Including Data Analysis in Your Grant Evaluation Section

Read this article for an overview of data analysis for your grant evaluation section.

Data analysis is not everyone's favorite topic, but it is a topic you cannot ignore if you want to be successful with grant writing. Not only do you need to be able to analyze your data appropriately to accurately and effectively describe your need for the project in the needs section, but you also need to describe how you will analyze data as part of your evaluation plan.

Many grant evaluation plans do a decent job of describing what data will be collected and how/when it will be collected. The majority also discuss how the data will be used for program improvement purposes. But when it comes to talking about how the data will be analyzed (one of the scoring criteria in most government grants, and many private ones, too), most grant writers fall apart.

We cannot discuss all of the detail you need to know regarding data analysis, but let's start with three basic concepts in analyzing the data that you should address.


Data Collection

Most people cover this pretty well in their evaluation plans. You need to include what data you will be collecting, how you will collect it, when you will collect it, and who will collect it. If new instruments (surveys, etc.) are going to be developed, you will need to describe that process, too. Think through the whole process from developing or acquiring the instruments to getting the data into your computer for analysis. The days of tallying surveys by hand on paper are over.


Descriptive Statistics 

This is a fancy way of saying that you will use the data to describe something. Descriptive statistics include frequency counts, percentages, means, etc. You will use descriptive statistics to describe the population you served. You will use them to describe your basic outcome data (survey results, etc.). Of course, whenever possible, you should disaggregate your descriptive statistics by important subgroups to make sure you painting an accurate picture. Most of the time, descriptive statistics are all you need for basic program evaluation, but not always.


Inferential Statistics

Inferential statistics are used to help you make judgments about the data beyond what can be said by looking at the descriptive data alone. Inferential statistics help you determine the statistical significance of the changes you see (the likelihood that the changes occurred as a result of your treatment, rather than by chance). They help you predict things, too. If you ever studied anything beyond descriptive statistics in school, you entered the world of inferential statistics. It is a scary place for some, but it is the only place to go if you really want to show causation (that your program really made a difference), and is not that what evaluation is all about?


Source: Veronica Robbins,
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Last modified: Monday, October 12, 2020, 6:00 PM