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Causal Complexity and Party Preference

Bibliographic Data

ID6258243
AuthorsGunnar Grendstad (0000-0002-2914-8856, University of Bergen, corresponding author)
Year2007
Volume46
Issue1
Pages121-149
Publication date2007-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEuropean Journal of Political Research (JOURNAL)
Journal identifiersISSN: 0304-4130 • E-ISSN: 1475-6765
PublisherWiley (PUBLISHER • GB)
DOI10.1111/j.1475-6765.2006.00689.x
OpenAlexW2068787766
LanguageEN
Citations received7
References cited67

Qualitative Comparative Analysis (QCA) overlaps logistic regression in explaining events, but challenges the latter's lack of accounting for causal complexity. QCA has only to a limited degree been applied to large‐N studies or individuals as cases and has not incorporated the logic of probability. QCA and logistic regression are compared with respect to logic, procedure and outcome. Political orientations from five national surveys are adapted to the requirements of the two methods. The methods are demonstrated on explanations of individuals' party preferences. QCA and logistic regression converge and overlap in identifying degrees of causal complexity, in ascertaining model significance and in identifying antecedents to party preference. Results differ in degree, not in kind. A slightly more nuanced picture emerges using the QCA approach, whereas logistic regression delivers greater parsimony. Choice of method(s) is not arbitrary. QCA can easily be used on any large‐N research problem. It should apply probability when appropriate

Causal inference · Causal model · Degree (music · Econometrics · Logistic regression · Preference · Qualitative comparative analysis · Regression · Statistics · Computer Science · Mathematics · Qualitative Comparative Analysis Research

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Unique citing works7
Citations per year0,41
Citation span2009 - 2023 (15)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 7

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