Professional Statistical Analysis of Electoral Data for Gubernatorial Elections in South East Nigeria

Statistical analysis of electoral data provides objective characterisation of voting patterns, turnout determinants, and inter-election change that informs academic and policy understanding of democratic processes in Nigerian states. This study professionally analyses electoral data from the 2019 and 2023 governorship elections in the five South East states: Abia, Anambra, Ebonyi, Enugu, and Imo. Official INEC result data disaggregated by LGA and polling unit were used. Voter turnout rates, vote share distributions, and inter-election swing coefficients were computed. Coefficient of variation quantified within-state result variability as a proxy for electoral consistency. Regression analysis examined LGA-level turnout determinants using education, urbanisation, and poverty indices. Hierarchical cluster analysis grouped LGAs by voting pattern similarity. Mean voter turnout declined from 41.3 percent in 2019 to 28.7 percent in 2023, a statistically significant reduction (paired t = 7.4, p < 0.001). Coefficient of variation for polling unit vote shares exceeded 40 percent in 3 of 5 states, indicating high within-state electoral variability. Regression confirmed that urban LGAs (beta = 0.31, p = 0.004) and higher literacy rates (beta = 0.27, p = 0.018) were positively associated with turnout. Cluster analysis identified three distinct LGA voting profile types. The study provides statistically grounded electoral evidence for INEC and civil society organisations and recommends structured post-election statistical disclosure requirements for Nigerian state elections. Keywords: electoral statistics, gubernatorial elections, South East Nigeria, voter turnout, cluster analysis