Jenny Betz: 

Hello, hello, I’m glad to have you all here. We know some folks will trickle in, and that’s okay. But to make sure you’re in the right place, today we are here for the Proposals 101 series. It’s for school and district leaders. And this session is session three, so you may or may not have been to the first two sessions, and we’ll make sure you have access to the resources related to those later. But to know that in that process, we’re at session three, which is focused on designing with impact through the use of logic models and other data tools. So, we are glad that you’re here today and hope you are ready to get started. 

So, today’s session is hosted by the California Stronger Connections Technical Assistance Center. So, we provide support to local education agencies or school districts to foster safe, healthy and supportive learning environments, which can mean a lot of things. And some of you are stronger connections grantees, and some of you are from other districts, or schools or organizations, and that’s all great. So, this session really is for anyone that’s either new to proposal writing, or those who want to brush up or strengthen their knowledge and skills. Everyone is welcome. 

And today’s facilitation team includes Laura Markle, Shannon McCullough, who you can see also on the screen. And I’m Jenny Betz. Mostly you’ll be hearing from Shannon and I today. But we also have our colleagues, Rebecca and another Laura who are here today, and you may see them in the chat or otherwise participating. 

We talked about the series, and this is really where we’ve been, where we are now and then where we’re headed. So, we started this series with a panel and some overall insights from a few California Department of Ed or CDE proposal reviewers and grant directors. Then last week, oh, thank you, Laura, putting the recording and resources from that session in the chat. 

Then session two, which was last week. Or nope, it was the week before that. Was focused on really setting yourself up and your project up for success by identifying some of the important information needed before you even get started, like community needs and strengths, key data, potential partners, and opportunities for alignment and sustainability. All the things you think about before you even really start writing, or creating things to respond to a request for proposals, or however you’re submitting your application or proposal. 

So here we are today, session three, and we’re going to build on that learning. But again, if you weren’t at either of those or you were only at one, totally okay, you won’t be lost, it’ll be fine. But definitely go back and look at the materials later. So, we’re going to build on it by thinking what do we really need to know? What do we need to have? And what do we need to do to really develop a strong plan, and fundable plan for your project? 

Later on in further sessions, we’ll talk more about taking those plans, writing compelling narratives and really putting it all together before submission. But right now, we’re really talking about using this process of planning through a logic model to really set yourself up. 

So, the other thing we just want to remind you, if you are here for either of the other two, after each of these sessions, we are doing a discussion hour. It’s always the next morning. So, this one will be tomorrow, December 5th, and it’ll be from 9:00 to 10:00 AM. You can pop in, and it’s really a way to connect, ask questions, share ideas, or really show up for any support you need. So, they’re open. Everyone’s welcome who is at this session. It’ll focus on this session. But if there’s other things you want to talk about, that’s okay too. 

Oh, Robin, yep. We’ll put a link into these slides in just a little bit. There we go, thanks, Laura. So the slides are in there that you can follow along, and then you should also know that after this session, we’re going to compile the resources of these first three into a Padlet that will also have the recordings and everything, and we’ll make sure we get that out to you. So, we’re trying to consolidate, because it’s been a lot of stuff. So, hope to see you tomorrow. 

So, let’s get started with today’s topic, right? Logic models, and data tools and all that good stuff. We’re going to spend some time today learning, applying and getting more tips and tricks, which we’ve talked about in the previous sessions really for successful proposals. And we hope that you follow along, but again, we’ll post the transcript and resources and the recording of this one as well sometime next week, and we’ll let you know about it. 

So, logic models, it’s a lot. We’re going to watch a short video, and then we’ll really dig in. And when it’s over, Shannon will then be taking us into the next part of the session. But as you watch, I just wanted to make a note that the video itself is talking about logic models in a slightly different context that maybe you’re in. So, it’s more nonprofit related than school or district related, although some of you may also be nonprofits here. 

So as with everything, take what is helpful and leave the rest. But don’t get hung up on the little bit of difference between what is your context and what they might be talking about. So, Shannon, I’m going to let you start the video, and I’m going to stop talking and here we go. 

