The p value for the difference between those alpha values is:


              
This shows the plot and you can see a table of all the attenuated correlations for all the inputs you gave in the tab 'All_data'

              
You can use the dialogue below the plot to download it chosing the name and the format of the plot

              
 

              

              

                

                  
                  
                

                

                  
                  
                

                

                  
                  
                    
                    Download plot
                  
                

              

              
 

              

                Thanks to Keith Newman for the download handler: 
                shinyDownload
              

            

            

              
This is ancient psychometrics but still of some use as we may have published alpha values with the dataset sizes from the literature or one reported value and our one value from the same measure.  The p value given here is the usual null hypothesis test giving the probability that a difference (in either direction) would have happened by chance sampling vagaries given two datasets of the size you are inputting given that there were no difference in the population between the alpha values.

              
 

              
The p value is based on the assumption that the samples are independent (probably true), that the variables involved are all Gaussian (dodgy, value may be moderately robust to non-Gaussian distributions) and that there is some plausibility in the idea that the two datasets are random samples from enormous populations (can be dodgy!).

              
 

              
If you have raw item data from two samples I'm sure you are better off getting bootstrap CIs around the observed alpha values and comparing those but we often have data for one or neither alpha values so this can be useful.

              
 

              

                There is an explanation of the simulation I did to check Feldt's method and my implementation of it at my 
                Rblog
                post:
                Using simulation to check a function
              

            

            

              

                App created 26.x.24 by Chris Evans
                PSYCTC.org
              

              
Last updated 26.x.24.

              

                Licenced under a 
                Creative Commons, Attribution Licence-ShareAlike
                 Please respect that and put an acknowledgement and link back to here if re-using anything from here.
              

              


  
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