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Home » Archives for Margaret Gillis

Margaret Gillis

SRI Author

  • Margaret Gillis

    Senior Researcher, SRI Education

    View all posts

Education & learning publications October 1, 2021

Data Snapshot 1: Preschoolers with Disabilities in Subsidized Child Care in California: 2015-2019

Christine Morino, Margaret Gillis, Kathleen M. Hebbeler, Nicholas Ortiz October 1, 2021

This infographic summarizes key findings on children ages 3 through 5 years with disabilities in subsidized early learning and care in California. The data are from the Child Development Management Information System (CDMIS) from 2015 to 2019. Included in the analyses are children who participated in the California State Preschool Program, Alternative Payment, California State Preschool Full Day, California State Preschool Part Day, CalWORKS Stage 2, CalWORKS Stage 3, Family Child Care Home, General Child Care, General Migrant Care, Migrant Alternative Payment, and Severely Handicapped.

Education & learning publications October 1, 2021

Data Snapshot 2: Preschoolers Receiving Special Education: California and National Data (School Years 2011-12 to 2019-20)

Christine Morino, Nicholas Ortiz, Margaret Gillis, Kathleen M. Hebbeler October 1, 2021

This infographic summarizes key findings on where children ages 3 through 5 years with disabilities in California received their received special education services. The data are from the California Longitudinal Pupil Achievement Data System (CALPADS) from school years 2011–12 to 2019–20.

Education & learning publications January 1, 2021

Study Poster: Exploring Inclusion Through Administrative Data: Challenges & Opportunities

Kathleen M. Hebbeler, Margaret Gillis, Nicholas Ortiz, Kathryn Morrison January 1, 2021

Despite a focus on increasing inclusion in California, only 3% of preschoolers receiving subsidized care have disabilities. Moreover, the state is below the national average for the percentage of preschoolers with disabilities receiving services in inclusive settings. SRI has partnered with the California Department of Education in a study funded through a Child Care Policy Research Partnership (CCPRP) grant to identify strategies for increasing facilitators and decreasing barriers to the inclusion of preschoolers with disabilities in subsidized child care. Methods across the study include administrative data analysis, interviews with key informants, and community case studies.
This poster presents key findings from the analyses of administrative data about children ages 3 through 5 years with disabilities in subsidized care in California. The data are from:

The Child Development Management Information System (CDMIS) database, including children who participated in the California State Preschool Program, Alternative Payment, California State Preschool Full Day, California State Preschool Part Day, Calworks Stage 2, Calworks Stage 3, Family Child Care Home, General Child Care, General Migrant Care, Migrant Alternative Payment, and Severely Handicapped.
The California Longitudinal Pupil Achievement Data System (CALPADS) database, including all preschoolers with disabilities.

The poster also presents the challenges and opportunities identified through the data analyses and next steps in the study.
This study poster was presented at the Division for Early Childhood’s (DEC) 36th Annual International Conference on Young Children with Special Needs and Their Families in January 2021.

Education & learning publications December 10, 2019 Article

Evaluating implementation of evidence-based practices—Tip sheet series

Margaret Gillis December 10, 2019

The long-term goal of the State Systemic Improvement Plan (SSIP) and other federal and state early intervention and early childhood education initiatives is improved child and family outcomes. States play a critical role in supporting practitioners in the use of evidence-based practices to improve child and family outcomes. When practitioners implement evidence-based practices as intended (i.e., with fidelity), positive outcomes for children and/or families can be expected. Positive outcomes should not be expected when evidence-based practices are not implemented with fidelity. Therefore, it is essential for states and local programs to collect, analyze, and use data on the extent to which practitioners are implementing evidence-based practices as intended. Having high quality data on implementation, decision-makers can identify implementation successes and challenges and target valuable resources appropriately.
This tip sheet series provides concise guidance for collecting and analyzing high-quality data on the implementation of evidence-based practices. The content was designed for staff of state and local early intervention (IDEA Part C) and preschool programs for children with disabilities (IDEA Part B 619), but it is relevant for anyone evaluating the implementation of evidence-based practices. The tip sheets address topics that state personnel identified in webinars and workshops the DaSy and ECTA Centers offered in partnership with the National Center for Systemic Improvement and the IDEA Data Center. The tip sheets are not intended to be comprehensive; readers are encouraged to consult the resources listed in each tip sheet and to obtain support from federally funded technical assistance centers such as DaSy and ECTA, university partners, and others with evaluation expertise.

Education & learning publications December 9, 2019 Article

Part C child find funnel chart tool

Margaret Gillis December 9, 2019

The Child Find Funnel Chart tool is an Excel template for displaying data about infants and toddlers at each step of the Part C process, from referral through exit, for a set of infants and toddlers referred within a specified time span. On the Enter Data worksheet, you enter data for the entire state, an entire local program, or one or more subgroups that will be used to create the funnel chart. The dashboard is used to generate one or more funnel charts, and offers the opportunity to select the data elements to be included in the chart.
State or local Part C programs may use this tool to generate a funnel chart like the one below that allows for easy visualization of the data.

