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Babones

Applied Statistical Modeling

Medium: Buch
ISBN: 978-1-4462-0839-7
Verlag: SAGE PUBN
Erscheinungstermin: 31.03.2013
Lieferfrist: bis zu 10 Tage

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


Produkteigenschaften


  • Artikelnummer: 9781446208397
  • Medium: Buch
  • ISBN: 978-1-4462-0839-7
  • Verlag: SAGE PUBN
  • Erscheinungstermin: 31.03.2013
  • Sprache(n): Englisch
  • Auflage: Four Volumes
  • Serie: Sage Benchmarks in Social Rese
  • Produktform: Gebunden
  • Gewicht: 3416 g
  • Seiten: 1648
  • Format (B x H x T): 156 x 234 x 8 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Herausgeber

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.