On the Application of Discrete Marginal Graphical Models

Bibliographic Data

ID6169672
AuthorsRenáta Németh (0000-0001-6796-3563, Eötvös Loránd University, Budapest, Hungary), Tamás Rudas (0000-0002-9787-6900, Eötvös Loránd University, Budapest, Hungary)
Year2013
Volume43
Issue1
Pages70-100
Publication date2013-08-01
Open AccessYes
TypeARTICLE
VenueSociological Methodology (JOURNAL)
Journal identifiersISSN: 0081-1750 • E-ISSN: 1467-9531
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/0081175013481960
OpenAlexW1973762555
LanguageEN
Citations received5
References cited34

Graphical models are defined by general and possibly complex conditional independence assumptions and are well suited to model direct and indirect associations and effects that are of central importance in many problems of sociology. Such relevance is apparent in research on social mobility. This article provides a unified view of many of the graphical models discussed in a largely scattered literature. The marginal modeling framework proposed here relies on parameters that capture aspects of associations among the variables that are relevant for the graph and, depending on the substantive problem at hand, may lead to deeper insight than other approaches. In this context, model search, which uses a sequence of nested models, means the restriction of increasing subsets of parameters. As a special case, general path models for categorical data are introduced. These models are applied to the social status attainment process, generating substantive results and gaining new insights into the difference between liberal and conservative welfare systems. To help others use these models, all details of the analyses are posted on the Web site for this article at http://nemethr.web.elte.hu/discrete-graphical-models/ . Researchers can thus easily modify the analyses to their own data and models

Categorical variable · Causal model · Conditional independence · Context (archaeology · Data mining · Data science · Econometrics · Graphical model · Machine learning · Statistical graphics · Statistics · Advanced Causal Inference Techniques · Artificial Intelligence · Computer Science · Intergenerational and Educational Inequality Studies · Mathematics · Spatial and Panel Data Analysis · Theoretical Computer Science

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