Applied Statistical Modeling

Salvatore J. Babones is a senior lecturer in sociology and social policy at the University of Sydney and an associate fellow at the Institute for Policy Studies (IPS). Previously, he was an assistant professor of sociology, public health, and public and international affairs at the University of Pittsburgh. He holds both a PhD in sociology and an MSE in mathematical sciences from the Johns Hopkins University. Dr. Babones is the author or editor of eight books and more than thirty academic papers. He is the editor of Applied Statistical Modeling and Fundamentals of Regression Modeling, both published by SAGE as part of the Benchmarks in Social Research Methods reference series. His academic research focuses on globalization, economic development, and statistical methods for comparative social science research.

"This book will guide the reader far beyond textbook treatments right to the vanguard of methodological debates about the application of statistical and econometric models in the social sciences. The collection is exceptional in giving voice to various perspectives, thereby highlighting the fact that statistical analysis of social science data is more than just the application of techniques"Professor Bernhard Kittel, University of Vienna"This is an outstanding collection of articles that will amply repay the efforts of any aspiring social scientist"Professor Paul D. Allison, University of Pennsylvania

• 1. Variables and ColinearityExplaining Interstate Conflict and War - J.L. RayWhat Should Be Controlled for? The Moderator-Mediator Variable Distinction in Social Psychological Research - R.M. Baron and D.A. KennyConceptual, Strategic and Statistical Considerations Understanding and Using Mediators and Moderators - A.D. Wu and B.D. ZumboCollinearity, Power and Interpretation of Multiple Regression Analysis - C.H. Mason and W.D. Perreault Jr.A Caution Regarding Rules of Thumb for Variance Inflation Factors - R.M. O′BrienWhat to Do (and Not Do) with Multicollinearity in State Politics Research - K. Arceneaux and G.A. Huber2. Interaction ModelsTheory-Building and the Statistical Concept of Interaction - H.M. Blalock Jr.Testing for Interaction in Multiple Regression - P.D. AllisonIn Defense of Multiplicative Terms in Multiple Regression Equations - R.J. FriedrichHypothesis-Testing and Multiplicative Interaction Terms - B.F. BraumoellerUnderstanding Interaction Models - T. Brambor, W.R. Clark and M. GolderImproving Empirical Analyses PART FOUR: MULTILEVEL MODELSModeling Multilevel Data Structures - M.R. Steenbergen and B.S. JonesMultilevel Models - T.A. DiPrete and J.D. ForristalMethods and SubstanceMultilevel Analysis in Public Health Research - A.V. Diez-RouxMultilevel Modeling - R.F. Dedrick et alA Review of Methodological Issues and Applications Sufficient Sample Sizes for Multilevel Modeling - C.J.M. Maas and J.J. HoxPART FIVE: MODELS FOR PANEL DATAPanel Models in Sociological Research - C.N. HalabyTheory into Practice Problems with Repeated Measures Analysis - D.D. BerghDemonstration with a Study of the Diversification and Performance Relationship Modeling Error in Quantitative Macro-Comparative Research - S.J. BabonesAdvances in Analysis of Longitudinal Data - R.D. Gibbons, D. Hedeker and S. DuToitPART SIX: TIME SERIES CROSS-SECTIONAL ANALYSISWhat to Do (and Not to Do) with Time-Series Cross-Section Data - N. Beck and J.N. Katz Sense and Sensitivity in Pooled Analysis of Political Data - B. KittelDirty Pool - D.P. Green, S.Y. Kim and D.H. YoonTime Series Cross-Section Data - N. BeckWhat Have We Learned in the Past Few Years? A Lot More to Do - S.E. Wilson and D.M. ButlerThe Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications PART SEVEN: SPATIAL MODELSSpatial Autocorrelation - P. LegendreTrouble or New Paradigm? ′The Problem of Spatial Autocorrelation and Local Spatial Statistics - A.S. FotheringhamUnder the Hood - L. AnselinIssues in the Specification and Interpretation of Spatial Regression ModelsSpatial Regression Models for Demographic Analysis - G. Chi and J. ZhuSpace Is More Than Geography - N. Beck, K.S. Gleditsch and K. BeardsleyUsing Spatial Econometrics in the Study of Political EconomyPART EIGHT: LOGISTIC REGRESSIONAn Introduction to Logistic Regression Analysis and Reporting - C.-Y.J. Peng, K.L. Lee and G.M. IngersollA Tutorial in Logistic Regression - A. DeMarisLogistic Regression: Description, Examples and Comparisons - S.P. Morgan and J. D. TeachmanBinary Response Models - J.L. Horowitz and N.E. SavinLogits, Probits and Semi-Parametrics Logistic Regression - C. MoodWhy We Cannot Do What We Think We Can Do, and What We Can Do about ItPART NINE: MULTINOMIAL LOGITA Primer for Social Worker Researchers on How to Conduct a Multinomial Logistic Regression - C.J. PetrucciMultinomial Probit and Multinomial Logit - J.K. Dow and J.W. EndersbyA Comparison of Choice Models for Voting Research A Conceptual Framework for Ordered Logistic Regression Models - A.S. FullertonPART TEN: POISSON REGRESSIONAnalysis of Count Data Using Poisson Regression - M.K. Hutchinson and M.C. HoltmanThe Analysis of Count Data - D.N. BarronOver-Dispersion and Autocorrelation Negative Multinomial Regression Models for Clustered Event Counts - G. GuoA Comparison of Poisson, Negative Binomial and Semi-Parametric Mixed Poisson Regression Models - K.C. Land, P.L. McCall and D.S. NaginPART ELEVEN: INSTRUMENTAL VARIABLESInstrumental Variables and the Search for Identification - J.D. Angrist and A.B. KruegerFrom Supply and Demand to Natural Experiments Controlling for Endogeneity with Instrumental Variables in Strategic Management Research - G. BascleModel Specification in Instrumental-Variables Regression - T. DunningThat Instrument Is Lousy! In Search of Agreement When Using Instrumental Variables Estimation in Substance Use Research - M.T. French and I. PopoviciPART TWELVE: STRUCTURAL EQUATION MODELINGPath Analysis - O.D. DuncanSociological ExamplesStructural Equation Models - W.T. Bielby and R.M. HauserPractical Issues in Structural Modeling - P.M. Bentler and C.-P. ChouTotal, Direct and Indirect Effects in Structural Equation Models - K.A. BollenPrinciples and Practice in Reporting Structural Equation Analyses - R.P. McDonald and M.-H.R. HoInstrumental Variables in Sociology and the Social Sciences - K.A. BollenPART THIRTEEN: LATENT VARIABLE MODELSConfirmatory Factor-Analytic Structures and the Theory Construction Process - R.S. BurtLatent Variables in Psychology and the Social Sciences - K.A. BollenSpecification, Evaluation and Interpretation of Structural Equation Models - R.P. Bagozzi and Y. YiLatent Variable Models under Misspecification - K.A. Bollen et alTwo-Stage Least Squares (2SLS) and Maximum Likelihood (ML) EstimatorsThe Fallacy of Formative Measurement - J.R. Edwards