Controversy over which reliability estimators should be used persists due to a lack of knowledge about their accuracy. Simulation is an effective tool to obtain an answer, but existing simulation studies yield contradictory results regarding which reliability estimators are the best. The causes of these inconsistent conclusions have yet to be discussed. This study reanalyzes existing studies to understand these contradictions. The most important reason is that previous studies consider only a few reliability estimators. This study examines approximately 30 reliability estimators and finds that there is no single, most accurate reliability estimator across all data types. Instead, several reliability estimators are accurate to comparable levels for unidimensional data (congeneric reliability, Guttman’s lambda2, and ten Berge-Zegers’s mu). Likewise, multiple reliability estimators perform similarly for multidimensional data (multidimensional parallel reliability, correlated factors reliability, and second-order factor reliability). Whereas many recent studies support factor analysis (FA) reliability estimators, this study shows that not all FA reliability estimators are accurate and that some cause severe overestimation.

中文翻译：

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可靠性系数的准确性：对现有模拟的重新分析。


由于缺乏对其准确性的了解，关于应该使用哪种可靠性估计器的争议仍然存在。仿真是获得答案的有效工具，但现有的仿真研究得出了关于哪种可靠性估计器最好的矛盾结果。这些不一致结论的原因还有待讨论。本研究重新分析现有研究以理解这些矛盾。最重要的原因是以前的研究只考虑了少数可靠性估计量。本研究检查了大约 30 个可靠性估计器，发现没有一个针对所有数据类型的最准确的可靠性估计器。相反，一些可靠性估计器对于一维数据（同类可靠性、Guttman lambda2 和 10 个 Berge-Zegers mu）精确到可比较的水平。同样，多个可靠性估计器对于多维数据（多维并行可靠性、相关因素可靠性和二阶因素可靠性）执行类似的操作。尽管最近的许多研究支持因子分析 (FA) 可靠性估计器，但本研究表明，并非所有 FA 可靠性估计器都是准确的，有些会导致严重高估。