|Title:||Does Applied STEM CTE Strengthen the College and Career Pipeline for Low-Income High School Students?|
|Principal Investigator:||Gottfried, Michael||Awardee:||University of Pennsylvania|
|Program:||Career and Technical Education [Program Details]|
|Award Period:||2 years (09/01/2018 - 08/31/2020)||Award Amount:||$344,940|
Previous Award Number: R305A180096
Co-Principal Investigator: Klasik, Daniel
Purpose: Given the growth of science, technology, engineering, and math (STEM) jobs in the economy, it is important to identify and reduce disparities in access to STEM courses in high school. Unlike traditional academic STEM courses (e.g., math, physics) that focus on abstract, theoretical problems, applied STEM career technical education (AS-CTE) courses (e.g., computer/information sciences, engineering technologies) are focused on real-world problems and hands-on application. For low-income students – a population of students who are often disengaged from school and have few resources for upward mobility – taking AS-CTE courses may increase engagement and likelihood of pursuing STEM. This project explores whether and how AS-CTE coursetaking can help prepare low-income students for college, and for careers in STEM or with STEM applications.
Project Activities: Using two nationally-representative datasets, the researchers will examine the relationships between AS-CTE coursetaking in high school and post-high school STEM outcomes, and (2) determine whether these relationships differ by income level. They will estimate the statistical models with one dataset, the Education Longitudinal Study of 2002 (ELS:2002), and validate these models using the High School Longitudinal Study of 2009 (HSLS:2009). Importantly, the two cohorts represented by the two datasets span a major change in CTE legislation in 2006; thus, there may also be differences across cohorts in coursetaking and outcomes.
Products: Researchers will produce preliminary evidence about AS-CTE courses as a potentially promising strategy for increasing access to STEM careers for low-income youth. They will also produce peer-reviewed publications.
Setting: The project will use two nationally-representative samples of secondary school students: the Education Longitudinal Study of 2002 (ELS:2002) and the High School Longitudinal Study of 2009 (HSLS:2009).
Sample: The researchers will analyze ELS data first collected in 2002 from approximately 15,360 tenth-grade students, who were followed up in 2004, 2006, and 2012, as well as HSLS data first collected in 2009 from approximately 23,000 ninth-graders who were followed up in 2012, 2013, and 2016.
Malleable Factors: The malleable factor researchers will study is AS-CTE coursetaking behavior in high school.
Research Design and Methods: The researchers will code STEM and AS-SCTE courses in both datasets using the Secondary School Taxonomy. The researchers will first use ELS:2002 data to run descriptive analyses on the distribution of low-income students in AS-CTE courses, and then estimate a series of statistical models, adjusted for demographic and other student background variables. Specifically, they will examine the relationship of AS-CTE coursetaking and (1) the likelihood of attending college, (2) majoring in a STEM area, (3) entering a STEM career field, and (4) whether entering a STEM career field is related to postsecondary attainment. They will then determine whether any of these relationships differ by income. Next, they will re-estimate all of the models using the HSLS:2009 data (which provides the same measures as ELS:2002 up to 2-year college degree attainment) to validate and ensure generalizability of the results. Finally, they will examine whether the differences in estimates from the two datasets are statistically significant.
Control Condition: Due to the exploratory nature of the research design, there is no control condition. However, researchers will compare (1) students who take AS-CTE coursework to students who take traditional STEM coursework; (2) students who take AS-CTE coursework to students who take any CTE coursework that is not AS; and (3) students who take few AS-CTE courses to students who take three or more AS-CTE courses. To make these comparisons, the sample will be limited to just students who fall in either comparison category for each analysis.
Key Measures: Key variables include high school course credits; grades; family income; college enrollment and graduation; initial field of study and field of study upon completion of a postsecondary degree; type of employment; and a variety of control variables at the student and school levels.
Data Analytic Strategy: In addition to descriptive data analyses, the researchers will use multivariate regression techniques , with clustered standard errors at the high school level to account for nested data. They will employ chained multiple imputation for missing data. The researchers will include fixed effects for states and schools and conduct sensitivity analyses using instrumental variables and propensity score matching.