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

[journal article]

Schunck, Reinhard

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Please use the following Persistent Identifier (PID) to cite this document: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)
Keywords survey research; cluster analysis; sample; linear model; regression; estimation; parameter; simulation
Classification Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Free Keywords multilevel modeling; hierarchical linear model; sample size
Document language English
Publication Year 2016
Page/Pages p. 97-108
Journal Methods, data, analyses : a journal for quantitative methods and survey methodology (mda), 10 (2016) 1
ISSN 1864-6956
Status Published Version; peer reviewed
Licence Creative Commons - Attribution