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Applied Statistical Modeling : Sage Benchmarks in Social Research Methods - Salvatore J. Babones

Applied Statistical Modeling

By: Salvatore J. Babones (Editor)

Book with Other Items | 11 June 2013

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Book with Other Items


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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

Industry Reviews
"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

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