Surprising Causes: Propensity-Adjusted Treatment Scores for Multimethod Case Selection

Autor/inn/en	Galvin, Daniel J.; Seawright, Jason N.
Titel	Surprising Causes: Propensity-Adjusted Treatment Scores for Multimethod Case Selection
Quelle	In: Sociological Methods & Research, 52 (2023) 4, S.1632-1680 (49 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Galvin, Daniel J.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/00491241211004632
Schlagwörter	Social Sciences; Predictor Variables; Statistics; Labor Legislation; Selection; Case Studies + Suchen Sie Ihr Suchwort? Social science; Sozialwissenschaften; Gesellschaftswissenschaften; Prädiktor; Statistik; Labor law; Arbeitsrecht; Auslese; Case study; Fallstudie; Case Study
Abstract	Scholarship on multimethod case selection in the social sciences has developed rapidly in recent years, but many possibilities remain unexplored. This essay introduces an attractive and advantageous new alternative, involving the selection of extreme cases on the treatment variable, net of the statistical influence of the set of known control variables. Cases that are extreme in this way are those in which the value of the main causal variable is as surprising as possible, and thus, this approach can be referred to as seeking "surprising causes." There are practical advantages to selecting on surprising causes, and there are also advantages in terms of statistical efficiency in facilitating case-study discovery. We first argue for these advantages in general terms and then demonstrate them in an application regarding the dynamics of U.S. labor legislation. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01