COMMUNITY-CENTRED ASSESSMENT APPROACHES AND INCLUSIVE SOCIAL TRANSFORMATION IN CALABAR MUNICIPALITY LOCAL GOVERNMENT AREA OF CROSS RIVER STATE, NIGERIA
https://doi.org/10.83151/4ne4-hx73
The study examined the impact of community-based evaluation strategies on inclusive social change in Calabar Municipal Local Government Area of Cross River State, Nigeria. Guided by two objectives and corresponding research questions, a descriptive survey design was adopted. Data were collected using a structured instrument, the Community-Centred Assessment and Social Transformation Questionnaire (CCASTQ), developed by the researchers. Experts in Measurement and Evaluation validated the instrument, while reliability testing using Cronbach Alpha yielded coefficients of 0.78, 0.83, and 0.86, indicating strong internal consistency. Data were analysed using multiple linear regression at a 0.05 level of significance. Findings revealed that community assessment surveys significantly contribute to successful social transformation projects by enhancing relevance, ownership, and sustainability. Community-generated quantitative data were also found to be effective in improving programme outcomes by providing credible, context-sensitive evidence for planning, monitoring, and evaluation. Additionally, participatory approaches promoted accountability, inclusiveness, and sustainability in development interventions. The study concluded that active community involvement in assessment and data generation strengthens transparency and project outcomes, leading to inclusive social transformation. It is recommended that government and development organizations institutionalize community-based assessments, build local data generation capacity, and integrate participatory mechanisms throughout project planning and evaluation processes.
Keywords: Community-centred assessment, inclusive social transformation, participatory evaluation, community-generated data
Otu, Bernard Diwa, Uchegbue, Henrietta Osayi, Anele, Ezebunwo, Otu, Stelladeborah Bokan, 1Ogba, Uwaoma Flora, Ekereke, Aidam Benjamin, Ekaette Mfon Useh, Beshel, Ignatius Akwagiobe, Emmanuel King Ekon
COMMUNITY-CENTRED ASSESSMENT APPROACHES AND INCLUSIVE SOCIAL TRANSFORMATION IN CALABAR MUNICIPALITY LOCAL GOVERNMENT AREA OF CROSS RIVER STATE, NIGERIA https://doi.org/10.83151/4ne4-hx73
The study examined the impact of community-based evaluation strategies on inclusive social change in Calabar Municipal Local Government Area of Cross River State, Nigeria. Guided by two objectives and corresponding research questions, a descriptive survey design was adopted. Data were collected using a structured instrument, the Community-Centred Assessment and Social Transformation Questionnaire (CCASTQ), developed by the researchers. Experts in Measurement and Evaluation validated the instrument, while reliability testing using Cronbach Alpha yielded coefficients of 0.78, 0.83, and 0.86, indicating strong internal consistency. Data were analysed using multiple linear regression at a 0.05 level of significance. Findings revealed that community assessment surveys significantly contribute to successful social transformation projects by enhancing relevance, ownership, and sustainability. Community-generated quantitative data were also found to be effective in improving programme outcomes by providing credible, context-sensitive evidence for planning, monitoring, and evaluation. Additionally, participatory approaches promoted accountability, inclusiveness, and sustainability in development interventions. The study concluded that active community involvement in assessment and data generation strengthens transparency and project outcomes, leading to inclusive social transformation. It is recommended that government and development organizations institutionalize community-based assessments, build local data generation capacity, and integrate participatory mechanisms throughout project planning and evaluation processes. Keywords: Community-centred assessment, inclusive social transformation, participatory evaluation, community-generated data