An R Function for Cronbach’s Alpha Analysis: A Case-Based Approach

va-ABSTRACT Cronbach’s alpha is a very commonly used method in biomedical research. Cronbach’s alpha Indicates the extent to which the items in your questionnaire are related to each other, a useful coefficient for assessing the internal consistency of the items. Although this method is commonly used in medical research, the statistical software packages do not have the direct menu-driven operation for Cronbach’s alpha. Hence this paper intends to provide an R function (


INTRODUCTION
Many quantities in medicine, such as anxiety, stress, or degree of handicap, are not possible to measure explicitly. Instead, we ask a series of questions and combine the answers into a single numerical value. Often this is done by simply adding a score from each answer. When items are used to form a scale, they need to have internal consistency. The items should all measure the same concept, so they should be correlated with one another. Cronbach's alpha, is a correlation measure, if a scale consists of several items that are identical, then it indicates a questionnaire is very poorly formulated. So, the general idea that correlated items are the best ones has its flaws and we need to remember that.

CRONBACH'S ALPHA
Cronbach's alpha is a measure used to assess the reliability, or internal consistency, of a set of scale or test items. In other words, the reliability of any given measurement refers to the extent to which it is a consistent measure of a concept, and Cronbach's alpha is one way of measuring the strength of that consistency.
Cronbach's alpha is computed by correlating the score for each scale item with the total score for each observation (usually individual survey respondents or test takers), and then comparing that to the variance for all individual item scores. The formula is where k is the number of items, si 2 is the variance of the i th item and sT 2 is the variance of the total score formed by summing all the items. If the items are not simply added to make the score, but first multiplied by weighting coefficients, we multiply the item by its coefficient before calculating the variance si 2 . Clearly, we must have at least two items-that is k >1, or will be undefined.

INTERPRETATION OF OUTPUT
Cronbach's alpha has direct interpretation, a score of .70 or greater is generally considered to be acceptable 0.90 or greater indicates high consistent, 0.80-0.89 is good consistent,0.70-0.79 is acceptable consistent, 0.65-0.69 is marginal consistent and <0.5 indicates unacceptable consistent. For scales which are used as research tools to compare groups, alpha values of 0.7 to 0.8 are regarded as satisfactory. For the clinical application, much higher values of alpha are needed. The minimum is 0.90 and 0.95 is desirable.

CONCLUSION
Cronbach's alpha Indicates the extent to which the items in your questionnaire are related to each other, it ranges from 0 to 1 and not robust against missing data. It measures only internal consistency of the scale and higher values are always preferred over lower ones. Alpha is zero indicates items are not measuring what they supposed to measure. In this paper, the Cronbach's alpha is illustrated in a simpli-fied way to help the researchers. Also, the Cronbach. Alpha function provided in this paper will help to generate the alpha value.