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Data Collection for Homeless Education Programs

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... among the key elements of a project (impact, outcomes, activities) ... Data needed: Last day attended school of origin and first day attended new school ... – PowerPoint PPT presentation

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Title: Data Collection for Homeless Education Programs


1
Data Collection for Homeless Education Programs
  • Diana Bowman, NCHE
  • Carol Calfee, Santa Rosa School District (FL)
    Lynn Brown, Montgomery County Public Schools (MD)

2
Goals for Todays Session
  • A brief overview of using data collection for
    program improvement
  • Standards and Indicators for quality MV programs
  • Planning for data collection
  • Analyzing data
  • Managing data

3
Standards and Indicators for Quality
McKinney-Vento Programs
  • Developed in 2001 by representative task force
  • Included in U.S. Department of Education Guidance
  • Pilot tested in 2002-2004
  • Guidebook developed based on pilot test groups
    experiences
  • Revised in 2005 (http//www.serve.org/nche/product
    s_list.phpst_and_ind_2006_rev )

4
A Good Framework
  • 10 performance standards3 groups outcomes,
    school/LEA support, collaboration
  • Based on federal law
  • Reflect 5 years of effective practice in
    implementing the McKinney-Vento Act
  • Include items required for federal data
    collection
  • ( enrolled, services, tested, proficiency,
    services, served pre-K - 12)
  • Utilize standard and indicator language that
    focuses on quantifiable outcomes
  • Suggested indicators for each standard

5
Revised Standards
  • Student Achievement and Performance Outcomes
  • 1. All homeless students, identified and
    enrolled at the time of the state assessment,
    take the state assessment required for their
    grade levels.
  • 2. All homeless students demonstrate academic
    progress.

6
Standards cont.
  • School/LEA Support Outcomes
  • All children in homeless situations are
    identified.
  • Within one full day of an attempt to enroll in
    school, homeless students are in attendance.
  • All homeless students experience stability in
    school.
  • All homeless students receive specialized and
    comparable services when eligible.

7
Standards cont.
  • 7. All preschool-aged homeless children enroll
    in and attend preschool programs.
  • 8. All homeless unaccompanied youth enroll and
    attend school.

8
Standards cont.
  • Collaboration Outcomes
  • 9. All parents (or persons acting as parents) of
    homeless children and youth are informed of the
    educational and related opportunities available
    to their children and are provided with
    meaningful opportunities to participate in their
    childrens education.
  • 10. LEAs help with the needs of all homeless
    students through collaborative efforts both
    within and beyond the LEA.

9
Raising good questions
  • To what extent does our program align with the
    Standards for Quality McKinney-Vento programs?
  • Are numbers or percentages increasing or
    decreasing from year to year? What does that tell
    us?
  • How does performance compare to the school
    district average?
  • What strategies or activities support outcomes?
    Are the strategies and activities appropriate or
    sufficient?
  • Are current data sources sufficient for informing
    us about our program?

10
Planning for Data Collection
  • Get buy-in for data collection Were direct
    service providers! Do your school district,
    funders, state department of education believe
    that this is time well spent? Do you?
  • Assess your time and resources available and
    develop a realistic plan to meet your needs and
    purposes
  • Collect all the data and only the data that you
    will need.

11
Purposes for Data Collection
  • Be explicit about intended uses of data
  • Accountability
  • Program improvement
  • Advocacy
  • Understanding trends and comparisons
  • Funding
  • Develop a plan that matches purpose, audience,
    and data

12
Planning What Data to Collect
  • A logic model is a graphic representation of the
    relationships among the key elements of a project
    (impact, outcomes, activities).
  • Helps to articulate the key elements of the
    project.
  • Can lead to evaluation efficiency and
    effectiveness.
  • Promotes stakeholder buy-in by helping clarify
    how the project works.
  • Coffman, J. (1999). Learning from Logic Models.
    Cambridge, MA Harvard Family Research Project.

