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Quantile Lower Bounds to Reliability Based on Locally Optimal Splits

Published online by Cambridge University Press:  01 January 2025

Tyler D. Hunt and
Peter M. Bentler
Tyler D. Hunt*
Affiliation:
University of Utah
Peter M. Bentler
Affiliation:
University of California, Los Angeles
*
Requests for reprints should be sent to Tyler D. Hunt, University of Utah, Salt Lake City, USA. E-mail: tyler.hunt@utah.edu
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Abstract

Extending the theory of lower bounds to reliability based on splits given by Guttman (in Psychometrika 53, 63–70, 1945), this paper introduces quantile lower bound coefficients λ4(Q) that refer to cumulative proportions of potential locally optimal “split-half” coefficients that are below a particular point Q in the distribution of split-halves based on different partitions of variables into two sets. Interesting quantile values are Q=0.05,0.50,0.95,1.00 with λ4(0.05)≤λ4(0.50)≤λ4(0.95)≤λ4(1.0). Only the global optimum λ4(1.0), Guttman’s maximal λ4, has previously been considered to be interesting, but in small samples it substantially overestimates population reliability ρ. The three coefficients λ4(0.05), λ4(0.50), and λ4(0.95) provide new lower bounds to reliability. The smallest, λ4(0.05), provides the most protection against capitalizing on chance associations, and thus overestimation, λ4(0.50) is the median of these coefficients, while λ4(0.95) tends to overestimate reliability, but also exhibits less bias than previous estimators. Computational theory, algorithm, and publicly available code based in R are provided to compute these coefficients. Simulation studies evaluate the performance of these coefficients and compare them to coefficient alpha and the greatest lower bound under several population reliability structures.

Keywords

reliabilitysplit halflower boundslambda 4

Information

Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 1 , March 2015, pp. 182 - 195
Copyright
Copyright © 2013 The Psychometric Society

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