(From Video): 

Welcome, thanks for joining us. Today’s topic is logic models. We all know from experience that program planning and evaluation can feel overwhelming. Logic models can relieve some of the confusion by organizing the parts of planning and evaluation into an easy to follow, step-by-step process. That clearly demonstrates how, with the right inputs and the right activities, you can create what is needed to achieve the change you want to see in yourself, your organization, your community, your society. 

Most logic models have four main components. Inputs are the resources you need. Activities are what you do with those inputs. Outputs are what you create and who you reach with those activities, and outcomes are the change that results. Without a logic model to guide your planning, it’s easy to miss your target. 

Though they come in many shapes and sizes, logic models are all essentially designed to diagram the linkages that you expect to result in a set of desired outcomes. Logic models are like a series of if/then statements. If you have certain inputs, then you can do certain activities. If you do certain activities, then you can create certain outputs. If you create certain outputs, then you can expect to achieve certain outcomes. 

Logic models help to ensure all stakeholders have a shared understanding about how a program’s activities are meant to address the problem at hand, which makes for more productive and positive collaboration. And that makes it easier for everyone involved in the program to stay on target. 

Whether you call it a roadmap, a mental model, a program framework or a blueprint for change, a logic model can help you understand where you’re starting and where you’re going, plan how you’re going to get from here to there and determine how you will know when you finally arrived. After all, who doesn’t get excited about a well-organized road trip with clear directions, a full gas tank and an accurately estimated arrival time? Or a delicious, well-prepared meal, made with the right ingredients and clear instructions that fills your stomach and warms your heart. 

Logic models. They’re a recipe, a roadmap, a plan for the change you want to see in the world. 

Shannon McCullough: 

I love that little dancing excited logic model band at the end, it’s how I feel about logic models as well. So, as Jenny mentioned, it might not be the exact same context as a lot of us are working in, but I do think it gives a good introduction to what we’re going to be going through today. 

In addition to that, I do want to mention a few additional things. Specifically that not every one of the grants that you’re applying for will necessarily require you to have a logic model either built into your proposal or added in there somewhere, but we do think that it’s a pretty good place to start when you’re trying to pull your proposal together. Because as the video said, it really helps you make sure all of your pieces of your program are aligned. And then if you do include it in your proposal, it can help the funders really see that you have things planned and that you have this roadmap moving forward. 

So, the video outlined those four different steps of a logic model, and we’re just going to walk you through those. But first, we wanted to talk a little bit about what a logic model is, give you a little more information on that, and some more examples for why they might be good to use. 

So just like we saw in the video, we can think of a logic model as a really clear picture of how a program’s going to work from start to finish, all the way from beginning to end. It lays out the different resources you’re working with, what you plan to do with those resources, and then what you hope will end up happening as a result. 

And instead of having all of those ideas floating around separately in your minds and in different parts of your proposal, the logic model really pulls them all together so you can see a specific chain of logic behind your proposed program. And it’s helpful at the same time for getting everyone on the same page and making sure that the steps that we’re planning to take can actually connect to the outcomes that we’re aiming to see at the end. 

And so, a logic model is useful, because it helps us kind of slow down. I know we all see these funding opportunities, and we just want to get to work. But this can help us slow down and make our thinking clearer and more visible. It shows where we’re making assumptions, and it gives us a chance to check those assumptions before we start to put time into creating the proposal and start moving the train down the road. So, creating a logic model can be, like we said, a really great early step in planning a grant proposal. 

It’s also a really great communication tool. It can help your staff your partners and the funders hopefully really quickly see what your program is and why it makes sense. And because it helps you identify all of the data you need, it can make monitoring and evaluating your work down the line a lot easier. So at the end of the day, even though it takes a little bit of time to put together in the beginning, it can give everyone that’s working on this project or proposal a shared sense of what the plan looks like and how we’ll know if we’re getting there and if we’re successful in the end. 