13
Logic Model for McKinney-Vento
14
Data Questions Based on the Logic Model
  • Activity 1. Facilitate immediate enrollment
  • Are new program participants enrolled in school
    within one day?
  • Data needed Date family first came to school to
    enroll the child date child began attending
    classes
  • Outcome A. Students do not miss days
  • How many days did students miss between schools?
  • Data needed Last day attended school of origin
    and first day attended new school

15
Activity
  • Using the Logic Model template provided and the
    handout on the MV Standards and Indicators,
  • Choose one outcome (Standard)
  • Identify activities that will lead to this
    outcome
  • Develop questions for one of the activities that
    would indicate (Indicators) that this activity is
    working
  • Identify data that would enable you to answer
    these questions
  • Share with your neighbor

16
Types of Data
  • Quantitative Data Examples (How many)
  • Number of homeless students enrolled
  • Type and number of services provided
  • Qualitative Data Examples (How well)
  • Open-ended responses to survey items
  • Interviews/Focus Groups
  • In-take forms
  • In our school district, 54 percent of students
    identified
  • as homeless remained in their school of origin.
  • What questions does this data raise?
  • What qualitative data would be helpful?
  • By what methods could you collect it?

17
Types of Data
  • Perceptive Data Examples (What do you see? What
    do people think?)
  • Likert Scale Surveys SA-A-D-SD
  • Checklists
  • Informal data
  • Five-minute conversations
  • Phone logs and emails
  • Anecdotal data
  • Stories that create awareness
  • Affective

18
Analyzing Data
  • When and how often should we analyze our data?
  • Consider
  • Purpose for data collection
  • Time when the data will be most useful to you
  • Questions youre addressing
  • Resources and time available to analyze

19
Match the analysis to the type of data
  • Quantitative frequencies, percentages,
    distributions, averages, statistics
  • Qualitative - simple coding, key words, identify
    themes and trends
  • Perceptive frequencies, percent, distributions
  • Youth Survey
  • 1. I feel like school is a place where I can
    find help with personal problems
    SA(2)-A(5)-D(8)-SD(5)
  • How would you analyze this data? What does this
    tell you about your program?

20
Managing Data Collection
  • Ensure that quality data is collected
  • Provide clear guidelines for data collection -
    provide training, guidebook
  • No estimates avoid using unknown as a choice
  • Use a clear data collection form easy input
    (online) pilot test your form and/or instrument
  • Clarify terminology and methodology when working
    with other agencies
  • Spot check for errors provide analysis to
    submitters to review

21
Accommodate Changing Data Needs
  • Set up a flexible database to meet changing
    needs and requirements
  • Federal requirements will change
  • Data needs for program decision making will
    change
  • Make changes before the school year begins

22
Working with Collaborators
  • Strategies for sharing information across
    programs and agencies
  • Agree on what can and cannot be shared
  • Reinforce how all will benefit win-win
  • Note where definitions align/differ
  • Create awareness of MV program and data needs
  • Be organized and efficient in order to make the
    smallest demands on their time

23
Accessibility
  • Making data easily available for decision-making
  • Who has the data? Can we get it from one source?
  • What analyses and reports exist?
  • How quickly can the data be made available? Is it
    in a useful format?
  • Are there staff capacity issues for pulling data
    together?
  • Confidentiality issues? Are data-sharing
    agreements in place?
  • Are there data that have not been collected that
    we need to impact decision making? How might we
    obtain this data? (Short-term, long-term)

24
Would you be prepared?
  • A local foundation that is setting its budget
    priorities for the year contacted you to ask for
    information on the needs of homeless youth. The
    board needs this information for a meeting by the
    end of the week.
  • What should you have on hand to provide on short
    notice?
  • What should be in place to ensure that you can
    access data quickly from various sources?
  • How can you ensure that the data would be in a
    usable format on short notice?

25
NCHEs Data Collection Networking Group 2008
  • Online meetings, trainings, panel discussions
  • Networking with colleagues
  • Guiding NCHE in technical assistance offerings in
    data collection for homeless education programs
  • Email dbowman_at_serve.org
  • Deadline December 10

26
Presenter Contact Information
  • Diana Bowman
  • National Center for Homeless Education
  • SERVE Center, UNCG
  • dbowman_at_serve.org
  • 336-315-7453
  • Carol S. Calfee
  • Director of Federal Programs
  • Santa Rosa District Schools (FL)
  • 850-983-5001 (Fax) 850-983-5011
  • CalfeeC_at_mail.santarosa.k12.fl.us
  • Lynn T. Brown, Ph.D.
  • Coordinator of Enrollment and Attendance
    Compliance
  • Department of Reporting and Regulatory
    Accountability
  • Montgomery County Public Schools (MC)
  • 301-279-3211 (Fax) 301-279-3849
  • Lynn_T_Brown_at_mcpsmd.org
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