How to Write About Data Analysis in Grant Proposals
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Strong grant proposals use data to convincingly illustrate the potential impact and effectiveness of proposed programs. Yet, for many grant writers—whether applying for federal, state, or local funding—the evaluation section poses a significant challenge.
In the third installment of the Evaluation series, Drew discusses how to write about conducting data analysis when drafting successful grant proposals. He discusses:
How to align your proposed data analysis with grant requirements and your own goals and objectives
How to write about quantitative data analysis as a non-data expert
How to write about using qualitative and mixed-methods approaches in grant writing.
What grant makers are looking for you to demonstrate with respect to your proposed approach to data analysis
This episode is for those writing the evaluation section of grant proposals but aren’t necessarily experts when it comes to data and evaluation. Check out our recent blog post related to this podcast topic, The Evaluation Section Part #3: Data Analysis for Grant Proposals.
You’ll hear:
(02:22) How to align your data analysis with grantors' expectations and specific requirements.
(03:49) How to define clear objectives and goals to effectively measure and demonstrate program impact.
(05:44) Five ways to present quantitative data even if you’re not an expert in data analysis.
(11:18) How to incorporate qualitative data to provide depth and context to your findings.
(17:27) Why you should address potential challenges and limitations in your data analysis
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Drew Reynolds: Welcome to the Common Good Data podcast. My name is Drew Reynolds, and today we are going to put together a podcast for you. that focuses on data analysis and grant proposals. And for this episode, it's a part of a larger series, you'll see it on our blog at commongooddata. com slash blog, where we're putting a four part series together on things to think about when you're writing the evaluation section of a federal grant proposal or another type of grant proposal.
So this third installment focuses just on Data analysis for grant proposals and the reason we put this together was because many people who are writing grants federal grants state grants or even for local foundations there's always some part of the grant proposal that focuses on how you're going to evaluate the impact of what you do How are you going to be able to demonstrate?
And provide evidence that what you accomplished had some sort of meaningful impact in the world, right? And the evaluation section can sometimes be hard for folks to write because you say hey, maybe i'm not an expert in evaluation and quantitative data analysis or statistics or Doing qualitative analysis or things like that.
And so this podcast is really to help people who are Interested in doing grant writing and know that they have to write evaluation sections of proposals, but maybe they're not experts when it comes to data and evaluation. The key here for grant for data analysis and grant proposals is that what we I'll talk about today in this podcast are the things that you might want to consider when you're talking about how your organization will do data analysis.
Now, what's great is that you haven't done the work yet, so you don't actually have to do data analysis in this section, but you're just talking about it. How you're going to do it. Today we'll talk about four kind of key topics. Actually five, because what we're going to do is talk a little bit about First, you're meeting grant requirements, understanding how the grantors, what requirements they might have around data analysis, talking a little bit, secondly, about objectives and goals for data analysis and parts three and four, we'll talk a little bit about how you want to talk about quantitative data, and then fourthly, what you'd like to talk about with qualitative data.
And then lastly, some ethical considerations and some final factors to consider when you're addressing challenges and limitations with data analysis. So let's dive right on in. Okay, so first, knowing your requirements. Now, one critical mistake to avoid in grant writing is forgetting to mention the required outcomes from your grantor.
Most grantors, especially federal grantors, are going to have some type of standard measure that they're going to want for you to collect so that they can then understand the performance of all of their grant making, right? How many people did they serve through all the grant money that they provide?
Provided, for example, is one measure that almost Always shows up in almost every single grant opportunity that you'll find. It's just simply the number of people who are served by, through the grant, right? Grantors will typically expect some pretty detailed plans and ideas about how you're going to collect and analyze and report on grants.
Things like numbers of people you served, program effectiveness, outcomes, etc. But again, they especially expect for you to demonstrate how you're going to collect the data that they are requiring for you to collect. So one of the most important things, again, is to go back, read that funding announcement, read the information about the grantor, if The grantor is like a grant officer.
Sometimes you can get in touch with them to ask about requirements. If you're not sure from the funding announcement or from other information that you've received, you know what those requirements might be, but that's an absolute essential to be able to state back that you understand what those requirements are and that you know how to be able to analyze your data in a way that's going to be able to get that information back to the grantor.
