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The Effect of Examinee Motivation on...

Williams, Laura M.

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The Effect of Examinee Motivation on Value-Added Estimates.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Effect of Examinee Motivation on Value-Added Estimates./
Author:	Williams, Laura M.
Description:	151 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.
Contained By:	Dissertation Abstracts International76-09A(E).
Subject:	Educational evaluation. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3702699
ISBN:	9781321741360

The Effect of Examinee Motivation on Value-Added Estimates.
Williams, Laura M.

The Effect of Examinee Motivation on Value-Added Estimates. - 151 p.

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

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

Questions regarding the quality of education, both in K-12 systems and higher education, are common. Methods for measuring quality in education have been developed in the past decades, with value-added estimates emerging as one of the most well-known methods. Value-added methods purport to indicate how much students learn over time as a result of their attendance at a particular school. Controversy has surrounded the algorithms used to generate value-added estimates as well as the uses of the estimates to make decisions about school and teacher quality. In higher education, most institutions used cross-sectional rather than longitudinal data to estimate value-added. In addition, much of the data used to generate value-added estimates in higher education were gathered in low-stakes testing sessions. In low-stakes contexts, examinee motivation has been shown to impact test performance. Additionally, recent empirical evidence indicated that the change in test-taking motivation between pre-and post-test was a predictor of change in performance. Because of this, researchers have suggested that test-taking motivation may bias value-added estimates. Further, if interest truly lies in measuring student learning over time, the use of cross-sectional data is problematic, since the pre- and post-test data is gathered from two different groups of students, not the same students at two time points. The current study investigated two overarching questions related to value-added estimation in higher education: 1) are different methods of value-added estimation comparable?; and 2) how does test-taking motivation impact value-added estimates? In this study, first the results from value-added estimates calculated with cross-sectional and longitudinal data were compared. Next, estimates generated from two value-added models were compared: raw difference scores and a longitudinal hierarchical linear model. Finally, estimates were compared when motivation variables were included. Results indicated that at the institution under study, cross-sectional and longitudinal data and analyses yielded similar results and that changes in test-taking motivation between pre- and post-test did impact value-added estimates. Suggestions to combat the effect of motivation on value-added estimates included behavioral as well as statistical interventions.

ISBN: 9781321741360Subjects--Topical Terms:

526425
Educational evaluation.

The Effect of Examinee Motivation on Value-Added Estimates.
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245 1 4 $a The Effect of Examinee Motivation on Value-Added Estimates.
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500 $a Source: Dissertation Abstracts International, Volume: 76-09(E), Section: A.
500 $a Adviser: Donna L. Sundre.
502 $a Thesis (Ph.D.)--James Madison University, 2015.
520 $a Questions regarding the quality of education, both in K-12 systems and higher education, are common. Methods for measuring quality in education have been developed in the past decades, with value-added estimates emerging as one of the most well-known methods. Value-added methods purport to indicate how much students learn over time as a result of their attendance at a particular school. Controversy has surrounded the algorithms used to generate value-added estimates as well as the uses of the estimates to make decisions about school and teacher quality. In higher education, most institutions used cross-sectional rather than longitudinal data to estimate value-added. In addition, much of the data used to generate value-added estimates in higher education were gathered in low-stakes testing sessions. In low-stakes contexts, examinee motivation has been shown to impact test performance. Additionally, recent empirical evidence indicated that the change in test-taking motivation between pre-and post-test was a predictor of change in performance. Because of this, researchers have suggested that test-taking motivation may bias value-added estimates. Further, if interest truly lies in measuring student learning over time, the use of cross-sectional data is problematic, since the pre- and post-test data is gathered from two different groups of students, not the same students at two time points. The current study investigated two overarching questions related to value-added estimation in higher education: 1) are different methods of value-added estimation comparable?; and 2) how does test-taking motivation impact value-added estimates? In this study, first the results from value-added estimates calculated with cross-sectional and longitudinal data were compared. Next, estimates generated from two value-added models were compared: raw difference scores and a longitudinal hierarchical linear model. Finally, estimates were compared when motivation variables were included. Results indicated that at the institution under study, cross-sectional and longitudinal data and analyses yielded similar results and that changes in test-taking motivation between pre- and post-test did impact value-added estimates. Suggestions to combat the effect of motivation on value-added estimates included behavioral as well as statistical interventions.
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