Data analysis is one of the most crucial stage in the field of monitoring and evaluation. The main purpose of conducting data analysis is to convert raw data into useable information. Data analysis allows the researchers to interpret and convey the information and findings rationally and logically. It involves the process of understanding and summarising the collected data and organizing it in a manner that answers to the intervention’s objectives and indicators. Data analysis is important to understand ‘whethers, hows, and whys’ of the intervention i.e., whether the intervention under the study (M&E) is progressing towards completing its intended objectives or not and how it is/ it is not achieving it. The process is also helpful to test the null hypothesis, check for results, estimate parameters, and ultimately make insights and generalizations about the intervention’s approach in general.
The M&E team handling the project requires a well-thought-out plan for data analysis before the initiation of the study. Data analysis plan should include an outline of the time frame and characteristics of data, methods and data structure required by the statistical tools, necessary statistical tools or templates to achieve stated objectives, manpower responsible for carrying out the analysis and interpretation of the results of the study and, purpose of the data analysis. [1]
There are multiple methods to analyze quantitative and qualitative data. Quantitative data can primarily begin with descriptive analysis of the conditions, circumstances based on findings from the data. This involves the steps of summarization of the indicators of interest and tabulation for creating relationships. It will be followed by a comparative analysis of key indicators across respondents from various target groups, geographical areas, etc. which will provide sharper insights into the field realities. Adding to these steps, advanced tests such as variance test (ANOVA), T-test, simple regression analysis, and multiple regression analysis for establishing a cause-effect relationship, correlation coefficient, and Chi-square analysis for ascertaining association can be used. Qualitative data analysis helps in identifying patterns, deviants, groups, and others. Here, observations and findings can be made based on an existing theory. Under qualitative data, analyzing process often begins with coding. “Coding is the process of labeling as “belonging to” or representing some type of phenomenon that can be a concept, belief, action, theme, cultural practice or relationship.” [2] Adding to this, methods such as force field analysis, direct observations can be used.
This blog summarises various methods and techniques involved in data analysis in India for monitoring and evaluation and how it adds value to the studies. More importantly, it shows why data analysis is an integral part of any meaningful research work in the field of monitoring and evaluation.
[1] Nhepa T (2015), “Significance of Data analysis in Monitoring and Evaluation,” Department of Business Studies – Management Development Unit, University of Zimbabwe.
[2] IOM staff (2020), “Monitoring and Evaluation Methodologies for data collection and analysis for monitoring and evaluation,” Chapter 4, IOM Monitoring and Evaluation.