So first knowing those requirements, then you'll want to define a clear objective for data analysis. And I think here. You can really draw from your goals and objectives that you've already written in the grant somewhere. In the whether it's an action plan or a project description or something like that in your grant.
You'll have those goals and objectives about what you intend to accomplish. And in your analysis section, you want to talk about some of those goals and link, your analysis to those goals and objectives Sometimes too you can talk a little bit about the types of evaluation you might use so for example measuring doing a process evaluation that looks like an evaluation of how you implement the program so you can understand Challenges, barriers, problems, things that came up while you were doing the evaluation.
You can talk a little bit about outcomes evaluation. What changes in knowledge or skills or behaviors? What happened over time? What was the impact or the outcome that a a person experienced? Experience as a result of the services or programs that you provided. You can also look at, describe, talking about participant experiences.
So things like using qualitative data, like interviews or focus group data, sorry, interviews or focus group data to understand more deeply the experiences that participants are having while they're in your program. So there's some examples of some things you can think about when you're talking about the goal of this data analysis is to better understand.
Experiences of people in the program. The goal of this, data analysis is going to be able to understand the changes in outcomes or behaviors that we see over time. You can use that kind of language to talk about. the key goals of the analysis that you're trying to provide.
And because really not all evaluation is the same. Sometimes you're looking at outcomes, sometimes you're looking at process, sometimes you're looking at experiences, sometimes you're just trying to count the number of people who participated, right? So being clear about that can be helpful so you can provide a little bit more richness and detail as to how you intend to measure and demonstrate impact over time.
So a second thing to think about is actually how you analyze the data. Okay. And I'll start on the quantitative side, most funders will expect to see some type of quantitative data analysis in their grant proposal Not all some are going to be okay with more qualitative or descriptive analysis and some might not have too many requirements But certainly from your federal and state grantors, they're going to expect some type of quantitative data analysis and they really want to see how you'll use quantitative or numerical data to at least count the people you have served and then You count or measure progress, sorry, towards some type of stated goals or objectives.
So some types of outcomes work. Sometimes you'll see the term outputs, which is what you do or who you serve, and outcomes as the result of the work that you've done. So you might see both outputs and outcomes talked about when you're looking at data analysis here. So the challenge of course in a podcast is that data analysis is a very technical skill set it might not be something you're familiar with.
Maybe you had a class in college about research or program evaluation maybe you've had some experience professionally working in excel and spreadsheets. Maybe you haven't So it could be sometimes a little bit intimidating to think about how you want to write about analysis So I wanted to give some examples of some like words and some things that you can use as a grant writer Thank you to communicate some expertise on this topic without necessarily being an expert in data analysis.
Some examples. First, you can always talk about counts, right? Analyzing participant data in your programs by just simply counting the numbers of enrolled participants. For example, you might say, we're gonna count the number of participants who enrolled in our programs in the first quarter.
So that's a very simple thing. You can use the term counts to just describe that process. You can use percents. That's something people understand pretty well, right? So calculating the percentage of participants who meet a certain criteria. So for example, the percentage of participants who enrolled in your programs who don't have access to health insurance.
The percentage of participants who would identify with a particular cultural group. for example or who meet a certain age bracket. So that kind of gives a little bit more in detail about who you're serving and it can be a really helpful way to communicate a little bit about the nature of the programs that you're providing.
You can also do percents for things like a percent in who accomplished something. So the percent of people who completed a program, for example, or who accomplished some type of outcome. That can be really helpful as well. You could also look at trends and use the term trends to talk about, essentially changes over time.
So imagine a bar chart, right? Where you have the number of something accomplished, and then on the y axis and on the x axis, you have time measured in seconds. Years, quarters, months, days, something along those lines, you can define that time period. So trends are a way of showing how did things change over time throughout the course of your program.
So that might be a helpful way to visualize and think and communicate about the type of analysis you might do. So for example, maybe you want to be able to track the number of service hours provided by the staff in your organization. You can imagine what that bar chart might look like and then describe it in your grant proposal.
