Combatting Ceiling Effects: Modeling High-Ability Student Growth Using Multilevel Tobit Regression

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Hujar, J. (2022). Combatting Ceiling Effects: Modeling High-Ability Student Growth Using Multilevel Tobit Regression. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Pressures associated with accountability testing have resulted in a narrowing of both the curriculum and pedagogy that does not meet the needs of high ability learners. This study proposed that either a different measurement (an above-level computer adaptive assessment) or a different model (Tobit model) should be used to more accurately demonstrate high ability student achievement and growth in order to lessen the pressures on teachers and therefore create an environment better suited for high ability student learning. To answer the research questions under study, a two-part design was used. The first part of the study used an above-level assessment and imposed an artificial ceiling at grade-level with the goal of using Tobit modeling to reproduce uncensored growth estimates using censored data. The second part of the study used naturally censored data with the goal of increasing growth estimates through Tobit modeling. Ultimately, the Tobit models using artificially censored data were able to come close to replicating the uncensored growth estimates under certain conditions. The results indicated that Tobit regression was necessary when examining homogeneous groups of high ability students. Finally, the Tobit regression models were able to increase the growth estimates for high ability students using naturally censored data. The degree to which the models increased, and under which conditions the increases existed are described in detail.

Details
Author

Hujar, Julia

Title

Combatting Ceiling Effects: Modeling High-Ability Student Growth Using Multilevel Tobit Regression

Physical Description

1 online resource (114 pages) : PDF

Date

2022

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Pressures associated with accountability testing have resulted in a narrowing of both the curriculum and pedagogy that does not meet the needs of high ability learners. This study proposed that either a different measurement (an above-level computer adaptive assessment) or a different model (Tobit model) should be used to more accurately demonstrate high ability student achievement and growth in order to lessen the pressures on teachers and therefore create an environment better suited for high ability student learning. To answer the research questions under study, a two-part design was used. The first part of the study used an above-level assessment and imposed an artificial ceiling at grade-level with the goal of using Tobit modeling to reproduce uncensored growth estimates using censored data. The second part of the study used naturally censored data with the goal of increasing growth estimates through Tobit modeling. Ultimately, the Tobit models using artificially censored data were able to come close to replicating the uncensored growth estimates under certain conditions. The results indicated that Tobit regression was necessary when examining homogeneous groups of high ability students. Finally, the Tobit regression models were able to increase the growth estimates for high ability students using naturally censored data. The degree to which the models increased, and under which conditions the increases existed are described in detail.

Genre

doctoral dissertations

Subjects--Topics

Educational tests and measurements
Gifted children--Education
Statistics

Degree

Ph.D.

Keywords

Above-Level
Growth
High-Ability
Measurement
Modeling
Tobit

Subject Area

Educational Leadership

Advisor(s)

Lambert, Richard

Committee Members

Cox, Kyle
Kim, Stella
Matthews, Michael

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2022.

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Rights Holder Information

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Identifier

Hujar_uncc_0694D_13169

Permalink

http://hdl.handle.net/20.500.13093/etd:3085