Making Connections Across Indicators to Improve Post-School Outcomes:


Early State Efforts

  • Additional Resources
  • References
  • II. Making Connections Across Indicators to Understand Trends

    B. Making Connections Between Demographic Characteristics and Post-School Outcomes

    Not all former students will have the same post-school experiences and some groups may be consistently less likely to experience successful outcomes than others. For example, students with certain types of disabilities as well as lower income students have been found to experience less successful transitions after leaving high school than other groups. It is important to identify disparities among groups in outcomes and to explore why outcomes differ so that plans can be made to target identified high-risk student groups.

    Section Contents

    This section helps LEAs and schools examine differences in post-school outcomes across demographic groups by providing:

    • Guiding questions for analysis of demographic questions
    • Examples of analyses from Wisconsin (WI), Washington (WA), and New York (NY) that include data displays and ways that findings impacted local/state policy and practice
    Guiding Questions for Analysis of Demographic Differences
    Most of the contacted states analyze data collected for Indicator 14 by the demographic characteristics of age, gender, disability, and race/ethnicity. To assist with analysis of variations in outcomes across these groups, states can use the following guiding questions as a framework.
    How do post-school outcomes differ for youth with varying demographics?
    • Are there significant differences between
    • Males and females?
      • Ethnic groups?
      • Youth with different types of disabilities?
      • Youth with other characteristics, such as low income or residence in certain types of LEAs?
    • Are these differences consistent
      • For both employment and postsecondary school enrollment?
      • Over time? (Bost et al., 2007)
      • Across all state LEAs?
    Overall, which demographic subgroups are least likely to experience successful post-school outcomes and which are most likely?

    1Sources: Former students with MR or EBD (NYSED, 2005; Johnson et al, 2005; Wagner, Newman, Cameto, Garza, & Levine, 2005); and lower income (Wagner et al., 2005).

    1The following staff made contributions to this section: Doris Jamison, Joanne LaCrosse, Wendy Latimer, and Cynthia Wilson with the New York State Department of Education (NY); Cinda Johnson and Lisa Scheib at Seattle University in Washington State (WA); and Mary Kampa, Linda Maiterjean, and Lynese Gulczynski with the Wisconsin Department of Public Instruction (WI). Graphics included are derived from actual state data or, if state data were not available, prototypes were developed for the purposes of this guide.

    Important Reminder About Data Quality

    Unless data is based on a representative sample, collected through a rigorous research design, and analyzed using appropriate statistics, only tentative conclusions can be drawn. In the absence of these, however, information gathered can, when included with data from other sources, serve as a starting point for developing improvement plans and activities.

    Examples of State Analyses of Demographic Differences in Post-School Outcomes

    When analyzing demographic differences and answering questions such as those posed above, it is often helpful to develop visual displays to make trends easier to identify. Visual display examples from Wisconsin, Washington, and New York that illustrate trends across subgroups are provided below. Ways that results of these analyses impacted policy and practice in these states are also discussed.

    Wisconsin Analysis of Student Demographics and Post-School Outcomes

    Wisconsin's online survey reporting system provides a number of data analysis tools including generation of automated reports (http://www.posthighsurvey.org Link to an External Web Page). The system produces LEA and state tables and charts that summarize trends in responses to survey questions by gender, race/ethnicity, and disability (and also exit status). Table B-1 illustrates a table format that can be used to display responses from survey questions across these characteristics.

    Use of Findings in Policy and Practice

    • At 2-day special education data retreats, LEA teams are provided with automated survey reports to analyze and, with guidance from state staff, outline observations, develop hypotheses, and make connections. Teams develop a district improvement plan based on their review and discussion, and are encouraged to use the Wisconsin survey Web site to track improvement efforts and resources. County Transition Advisory Councils (TAC) (http://www.wsti.org Link to an External Web Page) have used results from outcomes data analysis to determine how to allocate funds across LEAs (WI).
    • The High School Transition Project Team of the Madison Public Schools in Wisconsin uses these types of data in local data retreats and post-high school data collection for program planning, decision-making, and resource allocation. Team members have shared their findings and resources with other LEAs around the state through conferences and workshops (WI). For LEA purposes, these data are available to teachers and staff year-round on a special Web site.

    Table B-1

    Percentage of Wisconsin Survey Respondents by Reported Post-School Outcomes
     
    %Postsecondary School
    % Competitively Employed
    % Both
    % Indicator #14
    Total Population
    46
    33
    40
    65
     
    Male
    42
    37
    36
    64
    Female
    52
    25
    46
    66
     
    White Youth
    47
    36
    42
    67
    Minority Youth
    44
    24
    35
    58
     
    Cognitive Disability (CD)
    11
    13
    9
    22
    Emotional Behavioral Disability (EBD)
    29
    29
    29
    45
    Learning Disability (LD)
    59
    44
    51
    83
    Low Incidence Disabilities (LI)
    52
    22
    44
    67
     
    Regular Diploma
    48
    33
    42
    67
    Certificate of Attendance
    33
    67
    33
    67
    Dropout/GED
    27
    23
    27
    44
    Maximum Age of Eligibility
    0
    0
    0
    0

    Note:   The values represent percentages of respondents. "Indicator #14 "includes an unduplicated count of students attending postsecondary school, competitively employed, and those involved in both. Due to possible duplication of students across the first three table columns, this percentage is not derived from the sum of the previous columns.

