Project Activities
Using two nationally representative datasets, the researchers examined the relationships between high school AS-CTE course taking and post-high school STEM outcomes and (2) determined whether these relationships differ by students' family income levels. With a secondary data analysis research design, they estimated the statistical models with one dataset, the Education Longitudinal Study of 2002 (ELS:2002) and validated 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, the researchers also explored differences across cohorts in course taking and outcomes.
Structured Abstract
Setting
Sample
The researchers analyzed ELS data first collected in 2002 from approximately 15,360 grade 10 students, who were followed up in 2004, 2006, and 2012, as well as HSLS data first collected in 2009 from approximately 23,000 grade 9 students who were followed up in 2012, 2013, and 2016.
The malleable factor researchers studied was AS-CTE course taking behavior in high school.
Research design and methods
The researchers coded STEM and AS-SCTE courses in both datasets using the Secondary School Taxonomy. The researchers first used ELS:2002 data to run descriptive analyses on the distribution of low-income students in AS-CTE courses and then estimated a series of statistical models, adjusted for demographic and other student background variables. Specifically, they examined the relationship of AS-CTE course taking and (1) improvement in behavioral engagement in high school, (2) STEM interest in high school, (3) the likelihood of attending college, (4) majoring in a STEM area, (5) entering a STEM career field, and (5) whether entering a STEM career field is related to postsecondary attainment. They then determined whether any of these relationships differ by students' family income levels. Next, they re-estimated 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 examined whether the differences in estimates from the two datasets are statistically significant.
Control condition
Due to the exploratory nature of the research design, there was no control condition. However, researchers compared (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; (3) students who take few AS-CTE courses to students who take three or more AS-CTE courses; (4) students who take a higher proportion of AS-CTE courses as a function of total STEM courses compared to students with a lower proportion. To make these comparisons, the sample was limited to just students who fall in either comparison category for each analysis.
Key measures
Key variables included high school course credits, grades, engagement, STEM interest, 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 used multivariate regression techniques, with clustered standard errors at the high school level to account for nested data. They employed chained multiple imputation for missing data. The researchers included fixed effects for states and schools and conduct sensitivity analyses using instrumental variables and propensity score matching.
Key outcomes
The main findings of this project are as follows:
- Over the decade between 2004 and 2013 (in between which Perkins identified the need to emphasize STEM-related technical skills), the proportion of low-income students participating in applied AS-CTE increased (Plasman, Gottfried, & Klasik, 2020)
- Low-income students who participated in applied AS-CTE coursework in high school exhibited higher levels of behavioral engagement in their junior year in high school than non-participants (Plasman, Gottfried, & Klasik, 2021).
- Taking more high school STEM credits is associated with a higher likelihood student had STEM-related work experience in college, though this benefit does not appear unique to low-income students (Gottfried et al., 2023).
People and institutions involved
IES program contact(s)
Products and publications
Publications:
ERIC Citations: Find available citations in ERIC for this award here and here.
Gottfried, M., Freeman, J. A., Odle, T. K., Plasman, J. S., Klasik, D., & Dougherty, S. M. (2023). Does High School STEMM Career Coursework Align With College Employment? Teachers College Record, 125(3), 207-236. https://doi.org/10.1177/01614681231175199
Plasman, J. S., Gottfried, M. A., & Klasik, D. (2020). Trending up: A cross-cohort exploration of STEM career and technical education participation by low-income students. Journal of Education for Students Placed at Risk (JESPAR), 25(1), 55-78, DOI: 10.1080/10824669.2019.1670066
Plasman, J. S., Gottfried, M. A., & Klasik, D. (2021). Do career-engaging courses engage low-income students? AERA Open, 7(1), 1-17, DOI: 10.1177/23328584211053324
Additional project information
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Supplemental information
Co-Principal Investigator: Klasik, Daniel
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