Counterfactuals and causal inference: Methods and principles for social research
Data(s) |
2011
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Resumo |
"Most quantitative empirical analyses are motivated by the desire to estimate the causal effect of an independent variable on a dependent variable. Although the randomized experiment is the most powerful design for this task, in most social science research done outside of psychology, experimental designs are infeasible. (Winship & Morgan, 1999, p. 659)." This quote from earlier work by Winship and Morgan, which was instrumental in setting the groundwork for their book, captures the essence of our review of Morgan and Winship's book: It is about causality in nonexperimental settings. |
Identificador |
https://serval.unil.ch/notice/serval:BIB_B252C50CF3AD https://serval.unil.ch/resource/serval:BIB_B252C50CF3AD.P001/REF http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_B252C50CF3AD7 urn:nbn:ch:serval-BIB_B252C50CF3AD7 http://nbn-resolving.org/urn/resolver.pl?urn=urn:nbn:ch:serval-BIB_B252C50CF3AD7 |
Idioma(s) |
eng |
Direitos |
info:eu-repo/semantics/openAccess Copying allowed only for non-profit organizations https://serval.unil.ch/disclaimer |
Fonte |
Structural Equation Modeling181152-159 |
Palavras-Chave | #causality; counterfactuals; field research |
Tipo |
info:eu-repo/semantics/article article |
Formato |
application/pdf |