So, to practice building a logic model together, we thought we would come up with a scenario from this theoretical high school that is seeking to improve graduation outcomes. So, I’m going to walk through this quick little brief description, and then we’ll map out the program’s inputs, activities, outputs and their hopeful outcomes at the end. 

So, as you can see on the slide, Riverside is looking to get some grant funding to launch a mentoring program for students that are at risk of maybe not graduating on time or show less interest in post-secondary education. And so many of these students might not have access to more focused guidance or exposure to college and career pathways. And to address this, the school’s really planning to match the students with trained community mentors who can be a consistent presence in their lives, offer support, academic encouragement and opportunities to explore those post-secondary options. 

This is something that a lot of schools put into place, and it’s something maybe your school is working on too. And through that weekly mentoring session, their hope is that the program can boost students’ engagement, strengthen their sense of belonging and broaden their awareness of the possibilities that are out there. So, we’re going to use this scenario, and build a logic model based on this over the next few slides. 

So, as you saw in the video, to start with, inputs are really just everything we’re putting into the program. So, it’s all the resources that we’re going to invest to try to make this work. That can include money, can include staff time, can include volunteers, textbooks, materials, transportation, technology, partners, whatever is going into this project. 

And so, for our example here where you can see that we have this budget, which I’m assuming would probably come from this proposed grant, a full-time program director, we have some mentors that we’re going to train. And then all of the materials really needed to run the program, transportation to colleges and things like that. And so, these are the ingredients that we’re starting with, and they’ll really set the stage for we hope what the program could accomplish. 

And as we move into the activities, we’re thinking about the things that the program will actually do. So, what the program will do with those resources that we outlined in the inputs. So, in this example, our mentoring program is going to recruit and train these volunteers. They’re going to hold weekly mentoring sessions with those students that we have identified and then come together and organize campus visits. 

So, these are the core pieces of work that move the program forward. And when you’re building a logic model, the activities should be concrete and specific. This is not quite the time where we’re looking at those big picture goals, this is where we’re thinking about the actual steps that your team’s going to take day-to-day to support those students. And I see that there was a question in the chat. Would you also want to include inputs that are not going to be grant funded? Yes. 

So, anything that’s going to become part of this program. So, if you’re grading your funding from different sources, you can definitely put those in here. And if you want to clarify where that funding is going to come from for the people that are reading this grant, that could probably be helpful. But yeah, definitely include anything that you’re going to add as part of the program. 

All right. And outputs are the direct countable results that we expect the program to produce. So, this is where we’re asking the question, what are we actually going to deliver? What are we actually doing? So, for this mentoring program, we are going to match 25 students with mentors. We’re anticipating we can do about 1,000 mentoring hours. Again, we might not know the exact details at this point, since we don’t have the funding and we can’t see the future. But we can take the best guess at what we would like this plan to look like. 

And these outputs aren’t going to necessarily show whether the students’ lives have totally changed at this point, but they are going to show what the program is intending to do, what it wants to carry out. And by outlining these outputs, it can help the funders see exactly what we’re planning to do with the funding and how we’re going to show them in the future that we are implementing the program the way that we intended to, or the way that we initially set out. 

And lastly, I think this is where people really… They always want to get to this point. So finally, you get to write about what you’re hoping to see out of all of this. This is the outcomes are the changes that you’re expecting to see as a result of the program. So, these are the benefits that our activities are designed to produce in the outputs. So here we have… What we’re hoping for is that 80% of our participants are going to have improved GPAs, that they’re going to take the college entrance exam, and hopefully they’re going to be staying on track to graduate in time. 

And you might see some logic models that have three columns for outcomes, like short, medium and long-term. You can totally do that. You can put them all in one. This is a guide. It’s not something that you’re bound to having it look exactly like this. So, if it makes more sense for you to show short, medium and long-term, by all means go ahead and do it. Either way, the outcomes really help us describe what the meaningful impact of this program is that we’re hoping to see. 

