ICC2: Stata module to compute Intraclass correlation coefficients based on crossed mixed regression

Niels Henrik Bruun

Statistical Software Components from Boston College Department of Economics

Abstract: Textbooks often calculate the ICC using sums of squares on a subject-by-measurement matrix with non-missing cells. The idea of the ICC is to compare the wanted variation explained by a factor variable on an outcome with the total variation, the total variation being the wanted variation by the factor variable plus the unwanted variation. Bias occurs sometimes from the measurement repetitions. The ANOVA-like calculations ignore all measurements by a subject if just one measurement for that subject is missing and may also return ICC estimates below zero. The latter is theoretically impossible. To better utilize subjects with missing measurements and avoid obtaining negative ICCs, it is better to use estimates from a mixed, crossed regression. The command icc2 returns a matrix with the absolute and consistency ICCs with a 95% confidence interval and a P-value for the ICCs equal to zero. The user can obtain more precise confidence intervals using the bootstrap.

Language: Stata
Requires: Stata version 13.1
Keywords: intraclass correlation; ICC; mixed regression (search for similar items in EconPapers)
Date: 2024-11-03
Note: This module should be installed from within Stata by typing "ssc install bap". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/i/icc2.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/i/icc2.sthlp help file (text/plain)

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