Identifying and Supporting Academically Low-Performing Schools in a Developing Country: An Application of a Specialized Multilevel IRT Model to PISA-D Assessment Data

Meredith Langi () and Minjeong Jeon ()
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Meredith Langi: NWEA
Minjeong Jeon: University of California

Psychometrika, 2023, vol. 88, issue 1, No 15, 332-356

Abstract: Abstract Performance-targeted interventions are an important tool in improving educational outcomes and are often applied at the school level, where low-performing schools are selected for participation. In this paper, we aim to identify low-performing schools in Cambodia that are in need of support on improving students’ abilities in formulating math problems. Using data from the PISA for Development project, we present an application of a structured multilevel mixture item response theory (IRT) model that utilizes strategic constraints in order to achieve our research aims. The approach utilized in this application draws on psychometric traditions in multilevel IRT modeling, mixture IRT modeling, and constrained mixture IRT modeling. Results support classifications of Cambodian schools participating in PISA-D as low- and non-low-performing schools, as well as provide insight into these schools various contexts. Implications for future school interventions in Cambodia as well as future extensions to this modeling approach are discussed.

Keywords: multilevel modeling; mixture modeling; item response theory; school identification (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11336-022-09893-3

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