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Data analysis using regression and multilevel/hierarchical models

Author: Gelman, Andrew ; Hill, Jennifer Series: Analytical methods for social research Publisher: Cambridge University Press (CUP) 2007.Language: EnglishDescription: 625 p. : Graphs ; 24 cm.ISBN: 052168689XType of document: BookBibliography/Index: Includes bibliographical references and index
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book Asia Campus
Main Collection
Print HA29 .G45 2007
(Browse shelf)
900245777
Available 900245777
Book Europe Campus
Main Collection
Print HA29 .G45 2007
(Browse shelf)
001220551
Available 001220551
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Includes bibliographical references and index

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Data Analysis Using Regression and Multilevel/Hierarchical Models Contents List of examples Preface 1 Why? 1.1 What is multilevel regression modeling? 1.2 Some examples from our own research 1.3 Motivations for multilevel modeling 1.4 Distinctive features of this book 1.5 Computing 2 Concepts and methods from basic probability and statistics 2.1 Probability distributions 2.2 Statistical inference 2.3 Classical confidence intervals 2.4 Classical hypothesis testing 2.5 Problems with statistical significance 2.6 55,000 residents desperately need your help! 2.7 Bibliographic note 2.8 Exercises Part 1A: Single-level regression 3 Linear regression: the basics 3.1 One predictor 3.2 Multiple predictors 3.3 Interactions 3.4 Statistical inference 3.5 Graphical displays of data and fitted model 3.6 Assumptions and diagnostics 3.7 Prediction and validation 3.8 Bibliographic note 3.9 Exercises page xvii xix 1 1 3 6 8 9 13 13 16 18 20 22 23 26 26 29 31 31 32 34 37 42 45 47 49 49 53 53 55 57 59 65 73 4 Linear regression: before and after fitting the model 4.1 Linear transformations 4.2 Centering and standardizing, especially for models with interactions 4.3 Correlation and "regression to the mean" 4.4 Logarithmic transformations 4.5 Other transformations 4.6 Building regression models for prediction68 4.7 Fitting a series of regressions ix x 4.8 Bibliographic note 4.9 Exercises CONTENTS 74 74 79 79 81 85 86 92 97 101 104 105 105 109 109 110 116 118 119 124 125 127 131 132 135 137 137 140 144 148 151 152 155 155 157 158 163 165 165 167 167 170 172 178 5 Logistic regression 5.1 Logistic regression with a single predictor 5.2 Interpreting the logistic regression coefficients 5.3 Latent-data formulation 5.4 Building a logistic regression model: wells in Bangladesh 5.5 Logistic regression with interactions 5.6 Evaluating, checking, and comparing fitted logistic regressions 5.7 Average predictive comparisons on the probability scale 5.8 Identifiability and separation 5.9 Bibliographic note 5.10 Exercises 6 Generalized linear models 6.1 Introduction 6.2 Poisson regression, exposure, and overdispersion 6.3 Logistic-binomial model 6.4 Probit regression: normally distributed latent data 6.5 Multinomial regression 6.6 Robust regression using the t model 6.7 Building more complex generalized linear models 6.8 Constructive choice models 6.9 Bibliographic note 6.10 Exercises Part 1B: Working with regression inferences 7 Simulation of probability models and statistical inferences 7.1 Simulation of probability models 7.2 Summarizing linear regressions using simulation: an informal Bayesian approach 7.3 Simulation for nonlinear predictions: congressional elections 7.4 Predictive simulation for generalized linear models 7.5 Bibliographic note 7.6 Exercises 8 Simulation for checking statistical procedures and model fits 8.1 Fake-data simulation 8.2 Example: using fake-data simulation to understand residual plots 8.3 Simulating from the fitted model and comparing to actual data 8.4 Using predictive simulation to check the fit of a time-series model 8.5 Bibliographic note 8.6 Exercises 9 Causal inference using regression on the treatment variable 9.1 Causal inference and predictive comparisons 9.2 The fundamental problem of causal inference 9.3 Randomized experiments 9.4 Treatment interactions and poststratification CONTENTS 9.5 Observational studies 9.6 Understanding causal inference in observational