Resumen
Despite impressive progress made in economic development, inequality still characterizes the pattern of socio-economic development especially in rural areas. This study aimed at investigating socio-economic development levels among the communities in rural space in Mkpat Enin LGA using linear discriminant analysis. To achieve this, data on 53socio-economic development indicators were collected from 87 communities in the study area using questionnaire and field observation. K-Mean Cluster analysis was used to group all the 87 communities into different development regions based on the levels of performance on six extracted factors for the purpose by determining dimensions of rural socio-economic development. Result showed a wider disparity in socio-economic development among regions in the study area. The socio-economic development characteristics earlier derived were used to demarcate the communities into development regions (clusters/groups) yielding a total of five (5) groupings. This result showed that even at the rural level, the problem of regional inequalities existed. This implied that some communities were weak in terms of performances on socio-economic development characteristics. To press further, Multiple Linear Discriminant Analysis (MLDA) was used to assess the optimality of earlier groupings of communities in the study area as well as identify the variables which differentiated the groups earlier derived. The result showed that MLDA correctly classified 87.4 per cent of the rural communities. The analysis correctly classified 54.5% of Group 1 communities but misclassified 45.5% of Group 1 rural communities as Group 2 rural communities. It correctly classified 50.0% of Group 2 and misclassified 50.0% as Group 5. It correctly classified 100% of Group 3, 4 and 5 rural communities with no misclassifications. In addition, Co-operative Societies and Medium Scale Industries, Modern Socio-economic and Infrastructural facilities, Neighbourhood Religious/Health and Infrastructural Factor, and Modern Agricultural Facilities were identified as the most important indicators which discriminated the five groups of communities of the study area earlier derived from the cluster analysis solution thus highlighting theirrole in improving the socio-economic development level in the study area.