Levent YAKAR, Nuri DOĞAN, Jimmy DE LA TORRE

Bu araştırmada çok kategorili bilişsel tanı ve çok boyutlu madde tepki kuramı (ÇBMTK) modellerinin birbiri yerine kullanımı durumunda birey parametrelerinin tekrar elde edilebilirliği incelenmiştir. Bu amaç doğrultusunda çok kategorili bilişsel tanı modellerinden polytomous generalized deterministic input noisy and gate (pG-DINA) ve fully-additive model (fA-M) ve ÇBMTK modellerinden ise telafi edici 2PL modeli için veriler üretilmiştir. Veriler, madde ayırt edicilik indeksi, maddelerin yapılarına göre testteki oranı, test uzunluğu ve yetenekler arası korelasyon değerleri farklılaştırılarak toplamda 54 koşul kullanılarak 25 tekrar ile üretilmiştir. Verinin her üç modelle de kestirimi sonucu ortaya çıkan birey parametreleri ile veri üretiminde kullanılan birey parametrelerinin karşılaştırılması ile bulgular elde edilmiştir. Bulgulara göre tüm veri türlerinin en yüksek doğrulukta kestirimi ait oldukları modeller tarafından gerçekleştirilmiştir. Uyarlama içeren analizlerde ise fA-M diğer iki model verilerini, ÇBMTK ise fA-M verisini, verinin ait olduğu model kestirimine yakın bir doğruluk oranında kestirmiştir. PG-DINA’nın diğer iki model verilerini, ÇBMTK’nin ise pG-DINA verisini kestirmede düşük performansa sahip olduğu gözlenmiştir. Araştırmada kullanılan koşullardan sırasıyla test uzunluğu ve madde ayırt ediciliğinin birey parametre doğruluğuna etki eden en kuvvetli faktörler olduğu görülmüştür. Madde yapısı oranı koşulunun ise ÇBMTK verisi analizlerinde ve uyarlamalarında çok daha etkili olduğu görülmüştür. Yetenekler arası korelasyonun varlığının birey parametre doğruluğuna etkisinin ise ÇBMTK verisinin analizlerinde daha belirgin ancak yine de sınırlı olduğu görülmüştür

Anahtar Kelimeler:

çok kategorili bilişsel tanı modelleri, pG-DINA, fA-M, çok boyutlu madde tepki kuramı, uyarlama

Retrofitting of Polytomous Cognitive Diagnosis and Multidimensional Item Response Theory Models

In this study, person parameter recoveries are investigated by retrofitting polytomous attribute cognitive diagnosis and multidimensional item response theory (MIRT) models. The data are generated using two cognitive diagnosis models (i.e., pG-DINA: the polytomous generalized deterministic inputs, noisy “and” gate and fA-M: the fully-additive model) and one MIRT model (i.e., the compensatory two-parameter logistic model). Twenty-five replications are used for each of the 54 conditions resulting from varying the item discrimination index, ratio of simple to complex items, test length, and correlations between skills. The findings are obtained by comparing the person parameter estimates of all three models to the actual parameters used in the data generation. According to the findings, the most accurate estimates are obtained when the fitted models correspond to the generating models. Comparable results are obtained when the fA-M is retrofitted to other data or when the MIRT model is retrofitted to fA-M data. However, the results are poor when the pG-DINA is retrofitted to other data or the MIRT is retrofitted to pG-DINA data. Among the conditions used in the study, test length and item discrimination have the greatest influence on the person parameter estimation accuracy. Variation in the simple to complex item ratio has a notable influence when the MIRT model is used. Although the impact on the person parameter estimation accuracy of the correlation between skills is limited, its effect on MIRT data is more significant.

Keywords:

polytomous attribute cognitive diagnosis models, pG-DINA, fA-M, multidimensional item response theory, retrofit,

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