Archived Resources
The Resource Library houses tools and products that were developed by IDC, developed with its collaborators, or submitted by IDC stakeholders. Search and filtering tools are available to help users navigate through the library.
Archived Resources 1 - 4 of 4
Format: Guides, Papers, and Reports
Examining Part C Exiting Data VariationUsing national averages for each of the exiting categories, this white paper helps state personnel examine differences in their Part C Exiting data. The paper explores Part C Exiting data category definitions, as well as general and specific trends in Part C Exiting data. It also includes suggestions for possible strategies to improve data quality, including clarifying policies and definitions, documenting procedures for implementation of policies, and developing training materials related to reporting exiting data.
Format: Guides, Papers, and Reports
CEIS and CCEIS Example Scenarios for Tracking Fiscal and Child DataOSEP requires SEAs to report data on the LEAs that use IDEA funds for either CEIS or CCEIS through the IDEA Part B Maintenance of Effort (MOE) Reduction, CEIS, and CCEIS data submission. The purpose of this resource is to provide examples of how SEAs and LEAs can accurately track the funds used for CEIS and CCEIS and track the children who receive those services.
Format: Online Applications
Part C Exiting CountsIDC's Part C Exiting Counts app allows users to test their knowledge of the 10 Part C Exiting categories by either starting with a child scenario and deciding which reason and category best fit the scenario or starting with a reason and category and deciding which child scenario best fits that reason and category.
Format: Online Applications
The Uses and Limits of Data: Supporting Data Quality With a Strong Data ChainThis 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
