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Cluster size and aggregated level 2 variables in multilevel models: a cautionary note

[Zeitschriftenartikel]

Schunck, Reinhard

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Zitationshinweis

Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):http://dx.doi.org/10.12758/mda.2016.005

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Abstract "This paper explores the consequences of small cluster size for parameter estimation in multilevel models. In particular, the interest lies in parameter estimates (regression weights) in linear multilevel models of level 2 variables that are functions of level 1 variables, as for instance the cluster-mean of a certain property, e.g. the average income or the proportion of certain people in a neighborhood. To this end, a simulation study is used to determine the effect of varying cluster sizes and number of clusters. The results show that small cluster sizes can cause severe downward bias in estimated regression weights of aggregated level 2 variables. Bias does not decrease if the number of clusters (i.e. the level 2 units) increases." (author's abstract)
Thesaurusschlagwörter survey research; cluster analysis; sample; linear model; regression; estimation; parameter; simulation
Klassifikation Erhebungstechniken und Analysetechniken der Sozialwissenschaften
Freie Schlagwörter multilevel modeling; hierarchical linear model; sample size
Sprache Dokument Englisch
Publikationsjahr 2016
Seitenangabe S. 97-108
Zeitschriftentitel Methods, data, analyses : a journal for quantitative methods and survey methodology (mda), 10 (2016) 1
ISSN 1864-6956
Status Veröffentlichungsversion; begutachtet (peer reviewed)
Lizenz Creative Commons - Namensnennung
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