The accuracy of certain internal consistency estimators have been questioned in recent years. The present study tests the accuracy of six reliability estimators (Cronbach’s alpha, omega, omega hierarchical, Revelle’s omega, and greatest lower bound) in 140 simulated conditions of unidimensional continuous data with uncorrelated errors with varying sample sizes, number of items, population reliabilities, and factor loadings. Estimators that have been proposed to replace alpha were compared with the performance of alpha as well as to each other. Estimates of reliability were shown to be affected by sample size, degree of violation of tau equivalence, population reliability, and number of items in a scale. Under the conditions simulated here, estimates quantified by alpha and omega yielded the most accurate reflection of population reliability values. A follow-up regression comparing alpha and omega revealed alpha to be more sensitive to degree of violation of tau equivalence, whereas omega was affected greater by sample size and number of items, especially when population reliability was low.

近年来，某些内部一致性估计器的准确性受到质疑。本研究测试了六个可靠性估计量（Cronbach's alpha、omega、omega hierarchical、Revelle's omega 和最大下限）在 140 个具有不相关误差的单维连续数据模拟条件下的准确性，这些数据具有不同的样本大小、项目数量、总体可靠性、和因子载荷。已提议替代 alpha 的估计器与 alpha 的性能以及彼此之间进行了比较。可靠性估计受样本量、违反 tau 等效性的程度、人口可靠性和量表中的项目数量的影响。在这里模拟的条件下，由 alpha 和 omega 量化的估计产生了人口可靠性值的最准确反映。