There are Many Greater Lower Bounds than Cronbach's α: A Monte Carlo Simulation Study

A Monte Carlo simulation study was conducted to examine the performance of α, λ 2 , λ 4 , μ 2 , ω T , GLB MRFA , and GLB Algebraic coefficients. Population reliability, distribution shape, sample size, test length, and number of response categories were varied simultaneously. The results indicate th...

Full description

Saved in:

Bibliographic Details
Published in:	Measurement (Mahwah, N.J.) Vol. 21; no. 1; pp. 1 - 28
Main Authors:	Novak, Josip, Rebernjak, Blaž
Format:	Journal Article
Language:	English
Published:	Routledge 02-01-2023
Subjects:	greatest lower bound internal consistency Monte Carlo simulation omega coefficient reliability
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	A Monte Carlo simulation study was conducted to examine the performance of α, λ 2 , λ 4 , μ 2 , ω T , GLB MRFA , and GLB Algebraic coefficients. Population reliability, distribution shape, sample size, test length, and number of response categories were varied simultaneously. The results indicate that α and λ 2 perform the worst overall. However, the performance of α is improved if the population reliability is high. λ 4 is relatively unbiased but the most imprecise. μ 2 and ω T perform relatively well under most conditions. GLB Algebraic outperforms other coefficients under many conditions. GLB MRFA is useful under few conditions if the population reliability is high. The results corroborate previous suggestions that large samples, longer tests, higher number of response categories, and normally distributed results can make reliability estimates more dependable. Some insights on the interaction of these factors are provided. We discuss the findings compared to previous research. The complete R code used for the simulation is provided in the online supplement.
ISSN:	1536-6367 1536-6359
DOI:	10.1080/15366367.2022.2031484