Download full text
(external source)
Citation Suggestion
Please use the following Persistent Identifier (PID) to cite this document:
https://doi.org/10.12758/mda.2016.005
Exports for your reference manager
Cluster size and aggregated level 2 variables in multilevel models: a cautionary note
[journal article]
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 cluste... view more
"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)... view less
Keywords
simulation; sample; parameter; cluster analysis; regression; linear model; survey research; estimation
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
2190-4936
Status
Published Version; peer reviewed
Licence
Creative Commons - Attribution