Applied Statistical Modeling (Sage Benchmarks in Social Research Methods)

Applied Statistical Modeling (Sage Benchmarks in Social Research Methods)

By: Salvatore J. Babones (editor)Hardback

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This new four-volume set on Applied Statistical Modeling brings together seminal articles in the field, selected for their exemplification of the specific model type used, their clarity of exposition and their importance to the development of their respective disciplines. The set as a whole is designed to serve as a master class in how to apply the most commonly used statistical models with the highest level of methodological sophistication. It is in essence a user's guide to statistical best-practice in the social sciences. This truly multi-disciplinary collection covers the most important statistical methods used in sociology, social psychology, political science, management science, media studies, anthropology and human geography. The articles are organised by model type into thematic sections that include selections from multiple disciplines. There are a total of thirteen sections, each with a brief introduction summarising common applications: Volume One: Control variables; Multicolinearity and variance inflation; Interaction models; Multilevel models Volume Two: Models for panel data; Time series cross-sectional analysis; Spatial models; Logistic regression Volume Three: Multinomial logit; Poisson regression; Instrumental variables Volume Four: Structural equation models; Latent variable models

About Author

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.


1. Variables and Colinearity Explaining Interstate Conflict and War - J.L. Ray What Should Be Controlled for? The Moderator-Mediator Variable Distinction in Social Psychological Research - R.M. Baron and D.A. Kenny Conceptual, Strategic and Statistical Considerations Understanding and Using Mediators and Moderators - A.D. Wu and B.D. Zumbo Collinearity, 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'Brien What to Do (and Not Do) with Multicollinearity in State Politics Research - K. Arceneaux and G.A. Huber 2. Interaction Models Theory-Building and the Statistical Concept of Interaction - H.M. Blalock Jr. Testing for Interaction in Multiple Regression - P.D. Allison In Defense of Multiplicative Terms in Multiple Regression Equations - R.J. Friedrich Hypothesis-Testing and Multiplicative Interaction Terms - B.F. Braumoeller Understanding Interaction Models - T. Brambor, W.R. Clark and M. Golder Improving Empirical Analyses PART FOUR: MULTILEVEL MODELS Modeling Multilevel Data Structures - M.R. Steenbergen and B.S. Jones Multilevel Models - T.A. DiPrete and J.D. Forristal Methods and Substance Multilevel Analysis in Public Health Research - A.V. Diez-Roux Multilevel Modeling - R.F. Dedrick et al A Review of Methodological Issues and Applications Sufficient Sample Sizes for Multilevel Modeling - C.J.M. Maas and J.J. Hox PART FIVE: MODELS FOR PANEL DATA Panel Models in Sociological Research - C.N. Halaby Theory into Practice Problems with Repeated Measures Analysis - D.D. Bergh Demonstration with a Study of the Diversification and Performance Relationship Modeling Error in Quantitative Macro-Comparative Research - S.J. Babones Advances in Analysis of Longitudinal Data - R.D. Gibbons, D. Hedeker and S. DuToit PART SIX: TIME SERIES CROSS-SECTIONAL ANALYSIS What 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. Kittel Dirty Pool - D.P. Green, S.Y. Kim and D.H. Yoon Time Series Cross-Section Data - N. Beck What Have We Learned in the Past Few Years? A Lot More to Do - S.E. Wilson and D.M. Butler The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications PART SEVEN: SPATIAL MODELS Spatial Autocorrelation - P. Legendre Trouble or New Paradigm? 'The Problem of Spatial Autocorrelation and Local Spatial Statistics - A.S. Fotheringham Under the Hood - L. Anselin Issues in the Specification and Interpretation of Spatial Regression Models Spatial Regression Models for Demographic Analysis - G. Chi and J. Zhu Space Is More Than Geography - N. Beck, K.S. Gleditsch and K. Beardsley Using Spatial Econometrics in the Study of Political Economy PART EIGHT: LOGISTIC REGRESSION An Introduction to Logistic Regression Analysis and Reporting - C.-Y.J. Peng, K.L. Lee and G.M. Ingersoll A Tutorial in Logistic Regression - A. DeMaris Logistic Regression: Description, Examples and Comparisons - S.P. Morgan and J. D. Teachman Binary Response Models - J.L. Horowitz and N.E. Savin Logits, Probits and Semi-Parametrics Logistic Regression - C. Mood Why We Cannot Do What We Think We Can Do, and What We Can Do about It PART NINE: MULTINOMIAL LOGIT A Primer for Social Worker Researchers on How to Conduct a Multinomial Logistic Regression - C.J. Petrucci Multinomial Probit and Multinomial Logit - J.K. Dow and J.W. Endersby A Comparison of Choice Models for Voting Research A Conceptual Framework for Ordered Logistic Regression Models - A.S. Fullerton PART TEN: POISSON REGRESSION Analysis of Count Data Using Poisson Regression - M.K. Hutchinson and M.C. Holtman The Analysis of Count Data - D.N. Barron Over-Dispersion and Autocorrelation Negative Multinomial Regression Models for Clustered Event Counts - G. Guo A Comparison of Poisson, Negative Binomial and Semi-Parametric Mixed Poisson Regression Models - K.C. Land, P.L. McCall and D.S. Nagin PART ELEVEN: INSTRUMENTAL VARIABLES Instrumental Variables and the Search for Identification - J.D. Angrist and A.B. Krueger From Supply and Demand to Natural Experiments Controlling for Endogeneity with Instrumental Variables in Strategic Management Research - G. Bascle Model Specification in Instrumental-Variables Regression - T. Dunning That Instrument Is Lousy! In Search of Agreement When Using Instrumental Variables Estimation in Substance Use Research - M.T. French and I. Popovici PART TWELVE: STRUCTURAL EQUATION MODELING Path Analysis - O.D. Duncan Sociological Examples Structural Equation Models - W.T. Bielby and R.M. Hauser Practical Issues in Structural Modeling - P.M. Bentler and C.-P. Chou Total, Direct and Indirect Effects in Structural Equation Models - K.A. Bollen Principles and Practice in Reporting Structural Equation Analyses - R.P. McDonald and M.-H.R. Ho Instrumental Variables in Sociology and the Social Sciences - K.A. Bollen PART THIRTEEN: LATENT VARIABLE MODELS Confirmatory Factor-Analytic Structures and the Theory Construction Process - R.S. Burt Latent Variables in Psychology and the Social Sciences - K.A. Bollen Specification, Evaluation and Interpretation of Structural Equation Models - R.P. Bagozzi and Y. Yi Latent Variable Models under Misspecification - K.A. Bollen et al Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) Estimators The Fallacy of Formative Measurement - J.R. Edwards

Product Details

  • ISBN13: 9781446208397
  • Format: Hardback
  • Number Of Pages: 1648
  • ID: 9781446208397
  • weight: 3420
  • ISBN10: 1446208397

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