RMC Arlington applies research and evaluation methods to answer clients' questions about the effectiveness and improvement of educational programs and practices. Our staff provides research and evaluation services to programs at the federal, state, and local levels.

Headline: Evaluation Contributes to Scaling up High School Innovation

RMC Arlington offers our clients these research and evaluation services:

Developing conceptual frameworks.
To guide the choice of research and evaluation methods, RMC Arlington works with clients to build an accurate, comprehensive description of the goals and objectives of educational programs, products, and practices. We develop research or evaluation questions, logic models, performance indicators, and theoretical context.

Implementing appropriate designs.
RMC Arlington helps clients select the right research or evaluation design for the purpose of the study. Possible designs address:

 • Different levels of rigor (including experimental, quasi-
   experimental, and correlational and descriptive designs)
   in determining causality;
 • Different time orientations (both cross-sectional and
   longitudinal); and
 • Different data structures (single or multiple/hierarchical
   data levels, and fixed or random design effects).

Collecting comprehensive, high-quality data.
As part of conducting evaluations, we use a variety of sampling strategies, including purposive, convenience, and random sampling, along with stratification, clustering and proportionality. RMC Arlington develops or helps clients select a variety of reliable, valid, cost-effective instrumentation such as achievement tests, survey questionnaires, and observation and interview protocols. RMC Arlington also has extensive experience with creating and managing large-scale, data files.

Conducting appropriate data analyses.
RMC Arlington employs data analysis methods that are appropriate for the research or evaluation questions and design, and for the measurement levels of the data collected. Our staff uses inferential methods, such as hypothesis testing and confidence intervals, along with a variety of analytic models. These models range from simple tabulations and cross-tabulations to the more complex including multi-level regression models, factor and component analyses, and structural equation modeling.