And so, if you take all of that together, this is what it looks like. And this is something that you could include in your proposal, or you could just use as a guiding light for your team in writing. We’re planning. As you can see in this example, we’re going to invest those resources, the funding, the director, the materials, and those inputs are then going to allow us to carry out the activities. 

So, the activities, again, are the specific things that we’re doing. We’re recruiting mentors, running mentoring sessions and organizing college visits, and those activities give us these very specific outputs. So, because we did the activities, we have 25 students that are matched to mentors. We have mentoring hours, and we have students that attend three college visits. 

And then ultimately, all of this is going to come together to be those outcomes that we see. The improved GPA, the college entrance exams, and then graduation on time. So, this full picture shows the logic behind your program, and how each step builds toward the impact that you’re hoping to see by receiving that funding from the funder. So, I’m going to pass it back to Jenny, and you’re going to be able to explore all of your new logic model knowledge. 

Jenny Betz: 

Awesome, thank you. And I see, Robin, I get confused with those two, and we actually in just a couple of minutes are going to dig down into that a little bit and have some tricks of how to remember which is which. So, we got you. 

That was a lot, first of all. That was the quickest overview of inputs, activities, outputs, and outcomes that maybe you’ve ever seen. So, we’re going to put some of your new knowledge to the test. So, before we do that, take a deep breath. If you need to, stretch if you need to. I need to do that every few minutes. Take a sip of water if you have it, whatever you need. And then we will together now travel back in time just a bit. We’re going to travel back in time to before Shannon wrote all the things in there, and we’re going to pretend that this is actually the logic model that we came up with, and it’s a little different. 

And as you’re looking at it, you may start to notice that some things aren’t quite adding up here, and really logic models are meant to be logical along the way. From one thing to build on this thing, and that thing, and that thing and then end up here. So, if you look at the inputs, activities and outputs, what do you notice that might be off, or what do you think a proposal reviewer or a funder might notice? And you can put it in the chat, you can unmute, whatever works. 

How about I’ll give you one example, because I don’t even know what my questions really meant. Maybe that wasn’t clear. But here. So, if you look, and in the inputs, we say we are going to train 50 mentors, but then in the outputs, we say we’re going to match 25 students with mentors. So that’s a disconnect. We don’t need 50 mentors if we only have 25 students who are going to participate in that. So that is just a quick like, “Oh, right, a thing that you can catch.” 

Ah, Randy’s saying that 100% here of students receive scholarships, but only 70% of them take the entrance exam. Absolutely. That may be a question. And if 100% of the participants are receiving scholarships, honestly, is that even realistic? Some of it is like, “Would this really happen?” Jason, I see you nodding. Getting 100% of anything is super rare, so you also want to be realistic, so you don’t set yourself up for stress down the line. And something like that, that every single student is going to get a scholarship just isn’t realistic. 

The activities don’t match the inputs, I’m seeing. We need a full-time director, but we only recruit volunteers. All right, yeah. And that we are talking about salaries for the mentors in the inputs, but it’s not needed for the volunteers. So that also is a piece. I’m seeing 90%, we’re saying graduate on time, but 100% have scholarships. So how does that work? Is there 10% that aren’t graduating on time but still have scholarships? 

And then also, a couple of things are maybe in the wrong place. So even with that, 90% of participants graduate on time, it’s right now in outputs. But it’s actually an outcome. If we do all these things, we hope, think, assume that 90% of students will graduate on time and that would go in the outcome column. 

So, you can see how there are different pieces that we can look, and this is also why it’s so important sometimes to have someone who is not on your team, has not been deep into your proposal, or logic model or whatever you’re doing. To have them look at it with fresh eyes. Because after you stare at your logic model for hours and hours over time, our brains don’t pick up on those little differences, and having someone to look at it really can be helpful in catching the things that we just don’t notice, even though if we did we knew we would know to change it. 

