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IES Grant

Title: The Relationship Between Course-taking Patterns and Postsecondary Outcomes
Center: NCER Year: 2019
Principal Investigator: Ogut, Burhan Awardee: American Institutes for Research (AIR)
Program: Postsecondary and Adult Education      [Program Details]
Award Period: 2 years (07/01/2019 – 06/30/2021) Award Amount: $583,210
Type: Exploration Award Number: R305A190073

Purpose: This study will provide a current and comprehensive description of the relationship between curricular intensity (the number of academic courses students complete and the level or difficulty of those courses) and postsecondary outcomes. The team is using recent data from a nationally representative study and a rigorous methodology that adjusts for self-selection issues. Researchers will focus on students with disadvantaged backgrounds, such as racial/ethnic minorities (Black, Hispanic), English language learners, students with disabilities, students from different socioeconomic status levels, or first-generation college students. Broadly, the results of the study will provide valuable information about the consequences of recent college and career readiness initiatives, such as Algebra for All, Race to the Top, and the Common Core.

Project Activities: The researchers will obtain restricted access to data from the High School Longitudinal Study of 2009 (HSLS:09) from the National Center for Education Statistics. The team will create measures of curricular intensity and carry our descriptive analyses to reveal the rates of students with access to more intensive curricula and advanced courses, characteristics of these students, whether there are gaps in access, and, if so, where the gaps might exist—for instance, in what type of schools and why, in differences in course offerings, or in the allocation of students to courses. The team will also complete a series of inferential analyses to help uncover which patterns of course taking are associated with higher postsecondary success, such as on-time graduation from high school, college enrollment, and the need for remedial course taking.

Products: The research team will produce preliminary evidence of the relationship between curricular intensity and postsecondary outcomes to inform policy and practice. The researchers will also produce conference presentations, peer reviewed publications, and policy briefs.

Structured Abstract

Setting: Data are from a nationally representative sample of U.S. high school students in 2009, followed up in 2012 and 2016.

Sample: The sample for this study will come from HSLS:09, using the 2016 Second Follow-Up and prior collections. The HSLS:09 comprises approximately 25,000 students who were in the ninth grade in 2009, selected using a stratified, two-stage random sample design, with schools selected in the first stage and students in the second. The nationally representative sample was contacted again in 2012, when most students were in the 11th grade, and transcript data were collected from their schools in 2013, after most had completed high school. The students were contacted a third time in 2016, 2 years after high school graduation, to learn about their educational and occupational experiences postgraduation. About 17,000 students responded to the 2016 follow-up.

Malleable Factor: The malleable factor in this study is high school curricular intensity.

Research Design and Methods: In this exploratory study using secondary data, the researchers will use cluster analysis, latent class analysis, and data mining to create and compare measures of curricular intensity (see Data Analytic Strategy below). They will then conduct (1) descriptive analyses of curricular intensity for the entire sample and subgroups of disadvantaged students and (2) inferential analyses using propensity score stratification to compare outcomes for students with different levels of curricular intensity minimizing selection bias.

Control Condition: There is no control condition. However, the researchers will compare the outcomes of students with different levels of curricular intensity.

Key Measures: From variables in the HSLS:09 dataset, the researchers will develop measures of curricular intensity to be used in exploratory and inferential analyses. Analyses will examine three major outcomes in the dataset: on-time high school graduation, college enrollment, and college without remedial course taking.

Data Analytic Strategy: Using secondary data, the researchers will use cluster analysis, latent class analysis, and data mining to create and compare measures of curricular intensity They will then examine the variation in curricular intensity for student- and school-related factors using multilevel models. Finally, they will create propensity scores in which different levels of curricular intensity are modeled as categorical 'treatments” influencing the probability of each of the three outcomes. They will also conduct these analyses separately for students from disadvantaged backgrounds.