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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Combatting Ceiling Effects :/
Reminder of title:	Modeling High-Ability Student Growth Using Multilevel Tobit Regression.
Author:	Hujar, Julia.
Description:	1 online resource (115 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
Subject:	Educational tests & measurements. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29253694click for full text (PQDT)
ISBN:	9798837541230

Combatting Ceiling Effects : = Modeling High-Ability Student Growth Using Multilevel Tobit Regression.
Hujar, Julia.

Combatting Ceiling Effects :Modeling High-Ability Student Growth Using Multilevel Tobit Regression. - 1 online resource (115 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798837541230Subjects--Topical Terms:

3168483
Educational tests & measurements.
Subjects--Index Terms:

Above-levelIndex Terms--Genre/Form:

542853
Electronic books.

Combatting Ceiling Effects : = Modeling High-Ability Student Growth Using Multilevel Tobit Regression.
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504 $a Includes bibliographical references
520 $a 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.
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538 $a Mode of access: World Wide Web
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