So, you all caught basically all of the changes in there, which is awesome. So, you’re applying your knowledge. So, we’re going to zoom back out now. So, we talked about that school specifically that wants to do the mentor program. We’re going to zoom out from that scenario, and then we’re going to go through some tips and tricks for creating logic models overall. So right now, we’re going to be talking about logic models regardless of the topic or program, regardless of the funder. Just the fundamental things that can be helpful to remember. And if you haven’t already gotten access to the slides, maybe we can put the link in again, follow along, and/or keep it with you. Some of them are a nice checklist. 

So after we talk about these ones that are global for logic models, or plans or theories of change, whatever type of way you want to do it, after that, Shannon’s going to talk again about how to make sure you’re aligning all of those things with your particular funder, or program or project. 

So, first thing is first, and we saw it in the chat, one of the most important things to understand and remember is the difference between outputs and outcomes. They often get mixed up or used interchangeably, myself even. Sometimes I’m like, “Wait, is it an output or an outcome?” And I’m like, “Sometimes it’s easier if I do the outputs, and not the outcomes.” And I may pick to only talk about one. But really, they go hand in hand. 

And your funders, even though most of us… It can feel muddy between the different pieces of the logic model. The funders who are reviewing your proposal or logic model, they’re paying attention. They look at these all the time, and they’ll see the differences, and see if something’s in the wrong column or whatever. 

So, one of the things is just remembering the difference between the two. So, outputs. That’s what you do. So those are the services that are delivered, the people served, we’re 150 hours of training. We did 70 trainings, whatever it is. It’s what you do. The outcomes are like, “We hope, after all this stuff that we do, that we will have this outcome.” And that’s really about your end user, or your students, your priorities, or families or whoever it is. It’s about what we hope, skills that they will gain, or behaviors that change or whatever, other conditions strengthened. And I see in the chat, yes. So, implementation versus effectiveness. Yeah, it’s action. Thank you, Robin. Action is the outputs. What are we doing? And then impact. So, outcomes are what actually changes. 

And I learned, actually from Shannon, a little trick. So, outcomes, the word. Outputs and outcomes have a lot of the same letters. But outcomes are the only one that has a C. So, you think C, change, and then you can remember that outcomes are related to the change. And if that works for you in your brain, awesome. I will probably never forget now that outcomes are about change, because I’ll just think of the C in my head. So that is a little tip from Shannon as well. 

Okay. So, speaking of our outputs and outcomes, a good thing to remember is that you don’t have to start from scratch with these things, and you aren’t writing your logic model in a vacuum. So just as someone was asking earlier, in the inputs, should I also put existing resources, or other funding or whatever? Yeah, that makes total sense. Any of the things that are leading to these outcomes that we want. The funder might want to know what are the things that they’re paying for, versus what someone else is paying for? But all of that could be in there. 

When we look at outputs and outcomes, sometimes it feels like we have to make up new data, new measures that really are meaningful measures. We start thinking about smart goals, and all those things which are important. But also, you can give yourself a headstart by thinking about the types of data that already exist, that we already collect somewhere or would already know, and then use them, leverage those as your meaningful measures. There may be additional things you need to measure. But some of it you can go like, “Oh, we want to do this thing. Maybe we measure it by this, because someone’s already collecting that information.” 

So, for example, maybe you are trying to increase the usage of your mentoring program. You already got the mentoring program funded, but you need to get more people doing it. So instead of coming up with new ways to record how many people participated or whatever, maybe send surveys out to your mentors or ask the students did they participate or not? You can actually rely on the types of things that are already collected. We already have our mentoring program, and maybe we have session logs already, or sign-in sheets. Those would be used regardless of if you are reporting on what you’re doing in the future or trying to use your logic model. It’s an obvious thing that you can use if it’s already being collected. 

And the same for outcome data. If you’re already, and you are in any school or district, tracking GPA, attendance, graduation rates, all those kinds of things. So, look and find which of those measures can actually inform your program and may help you see if you’re making change down the line. So as much as you can rely on, as long as they make sense and it’s meaningful, rely on some of the data that already exists out there so that you’re not doubling your efforts or tripling your efforts. 

