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Extending an IRT Mixture Model to Detect Random Responders on Non-Cognitive Polytomously Scored Assessments.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Extending an IRT Mixture Model to Detect Random Responders on Non-Cognitive Polytomously Scored Assessments./
作者:	Swanson, Mandalyn R.
面頁冊數:	1 online resource (153 pages)
附註:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.
標題:	Higher education. -
電子資源:	click for full text (PQDT)
ISBN:	9781321741339

Extending an IRT Mixture Model to Detect Random Responders on Non-Cognitive Polytomously Scored Assessments.
Swanson, Mandalyn R.

Extending an IRT Mixture Model to Detect Random Responders on Non-Cognitive Polytomously Scored Assessments. - 1 online resource (153 pages)

Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.

Thesis (Ph.D.)--James Madison University, 2015.

Includes bibliographical references

This study represents an attempt to distinguish two classes of examinees -- random responders and valid responders -- on non-cognitive assessments in low-stakes testing. The majority of existing literature regarding the detection of random responders in low-stakes settings exists in regard to cognitive tests that are dichotomously scored. However, evidence suggests that random responding occurs on non-cognitive assessments, and as with cognitive measures, the data derived from such measures are used to inform practice. Thus, a threat to test score validity exists if examinees' response selections do not accurately reflect their underlying level on the construct being assessed. As with cognitive tests, using data from measures in which students did not give their best effort could have negative implications for future decisions. Thus, there is a need for a method of detecting random responders on non-cognitive assessments that are polytomously scored.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321741339Subjects--Topical Terms:

1148448
Higher education.
Index Terms--Genre/Form:

554714
Electronic books.

Extending an IRT Mixture Model to Detect Random Responders on Non-Cognitive Polytomously Scored Assessments.
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245 1 0 $a Extending an IRT Mixture Model to Detect Random Responders on Non-Cognitive Polytomously Scored Assessments.
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500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.
500 $a Adviser: Dena A. Pastor.
502 $a Thesis (Ph.D.)--James Madison University, 2015.
504 $a Includes bibliographical references
520 $a This study represents an attempt to distinguish two classes of examinees -- random responders and valid responders -- on non-cognitive assessments in low-stakes testing. The majority of existing literature regarding the detection of random responders in low-stakes settings exists in regard to cognitive tests that are dichotomously scored. However, evidence suggests that random responding occurs on non-cognitive assessments, and as with cognitive measures, the data derived from such measures are used to inform practice. Thus, a threat to test score validity exists if examinees' response selections do not accurately reflect their underlying level on the construct being assessed. As with cognitive tests, using data from measures in which students did not give their best effort could have negative implications for future decisions. Thus, there is a need for a method of detecting random responders on non-cognitive assessments that are polytomously scored.
520 $a This dissertation provides an overview of existing techniques for identifying low-motivated or amotivated examinees within low-stakes cognitive testing contexts including motivation filtering, response time effort, and item response theory mixture modeling, with particular attention paid to an IRT mixture model referred to in this dissertation as the Random Responders model -- Graded Response model (RRM-GRM). Two studies, a simulation and an applied study, were conducted to explore the utility of the RRM-GRM for detecting and accounting for random responders on non-cognitive instruments in low-stakes testing settings. The findings from the simulation study show considerable bias and RMSE in parameter estimates and bias in theta estimates when the proportion of random responders is greater than 5%. Use of the RRM-GRM with the same data sets provides parameter estimates with minimal to no bias and RMSE and theta estimates that are essentially bias free. The applied study demonstrated that when fitting the RRM-GRM to authentic data, 5.6% of the responders were identified as random responders. Respondents classified as random responders were found to have higher odds of being males and of having lower scores on importance of the test, as well as lower average total scores on the UMUM-15 measure used in the study. Limitations of the RRM-GRM technique are discussed.
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