Applied Statistical Modeling
1st Edition
Published on 13. March 2013
Book
Hardback
1648 pages
978-1-4462-0839-7 (ISBN)
Description
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
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
Reviews / Votes
"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
More details
Series
Edition
Four Volumes
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 234 mm
Width: 156 mm
Weight
3416 gr
ISBN-13
978-1-4462-0839-7 (9781446208397)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
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.
Content
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
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