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

Schunck, Reinhard

[journal article]

dc.contributor.author	Schunck, Reinhard	de
dc.date.accessioned	2016-05-30T11:52:52Z
dc.date.available	2016-05-30T11:52:52Z
dc.date.issued	2016	de
dc.identifier.issn	2190-4936	de
dc.identifier.uri	http://www.ssoar.info/ssoar/handle/document/46951
dc.description.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)	en
dc.language	en	de
dc.subject.ddc	Sozialwissenschaften, Soziologie	de
dc.subject.ddc	Social sciences, sociology, anthropology	en
dc.subject.other	multilevel modeling; hierarchical linear model; sample size	de
dc.title	Cluster size and aggregated level 2 variables in multilevel models: a cautionary note	de
dc.description.review	begutachtet (peer reviewed)	de
dc.description.review	peer reviewed	en
dc.source.journal	Methods, data, analyses : a journal for quantitative methods and survey methodology (mda)
dc.source.volume	10	de
dc.publisher.country	DEU
dc.source.issue	1	de
dc.subject.classoz	Erhebungstechniken und Analysetechniken der Sozialwissenschaften	de
dc.subject.classoz	Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods	en
dc.subject.thesoz	simulation	en
dc.subject.thesoz	Cluster-Analyse	de
dc.subject.thesoz	Parameter	de
dc.subject.thesoz	sample	en
dc.subject.thesoz	Regression	de
dc.subject.thesoz	parameter	en
dc.subject.thesoz	Schätzung	de
dc.subject.thesoz	cluster analysis	en
dc.subject.thesoz	Simulation	de
dc.subject.thesoz	regression	en
dc.subject.thesoz	linear model	en
dc.subject.thesoz	Stichprobe	de
dc.subject.thesoz	survey research	en
dc.subject.thesoz	estimation	en
dc.subject.thesoz	lineares Modell	de
dc.subject.thesoz	Umfrageforschung	de
dc.rights.licence	Creative Commons - Namensnennung	de
dc.rights.licence	Creative Commons - Attribution	en
ssoar.contributor.institution	GESIS
internal.status	formal und inhaltlich fertig erschlossen	de
internal.identifier.thesoz	10040714
internal.identifier.thesoz	10035501
internal.identifier.thesoz	10051231
internal.identifier.thesoz	10054036
internal.identifier.thesoz	10037472
internal.identifier.thesoz	10056459
internal.identifier.thesoz	10037865
internal.identifier.thesoz	10057146
dc.type.stock	article	de
dc.type.document	journal article	en
dc.type.document	Zeitschriftenartikel	de
dc.source.pageinfo	97-108	de
internal.identifier.classoz	10105
internal.identifier.journal	614
internal.identifier.document	32
internal.identifier.ddc	300
dc.identifier.doi	https://doi.org/10.12758/mda.2016.005	de
dc.description.pubstatus	Published Version	en
dc.description.pubstatus	Veröffentlichungsversion	de
internal.identifier.licence	1
internal.identifier.pubstatus	1
internal.identifier.review	1
ssoar.wgl.collection	true	de
internal.check.abstractlanguageharmonizer	CERTAIN

Files in this item

Files	Size	Format	View
There are no files associated with this item.

This item appears in the following Collection(s)

Erhebungstechniken und Analysetechniken der Sozialwissenschaften
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods

Show simple item record