文章基本信息

标题：Cluster Size and Aggregated Level 2 Variables in Multilevel Models. A Cautionary Note
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作者：Reinhard Schunck
期刊名称：Methoden, Daten, Analysen
印刷版ISSN：1864-6956
电子版ISSN：2190-4936
出版年度：2016
卷号：10
期号：1
页码：120-131
DOI：10.12758/mda.2016.005
出版社：GESIS - Leibniz-Institute for the Social Sciences, Mannheim
摘要：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.
关键词：multilevel modeling; hierarchical linear model; sample size; survey research; cluster sampling