In statistics and research, internal consistency is a measure based on the correlations between different items on the same test (or the same subscale on a larger test). It measures whether several items that propose to measure the same general construct produce similar scores. For example, if a respondent expressed agreement with the statements, "I like to ride bicycles," "I've enjoyed riding bicycles in the past," and "I hate bicycles" (this item would be reverse scored), our test would have good internal consistency.
Internal consistency is usually measured with Cronbach's alpha, a statistic calculated from the pairwise correlations between items. A commonly-accepted rule of thumb is that an α of 0.6-0.7 indicates acceptable reliability, and 0.8 or higher indicates good reliability. Note that extremely high reliabilities (0.95 or higher) are not necessarily desirable, as this indicates that the items may be not just consistent, but redundant.
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