Improving Graduation Rate Estimates Using Regularly Updating Multi-Level Absorbing Markov Chains

Autor/inn/en	Boumi, Shahab; Vela, Adan Ernesto
Titel	Improving Graduation Rate Estimates Using Regularly Updating Multi-Level Absorbing Markov Chains
Quelle	In: Education Sciences, 10 (2020), Artikel 377 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Boumi, Shahab)
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2227-7102
Schlagwörter	Graduation Rate; Computation; Markov Processes; Accuracy; Statistical Bias; Sample Size; College Graduates + Suchen Sie Ihr Suchwort? Markowscher Prozess; Hochschulabsolvent; Hochschulabsolventin
Abstract	American universities use a procedure based on a rolling six-year graduation rate to calculate statistics regarding their students' final educational outcomes (graduating or not graduating). As an alternative to the six-year graduation rate method, many studies have applied absorbing Markov chains for estimating graduation rates. In both cases, a frequentist approach is used. For the standard six-year graduation rate method, the frequentist approach corresponds to counting the number of students who finished their program within six years and dividing by the number of students who entered that year. In the case of absorbing Markov chains, the frequentist approach is used to compute the underlying transition matrix, which is then used to estimate the graduation rate. In this paper, we apply a sensitivity analysis to compare the performance of the standard six-year graduation rate method with that of absorbing Markov chains. Through the analysis, we highlight significant limitations with regards to the estimation accuracy of both approaches when applied to small sample sizes or cohorts at a university. Additionally, we note that the Absorbing Markov chain method introduces a significant bias, which leads to an underestimation of the true graduation rate. To overcome both these challenges, we propose and evaluate the use of a regularly updating multi-level absorbing Markov chain (RUML-AMC) in which the transition matrix is updated year to year. We empirically demonstrate that the proposed RUML-AMC approach nearly eliminates estimation bias while reducing the estimation variation by more than 40%, especially for populations with small sample sizes. (As Provided).
Anmerkungen	MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. Tel: e-mail: indexing@mdpi.com; Web site: http://www.mdpi.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01