Thèse de Doctorat
DOI
https://doi.org/10.11606/T.6.1999.tde-01102014-105050
Document
Auteur
Nom complet
Denise Pimentel Bergamaschi
Unité de l'USP
Domain de Connaissance
Date de Soutenance
Editeur
São Paulo, 1999
Directeur
Jury
Souza, Jose Maria Pacheco de (Président)
Moraes, Suzana Alves de
Oishi, Jorge
Peres, Clovis de Araujo
Santos, Jair Licio Ferreira
Titre en portugais
Correlação intraclasse de Pearson para pares repetidos: comparação entre dois estimadores
Mots-clés en portugais
Dois Estimadores da Correlação Intraclasse de Pearson para Pares Repetidos
Simulação Monte Carlo
Resumé en portugais
Titre en anglais
Intraclass correlation of Pearson repeated for couples: comparison between two estimators
Mots-clés en anglais
Monte Carlo Study
Two Estimators of Pearson's Pairwise Correlation Coefficient
Resumé en anglais
Objective. This thesis presents and compares, theoretically and empirically, two estimators of the intraclass correlation coefficient pI, defined as Pearson's pairwise intraclass correlation coefficient. The first is the "natural" estimator, obtained by Pearson's moment-product correlation for members of one class (rI) while the second was obtained as a function of components of variance (icc). Methods. Theoretical and empirical comparison of the parameters and estimators are performed. The theoretical comparison involves two definitions of the intrac1ass correlation coefficient pI as a measure of reliability (*) for two repeated measurements in the same class and the presentation of the technique of analysis of variance, as well as for the definition and interpretation of the estimators ri and icc. The empirical comparison was carried out by means of a Monte Carlo simulation study of pairs of correlated values according Pearson's pairwise correlation. The pairs of values follow a normal bivariate distribution, with correlation values and sample size previously fixed: n= 15, 30 e 45 and Pl = {O; 0,15; 0,30; 0,45; 0,60; 0,75; 0,9}. Results. Bias and mean square error for the estimators were compared as well as the range of the intervals of confidence. The comparison shows that the bias of icc is always smaller than of rI This also applies to the mean square error. Conclusions. The icc is a better estimator, especially for n less than or equal to 15. For larger samples sízes (n 30 or more), the estimators produce results that are equal to the second decimal place. (*) Fórmula

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Date de Publication
2014-10-01

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