Upper bounds on correlations and Cronbach’s alpha for discrete random variables

CLICK HERE FOR THE ACTUAL ARTICLE

This is a small tutorial on how to either use the R code (if you are an R user) or the web application (if you prefer a user-friendly GUI) that will calculate the upper bound on (positive) correlations between random variables with an arbitrary number of categories.

The first thing to know is that your categories should start at 0 (not 1). If you are using the R code, please make sure your categories start there. If you are using the GUI, it will check (variable by variable) whether or not the categories start at 0 and will make the necessary adjustments for you.

The overall interface is very simple:

cronbach1The first step will be to upload your data where it says “Browse”. Notice that:

(1) This app ONLY takes .csv files. If you are an SPSS user and are unfamiliar with how to save your SPSS (extension .sav) data as a .csv file, here is an easy explanation. Trust me, it’s A LOT easier than you think.

(2) It will use ALL the variables in your data file. If you have other variables or covariates which you are not interested in correlating, you have to remove them before uploading the data.

(3) It doesn’t handle missing data. You need to provide a .csv file with no missing elements so you need to ‘pre-process’ it before you upload it.

(4) It can only handle numbers. If you leave your variable names, it will give you an error. So make sure those are removed as well before uploading anything.

And…. that’s pretty much it. Once you’ve pre-processed the data and uploaded the file just click “Run” and it should give you something that looks like this:

cronbach2

The interpretation is very simple. It uses the number of categories and the probabilities of endorsing each category to calculate the maximum possible correlations that you could have observed. Then it uses this convenient formula that operates exclusively on the correlation (or the covariance) matrix to calculate Cronbach’s alpha. Keep in mind that this would be the standardized Cronbach’s alpha (as opposed to the raw one). But you can always use it as a point of reference. Or work backwards from the maximum possible correlation matrix and “unstandardize” it to obtain the maximum possible covariance matrix.

If you have many variables, the correlation matrix won’t be displayed. But you can download it as a .csv file for inspection or click on ‘copy’ to copy-paste it in Microsoft Words or some text editor of your choice. Cronbach’s alpha is always calculated.

And… that is pretty much it.

YOU CAN CLICK HERE TO ACCESS THE APP

 

And if you are an R user and feel more at home with code, HERE IS THE CODE THAT RUNS THE ANALYSIS.