Applied Statistical Modeling

Applied Statistical Modeling

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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

An Orientation to Applied Statistical Modeling in the Social Sciences
SalvatoreBabones

Statistics tatistics means one thing to the mathematician, quite another to the social scientist. For the mathematician, the challenge is estimation: given a model of how the world works, how can its parameters be estimated from the data at hand? In archetypical physical science applications, the choice of model is trivial: objects in freefall near a planet’s surface are modeled as accelerating downward at a fixed rate; the challenge is to estimate the rate. The model is given by theory. Even in higher-order sciences like biology and geology theoretical models often generate well-defined statistical models, as in Galton’s genetic regression to the mean or modern descriptions of sediment falling out of suspension where rivers widen ...

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