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Evaluation of the Goodness-of-Fit Index Mord in Polytomous DCMS with Hierarchical Attribute Structures.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evaluation of the Goodness-of-Fit Index Mord in Polytomous DCMS with Hierarchical Attribute Structures./
Author:	Yuan, Haimiao.
Description:	1 online resource (145 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
Contained By:	Dissertations Abstracts International84-03A.
Subject:	Educational tests & measurements. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29321981click for full text (PQDT)
ISBN:	9798845426345

Evaluation of the Goodness-of-Fit Index Mord in Polytomous DCMS with Hierarchical Attribute Structures.
Yuan, Haimiao.

Evaluation of the Goodness-of-Fit Index Mord in Polytomous DCMS with Hierarchical Attribute Structures. - 1 online resource (145 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: A.

Thesis (Ph.D.)--The University of Iowa, 2022.

Includes bibliographical references

The application of diagnostic classification models (DCMs) in the field of educational measurement is getting more attention in recent years. To make a valid inference from the model, it is important to ensure that the model fits the data. The purpose of the present study was to investigate the performance of the limited information goodness-of-fit statistic Mord in the polytomous response DCMs with the presence of hierarchical attribute structures. The first simulation study investigated the empirical Type I error rates of the Mord statistic under the null hypothesis when the model and data were perfectly fitted. The second simulation study explored the empirical rejection rate of Mord under different types of misspecifications: model misspecification, attribute hierarchy misspecification, and Q-matrix misspecification. The impact of test length, item quality, attribute structure, marginal/conditional probability of the mastery of attributes, and the number of response categories were investigated. The results indicated that the Mord statistic demonstrated well-calibrated Type I error rates under the null conditions with different types of hierarchical attribute structures. When there were model-data misfits, the Mord statistic showed high empirical rejection rates in detecting the misspecified sDINA and sDINO but didn't show enough power to detect the sC-RUM. The Mord also exhibited high empirical rejection rates when the generating attribute structure was non-strict hierarchical, but the fitted structure was strict hierarchical. Besides, it was sensitive when the sequence of parent and child attributes was reversed. However, the Mord statistic couldn't detect the omission of hierarchical attribute connections. When there were Q-matrix misspecifications, the Mord statistic showed extremely high empirical rejection rates in the condition of Q-matrix under-specification but was not sensitive to the Q-matrix over-specification. The higher proportion of Q-matrix misspecification led to higher Mord rejection rates. The item quality exhibited a huge influence on the performance of the Mord statistic. The Mord statistic demonstrated higher empirical rejection rates with higher item quality, longer test length, fewer item response categories, and lower marginal/conditional probability of the mastery of attributes. The types of attribute structure also demonstrated slight influences on the rejection rates of the Mord statistics under some circumstances. The performance of the RMSEAord and SRMSR were explored in the present study to assess the degree of misfit and absolute model fit. The results indicated that the frequently used cut-off value of 0.05 was not appropriate in the framework of polytomous response DCMs. The magnitude of RMSEAord and SRMSR values varied in different situations.

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

Mode of access: World Wide Web

ISBN: 9798845426345Subjects--Topical Terms:

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

Diagnostic classification modelIndex Terms--Genre/Form:

542853
Electronic books.

Evaluation of the Goodness-of-Fit Index Mord in Polytomous DCMS with Hierarchical Attribute Structures.
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500 $a Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
500 $a Advisor: Templin, Jonathan.
502 $a Thesis (Ph.D.)--The University of Iowa, 2022.
504 $a Includes bibliographical references
520 $a The application of diagnostic classification models (DCMs) in the field of educational measurement is getting more attention in recent years. To make a valid inference from the model, it is important to ensure that the model fits the data. The purpose of the present study was to investigate the performance of the limited information goodness-of-fit statistic Mord in the polytomous response DCMs with the presence of hierarchical attribute structures. The first simulation study investigated the empirical Type I error rates of the Mord statistic under the null hypothesis when the model and data were perfectly fitted. The second simulation study explored the empirical rejection rate of Mord under different types of misspecifications: model misspecification, attribute hierarchy misspecification, and Q-matrix misspecification. The impact of test length, item quality, attribute structure, marginal/conditional probability of the mastery of attributes, and the number of response categories were investigated. The results indicated that the Mord statistic demonstrated well-calibrated Type I error rates under the null conditions with different types of hierarchical attribute structures. When there were model-data misfits, the Mord statistic showed high empirical rejection rates in detecting the misspecified sDINA and sDINO but didn't show enough power to detect the sC-RUM. The Mord also exhibited high empirical rejection rates when the generating attribute structure was non-strict hierarchical, but the fitted structure was strict hierarchical. Besides, it was sensitive when the sequence of parent and child attributes was reversed. However, the Mord statistic couldn't detect the omission of hierarchical attribute connections. When there were Q-matrix misspecifications, the Mord statistic showed extremely high empirical rejection rates in the condition of Q-matrix under-specification but was not sensitive to the Q-matrix over-specification. The higher proportion of Q-matrix misspecification led to higher Mord rejection rates. The item quality exhibited a huge influence on the performance of the Mord statistic. The Mord statistic demonstrated higher empirical rejection rates with higher item quality, longer test length, fewer item response categories, and lower marginal/conditional probability of the mastery of attributes. The types of attribute structure also demonstrated slight influences on the rejection rates of the Mord statistics under some circumstances. The performance of the RMSEAord and SRMSR were explored in the present study to assess the degree of misfit and absolute model fit. The results indicated that the frequently used cut-off value of 0.05 was not appropriate in the framework of polytomous response DCMs. The magnitude of RMSEAord and SRMSR values varied in different situations.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Educational tests & measurements. $3 3168483
653 $a Diagnostic classification model
653 $a Goodness of fit
653 $a Hierarchical
653 $a Polytomous
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0288
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The University of Iowa. $b Psychological & Quantitative Foundations. $3 1669926
773 0 $t Dissertations Abstracts International $g 84-03A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29321981 $z click for full text (PQDT)