Data Collection and Management

Expertise and infrastructure to carry out qualitative and quantitative studies, from conception to publication and dissemination of study results


Study Design and Implementation

Experience with various research designs to collect data: from leading qualitative studies (focus groups and cognitive interviews) to guide early scale development to running large scale longitudinal studies with thousands of participants.


Database platforms

Experience setting up and collecting data via many database platforms. (REDCap, Assessment Center, WebQ, Microsoft Access, Teleform)


Study populations

Experience recruiting many different study populations: children and adults with physical disabilities (multiple sclerosis, spinal cord injury, post-polio syndrome, muscular dystrophy, burn injury, lower limb amputations), people with chronic pain, caregivers of children with health conditions (such as epilepsy, Down syndrome, muscular dystrophy), people with communication disorders, community sample of adults representative of the US general population.


Recruitment Sources

Ensure that we meet goals by collaborating with various recruitment sources (e.g., clinicians and researchers across the country; online panel companies; condition-specific organizations such as the National MS Society, the American Pain Association, Northwest Regional Spinal Cord Injury Model System, Dravet Syndrome Foundation).


Targeted recruitment

Experience with targeted enrollment of specific demographic (such as age, gender, race/ethnicity, education) and clinical (such as pain level, amputation level and etiology) characteristics to ensure collected data is representative of the population being studied.


Data entry and Processing

Establish data entry and processing protocols to ensure the quality of collected data (eg, double entry of paper surveys; daily or weekly reports on collected data; follow up on missing and inconsistent data).


Datasets and Dissemination

Create cleaned, scored, and de-identified final datasets for analysis. Data dictionary documents variable names and coded values. Set up data sharing protocols to promote secondary analysis of collected data. Run advanced data visualization techniques to disseminate complex information