Education & learning publications December 8, 2019 Article

Child find self-assessment

Margaret Gillis December 8, 2019

The Child Find Self-Assessment (CFSA) is a valuable tool for State IDEA Part C programs seeking to improve their child find efforts. Developed by the Office of Special Education Programs (OSEP) with support from the Center for IDEA Early Childhood Data Systems (DaSy) and the Early Childhood Technical Assistance Center (ECTA), the CFSA can help states identify strategies to promote efficiency in their Part C child find systems, with the goal of ensuring children eligible for services are referred and enrolled. States can use the CFSA as a monitoring tool to ensure they have met the regulatory components of a comprehensive child find system, and as an informational tool to assist with identification and implementation of best practices for child find. The CFSA is voluntary and intended to support states in their child find efforts.

Education & learning publications July 10, 2019 Article

Linking expectations to evaluations: Using your logic model to create your evaluation plan

Margaret Gillis July 10, 2019

This brief provides guidance and examples for creating strong alignment between project evaluation plans and logic models. The project logic model graphically displays the project investments, planned activities, and expected results. It guides a project’s design and implementation and can provide the foundation for a strong evaluation plan. Whereas a theory of change or theory of action may depict complex relationships and confounding factors, the logic model generally provides a more linear, simpler depiction of the process of change expected. The evaluation plan is the blueprint for assessing how well the project components have been implemented and analyzing the extent to which project objectives and outcomes have been achieved. An evaluation plan that is aligned to your logic model will help you target evaluation resources and focus your energy.
For more specific information about developing a logic model or evaluation plan, see the CIPP resources listed at the end of this document. This document can be used in conjunction with those resources to ensure your logic model and evaluation plan are aligned, accurately reflect your project, and are of high quality.

Education & learning publications June 22, 2019 Article

Taking a critical look at your child find efforts

Margaret Gillis June 22, 2019

In reviewing the data on the percentage of children birth to 3 years old that my state’s Part C program serves, I noticed that we serve a much lower percentage of children as compared with the national average. I want to understand why my state’s percentage is so much lower and identify some ways to improve our system. How can I examine our data and child find efforts? — Jane Coordinator
Background
Child find is a component of Part C of the Individuals with Disabilities Education Act (IDEA) that requires states to establish a comprehensive system to identify, evaluate, and enroll infants and toddlers who are eligible for early intervention. The comprehensiveness of the child find system affects the number of children Part C programs serve. In fall 2017, the percentage of infants and toddlers, birth to 3, served by Part C ranged across states from 0.8% to 9.5%, with a national average of 3.3%. The state percentages are expected to vary somewhat because of differences in each state’s eligibility criteria. However, big differences in the percentage of the population served between states with similar eligibility criteria would suggest that some states might not be serving all eligible children. This blog post provides guidance on where to begin to examine child find data and practices and describes several resources that are available.
Where to Start
Limited staff and resources pose challenges for many states, including how to improve the child find system. Program improvement can be a daunting process, and it can be difficult to know where to begin. However, taking a close look at your policies, procedures, practices, and data are first steps toward making your child find efforts more effective.

Start by reviewing the federal regulations that guide child find and consider how your state is meeting them.
Examine state and local program data related to child find to better understand your system. This includes reviewing public awareness efforts, referral procedures, and whether your state or local area uses a screening tool as a first step. Looking at difference across local programs in the percentage of the population served and the number and demographics of children (e.g., age, geographic location, gender) in each step of the child find process, from referral to enrollment, are also useful for identifying patterns.

Resources and Help Are Available
The Office of Special Education (OSEP) and the Technical Assistance centers are here to help! In addition to providing TA, they have developed tools that can assist you in evaluating and improving your child find system.

Child Find Self-Assessment : A four-part toolkit that can help states examine how they are meeting child find regulations, identify critical child find best practices, locate resources to help them implement best practices, and easily access OSEP’s policy letters and guidance on child find.
Part C Child Find Funnel Chart Tool : This Excel template displays data on each step of the Part C process, from referral through exit, for a set of infants and toddlers referred within a specified time span. Data can be entered for the entire state, a local program, or one or more subgroups. The tool enables Part C program administrators to easily visualize the differences in the number of children in each step of the process.
Meaningful Differences in Child Find Calculator : This Excel-based calculator allows states to make several comparisons related to the percentage of infants and toddlers served: state percentage compared with state target, local program percentage compared with state target, and year-to-year comparisons of the state percentages. It also computes confidence intervals to determine whether the difference between two numbers is large enough to be considered meaningful (statistically significant).

When Jane Coordinator looked at the state data, she noticed that not many referrals were coming from one region of the state. The Part C Child Find Funnel Chart Tool helped her determine that the point of difference from other regions was at referral. The Meaningful Differences Calculator determined that the difference was statistically significant. The Child Find Self-Assessment helped Jane identify several best practices the state could implement to address the low number of referrals from the region.
For more information on how to use any of the tools listed above or to receive other support related to child find, contact Margaret Gillis (margaret.gillis@sri.com) or Evelyn Shaw (evelyn.shaw@unc.edu).

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