Grant proposal and budget preparation

  •  Research aims and objectives
  • Structure of the research, including consideration of alternative study plans
  •  Data analysis options
  •  Estimated budget, including line-item calculations
  •  Project timeline creation

Survey project design

  • Appropriate survey methods, such as written or web-based questionnaires
  • Study populations
  • Appropriate sampling techniques, including whether respondents should be identified or anonymous
  • Appropriate contact protocols
  • Participant compensation and use of incentives
  • Data management planning
  • Institutional Review Board documentation

Questionnaire design and evaluation

  • Implementation of questionnaire design principles
  • Formatted as internet surveys or paper booklets
  • Effective cover letters and/or quantitative methods, such as statistical item and scale analysis
  • Knowledge of instruments in the public domain that have been proven to be valid and reliable

Data Collection


RISR has state-of-the-art online data collection and management software applications, Qualtrics. Survey participants can be automatically contacted by e-mail as appropriate, with the system optionally tracking failed e-mail contact attempts. A remote data entry module is available so field staff can collect data on a laptop disconnected from the Internet. Upon completion, data are available in fully labeled and formatted files for analysis in your choice of statistical software (e.g., R, SAS, SPSS).

Data Management

Well-managed data are accurate, organized, and reflect the aims of your survey research project. Funding agencies are increasingly calling for publicly accessible data to allow verification of results by third parties. RISR will assist you to create, organize, and secure your project’s data.

  • Database design, development, and security
  • Data Entry
  • Participant tracking and tracing
  • Incentive tracking and audit procedures

Data Analysis

RISR staff can provide the statistical analysis services you need for your project, from simple data summaries to complex multivariate analysis.

Descriptive statistics of the data set

  1. Frequency distribution
  2. Central tendency (mean, median, mode)
  3. Dispersion (range, standard distribution)

Inferential quantitative analyses to test hypotheses

  1. Parametric tests (e.g., t-test, ANOVA)
  2. Non-parametric tests (e.g., Wilcoxon rank sum, Mann-Whitney)
  3. Regression analysis (linear or nonlinear, weighted or unweighted)

Presentation of Results

RISR can assist you with summarizing and presenting the research results based on the needs and requirements of your research partners and funders. This can range from a basic statistical report to a more elaborate narrative report with discussion of the findings.

  • Detailed, narrative reports of results
  • Discussion of the findings
  • Executive summary and professional briefings
  • Tabulations of raw data
  • Charts and graphs
  • PowerPoint presentation