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2. Data Analysis Using Regression and Multilevel/Hierarchical Models Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces and demonstrates a wide variety of models, at the same time instructing the reader in how to t these models using freely available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen in the authors own applied research, with pro- gramming code provided for each one. Topics covered include causal inference, including regression, poststratication, matching, regression discontinuity, and instrumental vari- ables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, tting, and understanding are provided throughout. Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published more than 150 articles in statistical theory, methods, and computation and in applications areas including decision analysis, survey sampling, polit- ical science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002). Jennifer Hill is Assistant Professor of Public Aairs in the Department of International and Public Aairs at Columbia University. She has coauthored articles that have appeared in the Journal of the American Statistical Association, American Political Science Review, American Journal of Public Health, Developmental Psychology, the Economic Journal, and the Journal of Policy Analysis and Management, among others. 3. Analytical Methods for Social Research Analytical Methods for Social Research presents texts on empirical and formal methods for the social sciences. Volumes in the series address both the theoretical underpinnings of analytical techniques and their application in social research. Some series volumes are broad in scope, cutting across a number of disciplines. Others focus mainly on method- ological applications within specic elds such as political science, sociology, demography, and public health. The series serves a mix of students and researchers in the social sciences and statistics. Series Editors: R. Michael Alvarez, California Institute of Technology Nathaniel L. Beck, New York University Lawrence L. Wu, New York University Other Titles in the Series: Event History Modeling: A Guide for Social Scientists, by Janet M. Box-Steensmeier and Bradford S. Jones Ecological Inference: New Methodological Strategies, edited by Gary King, Ori Rosen, and Martin A. Tanner Spatial Models of Parliamentary Voting, by Keith T. Poole Essential Mathematics for Political and Social Research, by Je Gill Political Game Theory: An Introduction, by Nolan McCarty and Adam Meirowitz 4. Data Analysis Using Regression and Multilevel/Hierarchical Models ANDREW GELMAN Columbia University JENNIFER HILL Columbia University 5. CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, So Paulo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK First published in print format ISBN-13 978-0-521-86706-1 ISBN-13 978-0-521-68689-1 ISBN-13 978-0-511-26878-6 Andrew Gelman and Jennifer Hill 2007 2006 Information on this title: www.cambridge.org/9780521867061 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. ISBN-10 0-511-26878-5 ISBN-10 0-521-86706-1 ISBN-10 0-521-68689-X Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Published in the United States of America by Cambridge University Press, New York www.cambridge.org hardback paperback paperback eBook (EBL) eBook (EBL) hardback 6. Data Analysis Using Regression and Multilevel/Hierarchical Models (Corrected nal version: 9 Aug 2006) Please do not reproduce in any form without permission Andrew Gelman Department of Statistics and Department of Political Science Columbia University, New York Jennifer Hill School of International and Public Aairs Columbia University, New York c 2002, 2003, 2004, 2005, 2006 by Andrew Gelman and Jennifer Hill To be published in October, 2006 by Cambridge University Press 7. For Zacky and for Audrey 8. Contents List of examples page xvii Preface xix 1 Why? 1 1.1 What is multilevel regression modeling? 1 1.2 Some examples from our own research 3 1.3 Motivations for multilevel modeling 6 1.4 Distinctive features of this book 8 1.5 Computing 9 2 Concepts and methods from basic probability and statistics 13 2.1 Probability distributions 13 2.2 Statistical inference 16 2.3 Classical condence intervals 18 2.4 Classical hypothesis testing 20 2.5 Problems with statistical signicance 22 2.6 55,000 residents desperately need your help! 23 2.7 Bibliographic note 26 2.8 Exercises 26 Part 1A: Single-level regression 29 3 Linear regression: the basics 31 3.1 One predictor 31 3.2 Multiple predictors 32 3.3 Interactions 34 3.4 Statistical inference 37 3.5 Graphical displays of data and tted model 42 3.6 Assumptions and diagnostics 45 3.7 Prediction and validation 47 3.8 Bibliographic note 49 3.9 Exercises 49 4 Linear regression: before and after tting the model 53 4.1 Linear transformations 53 4.2 Centering and standardizing, especially for models with interactions 55 4.3 Correlation and regression to the mean 57 4.4 Logarithmic transformations 59 4.5 Other transformations 65 4.6 Building regression models for prediction 68 4.7 Fitting a series of regressions 73 ix 9. x CONTENTS 4.8 Bibliographic note 74 4.9 Exercises 74 5 Logistic regression 79 5.1 Logistic regression with a single predictor 79 5.2 Interpreting the logistic regression coecients 81 5.3 Latent-data formulation 85 5.4 Building a logistic regression model: wells in Bangladesh 86 5.5 Logistic regression with interactions 92 5.6 Evaluating, checking, and comparing tted logistic regressions 97 5.7 Average predictive comparisons on the probability scale 101 5.8 Identiability and separation 104 5.9 Bibliographic note 105 5.10 Exercises 105 6 Generalized linear models 109 6.1 Introduction 109 6.2 Poisson regression, exposure, and overdispersion 110 6.3 Logistic-binomial model 116 6.4 Probit regression: normally distributed latent data 118 6.5 Multinomial regression 119 6.6 Robust regression using the t model 124 6.7 Building more complex generalized linear models 125 6.8 Constructive choice models 127 6.9 Bibliographic note 131 6.10 Exercises 132 Part 1B: Working with regression inferences 135 7 Simulation of probability models and statistical inferences 137 7.1 Simulation of probability models 137 7.2 Summarizing linear regressions using simulation: an informal Bayesian approach 140 7.3 Simulation for nonlinear predictions: congressional elections 144 7.4 Predictive simulation for generalized linear models 148 7.5 Bibliographic note 151 7.6 Exercises 152 8 Simulation for checking statistical procedures and model ts 155 8.1 Fake-data simulation 155 8.2 Example: using fake-data simulation to understand residual plots 157 8.3 Simulating from the tted model and comparing to actual data 158 8.4 Using predictive simulation to check the t of a time-series model 163 8.5 Bibliographic note 165 8.6 Exercises 165 9 Causal inference using regression on the treatment variable 167 9.1 Causal inference and predictive comparisons 167 9.2 The fundamental problem of causal inference 170 9.3 Randomized experiments 172 9.4 Treatment interactions and poststratication 178 10. CONTENTS xi 9.5 Observational studies 181 9.6 Understanding causal inference in observational studies 186 9.7 Do not control for post-treatment variables 188 9.8 Intermediate outcomes and causal paths 190 9.9 Bibliographic note 194 9.10 Exercises 194 10 Causal inference using more advanced models 199 10.1 Imbalance and lack of complete overlap 199 10.2 Subclassication: eects and estimates for dierent 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 eects indirectly using instrumental variables 215 10.6 Instrumental variables in a regression framework 220 10.7 Identication strategies that make use of variation within or between groups 226 10.8 Bibliographic note 229 10.9 Exercises 231 Part 2A: Multilevel regression 235 11 Multilevel structures 237 11.1 Varying-intercept and varying-slope models 237 11.2 Clustered data: child support enforcement in cities 237 11.3 Repeated measurements, time-series cross sections, and other non-nested structures 241 11.4 Indicator variables and xed or random eects 244 11.5 Costs and benets of multilevel modeling 246 11.6 Bibliographic note 247 11.7 Exercises 248 12 Multilevel linear models: the basics 251 12.1 Notation 251 12.2 Partial pooling with no predictors 252 12.3 Partial pooling with predictors 254 12.4 Quickly tting multilevel models in R 259 12.5 Five ways to write the same model 262 12.6 Group-level predictors 265 12.7 Model building and statistical signicance 270 12.8 Predictions for new observations and new groups 272 12.9 How many groups and how many observations per group are needed to t a multilevel model? 275 12.10 Bibliographic note 276 12.11 Exercises 277 13 Multilevel linear models: varying slopes, non-nested models, and other complexities 279 13.1 Varying intercepts and slopes 279 13.2 Varying slopes without varying intercepts 283 11. xii CONTENTS 13.3 Modeling multiple varying coecients using the scaled inverse- Wishart distribution 284 13.4 Understanding correlations between group-level intercepts and slopes 287 13.5 Non-nested models 289 13.6 Selecting, transforming, and combining regression inputs 293 13.7 More complex multilevel models 297 13.8 Bibliographic note 297 13.9 Exercises 298 14 Multilevel logistic regression 301 14.1 State-level opinions from national polls 301 14.2 Red states and blue states: whats the matter with Connecticut? 