High-quality evidence is fundamental for decision makers tasked with developing and implementing programs and policies to improve public well-being. For these audiences and others concerned with evidence-based decision making, the Center for Improving Research Evidence (CIRE) provides expertise in designing, conducting, and using a range of scientific research and evaluation approaches in varied settings. Building on more than 40 years of experience conducting a wide range of high-quality, applied research using cutting-edge qualitative and quantitative methods, CIRE is uniquely positioned to bridge the gap between policy research and practice.
CIRE is driven by a mission to:
- Build capacity to understand and use evidence
- Plan rigorous and relevant evaluations
- Improve research methods and standards
Systematic review is one of the primary tools that CIRE researchers use to help clients understand and use research evidence. Systematic reviews are a useful tool for decision makers because they identify relevant studies about a policy or program of interest, summarize findings across the various studies, and assess the strength of the evidence, given the different research designs Beyond systematic reviews, CIRE disseminates information to help clients understand what types of questions different research methods can and cannot address.
High-quality research must be based on a strong design that will provide reliable evidence to support policy changes and decision making, but it must also be relevant to decision makers. CIRE leverages Mathematica’s in-house design expertise to plan rigorous and useful evaluations. We also share our expertise with others through evaluation technical assistance and consulting activities.
Strong research incorporates sound methodologies and innovative approaches. CIRE uses transparent and scientific standards to assess and improve the quality of research methods. We also develop new approaches to assess program and policy implementation, outcomes, and impacts. In this area, our work includes the experimental approaches that Mathematica is known for, but also provides the latest thinking on quasi-experimental design and implementation science.