The Bee has a stack of IDC resources!

Resource Library

Guides. Briefs. Toolkits. Quick reference information. IDC and its partners created these data quality resources to help states better prepare to address their existing or emerging IDEA data quality needs. Use our search and filtering tools to navigate the library.

Resources 1 - 7 of 8

View Archived Resources
    An IDC Resource

    Format: Guides and Briefs

    Parent Involvement Data: How to Measure and Improve Representativeness for Indicator B8

    This interactive resource provides states with an overview on how to gather representative parent involvement data for Part B SPP/APR Indicator 8. The resource defines key concepts such as representativeness, sampling, nonresponse bias, response rates, and weighting. It also offers information on how to improve the quality of parent involvement data, including strategies that can help states collect representative data and evaluate and improve the representativeness of their data before, during, and after data collection. 

    An IDC Resource

    Format: Quick Reference

    SPP/APR Indicator Sampling Plan Checklist

    States are allowed to use sampling for collecting data for select Part B State Performance Plan/Annual Performance Report indicators. Sampling can provide an effective means for targeting resources for data collection and improving data quality. However, there are important requirements that states must consider when designing and implementing their sampling plans. States can use this interactive self-assessment tool to determine whether their state’s sampling plan addresses Office of Special Education Programs sampling requirements for best practice and to identify action steps to improve their sampling procedures.

    An IDC Resource

    Format: Applications and Spreadsheets

    Nonresponse Bias Analysis Application

    The NRBA App is an interactive nonresponse bias application powered by state-of-the-art statistical software. We developed this application to help states examine their SPP/APR Indicator 8 and Indicator 14 data through nonresponse bias analysis. The NRBA App allows users to conduct reproducible analyses of response rates, representativeness, and nonresponse bias, tailored to their survey’s data collection method.

    An IDC Resource

    Format: Guides and Briefs

    Methods for Assessing Racial/Ethnic Disproportionality in Special Education

    This TA guide addresses the more common methods for calculating racial/ethnic disproportionality in special education. IDC revised the guide, which the Data Accountability Center (DAC) originally published in October 2011, because of a change in SPP/APR Indicators 9 and 10 to remove underrepresentation from the measure.

    An IDC Resource

    Format: Quick Reference

    SPP/APR Indicator Card

    The SPP/APR indicators measure child and family outcomes as well as compliance with the requirements of IDEA. This quick-reference resource from IDC includes a list and brief definition of all the Part B FFY 2020–2025 SPP/APR indicators (including the new Indicator 18!), as well as a list of file specifications associated with each of the seven IDEA 618 data collections, all in one convenient package. Print one out here or contact your IDC State Liaison for a laminated version you can carry along wherever you go.

    An IDC Resource

    Format: Applications and Spreadsheets

    Equity Requirements in IDEA

    This resource compares the three equity requirements in IDEA (disproportionate representation, significant discrepancy, and significant disproportionality) across various elements to explain the similarities and the differences among the requirements.

    An IDC Resource

    Format: Guides and Briefs

    FFY 2020–25 Part B SPP/APR Changes at a Glance

    The FFY 2020–25 Part B SPP/APR Changes at a Glance resource is a quick overview for tracking updates to indicators in the new FFY 2020–25 SPP/APR package. For each of the 17 SPP/APR indicators, the table denotes whether there will be no changes, minor changes and/or clarifications, changes to response rates and representativeness, changes to data sources, and new components.