All right. Another strategy and trick, not really a trick. But is to go back to your days in high school, or college or whatever, maybe even middle school, and learning things like if A happens and B happens, then C happens. The if/then is a way to actually check your work and checking that the logic model is actually following a logical path. So, if we invest in the inputs and resources, then the activities can happen and the outputs then will be met, and then we will see the change in the outcomes. So, the if/then helps us. 

And what happens a lot, even in things that I’ve done, is that I may say, “I know what I want to do. We know we want this amount of money. I know what activities I want to do. Maybe I know what the end result is.” But then when I’m looking at it, I’m like, “Okay, let’s say we’re going to train 50 people, but I want this outcome.” They don’t align. It’s not the training that’s going to get us to this, maybe it’s a different part of what we’re doing that’s going to get to this. So, the if/then can help us go like, “Is it reasonable and realistic that if we have this input, we do these activities, these people participate, that we will have this change?” So, it’s a little bit of a check. 

Okay, so just a couple more things. And again, I’m not going to read every single one. Well, I’ll read some of them. Please do download the slides if it’s helpful. But we want to talk a little bit about what funders look for in a logic model. And again, this is regardless of topic or project. This is really like foundational logic model, or whatever you’re calling it. Your plan for how you’re going to use their money to have these great things happen. 

And some of these we talked about in the first two sessions as well, or we’ll talk about in the next two sessions. But you want to make sure your logic model is also saying or doing these same things. So, is it clear what the need is? And oftentimes you’ll have a… And I think in the last one, you talked a little bit about it. A needs and assets assessment, or you’ll work on a problem statement, or something like the… Why are we trying to make these changes, and do we need these changes? And making sure that somewhere in there you are showing and explaining not just how you think this will work, but why you needed it as well. Because that’s what they want to know. They can’t give funding to everyone, so they’re going to look for the highest needs. 

Is it realistic? Just like we talked about in the mismatch one. If you do those things with that amount of money and those resources, is it realistic that you can actually train 1,000 people or whatever it is? Hopefully. But that’s one of the things you can check to just make sure. Is that reasonable? Because sometimes we have big dreams and hope, but we don’t think about maybe how much money, or effort or time would go into it. Or we think everything’s going to go well, right? Everyone’s going to want to do what I want them to do. Everyone’s going to have the time to do what I want them to do, and it’ll all go smooth. 

Also want to think about coherence. So, is there a thread to follow? Is it logical that one thing leads to another, and that really even your outputs and outcomes make sense with each other too? That it’s overall either if you’re looking across at one thing, or you’re looking at the whole thing that it makes sense together, right? The puzzle pieces fit together. And when you look at all the puzzle pieces together, there is a coherent picture on them. 

And then lastly, often… And Shannon talked about this. Sometimes you might want to put things in there that will show progress along the way. It’s not always helpful to only have and plan for the outcomes or the end results that you want after a year, or two years or three years of this project. What are we going to do in the interim, and how do we check ourselves? So, are the things measurable? Is there evidence of progress? And also, is that aligning? Which Shannon’s going to talk about, aligning down with funders’ priorities and all of that. 

And then it’s just about being realistic. Not just because you want funders or proposal reviewers to think you did a great job and fund your project, but these are the things that if you’re really thoughtful now are going to save you a ton of effort and energy. And maybe even some stress down the line, because you already thought through the pieces enough that when something comes up and you have to pivot a little, you don’t have to redo everything, because you know really what you’re doing and you can adjust along the way. 

Those are some of those higher level tips and tricks, and then I am sending it back to Shannon to talk about really how do you customize your logic model to reflect funder priorities, their language or whatever it is so that it stands out to them that you aren’t just sending this logic model or proposal to every potential funder, but that you really are doing something specific for this one. So yeah. 

Shannon McCullough: 

Thanks, yeah. I think that’s definitely something we want, to make sure that it’s clear, coming across as clear. Is that you’re not just throwing this program out to anyone who will read it, but you’ve really read the RFP, that you’re really aligning your project with the specific goals that the funders have. 

