The Nexus between Project Monitoring and Evaluation and Quality of Residential Buildings in Nairobi County, Kenya

Mary Nyawira Dr. Mwenda, Dr. Otieno- Omutoko Lillian and Prof. Christopher Gakuu
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Mary Nyawira Dr. Mwenda: Lecturer, Department of Open Learning,University of Nairobi. Thika, Kenya
Dr. Otieno- Omutoko Lillian: Senior Lecturer, Department of Open Learning, University of Nairobi, Nairobi, Kenya
Prof. Christopher Gakuu: Director, School of Open, Distance and e-Learning, University of Nairobi, Nairobi, Kenya

International Journal of Research and Innovation in Social Science, 2021, vol. 05, issue 1, 354-362

Abstract: Integrating monitoring and evaluation within project construction phases is considered in this research a means by which construction of buildings can foster sustainable development and alleviate their collapse as witnessed in many countries. The aim of monitoring and evaluation is to provide information that can help inform decisions, improve performance and achieve planned results. Projects with strong monitoring and evaluation components tend to stay on track. Additionally, problems are often detected earlier, which reduces the likelihood of risks, major cost overruns or time delays. Against this backdrop, this research sought to analyze the influence of project monitoring and evaluation on quality of buildings. Descriptive survey and correlational research designs were used in a mixed methods research approach. Quantitative data was collected through a questionnaire while qualitative data was collected through an interview guide. Research instruments were pilot tested for validity through content related method and reliability through split- half criterion. A sample of 192 respondents was selected by use of Yamane’s (1967) sampling size formula from a population of 3475 registered .contractors in Nairobi County by May 2017. A census survey was conducted among the 67 engineering consultants and 24 officers in top management teams of NCA and NBI. Arithmetic mean and standard deviation were used for analyzing descriptive data while Pearson Product Moment Correlation (r) and regression analysis (R2) were used for analyzing inferential data. F-tests were used to test the hypothesis in the study. Tests of statistical assumptions were carried out before data analysis to avoid invalidation of statistical analysis. With r = 0.409, R2=0.167, F (1,222) =49.770 at p= 0.000

Date: 2021
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