The effect of the item–attribute relation on the DINA model estimations in the presence of missing data
The objective of this study is to investigate the relation between the number of items and attributes and to analyze the manner in which the different rates of missing data affect the model estimations based on the simulation data. A Qmatrix contains 24 items, and data are generated using four attri...
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Published in | Pamukkale Üniversitesi Eğitim Fakültesi dergisi Vol. 2019; no. 46; pp. 290 - 306 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Pamukkale Üniversitesi
01.02.2019
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Subjects | |
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Abstract | The objective of this study is to investigate the relation between the number of items and attributes and to analyze the
manner in which the different rates of missing data affect the model estimations based on the simulation data. A Qmatrix
contains 24 items, and data are generated using four attributes. A dataset of n = 3,000 is generated by
associating the first, middle, and final eight items in the Q-matrix with one, two, and three attributes, respectively,
and 5%, 10%, and 15% of the data have been randomly deleted from the first, middle, and final eight-item blocks in
the Q-matrix, respectively. Subsequently, imputation was performed using the multiple imputation (MI) method with
these datasets, 100 replication was performed for each condition. The values obtained from these datasets were
compared with the values obtained from the full dataset. Thus, it can be observed that an increase in the amount of
missing data negatively affects the consistency of the DINA parameters and the latent class estimations. Further, the
latent class consistency becomes less affected by the missing data as the number of attributes associated with the
items increase. With an increase in the number of attributes associated with the items, the missing data in these items
affect the consistency level of the g parameter (guess) less and the s parameter (slip) more. Furthermore, it can be
observed from the results that the test developers using the cognitive diagnosis models should specifically consider
the item–attribute relation in items with missing data. |
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AbstractList | The objective of this study is to investigate the relation between the number of items and attributes and to analyze the
manner in which the different rates of missing data affect the model estimations based on the simulation data. A Qmatrix
contains 24 items, and data are generated using four attributes. A dataset of n = 3,000 is generated by
associating the first, middle, and final eight items in the Q-matrix with one, two, and three attributes, respectively,
and 5%, 10%, and 15% of the data have been randomly deleted from the first, middle, and final eight-item blocks in
the Q-matrix, respectively. Subsequently, imputation was performed using the multiple imputation (MI) method with
these datasets, 100 replication was performed for each condition. The values obtained from these datasets were
compared with the values obtained from the full dataset. Thus, it can be observed that an increase in the amount of
missing data negatively affects the consistency of the DINA parameters and the latent class estimations. Further, the
latent class consistency becomes less affected by the missing data as the number of attributes associated with the
items increase. With an increase in the number of attributes associated with the items, the missing data in these items
affect the consistency level of the g parameter (guess) less and the s parameter (slip) more. Furthermore, it can be
observed from the results that the test developers using the cognitive diagnosis models should specifically consider
the item–attribute relation in items with missing data. |
Abstract_FL | Bu araştırmanın amacı, farklı oranlarda kayıp veri varlığında madde-özellik sayısı ilişkisinin, DINA model
kestirimlerini nasıl etkilediğini simülasyon verileri üzerinden incelemektir. Verilerin üretilmesinde dört özellik ve 24
maddeden oluşan bir Q matris kullanılmıştır. Q matrixteki ilk, orta ve son 8 madde sırasıyla 1, 2 ve 3 özellikle
ilişkilendirilerek 3,000 kişilik bir veri seti üretilmiş ve bu verilerde yer alan her 8 maddelik bloktan sırası ile %5, %10
ve %15 veri rassal silinmiştir. Ardından, bu veri setlerine Mİ yöntemi ile imputasyon yapılmıştır. Bu işlemler, her bir
koşul için 100 kez tekrarlanmıştır. Bu veri setlerinden elde edilen kestirimler, kayıpsız veri setinden elde edilen
değerler ile karşılaştırılmıştır. Araştırmanın bulguları kayıp veri miktarındaki artışın, DINA model parametre ve örtük
sınıf kestirimlerindeki tutarlılığı olumsuz yönde etkilediğini göstermiştir. Maddenin ilişkili olduğu özellik sayısı
arttıkça örtük sınıf uyumu kayıp veriden daha az etkilenmiştir. Maddenin ilişkili olduğu attribute sayısı arttıkça bu
maddelerde gözlenen kayıp veri, testin g parametresi uyum düzeyini daha az, s parametresini daha çok etkilemiştir.
Araştırmanın sonuçları özellikle CDM modellerini kullanan test geliştiricilerinin kayıp veri gözlenen maddelerde,
madde-özellik ilişkisini göz önünde bulundurmaları gerektiğini göstermektedir. |
Author | Kalkan,Ömür Kaya Başokçu,Tahsin Oğuz |
AuthorAffiliation | Eğitim Bilimleri Bölümü, Eğitimde Ölçme ve Değerlendirme Anabilim Dalı |
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Keywords | kayıp veri latent class estimates madde-özellik ilişkisi DINA model item–attribute relation missing data örtük sınıf kestirimi |
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Title | The effect of the item–attribute relation on the DINA model estimations in the presence of missing data |
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