Q: Can you explain how to go about doing Cronbach's alpha analysis?

Cronbach’s alpha is a test used to measure the reliability of a scale used in social science or research projects. It tests whether the scale measures the outcome or variable it is intended to measure. The result is expressed through the alpha coefficient or simply alpha, depicted by the Greek letter α. It was devised by Lee Cronbach, an American educational psychologist, in 1951.

It is typically used to test Likert scales, which have responses ranging from ‘Strongly agree’ to ‘Strongly disagree,’ usually on a five- or seven-point scale. However, it may also be used to test dichotomous or binary scales, which have simple Yes-No responses. In the case of the latter, the actual measure used is the Kuder-Richardson Formula 20 (KR-20), with Cronbach’s alpha building on KR-20.

Consider a questionnaire intended to measure customers’ satisfaction with grievance redressal. Cronbach’s alpha can be used to assess if all the questions here indeed measure customer satisfaction around redressal. If not, the extraneous questions can be removed and replaced with new, more relevant ones as needed. So too, it can help gauge if the questionnaire has the right amount of questions, thus helping avoid redundancy.

In a Cronbach’s alpha analysis, a score of 0.7 or above is considered good, that is, the scale is internally consistent. A score of 0.5 or below means that the questions need to be revised or replaced, and in some cases, that the scale needs to be redesigned.

There are a couple of formulas to calculate Cronbach’s alpha, with one being slightly simpler and more popular. You may find both formulas here.

However, you may also use a statistical program to calculate α. The most popular is SPSS (earlier known as Statistical Package for the Social Sciences). Through this link, you may learn how to calculate α using SPSS. Through this link, you may also learn how to calculate α using programs such as Stata and R.

Related reading:

• Importance of statistical power in research design
• How should I design and score a Yes/No survey?
• If I use a minor statistical software in my research paper, could it be rejected?