克朗巴赫阿尔法
比例(比率)
可靠性(半导体)
质量(理念)
结构效度
有效性
阿尔法(金融)
心理学
一致性(知识库)
内部一致性
探索性因素分析
验证性因素分析
内容有效性
心理测量学
考试(生物学)
计算机科学
临床心理学
人工智能
地理
认识论
地图学
功率(物理)
哲学
物理
量子力学
标识
DOI:10.31234/osf.io/h47fv
摘要
Health Psychologists using questionnaires rely heavily on Cronbach’s alpha as indicator of scale reliability and internal consistency. Cronbach’s alpha is often viewed as some kind of quality label: high values certify scale quality, low values prompt removal of one or several items. Unfortunately, this approach suffers two fundamental problems. First, Cronbach’s alpha is both unrelated to a scale's internal consistency and a fatally flawed estimate of its reliability. Second, the approach itself assumes that scale items are repeated measurements, an assumption that is often violated and rarely desirable. The problems with Cronbach’s alpha are easily solved by computing readily available alternatives, such as the Greatest Lower Bound or Omega. Solving the second problem, however, is less straightforward. This requires forgoing the appealing comfort of a quantitative, seemingly objective indicator of scale quality altogether, instead acknowledging the dynamics of reliability and validity and the distinction between scales and indices. In this contribution, I will explore these issues, and provide recommendations for scale inspection that takes these dynamics and this distinction into account.
科研通智能强力驱动
Strongly Powered by AbleSci AI