Almost every grant proposal requires some form of needs assessment. More or less, the sentiment one must get across it that “It’s the end of the world as we know it and I feel fine,” as REM says. Essentially, the object is to make problems look overwhelming, but solvable with just a dollop of grant funds. So, how does a grant writer do this?
Start by making the end appear nigh, which requires a needs assessment. Look at the Census data available at American Fact Finder, which has a variety of geographic choices (e.g. county, city, zip code, census tract, etc.). It is almost always best to match the project target area with a census data geographic area to make assembling data easier, regardless of whether the census area perfectly matches the area you want to serve. Try not to make the target area, “the Westside of Dubuque,” unless that happens to conform with four census tracts. Most geographic areas have 2000 Census data, as well as estimates for 2005. Pick the date that is to your advantage, and being to your advantage means making the situation look worse. For example, if incomes have been trending downward and unemployment upward due to plant closings, the 2005 data may be better. Announce that, if current trends continue, Dubuque may be abandoned completely in 2010 because there are too few jobs, but the situation can be improved with the requested grant.
Once you have your target area, find useful socioeconomic indicators like ethnic breakdown, median family income, age cohort percentages, percent of people living below poverty, percent with disabilities, etc. Only include data that supports your case. A winning grant proposal is not like a thesis, so you are under no obligation to use all available data. Also, it is critical that you provide some data on a larger area for comparison purposes, so your readers understand the relative problems. This can be the city, the county, state or even national data—pick whichever makes your situation look worst, meaning with the greatest discrepancy between the target area and the larger sample. It doesn’t really matter which geographic level you compare to, as long as you can say something to effect of, “The target area median family income is just 2/3 that of Los Angeles County.” Depending on the target population, it may be advantageous to compare data for a particular ethnic group to all residents. For example, if the target area includes a significant African American population with lower incomes, you can set up tables showing African American indicators versus white indicators for the same geographic area, in essence comparing the target area to itself. American Fact Finder has a handy tool on the left button bar for “Fact Sheet for a Race, Ethnic or Ancestry Group” that makes this easy to do.
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You can also use census data to obfuscate the actual reality in the target area. For example, in many Southern California cities there are high percentages of Asian Americans, who in some communities have higher-than-average incomes. This can be used for statements such as, “over two in five residents is a person of color.” For better or worse, most grant reviewers will usually associate persons of color with lower incomes and higher risk factors whether this is true or not. Grant reviewers seldom have a deep background in statistics and they probably don’t even know statistics for journalists let alone real statistics. Even if they do, everything starts to become a haze after reading a dozen federal proposals that can be onerously long, so most reviewers are apt to begin looking more for conclusions than data not long into the process. Do you somehow fulfill the checkbox that asks whether educational attainment is lower in the target area than the nation? If so, give them five points and most on.
Other good sources of data include state and local departments of education. Some states and school districts have better data engines than others. For example, the California Department of Education has a great site, DataQuest, but other states’s data system are, as Borat would say, are “not so much”. If a good data engine/warehouse is not available, find the school/district reports cards mandated by the federal “No Child Left Behind” legislation. Many districts try to hide these reports, as they are often unflattering after you get past the mission/vision statement platitudes, but if you dig hard enough you will find them. If necessary, call the statistics unit at the district or state and force the reports out of them.
Once you have data, only use what helps the argument. So, if test scores for certain grades are low relative to the county or state, use those, not all test scores. If you want to use dropout data, use the four-year derived rate, not the single year, which will be much lower. In some states, such as Illinois, drop out data is wildly understated, due to the way the state treats students who are no longer in school, so if you have to use it, underscore this fact. Health data, including disease incidence, mortality, etc., can usually be found at state and local health department web sites, while crime and gang data are typically found at police department web sites.
If you’re having difficulty building your argument with data, a good technique is to call local “experts” for quotes. For example, find and call the police unit responsible for gang suppression in your target area, then ask leading questions. Invariably, the officer will tell horror stories about rampant gang activity. Just ask if you can quote her and she will almost always agree. It’s always fun to include the names of some local gangs in your proposal for a dash of reader titillation. This is particularly important if the reader is on proposal 35 out of 40 and just wants to go find the hotel bar. You can also find the name of any large social service provider or city official in the target area (other than the one for whom you are working) and ask them about local problems with the target population. For example, if you seek information about at-risk youth services and you talk to the local boys and girls club executive director or city parks director, this person will almost always say that new problems are erupting every minute while their funding is declining.
This gives you the opportunity to write something like, according to Conrad Cuttlebone, YMCA Director, “there are many more latch-key kids in the community since the Hindenburg Dirigible Factory closed, and we’re seeing many more cases of domestic violence, while at the same time the county cut our funding by 50%.” When all else fails, you can simply write, “although specific target area level data is not available, the agency knows anecdotally that teen pregnancy is on the rise, mirroring national trends.” Of course, you can do this even if the local area doesn’t match national trends, as most reviewers don’t have the vaguest idea about national trends for anything.
In other words, while it is not a good idea to make up data, it’s perfectly fair to exaggerate problems through obfuscation and specious analysis. You’re generally rewarded for such effects: the worse the target area, the more likely you are to get points, and the more likely you are to be funded.
The gentle art of writing needs assessments really comes down to painting word pictures that combine cherry-picked data with opinions and anecdotes strung together to meet the expectations of reviewers, who assume something terrible must be going on in your community, or you would be doing almost anything other than writing a grant proposal, such as watching my favorite college football team, the KU Jayhawks, trounce Virginia Tech in the upcoming Orange Bowl. Rock! Chalk! Jayhawk! KU!