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Comparing trend and gap statistics a...

Denbleyker, John Nickolas.

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Comparing trend and gap statistics across tests: Distributional change using ordinal methods and Bayesian inference.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Comparing trend and gap statistics across tests: Distributional change using ordinal methods and Bayesian inference./
Author:	Denbleyker, John Nickolas.
Description:	338 p.
Notes:	Source: Dissertation Abstracts International, Volume: 73-11(E), Section: A.
Contained By:	Dissertation Abstracts International73-11A(E).
Subject:	Education, Tests and Measurements. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3516615
ISBN:	9781267461094

Comparing trend and gap statistics across tests: Distributional change using ordinal methods and Bayesian inference.
Denbleyker, John Nickolas.

Comparing trend and gap statistics across tests: Distributional change using ordinal methods and Bayesian inference. - 338 p.

Source: Dissertation Abstracts International, Volume: 73-11(E), Section: A.

Thesis (Ph.D.)--The University of Iowa, 2012.

The shortcomings of the proportion above cut (PAC) statistic used so prominently in the educational landscape renders it a very problematic measure for making correct inferences with student test data. The limitations of PAC-based statistics are more pronounced with cross-test comparisons due to their dependency on cut-score locations. A better alternative is using mean-based statistics that can translate to parametric effect-size measures. However, these statistics as well can be problematic. When Gaussian assumptions are not met, reasonable transformations of a score scale produce non-monotonic outcomes.

ISBN: 9781267461094Subjects--Topical Terms:

1017589
Education, Tests and Measurements.

Comparing trend and gap statistics across tests: Distributional change using ordinal methods and Bayesian inference.
LDR:03675nam a2200313 4500 001 1965371
005 20141022133321.5
008 150210s2012 ||||||||||||||||| ||eng d
020 $a 9781267461094
035 $a (MiAaPQ)AAI3516615
035 $a AAI3516615
040 $a MiAaPQ $c MiAaPQ
100 1 $a Denbleyker, John Nickolas. $3 2102016
245 1 0 $a Comparing trend and gap statistics across tests: Distributional change using ordinal methods and Bayesian inference.
300 $a 338 p.
500 $a Source: Dissertation Abstracts International, Volume: 73-11(E), Section: A.
500 $a Advisers: Andrew D. Ho; Robert L. Brennan.
502 $a Thesis (Ph.D.)--The University of Iowa, 2012.
520 $a The shortcomings of the proportion above cut (PAC) statistic used so prominently in the educational landscape renders it a very problematic measure for making correct inferences with student test data. The limitations of PAC-based statistics are more pronounced with cross-test comparisons due to their dependency on cut-score locations. A better alternative is using mean-based statistics that can translate to parametric effect-size measures. However, these statistics as well can be problematic. When Gaussian assumptions are not met, reasonable transformations of a score scale produce non-monotonic outcomes.
520 $a The present study develops a distribution-wide approach to summarize trend, gap, and gap trend (TGGT) measures. This approach counters the limitations of PAC-based measures and mean-based statistics in addition to addressing TGGT-related statistics in a manner more closely tied to both the data and questions regarding student achievement. This distribution-wide approach encompasses visual graphics such as percentile trend displays and probability-probability plots fashioned after Receiver Operating Characteristic (ROC) curve methodology. The latter is framed as the P-P plot framework that was proposed by Ho (2008) as a way to examine trends and gaps with more consideration given to questions of scale and policy decisions. The extension in this study involves three main components: (1) incorporating Bayesian inference, (2) using a multivariate structure for longitudinal data, and (3) accounting for measurement error at the individual level. The analysis is based on mathematical assessment data comprising Grade 3 to Grade 7 from a large Midwestern school district.
520 $a Findings suggest that PP-based effect sizes provide a useful framework to measure aggregate test score change and achievement gaps. The distribution-wide perspective adds insight by examining both visually and numerically how trends and gaps are affected throughout the score distribution. Two notable findings using the PP-based effect sizes were (1) achievement gaps were very similar between the Focal and Audit test, and (2) trend measures were significantly larger for the Audit test. Additionally, measurement error corrections using the multivariate Bayesian CTT approach had effect sizes disattenuated from those based on observed scores. Also, the ordinal-based effect size statistics were generally larger than their parametric-based counterparts, and this disattenuation was practically equivalent to that seen by accounting for measurement error. Finally, the rank-based estimator of P(X>Y) via estimated true scores had smaller standard errors than for its parametric-based counterpart.
590 $a School code: 0096.
650 4 $a Education, Tests and Measurements. $3 1017589
650 4 $a Psychology, Psychometrics. $3 1017742
650 4 $a Education, Evaluation. $3 1669638
690 $a 0288
690 $a 0632
690 $a 0443
710 2 $a The University of Iowa. $b Psychological & Quantitative Foundations. $3 1669926
773 0 $t Dissertation Abstracts International $g 73-11A(E).
790 $a 0096
791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3516615