|Title:||Identifying Mediating and Moderating Mechanisms to Address Outcomes Associated with Poverty for Adolescents with Disabilities: Secondary Analysis of Data from the National Longitudinal Study-2 (NLTS2)|
|Principal Investigator:||Doren, Bonnie||Grantee:||Board of Regents of the University of Wisconsin System|
|Program:||Transition Outcomes for Secondary Students with Disabilities [Program Details]|
|Award Period:||7/01/2012–6/30/2014||Award Amount:||$688,422|
Co-Principal Investigators: Christopher Murray and Keith Zvoch
Purpose: Living in poverty during childhood can be predictive of lower school performance and increased likelihood of dropping out of school. Students with disabilities are twice as likely to be living in poverty as students without disabilities. However, little empirical research has explored the relationship between poverty and school/post-school outcomes focusing on students with disabilities. The research team will use extant data from the National Longitudinal Transition Study 2 (NLTS2) to investigate whether there are malleable individual, family, and school-based characteristics that act as risk or protective factors, mediating or moderating the effects of poverty on school performance and life outcomes of students with disabilities.
Project Activities: The research activities will accomplish three aims. First, the research team will investigate the relative salience and cumulative effects of poverty-related risk factors on key school and post-school outcomes among adolescents with disabilities. Second, the team will identify malleable individual, family, and school-based risk factors that mediate the relationship between poverty and key outcomes. Finally, they will identify malleable individual, family, and school-based protective factors that moderate the outcomes associated with exposure to poverty.
Products: The products of this project include published reports and presentations on the results of all analyses.
Setting: NLTS2 includes data on students with disabilities from across the United States.
Sample: The researchers will use the NLTS2 dataset, which includes a nationally representative sample of secondary students with disabilities. For this project, the researchers will focus on NLTS2 data (2001–2010) for the sample of over 3,000 students receiving special education services in the United States in each federally recognized disability category (i.e., autism, deaf blindness, emotional disturbance, hearing impairment, learning disability, mental retardation, multiple disabilities, orthopedic impairment, other health impairment, speech and language impairment, traumatic brain injury, and visual impairment).
Intervention: There is no intervention.
Research Design and Methods: The research team will conduct secondary data analyses using the NLTS2 dataset, which includes a nationally representative sample of secondary students with disabilities followed for 10 years. The data were collected through direct assessments, parental and student telephone interviews, teacher surveys, school program surveys, and the school characteristics survey. A two-stage sampling plan was used to select students with disabilities ranging from 13 to 16 years of age. Districts were randomly selected and then students with disabilities were randomly selected from the chosen districts.
Key Measures: The researchers will use the following key measures included in the NLTS2 dataset: poverty (i.e., federal poverty level, income, parental education, parental employment status), student characteristics and factors (i.e., disability status, race/ethnicity, self-determination, social skills, risk behaviors), family factors (i.e., parental involvement and support), school factors (i.e., transition planning, teacher-student relationships), and school/post-school outcomes (i.e., graduation, employment, postsecondary education).
Control Condition: Due to the nature of the research design, there is no control condition.
Data Analytic Strategy: Logistic regression models will be estimated when examining dichotomous dependent measures (e.g., employed vs. not employed) at one assessment wave. Cohort sequential longitudinal models will be applied when examining multiple assessment waves for a variety of dependent measures (e.g., hierarchical linear models for continuous and hierarchical generalized linear models for dichotomous outcome variables). Survival analyses will be applied to event history data (e.g., time to graduation). Mediation models will test proximal factors associated with poverty and outcomes. Moderation analysis will test the extent to which malleable factors may play a role in increasing positive outcomes despite exposure to poverty.