Square Contingency Tables	253
	8.3.5 Example: Coffee Brand Market Share Revisited	254
	8.4 COMPARING PROPORTIONS FOR ORDINAL MATCHED-PAIRS RESPONSES	255
	8.4.1 Marginal Homogeneity and Cumulative Logit Marginal Model	256
	8.4.2 Example: Recycle or Drive Less to Help the Environment?	256
	8.4.3 An Ordinal Quasi-Symmetry Model	257
	8.4.4 Example: Recycle or Drive Less Revisited?	258
	8.5 ANALYZING RATER AGREEMENT	259
	8.5.1 Example: Agreement on Carcinoma Diagnosis	259
	8.5.2 Cell Residuals for Independence Model	259
	8.5.3 Quasi-Independence Model	260
	8.5.4 Quasi Independence and Odds Ratios Summarizing Agreement	261
	8.5.5 Kappa Summary Measure of Agreement	262
	8.6 BRADLEY–TERRY MODEL FOR PAIRED PREFERENCES	263
	8.6.1 The Bradley–Terry Model and Quasi-Symmetry	263
	8.6.2 Example: Ranking Men Tennis Players	263
	Exercises	265
	9 Marginal Modeling of Correlated, Clustered Responses	269
	9.1 MARGINAL MODELS VERSUS SUBJECT-SPECIFIC MODELS	270
	9.1.1 Marginal Models for a Clustered Binary Response	270
	9.1.2 Example: Repeated Responses on Similar Survey Questions	270
	9.1.3 Subject-Specific Models for a Repeated Response	271
	9.2 MARGINAL MODELING: THE GENERALIZED ESTIMATING EQUATIONS (GEE) APPROACH	271
	9.2.1 Quasi-Likelihood Methods	271
	9.2.2 Generalized Estimating Equation Methodology: Basic Ideas	272
	9.2.3 Example: Opinion about Legalized Abortion Revisited	273
	9.2.4 Limitations of GEE Compared to ML	275
	9.3 MARGINAL MODELING FOR CLUSTERED MULTINOMIAL RESPONSES	276
	9.3.1 Example: Insomnia Study	276
	9.3.2 Alternative GEE Specification of Working Association	278
	9.4 TRANSITIONAL MODELING, GIVEN THE PAST	279
	9.4.1 Transitional Models with Explanatory Variables	279
	9.4.2 Example: Respiratory Illness and Maternal Smoking	279
	9.4.3 Group Comparisons Treating Initial Response as a Covariate	281
	9.5 DEALING WITH MISSING DATA	282
	9.5.1 Missing at Random: Impact on ML and GEE Methods	282
	9.5.2 Multiple Imputation: Monte Carlo Prediction of Missing Data	283
	Exercises	284
	10 Random Effects: Generalized Linear Mixed Models	289
	10.1 RANDOM EFFECTS MODELING OF CLUSTERED CATEGORICAL DATA	289
	10.1.1 The Generalized Linear Mixed Model (GLMM)	290
	10.1.2 A Logistic GLMM for Binary Matched Pairs	290
	10.1.3 Example: Environmental Opinions Revisited	291
	10.1.4 Differing Effects in GLMMs and Marginal Models	292
	10.1.5 Model Fitting for GLMMs	293
	10.1.6 Inference for Model Parameters and Prediction	294
	10.2 EXAMPLES: RANDOM EFFECTS MODELS FOR BINARY DATA	294
	10.2.1 Small-Area Estimation of Binomial Probabilities	294
	10.2.2 Example: Estimating Basketball Free Throw Success	295
	10.2.3 Example: Opinions about Legalized Abortion Revisited	297
	10.2.4 Item Response Models: The Rasch Model	299
	10.2.5 Choice of Marginal Model or Random Effects Model	299
	10.3 EXTENSIONS TO MULTINOMIAL RESPONSES AND MULTIPLE RANDOM EFFECT TERMS	300
	10.3.1 Example: Insomnia Study Revisited	300
	10.3.2 Meta-Analysis: Bivariate Random Effects for Association Heterogeneity	301
	10.4 MULTILEVEL (HIERARCHICAL) MODELS	304
	10.4.1 Example: Two-Level Model for Student Performance	304
	10.4.2 Example: Smoking Prevention and Cessation Study	305
	10.5 LATENT CLASS MODELS	307
	10.5.1 Independence Given a Latent Categorical Variable	307
