NCEO Tool 2

Data Analysis and Use Planning Tool for Examining AA-AAAS Participation: Addressing the Percentage of Students Participating in the Alternate Assessment

 

Cesar D’Agord, Tony Ruggiero, Kathy Strunk, and Martha Thurlow

A publication of:
NATIONAL CENTER ON EDUCATIONAL OUTCOMES

In collaboration with
NCEO’s 2019 1.0% Peer Learning Group (PLG 1)

 

The Center is supported through a Cooperative Agreement (#H326G160001) with the Research to Practice Division, Office of Special Education Programs, U.S. Department of Education. The Center is affiliated with the Institute on Community Integration at the College of Education and Human Development, University of Minnesota. The contents of this report were developed under the Cooperative Agreement from the U.S. Department of Education, but does not necessarily represent the policy or opinions of the U.S. Department of Education or Offices within it. Readers should not assume endorsement by the federal government.

Project Officer: David Egnor

All rights reserved. Any or all portions of this document may be reproduced and distributed without prior permission, provided the source is cited as:

D’Agord, C., Ruggiero, T., Strunk, K., & Thurlow, M. L. (2019). Data analysis and use planning tool for examining AA-AAAS participation: Addressing the percentage of students participating in the alternate assessment. Minneapolis, MN: University of Minnesota, National Center on Educational Outcomes. Available at www.nceo.info.

Acknowledgments

This Data Analysis and Use Planning Guide was developed through the work of the National Center on Educational Outcomes (NCEO) 1.0% 2019 Peer Learning Group (PLG) 1, Digging into Your Data: Building a 1% Data Analysis and Use Plan, which took place from February – May, 2019.1

In addition to the authors of this document, it took a team to ensure that PLG 1 was successful. Members of this team included, in addition to the authors (in alphabetical order): Anthea Brady, Duane Brown, Maureen Hawes, Susan Hayes, Sheryl Lazarus, Judy Lee, Kate Nagle, Travis Peterson, Tanner Petry, Chris Rogers, Stephen Ruffini, and Mari Shikuzawa.

Staff from 32 states participated in the PLG 1 webinar calls. The participating states are listed here. This Guide would not exist had it not been for their active participation in PLG 1.

Arizona
Arkansas
Colorado
Delaware
Florida
Georgia
Hawaii
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Minnesota
Mississippi
Montana
Nebraska
New Jersey
New Mexico
New York
North Carolina
Ohio
Pennsylvania
Tennessee
Texas
Utah
Washington
West Virginia
Wisconsin
Wyoming

1 This tool was inspired by the resource, “Essential Elements of Comprehensive Data Literacy,” currently in development by the National Center on Systemic Improvement (NCSI), the National Center on Educational Outcomes (NCEO), IDEA Data Center (IDC), and the Center for the Integration of IDEA Data (CIID).

 

The 2015 reauthorization of the Elementary and Secondary Education Act, known as the Every Student Succeeds Act (ESSA), includes a 1.0% cap on state-level participation rates in the alternate assessment aligned to alternate academic achievement standards (AA-AAAS). No limit was placed on district or school rates, but districts must provide justifications if they expect their rate to be above the 1.0% threshold. In addition, states are to provide oversight to districts.

These requirements mean that states, districts, schools, and Individualized Education Program (IEP) teams need to think carefully about which students should be included in the AA-AAAS. Further, districts should examine their data frequently to ensure that state guidelines are being followed and that appropriate decisions are being made for individual students. Having a data analysis and use plan is essential to being able to examine and discuss data in ways that inform how states and districts act on their data.

Purpose of this Data Analysis and Use Planning Tool

This Data Analysis and Use Planning Tool is designed to help states and districts develop a plan for analyzing and using their AA-AAAS data. It presents a four-step data analysis framework that is intended to serve as an example and a starting point for states and districts. It is expected that states and districts may have their own information to enter into each step.

