Topics: Qualitative research, Quantitative research, Data Pages: 12 (4931 words) Published: November 1, 2014
WHY, WHEN AND HOW - Savitri Abeyasekera
Statistical Services Centre, University of Reading, P.O. Box 240, Harry Pitt Building, Whiteknights Rd., Reading RG6 6FN, UK.
Phone 0118 931 8459, e-mail


In many research studies involving the use of participatory tools, much of the information gathered is of a qualitative nature. Some of this will contribute to addressing specific research questions, while other parts provide a general understanding of peoples’ livelihoods and constraints. The aim of this paper is to focus on the former. The paper concentrates on some quantitative analysis approaches that can be applied to qualitative data. A major objective is to demonstrate how qualitative information gathered during PRA work can be analysed to provide conclusions that are applicable to a wider target population. Appropriate sampling is of course essential for this purpose, and it will be assumed in what follows that any sampling issues have been satisfactorily addressed to allow generalization of results from the data analysis to be meaningful.

Most emphasis in this paper will be given to the analysis of data that can be put in the form of ranks, but some analysis approaches suitable for other types of qualitative data will also be considered. The general questions of why and when are discussed first, but the main focus will be on issues relating to the how component of data analysis. It is not the intention to present implementation details of any statistical analysis procedures, nor to discuss how output resulting from the application of statistical software could be interpreted. The aim is to highlight a few types of research questions that can be answered on the basis of qualitative information, to discuss the types of data format that will lend themselves readily to appropriate data analysis procedures and to emphasise how the data analysis can be benefited by recognizing the data structure and paying attention to relevant sources of variation.


Why use quantitative approaches?

Quantitative methods of data analysis can be of great value to the researcher who is attempting to draw meaningful results from a large body of qualitative data. The main beneficial aspect is that it provides the means to separate out the large number of confounding factors that often obscure the main qualitative findings. Take for example, a study whose main objective is to look at the role of non-wood tree products in livelihood strategies of smallholders. Participatory discussions with a number of focus groups could give rise to a wealth of qualitative information. But the complex nature of inter-relationships between factors such as the marketability of the products, distance from the road, access to markets, percent of income derived from sales, level of women participation, etc., requires some degree of quantification of the data and a subsequent analysis by quantitative methods. Once such quantifiable components of the data are separated, attention can be focused on characteristics that are of a more individualistic qualitative nature. Quantitative analytical approaches also allow the reporting of summary results in numerical terms to be given with a specified degree of confidence. So for example, a statement such as “45% of households use an unprotected water source for drinking” may be enhanced by providing 95% confidence limits for the true proportion using unprotected water as ranging from 42% to 48%. Here it is possible to say with more than 95% confidence that about half the households had no access to a protected water supply, since the confidence interval lies entirely below 50%. Likewise, other statements which imply that some characteristic differed across two or more groups, e.g. that “infant mortality differed significantly between households with and without access to a community based health care clinic”, can be...
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