Advancing Frontiers
Disability statistics are powerful tools that can support efforts to promote positive change. For instance, they may be used to (a) positively influence the formation of the individual and group identities of people with disabilities and the perception of people without disabilities, (b) frame issues by characterizing the size, composition, and experiences of the population with disabilities, (c) monitor key trends, (d) test hypotheses, and (e) evaluate programs.
The goal of the Rehabilitation Research and Training Center on Disability Statistics and Demographics (StatsRRTC) is to effect positive change in the lives of people with disabilities, as individuals and as a community. The StatsRRTC is pursuing this goal by seeking to advance frontiers in the production and utilization of disability statistics, such that disability statistics (1) accurately represent the experiences of people with disabilities and (2) accurately shape policies and programs that influence the lives of people with disabilities.
Partners
Mathematica
Kessler Foundation
Association of University Centers on Disabilities
The StatsRRTC has been developed under a grant from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR grant number 90RTGE0005). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this website do not necessarily represent the policy of NIDILRR, ACL, or HHS, and you should not assume endorsement by the Federal Government.
- Investigating Statistical Bias
- Project lead – Andrew Houtenville, University of New Hampshire
- This project investigates sources of “statistical bias” that threaten the accuracy of disability statistics.
- Piloting a Promising Approach
- Project lead – Kimberly Aguillard, Mathematica
- This project examines the feasibility of using Respondent-Driven Sampling—a robust version of network sampling—to recruit samples of people with disabilities from hard-to-reach groups, a method that permits variance estimation using sample weights.
- Demonstrating Advanced Techniques
- Project lead – Kimberly Aguillard, Mathematica
- This project will analyze disability data to demonstrate the ability of two advanced statistical techniques to overcome the very common data limitations of small sample sizes.
- Community Engaged Analyses
- Project lead – Megan Henly, University of New Hampshire
- This project will conduct detailed analyses of key issues selected with input from consumer groups.
- Exploring Impact of the Environment
- Project lead – Debra Brucker, University of New Hampshire
- This project will use survey data and administrative records to investigate the role of the environment in (a) housing disparities experienced by vulnerable populations, (b) participation in multiple disability and/or non-disability government programs, and (c) local-level social determinants of health for people with disabilities.
- Conveying Research Findings
- Project lead – Andrew Houtenville, University of New Hampshire
- This project encompasses and tracks the outputs of each research project, which will produce at least one manuscript for publication in peer-review journals accompanied by corresponding conference/webinar presentations and plain language briefs.
- Annual Disability Statistics Collection
- Project lead – Andrew Houtenville, University of New Hampshire
- This project expands and refines the current Center’s center-piece dissemination approach—a comprehensive and collaborative series of annually updated statistical abstracts, reports, and infographics—providing ready, timely, and accessible disability statistics.
- Annual Disability Statistics Conference and State of the Science Conference
- Project lead – Andrew Houtenville, University of New Hampshire
- The Collection will continue to be released via the Center’s Annual Disability Statistics Conference, a one-day conference that serves as a collaboration platform, bringing together other NIDILRR grantees, federal and state partners, and advocacy organizations.
- Monthly nTIDE Deeper Dive
- Project lead – Andrew Houtenville, University of New Hampshire
- This project is an online webinar and corresponding press release—will broadens an existing platform to include the interrelated aspects of NIDILRR’s mission (health, community living, technology), providing timely, consistent, and numerous opportunities to connect with audience members and foster collaboration.
- Social Media Strategy
- Project lead – Megan Henly, University of New Hampshire
- This project is a concerted effort, via Twitter and Facebook, to disseminate our resources broadly.
- Environmental Factors Data Infrastructure
- Project lead – Andrew Houtenville, University of New Hampshire
- This project creates a methodological (TA) infrastructure—containing highly structured information (data and documentation within a web-based application) and TA (consultation regarding the data and use of advanced statistical techniques)—to support the systematic collection and use of environmental-level variables.
- Info, Referral, and Estimation
- Project lead – Megan Henly, University of New Hampshire
- The=is project is response to requests of information for and about disability statistics, supplemented with customized estimates in cases when statistics are not available but possible to estimate.
- Expert Consultation
- Project lead – Megan Henly, University of New Hampshire
- This project provides expert consultation on data collection methods and advanced statistical techniques.
- Engaged Scholars Program
- Project lead – Shreya Paul, University of New Hampshire
- This is a new project that will recruit (and pay) people with disabilities to be members of research and knowledge translation project teams, participating in the evolving design and implementation of these projects.
- Disability Statistics Curriculum
- Project lead – Andrew Houtenville, University of New Hampshire
- This project will refine and expand the current online Disability Statistics Curriculum to include guidance on data extraction.
- Application of Advanced Statistics
- Project lead – Andrew Houtenville, University of New Hampshire
- This project will create and subsequently offer a credit-bearing, graduate-level, hybrid course: Applications of Advanced Statistics in Disability.