Utilizing Your Institutional Research Office Resources When Writing a Grant Application

There are a number of guiding questions that must be answered to develop a successful grant project evaluation plan. The answers to these questions also provide guidance to demonstrate need and develop ambitious, yet attainable, objectives. Data does not exist in a vacuum and can be evaluated and transformed into insight only if it is contextualized with associated activities. This is best accomplished in collaboration with the Institutional Research (IR) office. The Association for Institutional Research’s aspirational statement “highlights the need for IR to serve a broader range of decision makers.”

We emphasize the critical need to incorporate fundamental knowledge of experimental and quasi-experimental design at the beginning of any grant project. In essence, grant projects are experiments—just not necessarily being performed in a laboratory. The design of any experiment is to introduce new conditions. The independent variable is the grant project and the dependent variable is the success of the target population (students, faculty). The ability to properly measure and replicate this scientific process must be established during project planning, and the IR office can be instrumental in the design of your evaluation.

Responding to a program solicitation (or RFP, RFA, etc.) provides the opportunity to establish the need for the project, measurable outcomes, and an appropriate plan for evaluation that can win over the hearts and minds of reviewers, and lead to a successful grant award. Institutional researchers work with the grant office not only to measure outcomes but also to investigate and provide potential opportunities for improvement. IR staff act as data scientists and statisticians while working with grants and become intimately acquainted with the data, collection process, relationships between variables, and the science being investigated. While the term statistician and data scientist are often used synonymously, data scientists do more than just answer hypothesis tests and develop forecasting models; they also identify how variables not being studied may affect outcomes. This allows IR staff to see beyond the questions that are being asked and not only contribute to the development of the results but also identify unexpected structures in the data. Finding alternative structure may lead to further investigation in other areas and more opportunities for other grants.

If a project’s objective is to affect positive change in student retention, it is necessary to know the starting point before any grant-funded interventions are introduced. IR can provide descriptive statistics on the student body and target population before the intervention. This historical data is used not only for trend analysis but also for validation, correcting errors in the data. Validation can be as simple as looking for differences between comparison groups and confirming potential differences are not due to error. IR can also assist with the predictive analytics necessary to establish appropriate benchmarks for measurable objectives. For example, predicting that an intervention will increase retention rates by 10-20% when a 1-2% increase would be more realistic could lead to a proposal being rejected or set the project up for failure. Your IR office can also help ensure that the appropriate quantitative statistical methods are used to analyze the data.

Tip: Involve your IR office from the beginning, during project planning. This will contribute greatly to submitting a competitive application, the evaluation of which provides the guidance necessary for a successful project.