You may wonder why my sympathy for Cronbach’s Alpha (also called Coefficient Alpha).
Well, it’s because alpha is one of the most frequently misunderstood and misinterpreted statistics in theses, dissertations, proposals and other academic publications involving measurement 🥲.
SO, WHAT IS CRONBACH’S ALPHA?
Cronbach’s alpha is a measure of the internal consistency of a measurement instrument. It is the most commonly reported type of reliability.
If the internal consistency of a measurement instrument is low, then there is heterogeneity – or lack of consistency – among the items of the instrument, making it uncertain what the total score is measuring and implying the presence of random error in its scores.
WHY IS ALPHA MISUNDERSTOOD?
First, Cronbach’s alpha refers to the reliability of measurement, not the reliability of the overall research or study.
Second, some students doing qualitative interviews say they will calculate alpha for the questions of their qualitative interview instrument. Alpha can only be computed for a quantitative measurement instrument.
Third, internal consistency does not imply unidimensionality. A high value of alpha does not mean that all the items in an instrument measure the same thing. Rather, it means that every item in the instrument measures something similar to some of the other items, but the instrument may still be multidimensional.
Fourth, unless the measurement instrument is unidimensional (check this via factor analysis or similar), the total score may not be interpretable, even if alpha is high.
Fifth, don’t assume that an instrument with an alpha value of .7 indicates a sufficient measure of reliability or internal consistency. This value is arbitrary and increases with the number of items in the instrument.
Sixth, a very high value of alpha is not necessarily good as it may imply redundant items in the instrument that should be dropped.
Seventh, you cannot assume that published reliability values will apply to your own sample and test situation. You need to quote your own alpha value in addition to those published.
Finally, it is inappropriate to calculate the alpha value for a knowledge test made up of different facets, or dimensions. Calculate alpha per facet.
(Cortina, 1993; Cronbach, 1951; Gardner, 1995; Kline, 2011; Revelle & Condon, 2019; Taber, 2018).
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