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The 5 Forms of Data Analysis in M&E

a professional undertaking a data analysis assignment on computer

The 5 Forms of Data Analysis in M&E

In our previous blog, we talked about data collection methods in M&E. Today we discuss the five main types of data analysis in M&E. We’ll explain how the complexity and uncertainty of each of them increases. This isn’t to say that one method is superior to the other; rather, both have a place in an analyst’s toolkit.

  • Descriptive: Answers the question, “What transpired?”
  • Inferential: Answers the question, “And what about the rest?”
  • Diagnostic: Answers the question, “What’s going on below the surface?”
  • Predictive: Answers the question, “What is expected to occur next?”
  • Prescriptive: Answers the question, “How should we proceed?”

1.      Descriptive Analysis: This is the most fundamental and common sort of analysis. It is the simplest to accomplish from a technological standpoint. There are several descriptive statistics that provide us with methods for summarizing a set of data. (For example, counts, minimum and maximum values, sums, ratios, proportions and percentages, mean/median/mode, and dispersion measures (standard deviation and range)).

2.      Inferential Analysis: Inferential data analysis, like descriptive data analysis, is interested in what happened in the past. In contrast to descriptive analysis, which uses data that we already have, inferential analysis uses data that we don’t have. This form of study is used when collecting data on every single individual part of a population is impossible (due to cost or time constraints). Inferential analysis aims to give generalizations about a population using data from a sample.

3.      Diagnostic Analysis: This form of analysis looks for patterns beneath the surface, attempting to answer the question “why is this happening?”

4.      Predictive Analysis: This entails making predictions about future outcomes based on historical data.

5.      Prescriptive Analysis: This narrows our focus on the many courses of action we could follow and the possible results. The goal of prescriptive analysis is to provide a solution to the question, “What should we do?”

Ben Jones, a data literacy expert, presents this helpful metaphor to demonstrate how these five forms of analysis are related to one another. A patient attends a doctor’s appointment. The doctor keeps track of the patient’s symptoms and takes measurements (this is like descriptive analysis). The doctor may then have to draw some conclusions about other patients (inferential analysis.) The doctor digs further to figure out what’s causing the symptoms (diagnostic analysis.) The doctor makes some educated guesses about how the patient’s illness may progress with and without treatment (predictive analysis). Finally, the doctor may prescribe medication to help the patient recuperate (prescriptive analysis.) Whether you rely on one or all of these types of analytics, you will find they come in handy together. Our Monitoring, Evaluation, Accountability & Learning (MEAL) course has in-depth information regarding data analysis. Enroll today for a 10% discount!

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