Testing ‘Crowdcoding’ Methods in Sub-Saharan African Settings: Using the 2020 Tanzanian Elections to Test its Validity and Reliability

This study replicates existing research on crowdcoding, and content analysis approaches to test the validity and reliability of content analysis methods in the African setting. We use data from the 2020 Tanzanian presidential elections as a case study. Instead of MTurk for crowdsourcing, the study utilized WhatsApp groups and university students from Tanzania to code the data. Using a collected and controlled sample of 400 tweets to represent Tanzania's ruling and opposition parties, respectively, our overall findings suggested that crowdcoding produced more reliable data than qualitative content analysis (QCA). However, further analysis suggests that although Crowdcoding recorded higher agreement on validity scores, trained coders seemed to provide more reliability accuracy scores. Besides, data indicates that the traditional training of the coders was statistically insignificant in providing accurate validity and reliability scores for QCA.