1. [Cohens_kappa.py] contains the code used to determine the Cohen's Kappa of each code. For this, the initial coding (Initial_Coding.xlsx) is converted into a CSV file (My_encoding.csv) and the second coding (Second_Coding.xlsx) is converted into a CSV file (Second_Encoding.csv). These are inputted into the cohens_kappa program. The output can be found in [Console_Output_Cohens_Kappa.txt]. The Cohen's Kappa per code and Average Cohen's Kappa for each theme can also be found in [Cohens_Kappa.xlsx]. The formula used to calculate the average Cohen's Kappa can be found by clicking on the entry holding the average.    
2. The coding that has been performed by me (Mahira Ali) can be found in [Initial_Coding.xlsx]. Here, on the left side of the sheet, the codes have been transformed into a hot-one encoding. The formula used for this can be found when clicking on the entry. Additionally, the frequency of the code has been stored below each hot-one encoding. Again, the formula used for this can be found when clicking on the entry.
3. The coding that has been performed by the double coder can be found in [Second_Coding.xlsx]. Here, on the left side of the sheet, the codes have been transformed into a hot-one encoding. The formula used for this can be found when clicking on the entry.
4. The final coding that has been derived after the discussion with the double coder can be found in [Final_Coding.xlsx].
5. The transformation of the coding scheme can be viewed in [Thematic Analysis Compared Codes.jpg].  
6. The frequency of the codes can be found in [Frequencies.png].
7. The excel used to create the frequency charts can be found in [Chart_Initial_code.xlsx] 
8. [pbcc.py][phi_coef.py][spearman.py] have been used to calculate the correlation coefficients. Please refer to the methodology section, when to use which. In order to calculate the coefficients, the CSV strings are pasted in [data1.csv] and [data2.csv]. Please see point 9 how the CSV strings are obtained.  
9. The excel sheet holding the CSV String used to calculate the correlation coefficient can be found in [CSV Strings.xlsx]. These have been generated by using the following formula on the hot-one encoding of each theme: =CONCAT(TRANSPOSE(##:##)&","). For an example, please click on an entry below the encoding in [Final_Coding.xlsx].
10. [heatmap_matplot.py] has been used to generate the heatmaps used to report the correlation values.  

 
  
 