Robust estimation of Cronbach's alpha

Authors:
A. Christmann;S. Van Aelst
Affiliations:
University of Dortmund, Fachbereich Statistik, Dortmund, Germany;Department of Applied Mathematics and Computer Science, Ghent University (UGENT), Gent, Belgium
Venue:
Journal of Multivariate Analysis
Year:
2006

Citing 6
Cited 1

Robust regression and outlier detection

Robust regression and outlier detection
A fast algorithm for the minimum covariance determinant estimator

Technometrics
Influence function and efficiency of the minimum covariance determinant scatter matrix estimator

Journal of Multivariate Analysis
Robust factor analysis

Journal of Multivariate Analysis
Influence functions and efficiencies of the canonical correlation and vector estimates based on scatter and shape matrices

Journal of Multivariate Analysis
The multivariate least-trimmed squares estimator

Journal of Multivariate Analysis

A simulation approach on Cronbach's alpha statistical significance

Mathematics and Computers in Simulation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cronbach's alpha is a popular method to measure reliability, e.g. in quantifying the reliability of a score to summarize the information of several items in questionnaires. The alpha coefficient is known to be non-robust. We study the behavior of this coefficient in different settings to identify situations where Cronbach's alpha is extremely sensitive to violations of the classical model assumptions. Furthermore, we construct a robust version of Cronbach's alpha which is insensitive to a small proportion of data that belong to a different source. The idea is that the robust Cronbach's alpha reflects the reliability of the bulk of the data. For example, it should not be possible that some small amount of outliers makes a score look reliable if it is not.