310 14.3 Item-response and ideal-point models 314 14.4 Non-nested overdispersed model for death sentence reversals 320 14.5 Bibliographic note 321 14.6 Exercises 322 15 Multilevel generalized linear models 325 15.1 Overdispersed Poisson regression: police stops and ethnicity 325 15.2 Ordered categorical regression: storable votes 331 15.3 Non-nested negative-binomial model of structure in social networks 332 15.4 Bibliographic note 342 15.5 Exercises 342 Part 2B: Fitting multilevel models 343 16 Multilevel modeling in Bugs and R: the basics 345 16.1 Why you should learn Bugs 345 16.2 Bayesian inference and prior distributions 345 16.3 Fitting and understanding a varying-intercept multilevel model using R and Bugs 348 16.4 Step by step through a Bugs model, as called from R 353 16.5 Adding individual- and group-level predictors 359 16.6 Predictions for new observations and new groups 361 16.7 Fake-data simulation 363 16.8 The principles of modeling in Bugs 366 16.9 Practical issues of implementation 369 16.10 Open-ended modeling in Bugs 370 16.11 Bibliographic note 373 16.12 Exercises 373 17 Fitting multilevel linear and generalized linear models in Bugs and R 375 17.1 Varying-intercept, varying-slope models 375 17.2 Varying intercepts and slopes with group-level predictors 379 17.3 Non-nested models 380 17.4 Multilevel logistic regression 381 17.5 Multilevel Poisson regression 382 17.6 Multilevel ordered categorical regression 383 17.7 Latent-data parameterizations of generalized linear models 384 12. CONTENTS xiii 17.8 Bibliographic note 385 17.9 Exercises 385 18 Likelihood and Bayesian inference and computation 387 18.1 Least squares and maximum likelihood estimation 387 18.2 Uncertainty estimates using the likelihood surface 390 18.3 Bayesian inference for classical and multilevel regression 392 18.4 Gibbs sampler for multilevel linear models 397 18.5 Likelihood inference, Bayesian inference, and the Gibbs sampler: the case of censored data 402 18.6 Metropolis algorithm for more general Bayesian computation 408 18.7 Specifying a log posterior density, Gibbs sampler, and Metropolis algorithm in R 409 18.8 Bibliographic note 413 18.9 Exercises 413 19 Debugging and speeding convergence 415 19.1 Debugging and condence building 415 19.2 General methods for reducing computational requirements 418 19.3 Simple linear transformations 419 19.4 Redundant parameters and intentionally nonidentiable models 419 19.5 Parameter expansion: multiplicative redundant parameters 424 19.6 Using redundant parameters to create an informative prior distribution for multilevel variance parameters 427 19.7 Bibliographic note 434 19.8 Exercises 434 Part 3: From data collection to model understanding to model checking 435 20 Sample size and power calculations 437 20.1 Choices in the design of data collection 437 20.2 Classical power calculations: general principles, as illustrated by estimates of proportions 439 20.3 Classical power calculations for continuous outcomes 443 20.4 Multilevel power calculation for cluster sampling 447 20.5 Multilevel power calculation using fake-data simulation 449 20.6 Bibliographic note 454 20.7 Exercises 454 21 Understanding and summarizing the tted models 457 21.1 Uncertainty and variability 457 21.2 Superpopulation and nite-population variances 459 21.3 Contrasts and comparisons of multilevel coecients 462 21.4 Average predictive comparisons 466 21.5 R2 and explained variance 473 21.6 Summarizing the amount of partial pooling 477 21.7 Adding a predictor can increase the residual variance! 480 21.8 Multiple comparisons and statistical signicance 481 21.9 Bibliographic note 484 21.10 Exercises 485 13. xiv CONTENTS 22 Analysis of variance 487 22.1 Classical analysis of variance 487 22.2 ANOVA and multilevel linear and generalized linear models 490 22.3 Summarizing multilevel models using ANOVA 492 22.4 Doing ANOVA using multilevel models 494 22.5 Adding predictors: analysis of covariance and contrast analysis 496 22.6 Modeling the variance parameters: a split-plot latin square 498 22.7 Bibliographic note 501 22.8 Exercises 501 23 Causal inference using multilevel models 503 23.1 Multilevel aspects of data collection 503 23.2 Estimating treatment eects in a multilevel observational study 506 23.3 Treatments applied at dierent levels 507 23.4 Instrumental variables and multilevel modeling 509 23.5 Bibliographic note 512 23.6 Exercises 512 24 Model checking and comparison 513 24.1 Principles of predictive checking 513 24.2 Example: a behavioral learning experiment 515 24.3 Model comparison and deviance 524 24.4 Bibliographic note 526 24.5 Exercises 527 25 Missing-data imputation 529 25.1 Missing-data mechanisms 530 25.2 Missing-data methods that discard data 531 25.3 Simple missing-data approaches that retain all the data 532 25.4 Random imputation of a single variable 533 25.5 Imputation of several missing variables 539 25.6 Model-based imputation 540 25.7 Combining inferences from multiple imputations 542 25.8 Bibliographic note 542 25.9 Exercises 543 Appendixes 545 A Six quick tips to improve your regression modeling 547 A.1 Fit many models 547 A.2 Do a little work to make your computations faster and more reliable 547 A.3 Graphing the relevant and not the irrelevant 548 A.4 Transformations 548 A.5 Consider all coecients as potentially varying 549 A.6 Estimate causal inferences in a targeted way, not as a byproduct of a large regression 549 B Statistical graphics for research and presentation 551 B.1 Reformulating a graph by focusing on comparisons 552 B.2 Scatterplots 553 B.3 Miscellaneous tips 559 14. CONTENTS xv B.4 Bibliographic note 562 B.5 Exercises 563 C Software 565 C.1 Getting started with R, Bugs, and a text editor 565 C.2 Fitting classical and multilevel regressions in R 565 C.3 Fitting models in Bugs and R 567 C.4 Fitting multilevel models using R, Stata, SAS, and other software 568 C.5 Bibliographic note 573 References 575 Author index 601 Subject index 607 15. List of examples Home radon 3, 36, 252, 279, 479 Forecasting elections 3, 144 State-level opinions from national polls 4, 301, 493 Police stops by ethnic group 5, 21, 112, 325 Public opinion on the death penalty 19 Testing for election fraud 23 Sex ratio of births 27, 137 Mothers education and childrens test scores 31, 55 Height and weight 41, 75 Beauty and teaching evaluations 51, 277 Height and earnings 53, 59, 140, 288 Handedness 66 Yields of mesquite bushes 70 Political party identication over time 73 Income and voting 79, 107 Arsenic in drinking water 86, 128, 193 Death-sentencing appeals process 116, 320, 540 Ordered logistic model for storable votes 120, 331 Cockroaches in apartments 126, 161 Behavior of couples at risk for HIV 132, 166 Academy Award voting 133 Incremental cost-eectiveness ratio 152 Unemployment time series 163 The Electric Company TV show 174, 503 Hypothetical study of parenting quality as an intermediate outcome 188 Sesame Street TV show 196 Messy randomized experiment of cow feed 196 Incumbency and congressional elections 197 xvii 16. xviii LIST OF EXAMPLES Value of a statistical life 197 Evaluating the Infant Health and Development Program 201, 506 Ideology of congressmembers 213 Hypothetical randomized-encouragement study 216 Child support enforcement 237 Adolescent smoking 241 Rodents in apartments 248 Olympic judging 248 Time series of childrens CD4 counts 249, 277, 449 Flight simulator experiment 289, 464, 488 Latin square agricultural experiment 292, 497 Income and voting by state 310 Item-response models 314 Ideal-point modeling for the Supreme Court 317 Speed dating 322 Social networks 332 Regression with censored data 402 Educational testing experiments 430 Zinc for HIV-positive children 439 Cluster sampling of New York City residents 448 Value added of school teachers 458 Advanced Placement scores and college grades 463 Prison sentences 470 Magnetic elds and brain functioning 481 Analysis of variance for web connect times 492 Split-plot latin square 498 Educational-subsidy program in Mexican villages 508 Checking models of behavioral learning in dogs 515 Missing data in the Social Indicators Survey 529 17. Preface Aim of this book This book originated as lecture notes for a course in regression and multilevel mod- eling, oered by the statistics department at Columbia University and attended by graduate students and postdoctoral researchers in social sciences (political sci- ence, economics, psychology, education, business, social work, and public health) and statistics. The prerequisite is statistics up to and including an introduction to multiple regression. Advanced mathematics is not assumedit is important to understand the linear model in regression, but it is not necessary to follow the matrix algebra in the derivation of least squares computations. It is useful to be familiar with exponents and logarithms, especially when working with generalized linear models. After completing Part 1 of this book, you should be able to t classical linear and generalized linear regression modelsand do more with these models than simply look at their coecients and their statistical signicance. Applied goals include causal inference, prediction, comparison, and data description. After completing Part 2, you should be able to t regression models for multilevel data. Part 3 takes you from data collection, through model understanding (looking at a table of estimated coecients is usually not enough), to model checking and missing data. The appendixes include some reference materials on key tips, statistical graphics, and software for model tting. What you should be able to do after reading this book and working through the examples This text is structured through models and examples, with the intention that after each chapter you should have certain skills in tting, understanding, and displaying models: Part 1A: Fit, understand, and graph classical regressions and generalized linear models. Chapter 3: Fit linear regressions and be able to interpret and display estimated coecients. Chapter 4: Build linear regression models by transforming and combining variables. Chapter 5: Fit, understand, and display logistic regression models for binary data. Chapter 6: Fit, understand, and display generalized linear models, including Poisson regression with overdispersion