studies 9.7 Do not control for post-treatment variables 9.8 Intermediate outcomes and causal paths 9.9 Bibliographic note 9.10 Exercises xi 181 186 188 190 194 194 10 Causal inference using more advanced models 199 10.1 Imbalance and lack of complete overlap 199 10.2 Subclassification: effects and estimates for different subpopulations 204 10.3 Matching: subsetting the data to get overlapping and balanced treatment and control groups 206 10.4 Lack of overlap when the assignment mechanism is known: regression discontinuity 212 10.5 Estimating causal effects indirectly using instrumental variables 215 10.6 Instrumental variables in a regression framework 220 10.7 Identification strategies that make use of variation within or between groups 226 10.8 Bibliographic note 229 10.9 Exercises 231 Part 2A: Multilevel regression 11 Multilevel structures 11.1 Varying-intercept and varying-slope models 11.2 Clustered data: child support enforcement in cities 11.3 Repeated measurements, time-series cross sections, and other non-nested structures 11.4 Indicator variables and fixed or random effects 11.5 Costs and benefits of multilevel modeling 11.6 Bibliographic note 11.7 Exercises 12 Multilevel linear models: the basics 12.1 Notation 12.2 Partial pooling with no predictors 12.3 Partial pooling with predictors 12.4 Quickly fitting multilevel models in R 12.5 Five ways to write the same model 12.6 Group-level predictors 12.7 Model building and statistical significance 12.8 Predictions for new observations and new groups 12.9 How many groups and how many observations per group are needed to fit a multilevel model? 12.10 Bibliographic note 12.11 Exercises 13 Multilevel linear models: varying slopes, non-nested models, and other complexities 13.1 Varying intercepts and slopes 13.2 Varying slopes without varying intercepts 235 237 237 237 241 244 246 247 248 251 251 252 254 259 262 265 270 272 275 276 277 279 279 283 xii CONTENTS 13.3 Modeling multiple varying coefficients using the scaled inverse Wishart distribution 13.4 Understanding correlations between group-level intercepts and slopes 13.5 Non-nested models 13.6 Selecting, transforming, and combining regression inputs 13.7 More complex multilevel models 13.8 Bibliographic note 13.9 Exercises 284 287 289 293 297 297 298 301 301 310 314 320 321 322 325 325 331 332 342 342 343 345 345 345 348 353 359 361 363 366 369 370 373 373 14 Multilevel logistic regression 14.1 State-level opinions from national polls 14.2 Red states and blue states: what's the matter with Connecticut? 14.3 Item-response and ideal-point models 14.4 Non-nested overdispersed model for death sentence reversals 14.5 Bibliographic note 14.6 Exercises 15 Multilevel generalized linear models 15.1 Overdispersed Poisson regression: police stops and ethnicity 15.2 Ordered categorical regression: storable votes 15.3 Non-nested negative-binomial model of structure in social networks 15.4 Bibliographic note 15.5 Exercises Part 2B: Fitting multilevel models 16 Multilevel modeling in Bugs and R: the basics 16.1 Why you should learn Bugs 16.2 Bayesian inference and prior distributions 16.3 Fitting and understanding a varying-intercept multilevel model using R and Bugs 16.4 Step by step through a Bugs model, as called from R 16.5 Adding individual- and group-level predictors 16.6 Predictions for new observations and new groups 16.7 Fake-data simulation 16.8 The principles of modeling in Bugs 16.9 Practical issues of implementation 16.10 Open-ended modeling in Bugs 16.11 Bibliographic note 16.12 Exercises 17 Fitting multilevel linear and generalized linear models in Bugs and R 17.1 Varying-intercept, varying-slope models 17.2 Varying intercepts and slopes with group-level predictors 17.3 Non-nested models 17.4 Multilevel logistic regression 17.5 Multilevel Poisson regression 17.6 Multilevel ordered categorical regression 17.7 Latent-data parameterizations of generalized linear models 375 375 379 380 381 382 383 384 CONTENTS 17.8 Bibliographic note 17.9 Exercises 18 Likelihood and Bayesian inference and computation 18.1 Least squares and maximum likelihood estimation 18.2 Uncertainty estimates using the likelihood surface 18.3 Bayesian inference for classical and multilevel regression 18.4 