SayPro Analysing Quantitative and Qualitative Data12

The analysis of quantitative data is often better known in projects than that of qualitative data. Quantitative data analysis often – but not exclusively – encompasses calculations, such as total and average numbers of activities implemented or percentages as compared to plans or targets. More elaborate statistical analysis may also be required, for example with cost-benefit analysis (see Annex D). Discussing the specific procedures of statistical analysis lies beyond the scope of this Guide, so the focus here is on ways to deal with qualitative information.The analysis of qualitative information is very different and can be more difficult than that of quantitative data for those who are not used to dealing with opinions and non-standard answers. Through content analysis of collected information, conclusions can be formulated for each of the performance questions or indicators. The analysis process involves identifying the categories of responses found in the raw data.Involve the data collectors in analysis. All M&E data collectors and facilitators – whether they are project staff, implementing partner staff or primary stakeholders – should participate in sessions to analyse qualitative data. Because of the nature of qualitative data, it is critical that those who were present when the data were gathered also participate in analysis. Much happens in open-ended discussions that is observed by facilitators and helps to explain the data.Collect and analyse qualitative data concurrently. Qualitative data collection is intended to trigger an iterative learning process. This means that information from one discussion or interview will indicate aspects of the topic that you will need to pursue with other questions and methods. So analysis of one set of interview data may indicate changes needed in subsequent interviews or discussions. A second reason for immediate analysis of information is that it is impossible to note everything that is said in open-ended discussions. Additional information, such as about the group dynamics and how they influenced what was said, will not be recorded but are critical to interpreting information. So the sooner the analysis takes place, the easier it is to remember aspects that were not noted.Structure analysis around each performance question and each category of interviewees. For example, if M&E field staff conducted individual interviews with two farmer leaders and with the village council (VC) in one day, then the two sets of data (farmers and VCs) would be analysed separately. During the analysis, the team may need to refer back to the performance questions to clarify the objectives of the different discussions.Follow these five steps to analyse the data.

• Re-read the interview questions to the group. This allows everyone to remember the focus of the M&E work.
• The note-taker(s) read aloud the responses for each question. If there is more than one set of notes, each set of notes should be read.
• Discuss the responses and share other comments that may not have been written down, to clarify exactly what the interviewees were saying.
• Cluster the responses and summarise findings. Together, identify the categories of responses in collected information and summarise the findings concisely. The summary should indicate the trends in the information in terms of whether the attitudes or ideas expressed were shared by all interviewees, the majority, half, a minority or only a few. Although you cannot quantify the different types of answers, do report trends.
• Identify unclear or missing information. Determine whether there is missing or unclear information that should be investigated in subsequent M&E work.

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