and ordered logit and probit models. Part 1B: Use regression to learn about quantities of substantive interest (not just regression coecients). Chapter 7: Simulate probability models and uncertainty about inferences and predictions. xix 18. xx PREFACE Chapter 8: Check model ts using fake-data simulation and predictive simu- lation. Chapter 9: Understand assumptions underlying causal inference. Set up re- gressions for causal inference and understand the challenges that arise. Chapter 10: Understand the assumptions underlying propensity score match- ing, instrumental variables, and other techniques to perform causal inference when simple regression is not enough. Be able to use these when appropriate. Part 2A: Understand and graph multilevel models. Chapter 11: Understand multilevel data structures and models as generaliza- tions of classical regression. Chapter 12: Understand and graph simple varying-intercept regressions and interpret as partial-pooling estimates. Chapter 13: Understand and graph multilevel linear models with varying in- tercepts and slopes, non-nested structures, and other complications. Chapter 14: Understand and graph multilevel logistic models. Chapter 15: Understand and graph multilevel overdispersed Poisson, ordered logit and probit, and other generalized linear models. Part 2B: Fit multilevel models using the software packages R and Bugs. Chapter 16: Fit varying-intercept regressions and understand the basics of Bugs. Check your programming using fake-data simulation. Chapter 17: Use Bugs to t various models from Part 2A. Chapter 18: Understand Bayesian inference as a generalization of least squares and maximum likelihood. Use the Gibbs sampler to t multilevel models. Chapter 19: Use redundant parameterizations to speed the convergence of the Gibbs sampler. Part 3: Chapter 20: Perform sample size and power calculations for classical and hier- archical models: standard-error formulas for basic calculations and fake-data simulation for harder problems. Chapter 21: Calculate and understand contrasts, explained variance, partial pooling coecients, and other summaries of tted multilevel models. Chapter 22: Use the ideas of analysis of variance to summarize tted multilevel models; use multilevel models to perform analysis of variance. Chapter 23: Use multilevel models in causal inference. Chapter 24: Check the t of models using predictive simulation. Chapter 25: Use regression to impute missing data in multivariate datasets. In summary, you should be able to t, graph, and understand classical and mul- tilevel linear and generalized linear models and to use these model ts to make predictions and inferences about quantities of interest, including causal treatment eects. 19. PREFACE xxi Data for the examples and homework assignments and other resources for teaching and learning The website www.stat.columbia.edu/gelman/arm/ contains datasets used in the examples and homework problems of the book, as well as sample computer code. The website also includes some tips for teaching regression and multilevel modeling through class participation rather than lecturing. We plan to update these tips based on feedback from instructors and students; please send your comments and suggestions to gelman@stat.columbia.edu. Outline of a course When teaching a course based on this book, we recommend starting with a self- contained review of linear regression, logistic regression, and generalized linear mod- els, focusing not on the mathematics but on understanding these methods and im- plementing them in a reasonable way. This is also a convenient way to introduce the statistical language R, which we use throughout for modeling, computation, and graphics. One thing that will probably be new to the reader is the use of random simulations to summarize inferences and predictions. We then introduce multilevel models in the simplest case of nested linear models, tting in the Bayesian modeling language Bugs and examining the results in R. Key concepts covered at this point are partial pooling, variance components, prior distributions, identiability, and the interpretation of regression coecients at dif- ferent levels of the hierarchy. We follow with non-nested models, multilevel logistic regression, and other multilevel generalized linear models. Next we detail the steps of tting models in Bugs and give practical tips for repa- rameterizing a model to make it converge faster and additional tips on debugging. We also present a brief review of Bayesian inference and computation. Once the student is able to t multilevel models, we move in the nal weeks of the class to the nal part of the book, which covers more advanced issues in data collection, model understanding, and model checking. As we show throughout, multilevel modeling ts into a view of statistics that unies substantive modeling with accurate data tting, and graphical methods are crucial both for seeing unanticipated features in the data and for understanding the implications of tted models. Acknowledgments We thank the many students and colleagues who have helped us understand and implement these ideas. Most important have been Jouni Kerman, David Park, and Joe Bafumi for years of suggestions throughout this project, and for many insights into how to present this material to students. In addition, we thank Hal Stern and Gary King for discussions on the structure of this book; Chuanhai Liu, Xiao-Li Meng, Zaiying Huang, John Boscardin, Jouni Kerman, and Alan Zaslavsky for discussions about statistical computation; Iven Van Mechelen and Hans Berkhof for discussions about model checking; Iain Par- doe for discussions of average predictive eects and other summaries of regression models; Matt Salganik and Wendy McKelvey for suggestions on the presentation of sample size calculations; T. E. Raghunathan, Donald Rubin, Rajeev Dehejia, Michael Sobel, Guido Imbens, Samantha Cook, Ben Hansen, Dylan Small, and Ed Vytlacil for concepts of missing-data modeling and causal inference; Eric Loken for help in understanding identiability in item-response models; Niall Bolger, Agustin 20. xxii PREFACE Calatroni, John Carlin, Rafael Guerrero-Preston, Reid Landes, Eduardo Leoni, and Dan Rabinowitz for code in Stata, SAS, and SPSS; Hans Skaug for code in AD Model Builder; Uwe Ligges, Sibylle Sturtz, Douglas Bates, Peter Dalgaard, Martyn Plummer, and Ravi Varadhan for help with multilevel modeling and general advice on R; and the students in Statistics / Political Science 4330 at Columbia for their invaluable feedback throughout. Collaborators on specic examples mentioned in this book include Phillip Price on the home radon study; Tom Little, David Park, Joe Bafumi, and Noah Kaplan on the models of opinion polls and political ideal points; Jane Waldfogel, Jeanne Brooks-Gunn, and Wen Han for the mothers and childrens intelligence data; Lex van Geen and Alex Pfa on the arsenic in Bangladesh; Gary King on election forecasting; Jerey Fagan and Alex Kiss on the study of police stops; Tian Zheng and Matt Salganik on the social network analysis; John Carlin for the data on mesquite bushes and the adolescent-smoking study; Alessandra Casella and Tom Palfrey for the storable-votes study; Rahul Dodhia for the ight simulator exam- ple; Boris Shor, Joe Bafumi, and David Park on the voting and income study; Alan Edelman for the internet connections data; Donald Rubin for the Electric Com- pany and educational-testing examples; Jeanne Brooks-Gunn and Jane Waldfogel for the mother and child IQ scores example and Infant Health and Development Program data; Nabila El-Bassel for the risky behavior data; Lenna Nepomnyaschy for the child support example; Howard Wainer with the Advanced Placement study; Iain Pardoe for the prison-sentencing example; James Liebman, Jerey Fagan, Va- lerie West, and Yves Chretien for the death-penalty study; Marcia Meyers, Julien Teitler, Irv Garnkel, Marilyn Sinkowicz, and Sandra Garcia with the Social Indi- cators Study; Wendy McKelvey for the cockroach and rodent examples; Stephen Arpadi for the zinc and HIV study; Eric Verhoogen and Jan von der Goltz for the Progresa data; and Iven van Mechelen, Yuri Goegebeur, and Francis Tuerlincx on the stochastic learning models. These applied projects motivated many of the methodological ideas presented here, for example the display and interpretation of varying-intercept, varying-slope models from the analysis of income and voting (see Section 14.2), the constraints in the model of senators ideal points (see Section 14.3), and the diculties with two-level interactions as revealed by the radon study (see Section 21.7). Much of the work in Section 5.7 and Chapter 21 on summarizing regression models was done in collaboration with Iain Pardoe. Many errors were found and improvements suggested by Brad Carlin, John Car- lin, Samantha Cook, Caroline Rosenthal Gelman, Kosuke Imai, Jonathan Katz, Uwe Ligges, Wendy McKelvey, Jong-Hee Park, Martyn Plummer, Phillip Price, Song Qian, Dylan Small, Elizabeth Stuart, Sibylle Sturtz, and Alex Tabarrok. Brian MacDonalds copyediting has saved us from much embarrassment, and we also thank Yu-Sung Su for typesetting help, Sarah Ryu for assistance with index- ing, and Ed Parsons and his colleagues at Cambridge University Press for their help in putting this book together. We especially thank Bob OHara and Gregor Gorjanc for incredibly detailed and useful comments on the nearly completed manuscript. We also thank the developers of free software, especially R (for statistical com- putation and graphics) and Bugs (for Bayesian modeling), and also Emacs and LaTex (used in the writing of this book). We thank Columbia University for its collaborative environment for research and teaching, and the U.S. National Science Foundation for nancial support. Above all, we thank our families for their love and support during the writing of