    Source: Data are from the Wisconsin Post High School Outcomes Survey for Individuals with Disabilities of 2005-06 school leavers (exiters), surveyed in the spring of 2007, M. Kampa and L. Gulczynski, CESA #11, Wisconsin Department of Public Instruction, 2007.

    Washington Analysis of Community Type, Student Demographics, and Post-School Outcomes

    For their 2006 graduates, Washington State analyzed differences in successful post-school transitions or "engagement" across varied demographic characteristics of graduates. As illustrated in Figure B-1, staff compared results by community type, gender, race/ethnicity, and type of disability.

    Figure B-1. Percentage of Washington 2006 graduates engaged by county type, gender, race/ethnicity, and type of disability, one year out.

    Figure B-1. Percentage of Washington 2006 graduates engaged by county type, gender, race/ethnicity, and type of disability, one year out

    Note:   "Engaged" indicates that a former student is either employed full- or part-time, in postsecondary school full-or part-time, or doing both at time of follow-up survey.

    Source: Data are from the 2006 Washington Post-School Status of Special Education Students survey, C. Johnson and L. Scheib, Center for Change in Transition Services, Seattle University, 2007.

    Use of Findings in Policy and Practice

    Technical assistance and training for LEAs in Washington State focuses on helping teachers and district staff identify and address gaps between students' goals and post-school outcomes. Links between IEP plans and students' reality are examined to identify possible reasons why student goals were not met. If a student's goal was college, for example, did the student take the right courses to be prepared for college? Were they able to identify and locate services at local colleges that could help them? Staff then shares research evidence outlining ways to address identified gaps. Results from these training sessions have been used to inform IEP meetings, planning, offerings, and policy (WA).

    New York Analysis of Community Level of Resources and Post-School Outcomes

    In addition to demographic characteristics, New York also analyzes differences in post-school outcomes between students across different types of districts. For example in Figure B-2, comparisons are made on the percentages of successful transitions reported by former students across specific cities or groups of cities.

    Figure B-2. Successful and unsuccessful transitions in the five big cities in New York, 1995 -1996 exiters one year out.

    Figure B-2. Successful and unsuccessful transitions in the five big cities in New York, 1995 -1996 exiters one year out.

    Note:   ""Success" indicates that the individual is involved in learning, working or similar daily activity as opposed to staying home. The Big Five City schools of New York State include Buffalo, New York, Rochester, Syracuse, and Yonkers. The "Reference Group" consists of a sample of 217 former general education students surveyed from the Big Four cities of Buffalo, Rochester, Syracuse, and Yonkers. In New York City, general education students were not sampled due to logistical difficulties in locating these students a year after school exit.

    Source: Data are from the New York State Department of Education Longitudinal Post School Indicators Study survey of 1997 of former special education students from the Big Five City Schools of New York State who exited school during the 1995-96 school year. Adapted from "The Post School Status of Former Special Education Students In The Big Five Cities: Transition Planning," by the Office of Vocational and Educational Services for Individuals with Disabilities, New York State Education Department, n.d., Figure 10, page 4.

    Comparisons in post-school outcomes have also been made by researchers in New York across districts with varying levels of resources. In Figure B-3, researchers compared differences between low- and high-needs districts in the percentage of exiters employed or in postsecondary school one year out of high school

    Figure B-3. 	“Statewide Benchmark,” reported post-school outcomes by high and low needs New York districts.

    Note:   "Need" is defined by a weighted average of the 2000-01 and 2001-02 Kindergarten through Grade 6 Free/Reduced Price Lunch percentage and the percentage of children aged 5 to 17 in poverty, according to the 2000 Decennial Census. (An average was used to mitigate errors in each measure.)

    Source: Data are from the New York State Department of Education Longitudinal Post School Indicators Study survey. Adapted from "2006 National Forum on Post-School Outcomes: Using the Data to Inform State and Local Practices, by B. Shepherd, D. Jamison, and J. P. Turbett, PowerPoint presentation, Slide # 16, Office of Vocational and Educational Services for Individuals with Disabilities, New York State Education Department and SUNY at Potsdam, PSO Forum, March 2006.

    Use of Findings in Policy and Practice

    For each participating district in New York State, special reports are produced with data collected through the post-school outcomes survey. Staff of the Office of Vocational and Educational Services for Individuals with Disabilities in the New York State Education Department develops targeted PowerPoint presentations that highlight five or six primary findings from these data that are tailored to specific audiences. Staff makes these presentations based on requests but also have used these data to target certain cities or groups of districts for special training efforts.

    III. Additional Resources

    IV. References