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- W2896427553 abstract "In this article, we introduce clusteff, a community-contributed command for checking the severity of cluster heterogeneity in cluster–robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2017, Review of Economics and Statistics 99: 698–709) develop the effective number of clusters to reflect a reduction in the degrees of freedom, thereby mirroring the distortion caused by assuming homogeneous clusters. clusteff generates the effective number of clusters. We provide a decision tree for cluster–robust analysis, demonstrate the use of clusteff, and recommend methods to minimize the size distortion." @default.
- W2896427553 created "2018-10-26" @default.
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- W2896427553 date "2018-06-01" @default.
- W2896427553 modified "2023-10-01" @default.
- W2896427553 title "Inference for Clustered Data" @default.
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- W2896427553 doi "https://doi.org/10.1177/1536867x1801800210" @default.
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