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 | |
Online Access | Get full text |
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Summary: | 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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ISSN: | 1301-0085 1309-0275 |
DOI: | 10.9779/pauefd.546797 |