Okay. Also thinking about changes. This is the most, probably one of the more important ones because it really gets at outcomes, right? So what are the changes in behavior and knowledge and skills and any of those different types of characteristics that we want to see change in as a result of the programs, how do you explore those changes over time?
So for example, Say you ran a nutrition program and you wanted to explore changes in dietary habits for your participants. You might look and explore changes in how people respond to a survey that asks them about those changes. particular dietary habits at a pre or before the program starts and at a post or after the program starts.
So you can use things like pre and post or some of those time indicators to be able to explore when you're measuring a certain outcome and how that might change over time. That's an important one. I really encourage folks to find some way to do that over time. Now, there's different ways to do it, of course.
You can have pre post surveys. You can add control groups. You can do randomized control trials. There's all these different things you can do from a methods perspective. But I think for folks who maybe are just trying to put together a proposal that is going to work for a grant proposal. Just talk about changing over time and use language that you know, and that is familiar to you.
You don't have to sound like a research person to be able to do most of these grants. Lastly, also I think it can be helpful to talk about visualizations you might want to create. So like visualization, physical, sorry, visualization tools like pie charts or bar charts, line graphs, mapping and others.
You can describe, have that image in your mind, what is like a cool graph that you've seen maybe one of your peer organizations put together that you would like to try to modify and use for one that you use. That can be a helpful way to, to just get a sense of, okay, how do I want to visualize this data to communicate something to, in this case, the grantor, but also your audience, broadly speaking.
When you're doing quantitative data analysis, I think it's also a good idea to try to find ways to integrate it into what you're already doing. So when you're writing a grant proposal, think about, okay, what data do we already have? What data are already being collected? And try and use that as much as you can or rely on it so that, each time you're writing a grant proposal, you don't feel like you have to reinvent the wheel about how you're collecting data.
So that's another thing too, to definitely keep in mind is how can you use what you've already got when you're describing data analysis and grant proposals. Okay. So that's quantitative. Let's talk about qualitative data. It's a totally different set of data, right? Now we're talking about typically words, plainly said, it's words versus numbers.
But qualitative data is actually not just words. It could be images, it could be all kinds of things. Basically anything that's not numeric. And quantitative data really offers a perspective that allows you to go deeper richer. More contextual in the insights that you're trying to gather about program effectiveness.
So again, as I described before with the data analysis, you don't have to be. An expert in qualitative data analysis, just like you don't have to be an expert in statistics to be able to do this work. Two words that I think come up pretty consistently when you're talking about qualitative data, is coding and themes.
So first, coding involves systematically labeling, categorizing something in qualitative data to identify themes and concepts and patterns. So you, we have two types of coding. One is open coding, where you do an exploration of the data without any predefined categories. You just read a set of interviews, read some focus groups to identify themes.
Or alternatively, you might use selective coding, where you actually have the themes We use the term a priori in the research world, but beforehand you come in with a predefined set of themes and look for them in the data that you're doing analysis for. And there's advantages and disadvantages to both, but that can be a really cool way to talk about, Oh, look, this person really knows what they're talking about with qualitative data.
They're talking about coding. That's awesome. So you can explore that as an approach to talk about qualitative data analysis. For example, our evaluation team is going to. Analyze 10 interviews and examine codes related to, themes for your outcome. Could be a sentence that you write in a grant proposal.
Then there's this term, themes, right? So a theme in qualitative research refers to some type of pattern or meeting that emerges over time. So it's the thing you're looking for when you're doing coding. So those themes could be say for example, you're doing You're offering a program that focuses on mental health or mental wellness or mental illness, right?
And you're trying to identify different themes that are related to the experiences of individuals who are living with a mental illness, for example. You might identify as a theme, for example, strategies for coping with symptoms emotional, impact of the illness on your life or your family or support systems.
Who are the, what are the sources of support that people are identifying in interviews that they rely upon as a way of managing the symptoms that they're experiencing? So you can see each of those are clearly defined cohesive ideas that might be described in lots of different places.
