Voters' literacy levels and the number of rejected votes in their constituencies

You know I like playing with data. Actually, that’s what my work entails. I have to make sense of some array of data and either validate a previously hypothesized theory or reject the same. Occasionally, the data may not produce any meaningful information. This could be as a result of many things. It could, for example, be caused by human error - the most common cause. It’s courtesy of this that we have the infamous GIGO - garbage in garbage out. If the data clerk, or even the analyst, makes a mistake, then that would in most likelihood result in a bias. This would thus, distort the conclusion inferred from the analysis.

In some instances, there could have been mistakes at the data collection stage; either due to a poorly produced tool (questionnaire etc) or the method of administration. Rarely, does this result from a faulty computer or software. When the latter occurs it could be as a result of a bug in the software or even a computer virus.

Deliberate distortion of data can also lead to a bias as well. This may be influenced by some ulterior motives, where the user selectively chooses what to include in the analysis and what not to include.

Anyhow, while reading the online version of the Daily Nation, I came across some breaking news saying that the Kenyan referendum results have been gazetted. This somewhat sprung my statistical curiosity into action.

Since I had preciously visited the IIEC website, I thought I should just check the results once again. Just in case I ‘discovered’ something useful. Anyway, I was happy to see that the results are easily accessible in a .pdf format. I therefore, saved the results on my desktop and exported the tables to an excel sheet.

The first thing that I noticed once I moved the data to an excel sheet was the ‘rejected votes’ column. I wasn’t looking for anything in particular but just happened on that. Anyway, it didn’t take me long to see some negative correlation between the assumed illiteracy and poverty levels of a constituency and the low number of rejected votes. Before you run away, let me explain what I mean by this technical term.

In negative correlation, as the value of one variable increases another somewhat related variable decreases. By variables, here, I mean the poverty and illiteracy levels on the one hand and the number of rejected votes on the other. However, this may not be true if you check for a relationship using the percentage figures. You can only see the association if you use the absolute number of rejected votes.

Please note that I do not, as of now, have data to validate my assumption about the levels of poverty and illiteracy among the constituencies that recorded the least number of rejected votes. I based my hypothesis on a recent study that I read on the Daily Nation portraying NEP as the province that, unfortunately, leads in these two areas. The same was true of the districts in ‘upper eastern’ and some other pastoralist’s areas. I remember Nyanza was also considered as one of the impoverished regions in the country.

Thus, when I saw constituencies mostly from the above regions having the least number of rejected votes, then I thought the reason for this could be the poverty and illiteracy associated with these areas. Here, I am talking of all the constituencies in NEP with the exception of Dujis having less than 100 spoilt ballots. The expansive arid north was also well represented with Turkana North, Moyale, Samburu East, Saku, Laisamis, Isiolo South and North Horr all having less than 100 rejected votes. However, for some reasons, Kathiani and Vihiga are also represented in this category of constituencies with the least number of rejected votes.

Anyway, I can only hypothesize that the higher the literacy level in an area, the more the illiterate voters would want to fit in. They might probably feel ashamed in asking for help during the actual casting of the ballot and thus making them prone to commit some mistakes. On the other hand, the voters from the regions known for their low literacy levels would have no problem informing the IIEC personnel of their inability to vote unaided and thus reducing the probability of rejected vote. This could be the reason behind the low number of rejected votes in the least literate constituencies. Conversely, Starehe, Langata and Embakasi having the highest absolute number of rejected votes.

However, this might not be the only valid conclusion. One other reason could be the high number of voters registered in a constituency and could certainly increase the probability of rejected votes. I will test this last hypothesis in my next article.