	10.5.2 Example: Latent Class Model for Rater Agreement	308
	Exercises	311
	11 Classification and Smoothing	315
	11.1 CLASSIFICATION: LINEAR DISCRIMINANT ANALYSIS	316
	11.1.1 Classification with Fisher’s Linear Discriminant Function	316
	11.1.2 Example: Horseshoe Crab Satellites Revisited	317
	11.1.3 Discriminant Analysis Versus Logistic Regression	318
	11.2 CLASSIFICATION: TREE-BASED PREDICTION	318
	11.2.1 Classification Trees	318
	11.2.2 Example: A Classification Tree for Horseshoe Crab Mating	319
	11.2.3 How Does the Classification Tree Grow?	320
	11.2.4 Pruning a Tree and Checking Prediction Accuracy	320
	11.2.5 Classification Trees Versus Logistic Regression and Discriminant Analysis	321
	11.3 CLUSTER ANALYSIS FOR CATEGORICAL RESPONSES	322
	11.3.1 Measuring Dissimilarity Between Observations	322
	11.3.2 Hierarchical Clustering Algorithm and Dendrograms	323
	11.3.3 Example: Clustering States on Presidential Elections	324
	11.4 SMOOTHING: GENERALIZED ADDITIVE MODELS	326
	11.4.1 Generalized Additive Models	326
	11.4.2 Example: GAMs for Horseshoe Crab Data	327
	11.4.3 How Much Smoothing? The Bias/Variance Tradeoff	327
	11.4.4 Example: Smoothing to Portray Probability of Kyphosis	328
	11.5 REGULARIZATION FOR HIGH-DIMENSIONAL CATEGORICAL DATA (LARGE p)	329
	11.5.1 Penalized-Likelihood Methods and Lq-Norm Smoothing	330
	11.5.2 Implementing the Lasso	331
	11.5.3 Example: Predicting Opinion on Abortion with Student Survey	331
	11.5.4 Why Shrink ML Estimates Toward 0?	334
	11.5.5 Issues in Variable Selection (Dimension Reduction)	334
	11.5.6 Controlling the False Discovery Rate	335
	11.5.7 Large p also Makes Bayesian Inference Challenging	337
	Exercises	337
	12 A Historical Tour of Categorical Data Analysis	341
	The Pearson–Yule Association Controversy	341
	R.A. Fisher’s Contributions	342
	Logistic Regression	343
	Multiway Contingency Tables and Loglinear Models	344
	Final Comments	345
	Appendix: Software for Categorical Data Analysis	347
	A.1 R FOR CATEGORICAL DATA ANALYSIS	347
	A.2 SAS FOR CATEGORICAL DATA ANALYSIS	348
	Chapters 1–2: Introduction and Contingency Tables	348
	Chapters 3–5: Generalized Linear Models and Logistic Regression	350
	Chapters 6–7: Multicategory Logit Models and Loglinear Models	352
	Chapter 8: Matched Pairs	354
	Chapters 9–10: Marginal Models and Random Effects Models (GLMMs)	357
	Chapter 11: Non-Model-Based Classification and Clustering	358
	A.3 STATA FOR CATEGORICAL DATA ANALYSIS	358
	Chapters 1–2: Introduction and Contingency Tables	359
	Chapters 3–5: Generalized Linear Models and Logistic Regression	360
	Chapters 6–7: Multicategory Logit Models and Loglinear Models	361
	Chapters 8–11: Correlated Observations, Advanced Methods	362
	A.4 SPSS FOR CATEGORICAL DATA ANALYSIS	362
	Chapters 1–2: Introduction and Contingency Tables	363
	Chapters 3–5: Generalized Linear Models and Logistic Regression	363
	Chapters 6–7: Multicategory Logit Models and Loglinear Models	364
	Chapters 8–11: Correlated Observations, Advanced Methods	364
	Brief Solutions to Odd-Numbered Exercises	365
	Chapter 1	365
	Chapter 2	366
	Chapter 3	367
	Chapter 4	368
	Chapter 5	370
	Chapter 6	371
	Chapter 7	373
	Chapter 8	374
	Chapter 9	375
	Chapter 10	376
	Chapter 11	377
	Bibliography	379
	Examples Index	381
	Subject Index	385
	EULA	392