Is your organization or PACC thinking about responding to a RFP? Not sure how to incorporate data into your proposal in a way that will effectively tell your community’s story? This two part article by Chuck Putney from The Grantmanship Center might be just what you’re looking for to help build your capacity to submit a stronger application.

PART ONE: Data in Grant Proposals – What to Use, Where and Why

Grant proposals are not about what you hope, feel, believe, think, or wish. They are essentially factual. While it helps to have passion, proposals must be factual in describing the problem or need, proposed solutions, likely outcomes, methodology, and the track record of the applicant organization. (Click “read the rest of this entry” below for the complete article.)

Source: {Centered} June 2012 – Volume 5, Issue 6 © 2012 The Grantsmanship Center. All rights reserved. (www.tgci.com)

PART TWO: Finding Data

Getting right to the data that helps make part of your case in a grant proposal has never been easier. Just Google or Yahoo it, or use your preferred search engine. The data is all out there, and much of it has been posted. Gone are the days of having to try to track down printed reports. Mostly. (Click “read the rest of this entry” below for the complete article.)

Source: {Centered} July 2012 – Volume 5, Issue 7 © 2012 The Grantsmanship Center. All rights reserved. (www.tgci.com)

Interested in training opportunities through The Grantsmanship Center…their 5-day Grantsmanship Training Program is coming to Morehead City, North Carolina, August 6-10, 2012.

 

PART ONE: Data in grant proposals – What to use, where, and why

by Chuck Putney

Grant proposals are not about what you hope, feel, believe, think, or wish. They are essentially factual. While it helps to have passion, proposals must be factual in describing the problem or need, proposed solutions, likely outcomes, methodology, and the track record of the applicant organization.

Data–numbers–are critical in proposals. The ability to seek out and provide data to bolster an argument is an indication of the applicant’s level of professionalism. The less the argument is supported by appropriate data, the harder it is for the reviewer to see the proposal as a substantive and meaningful program plan.

At the same time, data alone will not make the argument for you. It’s not enough to lay out a mass of data and hope your reader reaches the desired conclusion. Unless you provide narrative that explains the data and its importance, your reader may miss the point or come to a different conclusion. It’s also important to be selective in the data you use, so the reader won’t have to wade through masses of information to get to the main point. Too much data can do as much harm as too little data.

The three most common ways to use data are a) to describe the nature of the need or problem, b) to create context by describing the community or population facing the problem, and c) to document the effectiveness of the applicant organization.

The Problem

When you are trying to describe the needs of a group of people–your target population–numbers are critical. Those numbers should describe how many people face a particular problem and how that has been ascertained.

For instance:

“Among 130 persons diagnosed with serious mental illness who visited Hammerville Mental Health during April 2012, 80%, or 104 persons, were identified by a physician as being obese, having hypertension, or having diabetes. All of these conditions can be life threatening. This rate compares to the national average of 75% for persons with severe mental illness, and to the average of 30% for residents of Hammerville (national and local figures from the State Department of Health, 2011). Among those 104 people, only 10% have a primary care physician who sees them on a regular basis.”

This passage presents several important pieces of information:

1. the size of the population from which the data has been taken,
2. a subset of persons who have a specific issue,
3. the definition of that issue,
4. two different comparisons with other groups,
5. the sources of the data, and
6. how current the data is.

The statement above also provides baseline data for possible outcomes, such as reducing the number of people with certain conditions or increasing the number seeing a primary care physician on a regular basis.

Data is often used to narrow the focus of your argument. The fact that 30% of the people in Hammerville are obese, have hypertension, or have diabetes might be a cause for concern. It’s not helpful, however, if we don’t know how many people live in Hammerville. If Hammerville is a city of 30,000, that means 9,000 have these conditions. While some organizations may be prepared to tackle a population of 9,000, many are not. For the purpose of this proposal, our focus is limited to those who have been diagnosed with serious mental illness.

The identification of the source for data tells the reader that project planning is well grounded. Simply saying “80%, or 104 persons, are obese, have hypertension, or have diabetes” doesn’t tell the reader how you know that. Were the 104 surveyed? Did someone simply look at them and guess? Similarly, having comparison data from a reliable and date-specific source (in this case, the State Department of Health, 2011) demonstrates your ability to find and use appropriate data.

Data can make the case for a proposal that addresses a specific subset of individuals within a larger population:

“Among the 1,000 students at Middleville High School, 50 are living in foster care. These 50 students have each lived in four different foster home placements during the past two years. Forty of these youth are absent more than 20 times a year, a rate that is five times that for the high school as a whole. Among these 40 students, all have failed at least two classes within the past six months.”

This begins to communicate to the reader that the target population you want to address is these 40 specific youth, all of whom have a common set of circumstances. It’s important to tell the reader this, but when it flows naturally from the data, so much the better.

The Community Context

The portion of the proposal where you may be tempted to present too much data is the section on community context. Choose the data that suggests the community-level circumstances that contribute to the problem or might contribute to the solution. For each piece of data you include about the community, ask “What am I trying to communicate?”

Simply throwing in the racial profile, unemployment rate, average income, or average educational level may not be useful. For our Hammerville example on persons with serious mental illness, it could be that the community context is not important; data on race, income, and educational levels won’t tell us much more. However, if Hammerville is a medically underserved community with only half of the health care providers it should have for a community its size, that may be a contributing factor to the medical needs of the target population, particularly if you can also document that many of those individuals have no primary care physician. If Hammerville is more than an hour’s drive to a larger community with more physicians and there is no public transit, that may be important. Be sure, however, to explain the significance of the information; don’t just cut-and-paste it into your narrative.