this book. 21. CHAPTER 1 Why? 1.1 What is multilevel regression modeling? Consider an educational study with data from students in many schools, predicting in each school the students grades y on a standardized test given their scores on a pre-test x and other information. A separate regression model can be t within each school, and the parameters from these schools can themselves be modeled as depending on school characteristics (such as the socioeconomic status of the schools neighborhood, whether the school is public or private, and so on). The student-level regression and the school-level regression here are the two levels of a multilevel model. In this example, a multilevel model can be expressed in (at least) three equivalent ways as a student-level regression: A model in which the coecients vary by school (thus, instead of a model such as y = +x+error, we have y = j +jx+error, where the subscripts j index schools), A model with more than one variance component (student-level and school-level variation), A regression with many predictors, including an indicator variable for each school in the data. More generally, we consider a multilevel model to be a regression (a linear or gen- eralized linear model) in which the parametersthe regression coecientsare given a probability model. This second-level model has parameters of its ownthe hyperparameters of the modelwhich are also estimated from data. The two key parts of a multilevel model are varying coecients, and a model for those varying coecients (which can itself include group-level predictors). Classi- cal regression can sometimes accommodate varying coecients by using indicator variables. The feature that distinguishes multilevel models from classical regression is in the modeling of the variation between groups. Models for regression coecients To give a preview of our notation, we write the regression equations for two multi- level models. To keep notation simple, we assume just one student-level predictor x (for example, a pre-test score) and one school-level predictor u (for example, average parents incomes). Varying-intercept model. First we write the model in which the regressions have the same slope in each of the schools, and only the intercepts vary. We use the 1 22. 2 WHY? notation i for individual students and j[i] for the school j containing student i:1 yi = j[i] + xi + i, for students i = 1, . . . , n j = a + buj + j, for schools j = 1, . . . , J. (1.1) Here, xi and uj represent predictors at the student and school levels, respectively, and i and j are independent error terms at each of the two levels. The model can be written in several other equivalent ways, as we discuss in Section 12.5. The number of data points J (here, schools) in the higher-level regression is typically much less than n, the sample size of the lower-level model (for students in this example). Varying-intercept, varying-slope model. More complicated is the model where in- tercepts and slopes both can vary by school: yi = j[i] + j[i]xi + i, for students i = 1, . . . , n j = a0 + b0uj + j1, for schools j = 1, . . . , J j = a1 + b1uj + j2, for schools j = 1, . . . , J. Compared to model (1.1), this has twice as many vectors of varying coecients (, ), twice as many vectors of second-level coecients (a, b), and potentially cor- related second-level errors 1, 2. We will be able to handle these complications. Labels Multilevel or hierarchical. Multilevel models are also called hierarchical, for two dierent reasons: rst, from the structure of the data (for example, students clustered within schools); and second, from the model itself, which has its own hier- archy, with the parameters of the within-school regressions at the bottom, controlled by the hyperparameters of the upper-level model. Later we shall consider non-nested modelsfor example, individual observations that are nested within states and years. Neither state nor year is above the other in a hierarchical sense. In this sort of example, we can consider individuals, states, and years to be three dierent levels without the requirement of a full ordering or hierarchy. More complex structures, such as three-level nesting (for example, students within schools within school districts) are also easy to handle within the general multilevel framework. Why we avoid the term random eects. Multilevel models are often known as random-eects or mixed-eects models. The regression coecients that are being modeled are called random eects, in the sense that they are considered random outcomes of a process identied with the model that is predicting them. In contrast, xed eects correspond either to parameters that do not vary (for example, tting the same regresslon line for each of the schools) or to parameters that vary but are not modeled themselves (for example, tting a least squares regression model with various predictors, including indicators for the schools). A mixed-eects model includes both xed and random eects; for example, in model (1.1), the varying intercepts j have a group-level model, but is xed and does not vary by group. 1 The model can also be written as yij = j + xij + ij, where yij is the measurement from student i in school j. We prefer using the single sequence i to index all students (and j[i] to label schools) because this ts in better with our multilevel modeling framework with data and models at the individual and group levels. The data are yi because they can exist without reference to the groupings, and we prefer to include information about the groupings as numerical data that is, the index variable j[i]rather than through reordering the data through subscripting. We discuss the structure of the data and models further in Chapter 11. 23. SOME EXAMPLES FROM OUR OWN RESEARCH 3 Fixed eects can be viewed as special cases of random eects, in which the higher- level variance (in model (1.1), this would be 2 ) is set to 0 or . Hence, in our framework, all regression parameters are random, and the term multilevel is all-encompassing. As we discuss on page 245, we nd the terms xed, random, and mixed eects to be confusing and often misleading, and so we avoid their use. 1.2 Some examples from our own research Multilevel modeling can be applied to just about any problem. Just to give a feel of the ways it can be used, we give here a few examples from our applied work. Combining information for local decisions: home radon measurement and remediation Radon is a carcinogena naturally occurring radioactive gas whose decay products are also radioactiveknown to cause lung cancer in high concentrations and esti- mated to cause several thousand lung cancer deaths per year in the United States. The distribution of radon levels in U.S. homes varies greatly, with some houses hav- ing dangerously high concentrations. In order to identify the areas with high radon exposures, the Environmental Protection Agency coordinated radon measurements in a random sample of more than 80,000 houses throughout the country. To simplify the problem somewhat, our goal in analyzing these data was to estimate the distribution of radon levels in each of the approximately 3000 counties in the United States, so that homeowners could make decisions about measuring or remediating the radon in their houses based on the best available knowledge of local conditions. For the purpose of this analysis, the data were structured hierarchically: houses within counties. If we were to analyze multiple measurements within houses, there would be a three-level hierarchy of measurements, houses, and counties. In performing the analysis, we had an important predictorthe oor on which the measurement was taken, either basement or rst oor; radon comes from un- derground and can enter more easily when a house is built into the ground. We also had an important county-level predictora measurement of soil uranium that was available at the county level. We t a model of the form (1.1), where yi is the logarithm of the radon measurement in house i, x is the oor of the measurement (that is, 0 for basement and 1 for rst oor), and u is the uranium measurement at the county level. The errors i in the rst line of (1.1) represent within-county vari- ation, which in this case includes measurement error, natural variation in radon levels within a house over time, and variation between houses (beyond what is ex- plained by the oor of measurement). The errors j in the second line represent variation between counties, beyond what is explained by the county-level uranium predictor. The hierarchical model allows us to t a regression model to the individual mea- surements while accounting for systematic unexplained variation among the 3000 counties. We return to this example in Chapter 12. Modeling correlations: forecasting presidential elections It is of practical interest to politicians and theoretical interest to political scientists that the outcomes of elections can be forecast with reasonable accuracy given in- formation available months ahead of time. To understand this better, we set up a 24. 