Asymptotic Distribution Free Interval Estimation-Reference-Cited by-同舟云学术

Asymptotic Distribution Free Interval Estimation

Published:2008-01 Issue:1 Volume:4 Page:4-9
ISSN:1614-1881
Container-title:Methodology
language:en
Short-container-title:Methodology

Author:

Coffman Donna L.¹,Maydeu-Olivares Alberto²,Arnau Jaume²

Affiliation:

1. Methodology and Prevention Research Centers, The Pennsylvania State University, USA

2. Faculty of Psychology, University of Barcelona, Spain

Abstract

Abstract. Confidence intervals for the intraclass correlation coefficient (ICC) have been proposed under the assumption of multivariate normality. We propose confidence intervals which do not require distributional assumptions. We performed a simulation study to assess the coverage rates of normal theory (NT) and asymptotically distribution free (ADF) intervals. We found that the ADF intervals performed better than the NT intervals when kurtosis was greater than 4. When violations of distributional assumptions were not too severe, both the intervals performed about the same. The point estimate of the ICC was robust to distributional violations. We provide R code for computing the ADF confidence intervals for the ICC.

Publisher

Hogrefe Publishing Group

Subject

General Psychology,General Social Sciences

Link

https://econtent.hogrefe.com/doi/pdf/10.1027/1614-2241.4.1.4

Reference16 articles.

1. Asymptotically distribution-free methods for the analysis of covariance structures

2. A Review of Inference Procedures for the Intraclass Correlation Coefficient in the One-Way Random Effects Model

3. A Comparison of Confidence Interval Methods for the Intraclass Correlation Coefficient

4. Alpha's Standard Error (ASE): An Accurate and Precise Confidence Interval Estimate.

5. Exact distributions of intraclass correlation and Cronbach's alpha with Gaussian data and general covariance

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