Contextual data may also be used to counter misconceptions. For example, contextual information may be necessary to explain why you want to expand a program for low-income individuals into a high-income area:

“Ten percent of the residents of Richville are living at or below poverty, despite the fact that the community as a whole has an average household income 20% higher than the state (U.S. Census, 2010). As a result, these low-income families live in an area where the average cost of food, housing, and other essentials is 10% higher than in the next nearest community, 25 miles away (cost data from comparison shopping by agency staff).”

About the Applicant

Data should also be used to document the scope and capacity of the applicant agency. The number of people served, the number of employees, budget size, and number and location of facilities is one kind of data, but readers want more than descriptive information about the agency. Details that point to the ability or capacity of the organization are also important.

If a community college’s graduation rate is 10% or 20% higher than that of the other community colleges in the state, or if a higher percentage of graduates go on for a four-year degree, this data makes the track record clearer. If 60% of the RNs who enter jobs in the county each year are graduates of that community college, that fact signals the importance of the college to the community.

Savvy proposal writers collect all the information they can about the track record of their agency so they can choose relevant data, as needed, to make a case for organizational capacity, experience, and ability to take on new projects.

Chuck Putney has been a consultant trainer for The Grantsmanship Center for more than 25 years. He has worked extensively on successful federal grant proposals funded by the Departments of Health and Human Services, Education, Labor, and Housing and Urban Development.

 

PART TWO: Finding data

by Chuck Putney

This is the second of two articles by Chuck Putney on the use of data in grant proposals. The first article (“Data in grant proposals: What to use, where, and why”) appeared in the June 2012 issue of {Centered}.

Getting right to the data that helps make part of your case in a grant proposal has never been easier. Just Google or Yahoo it, or use your preferred search engine. The data is all out there, and much of it has been posted. Gone are the days of having to try to track down printed reports.

Mostly.

There are several types of data to be considered:

• published data that helps support your problem statement. This can almost always be localized, at least to the county level.
• comparison data that allows you to contrast your community or population with other communities and populations to provide some context for your local data
• unpublished data from local agencies that they may share with you to help you understand an unmet need
• unpublished data from your own organization about your clients

A major concern for reviewers may be the reliability of your data. Hard data from reliable sources is always preferable to soft data, for which generally comparison is impossible. But soft data, numbers from a less rigorous process, is better than no data at all. [See our article on "Hard Data, Soft Data."]

Using the four divisions above, the approaches to finding data are fairly straightforward.

Published data:

This is available from many sources and much of it is online. The grandparent of all data is the U.S. Census. Originally this data was collected every 10 years (in part to facilitate the shifts in the population of congressional districts). Although the comprehensive count is still done every 10 years, much Census data is revised between the national head counts. Census data can be broken down to cities and even subsections of cities (based on Census tracts).

Other sources for “published” (as in “made public,” often on the Internet) data are state, county, and municipal governments; planning agencies; school districts; nonpolitical planning and study groups; community foundations; United Way affiliates; and the like. Some of the data may be repeated from one source to the next. A report by a planning organization may rely both on Census Bureau numbers and on data provided by state economic development agencies or education departments.

Finding this data is the easiest: use online search engines and do some logical thinking. Start with a simple phrase: “poverty level in Lancaster County, NE.” What comes up is U.S. Census Data along with data from the University of Nebraska/Lincoln; the Kids Count data bank, underwritten by the Annie E. Casey Foundation; and a variety of other sites that often repackage core information from the Census. As you look at the data, when you find a more general site that repackages data, look for citations so you can go directly to the source. Remember: not everything you read on the Web is true, unbiased, or reliable.

There is logic to this. If what you want isn’t on the Internet (or at least doesn’t show up in a search), then go to the state or local agency: education, human services, mental health, health, law enforcement.

Comparison data:

When looking for comparison data, use the same sources as the original data. Comparisons help readers judge the relative severity of a situation.

Finding it is also easy: If your data source has only local information, try to find out the original source. We live in a data-driven society. Much of what a local agency might have is likely to have come from a state agency or other common data source. Try to find out who on the state level is collecting that data and see if they have a statewide, county-by-county, or local data report.

Unpublished data:

This is where collaboration comes in. Local organizations that don’t have to report data may have information that can help you make a case. Free clinics, domestic violence and rape crisis centers, child care associations, arts alliances, sports clubs, and private schools may have very helpful data about community needs. Organizations often collect data during client intake. Colleges, for example, collect a great deal of information from students when they enroll and graduate. They may not, however, be used to sharing it beyond their walls.

Finding it takes relationships, common sense, and trust: Use your local contacts to identify unpublished information. This will be easiest if you are working together on a project. The natural questions from your colleagues will be: How will this data be used? How many people will see it? Is it going to embarrass us? How do we protect ourselves? If they have a vested interest in the project, you are more likely to get the information you need.

Unpublished data from your own organization:

Many proposal developers miss the opportunity to demonstrate their organization’s professionalism by neglecting to use the information already being collected. You may need to get your IT department or program heads to help you, but using information that shows you have a good handle on your clients and their needs is a sign that you’re doing good planning and assessment.

Finding it takes time: it means pulling together data that hasn’t been collected as part of the usual routine. Surveys of a month’s worth of intake data on new clients or participants in specific programs may be best collected at certain points during the year. Colleges, for example, find that collecting information about new students is most easily completed during orientation or at initial advising sessions. Information about the success rates of programs may need to be collected three or six months after program completion. You must tell your proposal readers how the data was collected and the degree to which you’re sure it’s an accurate picture of your clients.

By the way, national data is useful only as a comparison to local data, and even then statewide data may be more compelling.

Chuck Putney has been a consultant trainer for The Grantsmanship Center for more than 25 years. He has worked extensively on successful federal grant proposals funded by the Departments of Health and Human Services, Education, Labor, and Housing and Urban Development.