Staff capacity and data quality in Vi Agroforestry

Abstract

This study was conducted amongst the Vi Agroforestry partners. It is entitled “Staff capacity and data quality” the study was guide by three major objectives that is: To assess how capacity of staff in designing data collection tools affect project data quality. To evaluate the capacity of staff in data collection and its relationship to project data quality and to assess how the ability of staff to analyze data affect project data quality The main objective of this study can be summarized as the relationship between staff capacity and the quality of data among Vi Agroforestry partner staff. understanding the different capacities of staff who handle data and how the presence or the lack of these capacities have affected data quality. A correlational research design was applied as a research design in this study and correlational data was majorly used in the analysis. This non-experimental type of research was helpful in identifying the relationship between variables and seeing the frequency of occurrence in the variables. Purposive sampling was used to identify the sample and staff who are knowledgeable or who have handled data were interviewed. A total of 61 staff were interviewed out of a population of 70 staff and this sample was determined using a 95% confidence level. Other primary data collection methods used were key informant interviews and observations. Statistical data analysis involved the use of descriptive and inferential analytical techniques. Staff capacity in designing data management was found to have an impact on the data quality and this is backed by the analysis that on the different staff capacities that are discussed in the later chapters. Capacity of staff in designing data collection tools was assessed by analyzing the staff’s capacity to interpret indicators, their capacity to construct statements and their capacity to pretest developed tools and the results show that much as there are some staff with capacities in the design of data collection tools, there is still a lot of work to do to effect data quality positively. This is evidenced in only 38.4% of the respondents who agreed to having capacity to construct statements. There is still need to ensure good quality data collection through the selection, training and supervision of data collectors. The capacity of staff in data collection was looked at in different dimensions and these included their capacities in the use of different data collection methodologies, the results showed that only 48% of the respondents strongly agree using knowledge gained in interview skills trainings compared to 78% of the respondents who have had trainings on interview skills, the results reflect a compromise in the data quality produced more on the data quality dimension of accuracy and validity. It is advisable to use standardized data collection tools, which have already been tried and tested in real life situations, and improve these if necessary to maximize data quality. The capacity of staff in data analysis was assessed in their knowledge in data imputation, visualization, data editing and data integration. the results show that all the respondents who have done data analysis have not done data deduplication. And this is evidenced with the result of a coefficient of 0.000. The officers should be as critical of the methodological approaches to using qualitative data analysis software as they are about the fit between research question, methods and research design.