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Statistical Models for Causal Analysis.

Authors: Daniel Zelterman; Robert D. Retherford; Minja Kim Choe;

Statistical Models for Causal Analysis.

Abstract

This book provides a quick overview of statistical models commonly used in causal analyses of nonexperimental data in the social and biomedical sciences. Topics covered are simple bivariate regression multiple regression multiple classification analysis path analysis logit regression multinomial logit regression and survival models (proportional hazard models and hazard models with time dependence). The methods described are illustrated using data from the 1974 Fiji Fertility Survey conducted as part of the World Fertility Survey. (EXCERPT)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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