Now, as a qualitative data analysis person, one interview person might say that their source is a group of friends. Another might say it's their family. Another might say it's their place of worship or a church, right? So you see that what they say may be different examples, but you can identify that theme consistently across those different, right?
sources of qualitative data and then label them with that theme. And that's where, the more you do qualitative data, the more experience you'll get in being able to identify what those themes are and see beyond what is being said to understand something that emerges this pattern of meaning that emerges that we talk about in qualitative data analysis.
That's qualitative data. So a couple of things you can mention there, again, coding and themes when talking about qualitative data analysis. I think a really important thing to do too, and this is not just for grant proposals, but just when you're thinking about analysis in general, is how can you use both quantitative and qualitative data together to help paint a more comprehensive picture of the work you're doing and the program evaluation that you're conducting.
So a strong grant proposal might describe. What we call a mixed methods approach, so both qualitative and qualitative to be able to talk about their program effectiveness. I hear the phrase all the time when I'm talking with clients that the phrase, Oh, you can't reduce our program's effectiveness to a number.
People and the change that people experience is more than just the number of people who participated, right? Numbers can sometimes feel very reductive, like they don't, Capture the whole nature of the experiences of your programs and mixed methods and qualitative approaches are allow you to then add that richness Add that depth Some of that thick description sometimes is a word that comes up in qualitative research It's a better get beyond sometimes what can feel like an overly reductive approach to data analysis And I think the best organizations always have some combination of quantitative and qualitative analysis, because really you can't tell the whole picture of them all one without the other.
Now, there are some grantors who are really more interested in the quantitative than the qualitative, and that's been my experience, I think, with a lot of federal grants. That's, maybe it's not, more interest in, but at least they have requirements for quantitative data because it's more easily standardized and there might not necessarily be more of a clear vision from the grantor about what qualitative data might be helpful or what they might be interested in.
So know that you're going to anticipate that from grantors that sometimes you might see a bit of a lean towards the quantitative from your grantors. But that doesn't mean that you shouldn't do the qualitative piece. Especially when you're thinking about how to communicate and talk about and learn from your program, right?
Because again, all this evaluation work hopefully is making us better understand our work and get better at our work. Definitely include that qualitative even if you don't necessarily see it so clearly articulated by the grantor as a requirement. So there's some ideas about quantitative and qualitative analysis.
Certainly there's, an entire course or degree program in college that you could use to talk about stats and data analysis and qualitative and research methods and all of that. But again, to get started on grant writing, you don't have to have a whole degree to be able to do it. And that was really the purpose of, our conversation today.
A few notes before we wrap up on analysis. Sometimes it's good to also talk a little bit about anticipated challenges or limitations in data analysis. So maybe you're having trouble accessing data, especially if it comes from somebody outside your organization. That might be a barrier that you let you acknowledge or resources like, Hey, we would love to do a more in depth analysis here, but we have limited resources in our team to be able to do that.
So we're going to take a more limited approach. I think sometimes just acknowledging those limitations and challenges upfront. Sometimes people are afraid to do that. You're like, why would I talk about my limitations in a grant proposal? But I think in this case what you're doing is not every organization has limitations, right?
What you're doing is portraying your organization as experienced. An organization that clearly in the past has run into challenges and is better at anticipating them in the future. I think I always love that when I see organizations that kind of know where their pain points are, because it articulates a sense of self knowledge that means that they're going to be better equipped to handle problems when they inevitably come up especially in evaluation and data analysis work.
And then lastly, there's certainly a lot of ethical considerations in data and reporting, cultural considerations to keep in mind. We'll save that probably for another podcast episode because you could do tons on that. But I do want to point folks to part two of this series, which focused specifically on protecting participants, confidentiality, privacy, informed consent, and a lot of those issues.
So we've got a blog post on our website that speaks to those as well as a prior podcast episode that you can check out for some of those ethical considerations. So thanks again for listening today on this topic on data analysis, the quantitative and the qualitative side. This is the third of a three part series that focused on data analysis.
All kinds of things you want to be thinking about when you're writing the evaluation section of your grant and federal grant proposals. Stay tuned because we'll have a fourth one that's going to come up talking about reporting, communications, and continuous improvement that often shows up in grant grant proposals.
So stay tuned for that. Thanks again for listening and we'll see you on the next episode.