And one thing you’ll notice when you do take the time to read through those RFPs, is that specific funders often have really specific ways of describing what they care about, especially if it’s a foundation or an organization targeted to one specific thing. So maybe it’s student achievement, or equity, family engagement or school climate, whatever the focus of that grant might be. 

And so, when we go through and take the time to describe those outcomes and the goals that we have, we really want to try to use the same language. It makes it a lot easier for the reviewers to really immediately see how the program that we’ve come up with and that we’re planning really supports the priorities that they have as a funder. 

So, the goal is not to completely change our work. We don’t want to reshape our program specifically just to fit a grant. But all we want to do is translate what we’re already planning to accomplish into terms that the funders recognize and really value and want to scoop under their umbrella. So, this alignment can really help our proposal feel like it’s more clear, more coherent, and really responsive to that specific RFP or exactly what those funders are looking for. 

And so, we wanted to give you a few examples. This is for the school climate health and wellness, the focus of the stronger connections space. But you could do the same thing for pretty much anything, or any type of grant that you’re looking at. So on the slide, we just have some simple examples of how you can take the outcomes, which would go in that outcome column of your logic model, and target them so that they’re connected directly to the kinds of priorities that we know funders often include in their applications. 

So, for example, let’s say our program, one of our outcomes is that we want to have, I don’t know, 70% improved attendance. That can align with funders that are really looking for priorities of student engagement, or achievement or even reducing chronic absenteeism. Similarly, if one of our outcomes is that we have 50% of our students have an improved sense of belonging, there’s several different funder priorities that can fit with. So, things like equity, school climate, student engagement, wellness, wellbeing. 

And again, the similar approach can work with academic outcomes. If we want to have a certain percentage of students fully complete their assignments, that can fall under student achievement, academic engagement, several other kinds of funder priorities. So again, the goal is… We don’t just want to go apply to funding because there’s a grant for it. That’s not what we’re suggesting that we do. But the aim is to show the reviewers that the goals that we have really do align neatly into the categories that they care about, and we really want to show them that we’ve read the RFP and that we know what they value. 

And by using their language, you can really make that connection clear and help them see right away that your program is a strong match. And if you weren’t here for our first session where we heard the grant reviewers, I’d highly recommend going back and listening to that. And you’ll see why this is such an important piece for it. 

So just to wrap everything up, we wanted to leave you with a few key takeaways, and these are the big ideas that we hope you’ll walk away with. So first, we know that a logic model’s really just a tool. It’s helpful for laying out how your program’s going to work. Even if you don’t have to create one, it is helpful to have one in place. It can help keep everyone aligned, and clear and together. 

Second, remember that outputs and outcomes are not the same thing. Jenny, you said you’re never going to forget. We know that outcomes are what we’re hoping is going to happen at the end. And that’s the change that we want to be seeing. And we know that funders know the difference, and so that being precise really, really matters. 

Third, we know that strong proposals make sure that there’s some connection between each of those things in the logic model, making sure that the activities connect directly to the outcomes, and that they’re all things that you can actually measure. Fourth, we know that funders are looking for things that are clear and feasible, that can be measured and that are realistic. And lastly, your data plan doesn’t need to be complex. It doesn’t need to mean that you’re creating new surveys and collecting a ton of additional data. Simple tools can really be straightforward, and they can still tell a powerful story that are well aligned with what the goal of your program is. 

So, we wanted to thank you all again for joining us today. And just a reminder that you’re all invited for the discussion hour tomorrow. We are happy to help you walk through brainstorming some of these logic models. If you have something started, even if you’re not started. If you feel like you need me to go over all of this again, I’m happy to go through it with you. So tomorrow from 9:00 to 10:00 Pacific Time. And you do not have to register you’ll just get an invite directly to come and join us. 

And again, we still have two sessions coming up. We have one next week, next Thursday, and then we have a little bit of a break, and we come back in the new year with a final session to wrap everything up. And thanks again for joining us.