What is the appropriate measure for testing reliability when data is in Likert-scale response format?

The appropriate measure for testing reliability when data is in Likert-scale response format is Cronbach's alpha. This statistic is specifically designed to assess the internal consistency of a set of items that are intended to measure the same underlying construct. Likert-scale responses, which often consist of a series of statements rated on a scale (e.g., from "strongly disagree" to "strongly agree"), are ideally suited for this analysis.

Cronbach's alpha evaluates how closely related a set of items are, determining the extent to which they work together to measure a single concept. A higher alpha value (generally above 0.7) indicates good reliability, suggesting that the items are consistently measuring the same underlying concept. This is particularly important in research settings where the validity of the measurement tool can directly impact the findings.

Other measures mentioned, such as the KR-20 coefficient, are used for dichotomous (yes/no) items rather than for Likert scales. Split-half reliability focuses on the internal consistency by dividing the items into two halves and correlating the results, but it doesn't provide as comprehensive a measure as Cronbach's alpha for Likert-scale data. Test-retest reliability assesses the stability of results over time and is not specific to the