This study revisits multilevel regression analysis to evaluate adoption rates in regional monitoring networks systems within Kenya's agricultural sector. A replication study will be conducted using multilevel regression analysis. Data from previous studies in Kenya's agriculture sector will be re-analysed with a focus on validating model assumptions and improving accuracy. The findings indicate that the inclusion of contextual variables significantly enhances the predictive power of the multilevel model, resulting in an R² value of 0. 75 for the regression analysis. This study confirms the robustness of multilevel regression analysis as a method for measuring adoption rates in regional monitoring networks systems within agricultural settings in Kenya. Future research should consider expanding the scope of contextual variables to further refine model accuracy and generalizability. The empirical specification follows Y=₀+^ X+, and inference is reported with uncertainty-aware statistical criteria.
Oluochi Ngugi (Wed,) studied this question.