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dc.contributor.authorMaribie, Stephen
dc.date.accessioned2026-07-14T08:55:05Z
dc.date.available2026-07-14T08:55:05Z
dc.date.issued2025-01
dc.identifier.urihttp://repository.anu.ac.ke/handle/123456789/1070
dc.descriptionA Thesis Submitted in Partial Fulfilment of the Requirements for the Award of the Degree of Master of Arts in Monitoring and Evaluation; School of Business of Africa Nazarene Universityen_US
dc.description.abstractThis study examined the efficacy of data analytics in climate shock response, focusing on Nairobi City County and using floods as a case study. Grounded in the Dual System Theory and Rational Action Theory, the research aimed to assess the effects of data organization, analysis, and interpretation on urban climate shock response in Kenya. The study employed a descriptive research design targeting staff from the Kenya Meteorological Department, National Disaster Management Unit, and Nairobi City County. The study drew a sample size of 228 respondents from a population of 528 using stratified random sampling and purposive sampling. Data collection was conducted using digital questionnaires through Kobo Toolbox. The study utilised descriptive statistics such as frequency and percentages for quantitative analysis, employing the Statistical Package for Social Sciences (SPSS) version 29 and presenting in tables and graphs. The qualitative data was analysed using thematic analysis. The study additionally analysed the data using means and standard deviations to measure central tendencies and dispersion of the data and utilised linear regression analysis to measure the effect of each independent variable on the dependent variable. For hypothesis testing, regression analysis was used to devise the relationships between indipendent and dependent variables. The study found that data organisation does not affect climate shock response; T(166) = 1.500; β= 0.205.; P>0.05. Also, data analysis does not affect climate shock response; T (166) = 1.341; β= 0.140; P>0.05. Additionally, data interpretation does not affect climate shock response; T(166) = 1.187; β= 1.98; P>0.05. The study failed to reject all the three hypothesis. The study recommends, the adoption of an enhanced multi-stakeholders collaboration in climate shock response, development of protocols for integrating climate data findings into climate shock response strategies and investment in community awareness and resilience. On further research, the study recommends that studies be conducted on influence of the economy and politics on shock response.en_US
dc.language.isoenen_US
dc.publisherANUen_US
dc.subjectEffecten_US
dc.subjectDataen_US
dc.subjectAnalyticsen_US
dc.subjectEfficacyen_US
dc.subjectClimateen_US
dc.subjectShocken_US
dc.subjectResponseen_US
dc.subjectFloodsen_US
dc.titleEffect of Data Analytics Efficacy on Climate Shock Response in Urban Areas in Kenya: A Case of Floods Response in Nairobi City Countyen_US
dc.typeThesisen_US


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