The Effect of the Item–Attribute Relation on the DINA Model Estimations in the Presence of Missing Data

Yıl 2019, Cilt: 46 Sayı: 46, 290 - 306, 22.05.2019

Ömür Kaya Kalkan , Tahsin Oğuz Başokçu

https://doi.org/10.9779/pauefd.546797

Öz

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 Q-matrix contains 24 items,
and data are generated using four attributes. A dataset of n = 3000 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 (guessing) 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.

Anahtar Kelimeler

DINA model, missing data, latent class estimates, item–attribute relation

Kayıp Veri Varlığında DINA Model Madde-Özellik İlişkisinin Parametre Kestirimine Etkisi

Öz

Bu araştırmanın
amacı, farklı oranlarda kayıp veri varlığında madde-özellik sayısı ilişkisinin,
DINA model kestirimlerini nasıl etkilediğini incelediğini simülasyon verileri
üzerinden incelemektir. Verilerin üretimlesinde 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 3000 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 MI 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.

 

Anahtar Kelimeler

DINA model, kayıp veri, örtük sınıf kestirimi, madde-özellik ilişkisi

Kaynakça

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