4 WHY? model to forecast presidential elections. Our predicted outcomes were the Demo- cratic Partys share of the two-party vote in each state in each of the 11 elections from 1948 through 1988, yielding 511 data points (the analysis excluded states that were won by third parties), and we had various predictors, including the per- formance of the Democrats in the previous election, measures of state-level and national economic trends, and national opinion polls up to two months before the election. We set up our forecasting model two months before the 1992 presidential election and used it to make predictions for the 50 states. Predictions obtained using classical regression are reasonable, but when the model is evaluated historically (tting to all but one election and then using the model to predict that election, then repeating this for the dierent past elections), the associated predictive intervals turn out to be too narrow: that is, the predictions are not as accurate as claimed by the model. Fewer than 50% of the predictions fall in the 50% predictive intervals, and fewer than 95% are inside the 95% intervals. The problem is that the 511 original data points are structured, and the state-level errors are correlated. It is overly optimistic to say that we have 511 independent data points. Instead, we model yi = 0 + Xi11 + Xi22 + + Xikk + t[i] + r[i],t[i] + i, for i = 1, . . . , n, (1.2) where t[i] is a indicator for time (election year), and r[i] is an indicator for the region of the country (Northeast, Midwest, South, or West), and n = 511 is the number of state-years used to t the model. For each election year, t is a nationwide error and the r,ts are four independent regional errors. The error terms must then be given distributions. As usual, the default is the normal distribution, which for this model we express as t N(0, 2 ), for t = 1, . . . , 11 r,t N(0, 2 ), for r = 1, . . . , 4; t = 1, . . . , 11 i N(0, 2 ), for i = 1, . . . , 511. (1.3) In the multilevel model, all the parameters , , , are estimated from the data. We can then make a prediction by simulating the election outcome in the 50 states in the next election year, t = 12: yi = 0 + Xi11 + Xi22 + + Xikk + 12 + r[i],12 + i, for i = n+1, . . . , n+50. To dene the predictive distribution of these 50 outcomes, we need the point pre- dictors Xi = 0 + Xi11 + Xi22 + + Xikk and the state-level errors as before, but we also need a new national error 12 and four new regional errors r,12, which we simulate from the distributions (1.3). The variation from these gives a more realistic statement of prediction uncertainties. Small-area estimation: state-level opinions from national polls In a micro-level version of election forecasting, it is possible to predict the political opinions of individual voters given demographic information and where they live. Here the data sources are opinion polls rather than elections. For example, we analyzed the data from seven CBS News polls from the 10 days immediately preceding the 1988 U.S. presidential election. For each survey respondent i, we label yi = 1 if he or she preferred George Bush (the Republican candidate), 0 if he or she preferred Michael Dukakis (the Democrat). We excluded respondents who preferred others or had no opinion, leaving a sample size n of 25. SOME EXAMPLES FROM OUR OWN RESEARCH 5 about 6000. We then t the model, Pr(yi = 1) = logit1 (Xi), where X included 85 predictors: A constant term An indicator for female An indicator for black An indicator for female and black 4 indicators for age categories (1829, 3044, 4564, and 65+) 4 indicators for education categories (less than high school, high school, some college, college graduate) 16 indicators for age education 51 indicators for states (including the District of Columbia) 5 indicators for regions (Northeast, Midwest, South, West, and D.C.) The Republican share of the vote for president in the state in the previous election. In classical regression, it would be unwise to t this many predictors because the estimates will be unreliable, especially for small states. In addition, it would be necessary to leave predictors out of each batch of indicators (the 4 age categories, the 4 education categories, the 16 age education interactions, the 51 states, and the 5 regions) to avoid collinearity. With a multilevel model, the coecients for each batch of indicators are t to a probability distribution, and it is possible to include all the predictors in the model. We return to this example in Section 14.1. Social science modeling: police stops by ethnic group with variation across precincts There have been complaints in New York City and elsewhere that the police harass members of ethnic minority groups. In 1999 the New York State Attorney Generals Oce instigated a study of the New York City police departments stop and frisk policy: the lawful practice of temporarily detaining, questioning, and, at times, searching civilians on the street. The police have a policy of keeping records on every stop and frisk, and this information was collated for all stops (about 175,000 in total) over a 15-month period in 19981999. We analyzed these data to see to what extent dierent ethnic groups were stopped by the police. We focused on blacks (African Americans), hispanics (Latinos), and whites (European Americans). We excluded others (about 4% of the stops) because of sensitivity to ambiguities in classications. The ethnic categories were as recorded by the police making the stops. It was found that blacks and hispanics represented 50% and 33% of the stops, respectively, despite constituting only 26% and 24%, respectively, of the population of the city. An arguably more relevant baseline comparison, however, is to the num- ber of crimes committed by members of each ethnic group. Data on actual crimes are not available, of course, so as a proxy we used the number of arrests within New York City in 1997 as recorded by the Division of Criminal Justice Services (DCJS) of New York State. We used these numbers to represent the frequency of crimes that the police might suspect were committed by members of each group. When compared in that way, the ratio of stops to previous DCJS arrests was 1.24 for 26. 6 WHY? whites, 1.53 for blacks, and 1.72 for hispanicsthe minority groups still appeared to be stopped disproportionately often. These ratios are suspect too, however, because they average over the whole city. Suppose the police make more stops in high-crime areas but treat the dierent ethnic groups equally within any locality. Then the citywide ratios could show strong dierences between ethnic groups even if stops are entirely determined by location rather than ethnicity. In order to separate these two kinds of predictors, we performed a multilevel analysis using the citys 75 precincts. For each ethnic group e = 1, 2, 3 and precinct p = 1, . . . , 75, we model the number of stops yep using an overdispersed Poisson regression. The exponentiated coecients from this model represent relative rates of stops compared to arrests for the dierent ethnic groups, after controlling for precinct. We return to this example in Section 15.1. 1.3 Motivations for multilevel modeling Multilevel models can be used for a variety of inferential goals including causal inference, prediction, and descriptive modeling. Learning about treatment eects that vary One of the basic goals of regression analysis is estimating treatment eectshow does y change when some x is varied, with all other inputs held constant? In many applications, it is not an overall eect of x that is of interest, but how this eect varies in the population. In classical statistics we can study this variation using interactions: for example, a particular educational innovation may be more eective for girls than for boys, or more eective for students who expressed more interest in school in a pre-test measurement. Multilevel models also allow us to study eects that vary by group, for example an intervention that is more eective in some schools than others (perhaps because of unmeasured school-level factors such as teacher morale). In classical regression, estimates of varying eects can be noisy, especially when there are few observations per group; multilevel modeling allows us to estimate these interactions to the extent supported by the data. Using all the data to perform inferences for groups with small sample size A related problem arises when we are trying to estimate some group-level quan- tity, perhaps a local treatment eect or maybe simply a group-level average (as in the small-area estimation example on page 4). Classical estimation just using the local information can be essentially useless if the sample size is small in the group. At the other extreme, a classical regression ignoring group indicators can be mis- leading in ignoring group-level variation. Multilevel modeling allows the estimation of group averages and group-level eects, compromising between the overly noisy within-group estimate and the oversimplied regression estimate that ignores group indicators. Prediction Regression models are commonly used for predicting outcomes for new cases. But what if the data vary by group? Then we can make predictions for new units in existing groups or in new groups. The latter is dicult to do in classical regression: 27. MOTIVATIONS FOR MULTILEVEL MODELING 7 if a model ignores group eects, it will tend to understate the error in predictions for new groups. But a classical regression that includes group eects does not have any automatic way of getting predictions for a new group. A natural attack on the problem is a two-stage regression, rst including group indicators and then tting a regression of estimated group eects on group-level predictors. One can then forecast for a new group, with the group eect predicted from the group-level model, and then the observations predicted from the unit-level model. However, if sample sizes are small in some groups, it can be dicult or even impossible to t such a two-stage model classically, and fully accounting for the uncertainty at both levels leads directly to a multilevel model. Analysis of structured data Some datasets are collected with an inherent multilevel structure, for example, stu- dents within schools, patients within hospitals, or data from cluster sampling. Sta- tistical theorywhether sampling-theory or Bayesiansays that inference should include the factors used in the design of data collection. As we shall see, multi- level modeling is a direct way to include indicators for clusters at all levels of a design, without being overwhelmed with the problems of overtting that arise from applying least squares or maximum