# Applied Statistical Modeling

**Edited by:**

- Salvatore Babones - University of Sydney, Australia

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

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

"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