Using scales to measure constructs is widespread in the social sciences and beyond.  To support the application of these scales, researchers and practitioners need to show evidence of appropriate reliability and validity.  Many different types of reliability exist, but internal consistency reliability is perhaps the most popular.  Even yet, many metrics exist to provide evidence of internal consistency reliability, but Cronbach’s alpha is perhaps the most popular of these.  For this reason, I provide a guide below of how to calculate Cronbach’s alpha in Python.  If you have any questions or comments after reading, please contact me at MHoward@SouthAlabama.edu.

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Typically, I begin my guides with a brief review of the statistic; however, one of my prior Ph.D. students, Chad Marshall, wrote a fantastic introduction to Cronbach’s alpha which was previously featured on MattCHoward.com.  If you need to learn more about Cronbach’s alpha, click here to read it.

Once you are familiar with Cronbach’s alpha, we can then use Python to calculate it.  If you need a dataset, click here to download the example dataset.  Be aware, however, that this dataset is in the .xlsx format, and the current guide requires the file to be in .csv format.  For this reason, you must convert this file from .xlsx format to .csv format before you can follow along using this dataset.  If you do not know how to do this, please visit my page on converting a file to .csv format. While this guide was written for R, the portion that converts .xlsx to .csv only uses Excel.  After converting the file, you can continue with this guide.

There are two things that you first need to do before we type our syntax to calculate a Cronbach’s alpha.  First, you need to install the Pingouin and Pandas modules.  If you do not know how to install modules, then you should read my page on installing modules in Python.  Second, you need to import your data.  If you do not know how to import data in Python, then you should read my page on importing data in Python.  Once you have done those two things, your syntax should look something like this:

As seen above, we activate our packages in the first and third lines, and we input our data in the fifth line.

Now, to calculate a Cronbach’s alpha, our syntax is very simple.  We just need to type in: pg.cronbach_alpha(data= .  Then we enter the name of our data, followed by closing our parenthesis.  This should result in something similar to the syntax below. Then press enter.

Success! We got a (terrible) Cronbach’s alpha of .035 with a 95% confidence interval of [-.14, .19]. . .But what exactly did this syntax do? Well, the syntax calculated the Cronbach’s alpha of each variable in the dataset together. This can be helpful if your dataset only includes a single scale, but our datasets typically include multiple scales and we want to calculate a separate Cronbach’s alpha for each of them. So what do we do?

Well, we can add a little bit of code after importing our data that can separate our scales in the dataset.  To do this, we must first know which columns contain data for which scales.  For instance, we must know that columns 1, 2, and 3 contain data for Scale 1, whereas columns 4, 5, and 5 contain data for Scale 2.

To separate our scales and calculate separate Cronbach’s alphas, we need to add some additional code. Let’s just type it after the results that we calculated.

You first want to type in the name of the scale for which you will calculate the Cronbach’s alpha. For the current example, we will type: Scale1 = . This indicates that we will assign data to the label “Scale1.” Then, we want to type the following: MyData[[ . We are now referencing our dataset, but we need to tell it what data to use. To use certain columns of our original dataset, we need to enter the names of these columns enclosed by single quotes and separated by commas. So, if we wanted to calculate the Cronbach’s alpha of Var1, Var2, and Var3, we would then enter the following syntax: ‘Var1’, ‘Var2’, ‘Var3’ . We would want to close our two brackets by typing: ]] . Finally, we would press enter, and your syntax should appear as the following.

Scale1 now refers to the three columns that represent our scale. Or, at least, Scale1 represents the columns that we want to use to calculate our Cronbach’s alpha.

The last step is to calculate the Cronbach’s alpha again. We will type the same syntax as before, but we will replace MyData with Scale1. So, our syntax will be: pg.cronbach_alpha(data=Scale1). Press enter.

Great!  We got a much better (but still pretty low) Cronbach’s alpha of .57.  Did you get output that looked like the picture above?  If so, wonderful!  You calculated the Cronbach’s alpha for your scale.  Good work!  If not, try again and maybe even look at other sources.  If that still doesn’t work, email me at MHoward@SouthAlabama.edu.  I can try to help!