A Comparison of Significance Testing Procedures for the Intraclass Correlation from Family Data

Abstract
Different procedures for testing problems concerning intraclass correlation from familial data are considered in the case of varying number of siblings per family. Under the assumption of multivariate normality, the hypotheses that the intraclass correlation is equal to a specified value are tested. To assess the performance of the tests, Monte Carlo simulations are designed to compare their powers. The Neyman's (1959) C(α) test and the test based on the modified ANOVA F statistic are shown to be consistently more powerful than other procedures.