To the detriment of human resource development (HRD) theory building and research, many scholars may think that research data with a low coefficient alpha is destined for the file drawer; this does not have to be the case. Contemporary literature suggests that many scholars do not know how to move forward with data that yields α < .70. In addition, an investigation revealed that many scholars practice the method of item deletion to increase alpha. Besides supporting the case that discarding research simply because of low coefficient alphas may be unnecessary, a guide is presented to demonstrate how scholars and scholar–practitioners may be able to analyze data when an initial estimate of internal reliability is low. We caution that deleting items may increase reliability at the cost of validity. As an alternative, this study demonstrates that eliminating subjects can increase alpha and maintain the integrity of the scale. This guide presents generalizability theory as a means to identify the source of error variance in data as well as a step-by-step process to correct for low coefficient alpha. The guide is illustrated with data and R syntax.

有损于人力资源开发（HRD）理论的构建和研究，许多学者可能认为具有低系数alpha的研究数据被指定用于文件抽屉。并非必须如此。当代文学表明，许多学者不知道如何处理产生α<.70的数据。此外，一项调查显示，许多学者都实践删除项目以增加alpha的方法。除了支持不需要仅因系数α低而放弃研究的情况外，还提供了指南来说明学者和学者-从业者对内部可靠性的初步估计较低时如何能够分析数据。我们提醒您，删除项目可能会增加有效性，但会增加有效性。作为备选，这项研究表明，消除主体可以增加alpha并保持量表的完整性。本指南介绍了可推广性理论，可作为识别数据中误差方差的来源，以及逐步修正低系数α的过程。该指南以数据和R语法进行了说明。