likelihood to problems with large numbers of parameters. More ecient inference for regression parameters Data often arrive with multilevel structure (students within schools and grades, laboratory assays on plates, elections in districts within states, and so forth). Even simple cross-sectional data (for example, a random sample survey of 1000 Amer- icans) can typically be placed within a larger multilevel context (for example, an annual series of such surveys). The traditional alternatives to multilevel modeling are complete pooling, in which dierences between groups are ignored, and no pool- ing, in which data from dierent sources are analyzed separately. As we shall discuss in detail throughout the book, both these approaches have problems: no pooling ignores information and can give unacceptably variable inferences, and complete pooling suppresses variation that can be important or even the main goal of a study. The extreme alternatives can in fact be useful as preliminary estimates, but ultimately we prefer the partial pooling that comes out of a multilevel analysis. Including predictors at two dierent levels In the radon example described in Section 1.2, we have outcome measurements at the individual level and predictors at the individual and county levels. How can this information be put together? One possibility is simply to run a classical regression with predictors at both levels. But this does not correct for dierences between counties beyond what is included in the predictors. Another approach would be to augment this model with indicators (dummy variables) for the counties. But in a classical regression it is not possible to include county-level indicators as well along with county-level predictorsthe predictors would become collinear (see the end of Section 4.5 for a discussion of collinearity and nonidentiability in this context). Another approach is to t the model with county indicators but without the county-level predictors, and then to t a second model. This is possible but limited because it relies on the classical regression estimates of the coecients for those 28. 8 WHY? county-level indicatorsand if the data are sparse within counties, these estimates wont be very good. Another possibility in the classical framework would be to t separate models in each group, but this is not possible unless the sample size is large in each group. The multilevel model provides a coherent model that simultaneously incorporates both individual- and group-level models. Getting the right standard error: accurately accounting for uncertainty in prediction and estimation Another motivation for multilevel modeling is for predictions, for example, when forecasting state-by-state outcomes of U.S. presidential elections, as described in Section 1.2. To get an accurate measure of predictive uncertainty, one must account for correlation of the outcome between states in a given election year. Multilevel modeling is a convenient way to do this. For certain kinds of predictions, multilevel models are essential. For example, consider a model of test scores for students within schools. In classical regression, school-level variability might be modeled by including an indicator variable for each school. In this framework though, it is impossible to make a prediction for a new student in a new school, because there would not be an indicator for this new school in the model. This prediction problem is handled seamlessly using multilevel models. 1.4 Distinctive features of this book The topics and methods covered in this book overlap with many other textbooks on regression, multilevel modeling, and applied statistics. We dier from most other books in these areas in the following ways: We present methods and software that allow the reader to t complicated, linear or nonlinear, nested or non-nested models. We emphasize the use of the statistical software packages R and Bugs and provide code for many examples as well as methods such as redundant parameterization that speed computation and lead to new modeling ideas. We include a wide range of examples, almost all from our own applied research. The statistical methods are thus motivated in the best way, as successful practical tools. Most books dene regression in terms of matrix operations. We avoid much of this matrix algebra for the simple reason that it is now done automatically by computers. We are more interested in understanding the forward, or predic- tive, matrix multiplication X than the more complicated inferential formula (Xt X)1 Xt y. The latter computation and its generalizations are important but can be done out of sight of the user. For details of the underlying matrix algebra, we refer readers to the regression textbooks listed in Section 3.8. We try as much as possible to display regression results graphically rather than through tables. Here we apply ideas such as those presented in the books by Ramsey and Schafer (2001) for classical regression and Kreft and De Leeuw (1998) for multilevel models. We consider graphical display of model estimates to be not just a useful teaching method but also a necessary tool in applied research. Statistical texts commonly recommend graphical displays for model diagnostics. These can be very useful, and we refer readers to texts such as Cook and Weisberg 29. COMPUTING 9 (1999) for more on this topicbut here we are emphasizing graphical displays of the tted models themselves. It is our experience that, even when a model ts data well, we have diculty understanding it if all we do is look at tables of regression coecients. We consider multilevel modeling as generally applicable to structured data, not limited to clustered data, panel data, or nested designs. For example, in a random-digit-dialed survey of the United States, one can, and should, use multilevel models if one is interested in estimating dierences among states or demographic subgroupseven if no multilevel structure is in the survey design. Ultimately, you have to learn these methods by doing it yourself, and this chapter is intended to make things easier by recounting stories about how we learned this by doing it ourselves. But we warn you ahead of time that we include more of our successes than our failures. Costs and benets of our approach Doing statistics as described in this book is not easy. The diculties are not math- ematical but rather conceptual and computational. For classical regressions and generalized linear models, the actual tting is easy (as illustrated in Part 1), but programming eort is still required to graph the results relevantly and to simulate predictions and replicated data. When we move to multilevel modeling, the tting itself gets much more complicated (see Part 2B), and displaying and checking the models require correspondingly more work. Our emphasis on R and Bugs means that an initial eort is required simply to learn and use the software. Also, compared to usual treatments of multilevel models, we describe a wider variety of modeling options for the researcher so that more decisions will need to be made. A simpler alternative is to use classical regression and generalized linear modeling where possiblethis can be done in R or, essentially equivalently, in Stata, SAS, SPSS, and various other softwareand then, when multilevel modeling is really needed, to use functions that adapt classical regression to handle simple multilevel models. Such functions, which can be run with only a little more eort than simple regression tting, exist in many standard statistical packages. Compared to these easier-to-use programs, our approach has several advantages: We can t a greater variety of models. The modular structure of Bugs allows us to add complexity where needed to t data and study patterns of interest. By working with simulations (rather than simply point estimates of parameters), we can directly capture inferential uncertainty and propagate it into predictions (as discussed in Chapter 7 and applied throughout the book). We can directly obtain inference for quantities other than regression coecients and variance parameters. R gives us exibility to display inferences and data exibly. We recognize, however, that other software and approaches may be useful too, either as starting points or to check results. Section C.4 describes briey how to t multilevel models in several other popular statistical software packages. 1.5 Computing We perform computer analyses using the freely available software R and Bugs. Appendix C gives instructions on obtaining and using these programs. Here we outline how these programs t into our overall strategy for data analysis. 30. 