Quantitative Evaluation Methods for Grant-Funded Programs

Why Quantitative Evaluation Matters for Grant-Funded Programs

Funders invest in programs they believe will produce measurable change. Quantitative evaluation methods provide the numerical evidence that demonstrates whether a program achieved its intended outcomes, how many people it served, and whether the results were statistically meaningful. Without rigorous quantitative data, even the most well-designed program struggles to justify continued funding or make a case for replication.

Whether you are writing a federal grant proposal or reporting results to a private foundation, your ability to design and execute a sound quantitative evaluation plan signals organizational capacity and scientific credibility. This guide walks through the core quantitative methods used in grant-funded program evaluation, from experimental designs to descriptive statistics, so you can select the right approach for your project.

Experimental and Quasi-Experimental Designs

Randomized Controlled Trials

The randomized controlled trial (RCT) is widely regarded as the gold standard for establishing causal relationships between a program intervention and its outcomes. In an RCT, participants are randomly assigned to either a treatment group that receives the intervention or a control group that does not. Because random assignment distributes both known and unknown confounding variables equally across both groups, any observed differences in outcomes can be attributed to the intervention with high confidence.

Federal funders such as the Institute of Education Sciences and the National Institutes of Health frequently require or strongly prefer RCT designs. However, RCTs are not always feasible in community-based settings due to ethical concerns about withholding services or logistical barriers to randomization.

Quasi-Experimental Designs

When randomization is not possible, quasi-experimental designs offer a rigorous alternative. Common approaches include:

• Pre-post designs with comparison groups: Measuring outcomes before and after the intervention for both a treatment group and a non-randomized comparison group.
• Regression discontinuity: Assigning participants to treatment based on a cutoff score on a continuous measure, then analyzing outcomes around that threshold.
• Propensity score matching: Using statistical techniques to construct a comparison group that closely resembles the treatment group on observable characteristics.
• Interrupted time series: Analyzing trends in outcome data collected at multiple time points before and after the intervention to detect changes in level or slope.

For a deeper understanding of how evaluation methods connect to implementation science and grant design, see our guide on evaluation methods and implementation science for grants.

Descriptive and Inferential Statistics

Quantitative evaluation relies on two broad categories of statistical analysis. Descriptive statistics summarize the data you collected: means, medians, standard deviations, frequencies, and percentages. These numbers tell your funder how many people participated, what their characteristics were, and what the average outcomes looked like.

Inferential statistics go further by testing whether observed differences are statistically significant rather than the result of chance. Common inferential techniques used in grant evaluation include:

• T-tests: Comparing means between two groups or two time points.
• Chi-square tests: Examining associations between categorical variables.
• Analysis of variance (ANOVA): Comparing means across three or more groups.
• Regression analysis: Modeling the relationship between predictor variables and an outcome while controlling for covariates.
• Hierarchical linear modeling: Accounting for nested data structures common in educational and community-based programs.

Selecting the Right Quantitative Method for Your Grant

The evaluation method you propose must align with your program design, the outcomes specified in your SMART objectives and specific aims, and the expectations of the funding agency. Consider these factors when selecting your approach:

• Funder requirements: Some agencies specify the level of evidence they expect. Tiered evidence frameworks like those used by the Department of Education define what counts as strong, moderate, or preliminary evidence.
• Sample size: Statistical power analysis should drive your participant recruitment targets. Underpowered studies waste resources and cannot detect meaningful effects.
• Feasibility: Consider whether your organization has the data infrastructure, staffing, and institutional review board approval needed to execute the proposed design.
• Timeline: Longitudinal designs that track outcomes over multiple years require sustained data collection capacity that must be reflected in your budget and staffing plan.

Presenting Quantitative Results to Funders

Raw numbers alone do not tell a compelling story. Effective quantitative reporting translates statistical findings into clear, accessible language that connects back to the program's goals. Use tables and charts to present key findings, report effect sizes alongside p-values to communicate practical significance, and always contextualize your results within the broader evidence base.

Your logic model provides the roadmap for organizing quantitative findings. Map each reported metric back to the corresponding output or outcome in your logic model and theory of change so reviewers can see the direct connection between program activities and measured results.

Learn more about grant writing strategies at Subthesis.

Build Your Evaluation Expertise

Designing a rigorous quantitative evaluation plan is one of the most technical components of grant writing, and it is also one of the most heavily weighted in peer review. If you want to master evaluation design alongside every other element of a competitive proposal, enroll in The Complete Grant Architect course and gain the skills to write evaluation plans that earn top scores from reviewers.