This new four-volume major work presents a collection of landmark studies on the topic of regression modeling, identifying the most important, fundamental articles out of thousands of relevant contributions. The social sciences - particularly sociology and political science - have made extensive use of regression models since the 1960s, and regression modeling continues to be the staple method of the field. The collection is framed by an orienting essay which presents to a guide to regression modelling, written with applied practitioners in mind.
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.
VOLUME ONE PART ONE: THE MEANING OF P-VALUES The Non-Utility of Significance Tests - Sanford Labovitz The Significance of Tests of Significance Reconsidered Mindless Statistics - Gerd Gigerenzer Confusion over Measures of Evidence (p's) versus Errors (?'s) in Classical Statistical Testing - Raymond Hubbard and M.J. Bayarri Why We Don't Really Know What Statistical Significance Means - Raymond Hubbard and J. Scott Armstrong Implications for Educators Statistical Significance Researchers Should Make Thoughtful Assessments Instead of Null-Hypothesis Significance Tests - Andrea Schwab et al PART TWO: CONTROL VARIABLES Explaining Interstate Conflict and War - James Lee Ray What Should Be Controlled for? The Phantom Menace - Kevin Clarke Omitted Variable Bias in Econometric Research Beyond Baron and Kenny - Andrew Hayes Statistical Mediation Analysis in the New Millennium Equivalence of the Mediation, Confounding and Suppression Effect - David Mackinnon, Jennifer Krull and Chondra Lockwood Statistical Usage in Sociology - Sanford Labovitz Sacred Cows and Ritual Stepwise Regression in Social and Psychological Research - Douglas Henderson and Daniel Denison Return of the Phantom Menace - Kevin Clarke Stepwise Regression - Michael Lewis-Beck A Caution PART THREE: OUTLIERS AND INFLUENTIAL POINTS Teaching about Influence in Simple Regression - Frederick Lorenz Regression Diagnostics - Kenneth Bollen and Robert Jackman An Expository Treatment of Outliers and Influential Cases A Survey of Outlier Detection Methodologies - Victoria Hodge and Jim Austin Practitioners' Corner - Catherine Dehon, Marjorie Gassner and Vincenzo Verardi Some Observations on Measurement and Statistics - Sanford Labovitz PART FOUR: MULTICOLINEARITY AND VARIANCE INFLATION Issues in Multiple Regression - Robert Gordon A Caution Regarding Rules of Thumb for Variance Inflation Factors - Robert O'Brien What to Do (and Not Do) with Multicolinearity in State Politics Research - Kevin Arceneaux and Gregory Huber On the Misconception of Multicollinearity in Detection of Moderating Effects - Gwowen Shieh Multicollinearity Is Not Always Detrimental Correlated Independent Variables - H.M. Blalock Jr. The Problem of Multicollinearity PART FIVE: SAMPLE SELECTION BIASES Modeling Selection Effects - Thad Dunning and David Freedman An Introduction to Sample Selection Bias in Sociological Data - Richard Berk Models for Sample Selection Bias - Christopher Winship and Robert Mare Sample Selection Bias as a Specification Error - James Heckman How the Cases You Choose Affect the Answers You Get - Barbara Geddes Selection Bias in Comparative Politics When Less Is More - Bernhard Ebbinghaus Selection Problems in Large-N and Small-N Cross-National Comparisons PART SIX: IMPUTATION TECHNIQUES The Treatment of Missing Data - David Howell A Primer on Maximum Likelihood Algorithms Available for Use with Missing Data - Craig Enders What to Do about Missing Values in Time-Series Cross-Section Data - James Honaker and Gary King Multiple Imputation for Missing Data - Paul Allison A Cautionary Tale Multiple Imputation for Missing Data - Mark Fichman and Jonathon Cummings Making the Most of What You Know Imputation of Missing Item Responses - Mark Huisman Some Simple Techniques Analyzing Incomplete Political Science Data - Gary King et al An Alternative Algorithm for Multiple Imputation Landermanetal-1997 PART SEVEN: INTERACTION MODELS Testing for Interaction in Multiple Regression - Paul Allison Understanding Interaction Models - Thomas Brambor, William Roberts Clark and Matt Golder Improving Empirical Analyses Product-Variable Models of Interaction Effects and Causal Mechanisms - Lowell Hargens Limitations of Centering for Interactive Models - Richard Tate Decreasing Multicollinearity - Kent Smith and M.S. Sasaki A Method for Models with Multiplicative Functions Some Common Myths about Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression - Dev Dalal and Michael Zickar PART EIGHT: LONGITUDINAL MODELS A General Panel Model with Random and Fixed Effects - Kenneth Bollen and Jennie Brand A Structural Equations Approach A Lot More to Do - Sven Wilson and Daniel Butler The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications Panel Models in Sociological Research - Charles Halaby Theory into Practice Dynamic Models for Dynamic Theories - Luke Keele and Nathan Kelly The ins and outs of Lagged Dependent Variables Using Panel Data to Estimate the Effects of Events - Paul D. Allison PART NINE: INSTRUMENTAL VARIABLE MODELS Instrumental Variables and the Search for Identification - Joshua Angrist and Alan Krueger From Supply and Demand to Natural Experiments Improving Causal Inference: - Thad Dunning Strengths and Limitations of Natural Experiments Instrumental Variable Estimation in Political Science - Allison Sovey and Donald Green A Readers' Guide Instrumental Variables in Sociology and the Social Sciences - Kenneth Bollen Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variable Is Weak - John Bound et al PART TEN: STRUCTURAL MODELS Practical Issues in Structural Modeling - P.M. Bentler and Chih-Ping Chou As Others See Us - D.A. Freedman A Case Study in Path Analysis: Journal of Education and Behavioral Statistics Causation Issues in Structual Equation Modeling Research - Heather Bullock et al Structural Equation Modeling in Practice - James Anderson and David Gerbing A Review and Recommended Two-Step Approach Structural Equation Models in the Social and Behavioral Sciences - James Anderson Model-Building PART ELEVEN: CAUSALITY Statistical Models for Causation - David Freedman Structural Equations and Causal Explanations - Keith A. Markus Some Challenges for Causal Structural Equation Modeling The Estimation of Causal Effects from Observational Data - Christopher Winship and Stephen Morgan Statistical Models for Causation - David Freedman What Inferential Leverage Do They Provide? Pearl-2010