10 WHY? Our general approach to statistical computing In any statistical analysis, we like to be able to directly manipulate the data, model, and inferences. We just about never know the right thing to do ahead of time, so we have to spend much of our eort examining and cleaning the data, tting many dierent models, summarizing the inferences from the models in dierent ways, and then going back and guring how to expand the model to allow new data to be included in the analysis. It is important, then, to be able to select subsets of the data, to graph whatever aspect of the data might be of interest, and to be able to compute numerical sum- maries and t simple models easily. All this can be done within Ryou will have to put some initial eort into learning the language, but it will pay o later. You will almost always need to try many dierent models for any problem: not just dierent subsets of predictor variables as in linear regression, and not just minor changes such as tting a logit or probit model, but entirely dierent formulations of the modeldierent ways of relating observed inputs to outcomes. This is especially true when using new and unfamiliar tools such as multilevel models. In Bugs, we can easily alter the internal structure of the models we are tting, in a way that cannot easily be done with other statistical software. Finally, our analyses are almost never simply summarized by a set of parameter estimates and standard errors. As we illustrate throughout, we need to look carefully at our inferences to see if they make sense and to understand the operation of the model, and we usually need to postprocess the parameter estimates to get predictions or generalizations to new settings. These inference manipulations are similar to data manipulations, and we do them in R to have maximum exibility. Model tting in Part 1 Part 1 of this book uses the R software for three general tasks: (1) tting classical linear and generalized linear models, (2) graphing data and estimated models, and (3) using simulation to propagate uncertainty in inferences and predictions (see Sections 7.17.2 for more on this). Model tting in Parts 2 and 3 When we move to multilevel modeling, we begin by tting directly in R; however, for more complicated models we move to Bugs, which has a general language for writing statistical models. We call Bugs from R and continue to use R for preprocessing of data, graphical display of data and inferences, and simulation-based prediction and model checking. R and S Our favorite all-around statistics software is R, which is a free open-source version of S, a program developed in the 1970s and 1980s at Bell Laboratories. S is also available commercially as S-Plus. We shall refer to R throughout, but other versions of S generally do the same things. R is excellent for graphics, classical statistical modeling (most relevant here are the lm() and glm() functions for linear and generalized linear models), and various nonparametric methods. As we discuss in Part 2, the lmer() function provides quick ts in R for many multilevel models. Other packages such as MCMCpack exist to t specic classes of models in R, and other such programs are in development. 31. COMPUTING 11 Beyond the specic models that can be t by these packages, R is fully pro- grammable and can thus t any model, if enough programming is done. It is pos- sible to link R to Fortran or C to write faster programs. R also can choke on large datasets (which is one reason we automatically thin large Bugs outputs before reading into R; see Section 16.9). Bugs Bugs (an acronym for Bayesian Inference using Gibbs Sampling) is a program de- veloped by statisticians at the Medical Research Council in Cambridge, England. As of this writing, the most powerful versions available are WinBugs 1.4 and Open- Bugs. In this book, when we say Bugs, we are referring to WinBugs 1.4; however, the code should also work (perhaps with some modication) under OpenBugs or future implementations. The Bugs modeling language has a modular form that allows the user to put together all sorts of Bayesian models, including most of the multilevel models cur- rently t in social science applications. The two volumes of online examples in Bugs give some indication of the possibilitiesin fact, it is common practice to write a Bugs script by starting with an example with similar features and then altering it step by step to t the particular problem at hand. The key advantage of Bugs is its generality in setting up models; its main disad- vantage is that it is slow and can get stuck with large datasets. These problems can be somewhat reduced in practice by randomly sampling from the full data to create a smaller dataset for preliminary modeling and debugging, saving the full data until you are clear on what model you want to t. (This is simply a computational trick and should not be confused with cross-validation, a statistical method in which a procedure is applied to a subset of the data and then checked using the rest of the data.) Bugs does not always use the most ecient simulation algorithms, and currently its most powerful version runs only in Windows, which in practice reduces the ability to implement long computations in time-share with other processes. When tting complicated models, we set up the data in R, t models in Bugs, then go back to R for further statistical analysis using the tted models. Some models cannot be t in Bugs. For these we illustrate in Section 15.3 a new R package under development called Umacs (universal Markov chain sampler). Umacs is less automatic than Bugs and requires more knowledge of the algebra of Bayesian inference. Other software Some statistical software has been designed specically for tting multilevel mod- els, notably MLWin and HLM. It is also possible to t some multilevel models in R, Stata, SAS, and other general-purpose statistical software, but without the ex- ibility of modeling in Bugs. The models allowed by these programs are less general than available in Bugs; however, they are generally faster and can handle larger datasets. We discuss these packages further in Section C.4. Data and code for examples Data and computer code for the examples and exercises in the book can be down- loaded at the website www.stat.columbia.edu/gelman/arm/, which also includes other supporting materials for this book. 32. CHAPTER 2 Concepts and methods from basic probability and statistics Simple methods from introductory statistics have three important roles in regres- sion and multilevel modeling. First, simple probability distributions are the build- ing blocks for elaborate models. Second, multilevel models are generalizations of classical complete-pooling and no-pooling estimates, and so it is important to un- derstand where these classical estimates come from. Third, it is often useful in practice to construct quick condence intervals and hypothesis tests for small parts of a problembefore tting an elaborate model, or in understanding the output from such a model. This chapter provides a quick review of some of these methods. 2.1 Probability distributions A probability distribution corresponds to an urn with a potentially innite number of balls inside. When a ball is drawn at random, the random variable is what is written on this ball. Areas of application of probability distributions include: Distributions of data (for example, heights of men, heights of women, heights of adults), for which we use the notation yi, i = 1, . . . , n. Distributions of parameter values, for which we use the notation j, j = 1, . . . , J, or other Greek letters such as , , . We shall see many of these with the mul- tilevel models in Part 2 of the book. For now, consider a regression model (for example, predicting students grades from pre-test scores) t separately in each of several schools. The coecients of the separate regressions can be modeled as following a distribution, which can be estimated from data. Distributions of error terms, which we write as i, i = 1, . . . , nor, for group- level errors, j, j = 1, . . . , J. A distribution is how we describe a set of objects that are not identied, or when the identication gives no information. For example, the heights of a set of unnamed persons have a distribution, as contrasted with the heights of a particular set of your friends. The basic way that distributions are used in statistical modeling is to start by tting a distribution to data y, then get predictors X and model y given X with errors . Further information in X can change the distribution of the s (typically, by reducing their variance). Distributions are often thought of as data summaries, but in the regression context they are more commonly applied to s. Normal distribution; means and variances The Central Limit Theorem of probability states that the sum of many small inde- pendent random variables will be a random variable with an approximate normal 13 33. 14 BASIC PROBABILITY AND STATISTICS heights of women (normal distribution) heights of all adults (not a normal distribution) Figure 2.1 (a) Heights of women (which approximately follow a normal distribution, as predicted from the Central Limit Theorem), and (b) heights of all adults in the United States (which have the form of a mixture of two normal distributions, one for each sex). distribution. If we write this summation of independent components as z = n i=1 zi, then the mean and variance of z are the sums of the means and variances of the zis: z = n i=1 zi and z = n i=1 2 zi . We write this as z N(z, 2 z). The Central Limit Theorem holds in practicethat is, n i=1 zi actually follows an approximate normal distributionif the individual 2 zi s are small compared to the total variance 2 z . For example, the heights of women in the United States follow an approximate normal distribution. The Central Limit Theorem applies here because height is aected by many small additive factors. In contrast, the distribution of heights of all adults in the United States is not so close to normality. The Central Limit Theorem does not apply here because there is a single large factorsexthat represents much of the total variation. See Figure 2.1. Linear transformations. Linearly transformed normal distributions are still nor- mal. For example, if y are mens heights in inches (with mean 69.1 and standard deviation 2.9), then 2.54y are their heights in centimeters (with mean 2.54 69 = 175 and standard deviation 2.54 2.9 = 7.4). For an example of a slightly more complicated calculation, suppose we take inde- pendent samples of 100 men and 100 women and compute the dierence between the average heights of the men and the average heights of the women. This dif- ference will be normally distributed with mean 69.1 63.7 = 5.4 and standard deviation 2.92/100 + 2.72/100 = 0.4 (see Exercise 2.4). Means and variances of sums of correlated random variables. If x and y are ran- dom variables with means x, y, standard deviations x, y, and correlation , then x + y has mean x + y and standard deviation 2 x + 2 y + 2xy. More generally, the weighted sum ax+by has mean ax +by, and its