This guide is one of three developed by states and technical assistance centers working together in NCEO’s 2019 1.0% Peer Learning Group 1. The two other documents that were developed can support the data analysis and use planning tool presented here. They include:

In addition, other NCEO tools will be helpful in the implementation of this Planning Guide. Specifically:

 

Federal Law

Alternate assessments were first developed in response to the 1997 reauthorization of the Individuals with Disabilities Education Act (IDEA), which required that all states and districts develop, by the year 2000, alternate assessments for those students with disabilities unable to participate in regular assessments even with accommodations. IDEA did not define who the students were who could participate in an alternate assessment, nor did it use the term “significant cognitive disability.” In 2003, regulations added to the Elementary and Secondary Education Act (ESEA) allowed states to count as proficient those students with the most significant cognitive disabilities who participated in the alternate assessment and met rigorous alternate achievement standards set by the state.

In 2015, Congress reauthorized the Elementary and Secondary Education Act of 1965 as the Every Student Succeeds Act (ESSA). ESSA reaffirmed that an AA-AAAS is the appropriate assessment for students with the most significant cognitive disabilities to demonstrate their knowledge and skills. ESSA placed a 1.0% cap on the state participation rate for each subject, based on the total number of all students in the state assessed in the subject (34 CFR 200.6(c)(2)). ESSA specified that states cannot place a cap on the participation rates of local education agencies (LEAs) in any subject (34 CFR 200.6(c)(3)(i)). This means that LEAs can exceed the 1.0% participation threshold on an AA-AAAS in a given subject, but the state as a whole cannot exceed the 1.0% threshold in any subject. ESSA required LEAs that exceed the 1.0% participation threshold to submit information justifying the need to exceed the threshold; in addition, the state must provide oversight and monitoring of those LEAs (34 CFR 200.6(c)(3)(ii-iii)).

 

Data Analysis and Use Planning Tool Overview

Data analysis and use involves four steps, as shown in Figure 1. Each step is provided in more detail in the next several pages.

Figure 1. Steps in the Data Analysis and Use Planning Tool

Image showing 4 steps in a pie chart

 

Sample of Filled-In Data Analysis Plan

In this section, we present each step of the data analysis plan, with sample questions included. As a state or district develops a data analysis plan for its own purposes, it likely will want to insert its own questions in addition to some of those in this sample plan. (See a blank data analysis plan in the next section.)

1. Defining Purpose and Goals of the 1.0% Cap Data Analysis Plan

Main purpose of the analysis is to collect, manage and analyze data to answer questions such as:

Is the state implementing the alternate assessment based on alternate academic achievement standards (AA-AAAS) with the right students?
What is the state participation rate for the AA-AAAS?
What are the Local Education Agencies (LEAs) with over 1.0% participation rate for the AA-AAAS?
What are the reasons explaining why the State Education Agency (SEA) and/or LEAs are above the 1.0% participation rate for the AA-AAAS?

Other questions to consider in preparing for the data analysis:

  • Who is the audience for the analysis results?
    • District Superintendent
    • State Superintendent
    • Other decision makers and/or stakeholders be included in the audience of results
  • Who should participate on the data analysis team?
    • Special Education Office
      • State Special Education Director
      • Education Associate, Procedural Safeguards and Monitoring Personnel or Staff, Alternate Assessment Personnel
      • Data Manager
    • State Assessment Office
      • State Assessment Director
      • Education Associate, Student Assessment Data Personnel, Assessment
      • Education Associate, Special Populations Personnel, Assessment Staff
    • Data Management Office (State or District)
      • Education Specialist, District Support Personnel (Data Manager)

 

2. Managing the Data

Data needed to answer the analysis questions:

  • Assessment participation rate by state and local levels
  • Assessment raw scores
  • Assessment standard scores
  • District characteristics such as size, social-economic status (SES), location (rural, urban, suburban)
  • Student race and ethnicity
  • Student age
  • Student grade level
  • Student learning environment
  • Student primary disability
  • Individualized Education Plan (IEP) Team meeting notes
  • Other data to be included

What is the data collection plan?

