In our M.E.A.L. series we have learned about the design, planning and data collection phases. In this new module, we will have a look at the fourth phase of the System: the analysis of the data.
The Data Analysis Framework
The data your project team has collected so far now becomes useful through analysis, visualization, and interpretation. Data analysis is a process by which you bring order and structure to the data you’ve collected, turning individual pieces of data into actionable information. Through data visualization, on the other hand, you bring the data into a chart, graph, or other visual format that helps inform the analysis. This process allows you to interpret and communicate your results. Then, with the process of interpreting the data, you attach meaning to them. The purpose of interpretation is to answer key learning questions about your project. The three processes should support, shape, and influence each other in an iterative process that makes the data useful and more meaningful.
Introduction to Data Analysis
As noted above, data analysis is the process of bringing order and structure to collected data. Since, as we know, data can be quantitative or qualitative, obviously there are data analysis methods for both: quantitative data analysis is a numerical analysis and easily visualized using a graph, chart, or map. Quantitative data is analyzed using quantitative, statistical methods and computer packages; while qualitative analysis, also called “content analysis,” involves working with words, like transcripts of discussions. Qualitative analysis can be aided by software, but is most often done using paper and pens. By picking up your PMP and reviewing it, you can find practical guidance on how to conduct data analysis (in the column means of analysis). It will tell you which data you will analyze, when and how you will analyze it, and how you will use your results. The goal of data analysis is to provide timely and relevant responses to stakeholders, learn effectively, and find ways to make your data as useful as possible. The timing of data analysis depends on when the data are collected and the timing of stakeholder information needed. Output-level data change rapidly, so they are analyzed more frequently than data at the level of intermediate outcomes and strategic objectives in the logframe. Often the analysis and interpretation is done before an important quarterly project meeting or report deadline, or as part of an evaluation. Being able to coordinate the data analysis with the overall project implementation schedule is critical, as you must remember that your goal is to provide timely and relevant responses to stakeholders.
Download this module to know more about quantitave data, the mistakes and errors you might encounter and for an introduction to qualitative analysis.
MODULE 7: Analyzing M.E.A.L. Data – Download
Find the previous modules here.
If you are interested in knowing more about project writing and evaluation and would like to have the assistance of professionals, you can email us at firstname.lastname@example.org. At SIGNIS Services Rome we are experts in the sector and have been involved in project writing for the creation and development of communications projects all over the world for decades.
*This content was curated by Valeria Appolloni and inspired by the materials published on Kaya, published for non-commercial and educational use.