standard deviation is a22 x + b22 y + 2abxy. From this we can derive, for example, that xy has mean x y and standard deviation 2 x + 2 y 2xy. Estimated regression coecients. Estimated regression coecients are themselves linear combinations of data (formally, the estimate (Xt X)1 Xt y is a linear com- bination of the data values y), and so the Central Limit Theorem again applies, in this case implying that, for large samples, estimated regression coecients are approximately normally distributed. Similar arguments apply to estimates from lo- gistic regression and other generalized linear models, and for maximum likelihood 34. PROBABILITY DISTRIBUTIONS 15 log weights of men (normal distribution) weights of men (lognormal distribution) Figure 2.2 Weights of men (which approximately follow a lognormal distribution, as pre- dicted from the Central Limit Theorem from combining several small multiplicative fac- tors), plotted on the logarithmic and original scales. estimation in general (see Section 18.1), for well-behaved models with large sample sizes. Multivariate normal distribution More generally, a random vector z = (z1, . . . , zK) with a K-dimensional multivari- ate normal distribution with a vector mean and a covariance matrix is written as z N(, ). The diagonal elements of are the variances of the K individual random variables zk; thus, we can write zk N(k, kk). The o-diagonal elements of are the covariances between dierent elements of z, dened so that the cor- relation between zj and zk is jk/ jjkk. The multivariate normal distribution sometimes arises when modeling data, but in this book we encounter it in models for vectors of regression coecients. Approximate normal distribution of regression coecients and other parameter es- timates. The least squares estimate of a vector of linear regression coecients is = (Xt X)1 Xt y (see Section 3.4), which, when viewed as a function of data y (considering the predictors X as constants), is a linear combination of the data. Using the Central Limit Theorem, it can be shown that the distribution of will be approximately multivariate normal if the sample size is large. We describe in Chapter 7 how we use this distribution to summarize uncertainty in regression inferences. Lognormal distribution It is often helpful to model all-positive random variables on the logarithmic scale. For example, the logarithms of mens weights (in pounds) have an approximate normal distribution with mean 5.13 and standard deviation 0.17. Figure 2.2 shows the distributions of log weights and weights among men in the United States. The exponential of the mean and standard deviations of log weights are called the geo- metric mean and geometric standard deviation of the weights; in this example, they are 169 pounds and 1.18, respectively. When working with this lognormal distri- bution, we sometimes want to compute the mean and standard deviation on the original scale; these are exp( + 1 2 2 ) and exp( + 1 2 2 ) exp(2) 1, respectively. For the mens weights example, these come to 171 pounds and 29 pounds. 35. 16 BASIC PROBABILITY AND STATISTICS Binomial distribution If you take 20 shots in basketball, and each has 0.3 probability of succeeding, and if these shots are independent of each other (that is, success in one shot does not increase or decrease the probability of success in any other shot), then the number of shots that succeed is said to have a binomial distribution with n = 20 and p = 0.3, for which we use the notation y Binomial(n, p). As can be seen even in this simple example, the binomial model is typically only an approximation with real data, where in multiple trials, the probability p of success can vary, and for which outcomes can be correlated. Nonetheless, the binomial model is a useful starting point for modeling such data. And in some settingsmost notably, independent sampling with Yes/No responsesthe binomial model generally is appropriate, or very close to appropriate. Poisson distribution The Poisson distribution is used for count data such as the number of cases of cancer in a county, or the number of hits to a website during a particular hour, or the number of persons named Michael whom you know: If a county has a population of 100,000, and the average rate of a particular cancer is 45.2 per million persons per year, then the number of cancers in this county could be modeled as Poisson with expectation 4.52. If hits are coming at random, with an average rate of 380 per hour, then the num- ber of hits in any particular hour could be modeled as Poisson with expectation 380. If you know approximately 1000 persons, and 1% of all persons in the population are named Michael, and you are as likely to know Michaels as anyone else, then the number of Michaels you know could be modeled as Poisson with expectation 10. As with the binomial distribution, the Poisson model is almost always an ideal- ization, with the rst example ignoring systematic dierences among counties, the second ignoring clustering or burstiness of the hits, and the third ignoring fac- tors such as sex and age that distinguish Michaels, on average, from the general population. Again, however, the Poisson distribution is a starting pointas long as its t to data is checked. The model can be expanded to account for overdispersion in data, as we discuss in the context of Figure 2.5 on page 21. 2.2 Statistical inference Sampling and measurement error models Statistical inference is used to learn from incomplete or imperfect data. There are two standard paradigms for inference: In the sampling model, we are interested in learning some characteristics of a population (for example, the mean and standard deviation of the heights of all women in the United States), which we must estimate from a sample, or subset, of that population. In the measurement error model, we are interested in learning aspects of some underlying pattern or law (for example, the parameters a and b in the model 36. STATISTICAL INFERENCE 17 y = a + bx), but the data are measured with error (most simply, y = a + bx + , although one can also consider models with measurement error in x). These two paradigms are dierent: the sampling model makes no reference to mea- surements, and the measurement model can apply even when complete data are observed. In practice, however, we often combine the two approaches when creating a statistical model. For example, consider a regression model predicting students grades from pre- test scores and other background variables. There is typically a sampling aspect to such a study, which is performed on some set of students with the goal of general- izing to a larger population. The model also includes measurement error, at least implicitly, because a students test score is only an imperfect measure of his or her abilities. This book follows the usual approach of setting up regression models in the measurement-error framework (y = a + bx + ), with the sampling interpretation implicit in that the errors i, . . . , n can be considered as a random sample from a distribution (for example, N(0, 2 )) that represents a hypothetical superpopula- tion. We consider these issues in more detail in Chapter 21; at this point, we raise this issue only to clarify the connection between probability distributions (which are typically modeled as draws from an urn, or distribution, as described at the beginning of Section 2.1) and the measurement error models used in regression. Parameters and estimation The goal of statistical inference for the sorts of parametric models that we use is to estimate underlying parameters and summarize our uncertainty in these estimates. We discuss inference more formally in Chapter 18; here it is enough to say that we typically understand a tted model by plugging in estimates of its parameters, and then we consider the uncertainty in the parameter estimates when assessing how much we actually have learned from a given dataset. Standard errors The standard error is the standard deviation of the parameter estimate and gives us a sense of our uncertainty about a parameter and can be used in constructing condence intervals, as we discuss in the next section. When estimating the mean of an innite population, given a simple random sample of size n, the standard error is / n, where is the standard deviation of the measurements in the population. Standard errors for proportions Consider a survey of size n with y Yes responses and ny No responses. The estimated proportion of the population who would answer Yes to this survey is p = y/n, and the standard error of this estimate is p(1 p)/n. This estimate and standard error are usually reasonable unless y = 0 or ny = 0, in which case the resulting standard error estimate of zero is misleading.1 1 A reasonable quick correction when y or ny is near zero is to use the estimate p = (y+1)/(n+2) with standard error p p(1 p)/n; see Agresti and Coull (1998). 37. 18 BASIC PROBABILITY AND STATISTICS 2.3 Classical condence intervals Condence intervals from the normal and t distributions The usual 95% condence interval for large samples based on the normal distri- bution is an estimate 2 standard errors. Also from the normal distribution, an estimate 1 standard error is a 68% interval, and an estimate 2/3 of a standard error is a 50% interval. A 50% interval is particularly easy to interpret since the true value should be as likely to be inside as outside the interval. A 95% interval is about three times as wide as a 50% interval. The t distribution can be used to correct for uncertainty in the estimation of the standard error. Continuous data. For example, suppose an object is weighed ve times, with mea- surements y = 35, 34, 38, 35, 37, which have an average value of 35.8 and a standard deviation of 1.6. In R, we can create the 50% and 95% t intervals (based on 4 degrees of freedom) as follows: R code n