Site Search

Results 1 - 4 of 4

    An IDC Resource

    Format: Guides and Briefs

    Examining Representation and Identification: Over, Under, or Both?

    Significant disproportionality with regard to identifying children as children with disabilities or as children with specific disabilities is, by definition, overrepresentation. This resource defines overrepresentation and three related terms: over-identification, under-identification, and underrepresentation. States can use this resource, in conjunction with the Success Gaps Toolkit to help identify and address the factors contributing to significant disproportionality (i.e., overrepresentation) within school districts.

    An IDC Resource

    Format: Presentations

    Using the Success Gaps Toolkit to Support Improvement Activities

    LEAs in all states have many improvement initiatives underway at any one time. This workshop described how state and local staff can use the Success Gaps Toolkit to align various needs assessments and improvement strategies and use the data generated to support improved results for students with diverse learning needs. One state shared how it uses the Success Gaps materials with LEAs and some of the lessons it has learned.

    An IDC Resource

    Format: Presentations

    Developing Data Literacy

    Data literacy is a critical component of achieving and maintaining a culture of high-quality data and use. Working toward a high level of data literacy within an agency is an ongoing process that involves many steps and players. This session presented key considerations for improving data literacy, including how to develop and apply data to inform policy and practice and ensure that an agencies shares a meaningful story of the data with stakeholders that all can understand and use.

    An IDC Resource

    Format: Online Applications

    The Uses and Limits of Data: Supporting Data Quality With a Strong Data Chain

    This online learning module provides a general overview of how the methods and design of data collection and analysis affect interpretation of the data. The module presents the different links in the data chain (e.g., defining the question, measurement strategy) and describes how each link contributes to quality of data and data analyses. The module also includes examples from a selection of Part B and Part C SPP/APR indicators to illustrate how each step in the data chain contributes to the integrity of the data and its interpretation