|Title:||Improving the Education and Labor Market Outcomes of Students in Subbaccalaureate Postsecondary Institutions: What Can We Learn From Ohio's System of Public Career and Technical Centers?|
|Principal Investigator:||Bahr, Peter||Awardee:||University of Michigan|
|Program:||Postsecondary and Adult Education [Program Details]|
|Award Period:||3 years (07/01/2020 - 06/30/2023)||Award Amount:||$1,400,000|
Co-Principal Investigator: Cummins, Phyllis
Purpose: Many states are seeking to increase the number of individuals who hold sub-baccalaureate postsecondary certificates in occupational fields. Most postsecondary certificates are awarded by community colleges (CCs), but a number of states have a second system of public sub-baccalaureate institutions that award postsecondary certificates, known as career and technical centers (CTCs). Limited evidence suggests that student completion and employment rates are notably higher in CTCs than in CCs, but there has been little investigation of what contributes to these favorable outcomes. The overarching objective of this research is to pinpoint malleable factors associated with educational and labor market outcomes of students in CTCs that can be adapted to improve outcomes of CC students in postsecondary certificate programs or leveraged to improve outcomes of students enrolled in public CTCs with weaker performance.
Project Activities: Researchers will use multiple methods to explore which factors are associated with educational and labor market outcomes of students in CTCs. They will analyze statewide administrative records and will carry out individual interviews and focus groups with students, faculty, administrators, program directors, and employers.
Products: They will report findings from this study in conference presentations and peer-reviewed publications.
Setting: This study draws on existing quantitative data on students in all of Ohio's 54 public technical centers (OTCs) and 23 public community colleges (OCCs) and original qualitative data collected from these institutions, including interviews and focus groups at six 6 study institutions (four OTCs and two OCCs) and interviews at 40 to 50 of the other OTCs.
Population: Quantitative analyses will focus on the statewide population of students in Ohio who entered any of the OTCs as new students between 2013 and 2016 (N = 87,765) or who entered any of the OCCs seeking a terminal employment-related goal in the same time period (N= 75,207). Qualitative data will be collected through interviews with administrators and employers and focus groups with faculty and students in 6 case study institutions, as well as additional interviews with administrators and program directors at 40 to 50 of the other OTCs.
Research Design and Methods: Researchers are carrying out a multi-methods study, integrating findings from statistical analyses of a subset of the population of Ohio's OTC and OCC students with findings of qualitative analyses of data collected in key informant interviews and focus groups. Researchers willintegrate and triangulate emergent findings across the quantitative and qualitative components to maximize the clarity and validity of results.
Control Condition: There is no control condition due to the nature of the study design.
Key Measures: Quantitative measures address students' demographic characteristics and goals, pre-enrollment postsecondary participation and labor market experiences, duration and intensityof enrollment, course success rate, award of postsecondary certificates and degrees, transfer, and post-enrollment labor market outcomes. Using interviews and focus groups, the researchers will collect information about how students make enrollment decisions, progress through certificate programs, and obtain employment. They will also gather information about the barriers that students encounter and assess how institutions structure their educational experiences, interact with potential employers, and support their persistence, completion, and job placement.
Data Analytic Strategy: Researchers will use multilevel regression models to analyze the quantitative data. They will use a grounded theory approach to analyze interview and focus group data.