Gibbs sampler for multilevel linear models 18.5 Likelihood inference, Bayesian inference, and the Gibbs sampler: the case of censored data 18.6 Metropolis algorithm for more general Bayesian computation 18.7 Specifying a log posterior density, Gibbs sampler, and Metropolis algorithm in R 18.8 Bibliographic note 18.9 Exercises 19 Debugging and speeding convergence 19.1 Debugging and confidence building 19.2 General methods for reducing computational requirements 19.3 Simple linear transformations 19.4 Redundant parameters and intentionally nonidentifiable models 19.5 Parameter expansion: multiplicative redundant parameters 19.6 Using redundant parameters to create an informative prior distribution for multilevel variance parameters 19.7 Bibliographic note 19.8 Exercises Part 3: From data collection to model understanding to model checking 20 Sample size and power calculations 20.1 Choices in the design of data collection 20.2 Classical power calculations: general principles, as illustrated by estimates of proportions 20.3 Classical power calculations for continuous outcomes 20.4 Multilevel power calculation for cluster sampling 20.5 Multilevel power calculation using fake-data simulation 20.6 Bibliographic note 20.7 Exercises 21 Understanding and summarizing the fitted models 21.1 Uncertainty and variability 21.2 Superpopulation and finite-population variances 21.3 Contrasts and comparisons of multilevel coefficients 21.4 Average predictive comparisons 21.5 R2 and explained variance 21.6 Summarizing the amount of partial pooling 21.7 Adding a predictor can increase the residual variance! 21.8 Multiple comparisons and statistical significance 21.9 Bibliographic note 21.10 Exercises 385 385 387 387 390 392 397 402 408 409 413 413 415 415 418 419 419 424 427 434 434 435 437 437 439 443 447 449 454 454 457 457 459 462 466 473 477 480 481 484 485 xiii xiv 22 Analysis of variance 22.1 Classical analysis of variance 22.2 ANOVA and multilevel linear and generalized linear models 22.3 Summarizing multilevel models using ANOVA 22.4 Doing ANOVA using multilevel models 22.5 Adding predictors: analysis of covariance and contrast analysis 22.6 Modeling the variance parameters: a split-plot latin square 22.7 Bibliographic note 22.8 Exercises 23 Causal inference using multilevel models 23.1 Multilevel aspects of data collection 23.2 Estimating treatment effects in a multilevel observational study 23.3 Treatments applied at different levels 23.4 Instrumental variables and multilevel modeling 23.5 Bibliographic note 23.6 Exercises 24 Model checking and comparison 24.1 Principles of predictive checking 24.2 Example: a behavioral learning experiment 24.3 Model comparison and deviance 24.4 Bibliographic note 24.5 Exercises 25 Missing-data imputation 25.1 Missing-data mechanisms 25.2 Missing-data methods that discard data 25.3 Simple missing-data approaches that retain all the data 25.4 Random imputation of a single variable 25.5 Imputation of several missing variables 25.6 Model-based imputation 25.7 Combining inferences from multiple imputations 25.8 Bibliographic note 25.9 Exercises Appendixes - CONTENTS 487 487 490 492 494 496 498 501 501 503 503 506 507 509 512 512 513 513 515 524 526 527 529 530 531 532 533 539 540 542 542 543 545 547 547 547 548 548 549 549 551 552 553 559 A Six quick tips to improve your regression modeling A.1 Fit many models A.2 Do a little work to make your computations faster and more reliable A.3 Graphing the relevant and not the irrelevant A.4 Transformations A.5 Consider all coefficients as potentially varying A.6 Estimate causal inferences in a targeted way, not as a byproduct of a large regression B Statistical graphics for research and presentation B.1 Reformulating a graph by focusing on comparisons B.2 Scatterplots B.3 Miscellaneous tips CONTENTS B.4 Bibliographic note B.5 Exercises C Software C.1 Getting started with R, Bugs, and a text editor C.2 Fitting classical and multilevel regressions in R C.3 Fitting models in Bugs and R C.4 Fitting multilevel models using R, Stata, SAS, and other software C.5 Bibliographic note References Author index Subject index xv 562 563 565 565 565 567 568 573 575 601 607

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