  • Data available or already collected and state/district office responsible
  • Data that need to be collected or accessed

Describe accuracy, validity, and reliability of the data used for the analysis:

  • Assessment data for all students is collected by the state assessment vendor and goes through a rigorous cleaning process. Psychometrics analyzed by the vendor shows strong evidence to the validity and reliability of the data.
  • Other data…

Location and methods for data storage:

  • Assessment Data is stored in a Structured Query Language (SQL) Server database on a secured server by the SEA data management office. Access is provided to only deemed necessary to work with the data.
  • Other data…

Other:

 

 

3. Data Analysis

Main analysis methods:

  • Descriptive statistics to look for outliers, patterns, and trends in the data by the variables listed above (frequencies, percentages, cross-tabulations, etc.).
  • Inferential statistics exploring relationships among the data elements (regression models, chi-square analysis, etc.)
  • Qualitative analysis (document reviews, content analysis, narrative analysis, etc.)
  • Other?

Patterns, trends, themes emerging from the analysis:

  • Comparisons among school districts, based on district characteristics (urban, rural, suburban, district size, socio-economic status (SES), etc.)
  • Comparisons among groups of students (disabilities, race or ethnicity, SES, least restrictive environment (LRE), English Learners (EL), etc.)
  • IEP reviews (relationship of participation on AA-AAAS and present level of academic achievement and functional performance (PLAAFP), Specific, Measurable, Attainable, Relevant and Timely (SMART) goals, LRE, accommodations, modifications, meeting facilitation and decision processes, etc.)
  • What other patterns, trends do you see emerging from the analysis?

Potential root causes for the data patterns, trends, and themes emerging from the analysis:

  • Fishbone diagram (a visualization tool for categorizing the potential causes of a problem in order to identify its root causes)
  • Five Why’s (a team process used to identify root causes)
  • General brainstorming sessions
  • Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis (a technique used to identify strengths, weaknesses, opportunities, and threats that may assist with strategic planning)
  • Other

Additional data that would help explain or justify the data patterns, trends, and themes emerging from the data analysis and related potential root causes:

  • Based on what you learned from the data analysis so far, what else did your data analysis team and stakeholders identify as information that will help you understand or explain the participation level of students with disabilities in the AA-AAAS in your state or district(s)?

What else in terms of data or data analysis will help your state or district decide on a course of action for the successful implementation of the 1.0% threshold requirement?

 

 

4. Using the Data

Answers to main analysis questions:

 

How does the data analysis help the state and districts work on reducing the 1.0% threshold or on the justification for exceeding the 1% threshold?

 

How does the data analysis assist in building the capacity of IEP teams and parents in making decisions about assessment participation?

 

What are questions the data analysis did not answer?

 

Actions for continuing or revising the data analysis:

 

 

Blank Data Analysis Plan

This is a blank data analysis plan for use by states and districts. Questions may be pulled in from the sample data analysis plan presented above, as well as questions generated by the team developing the data analysis plan.

1. Defining Purpose and Goals of the 1.0% Cap Data Analysis Plan
Main purpose of the analysis is to collect, manage and analyze data to answer questions such as:











Other questions to consider in preparing for the data analysis:











 

2. Managing the Data
Data needed to answer the analysis questions:






What is the data collection plan?






Describe accuracy, validity and reliability of the data used for the analysis:






Location and methods for data storage:






Other:





 

3. Data Analysis
Main analysis methods:





Patterns, trends, themes emerging from the analysis:





Potential root causes for the data patterns, trends, and themes emerging from the analysis:





Additional data that would help explain or justify the data patterns, trends, and themes emerging from the data analysis and related potential root causes:





What else in terms of data or data analysis will help your state or district decide on a course of action for the successful implementation of the 1% threshold requirement?





 

4. Using the Data
Answers to main analysis questions:




How does the data analysis help the state and districts work on reducing the 1.0% threshold or on the justification for exceeding the 1% threshold?





How does the data analysis assist in building the capacity of IEP teams and parents in making decisions about assessment participation?





What are questions the data analysis did not answer?





Actions for continuing or revising the data analysis: