İhmal Edilebilir Kayıp Veri Durumunda Model Tabanlı Kayıp Veri Baş Etme Yöntemlerinin Şans Parametresine Etkisi

Bu çalışmada, kayıp veri durumunda model tabanlı kayıp veri baş etme yöntemlerinin ihmal edilebilir şans parametresi üzerindeki etkilerinin belirlenmesi amaçlanmıştır. Bu amaçla 500, 1000 ve 3000 örneklem büyüklüğünde tek boyutlu Madde Tepki Kuramı 3 parametreli lojistik modeline uygun olarak üretilen verilerde %2.00, %5.00 ve %10.00 oranlarında tamamen rastgele kayıp ve rastgele kayıp mekanizmalarına uygun olacak şekilde kayıp veri oluşturulmuştur. Oluşturulan kayıp veriler, beklenti maksimizasyon algoritması ve çoklu atama yöntemleri ile tamamlanmıştır. Veri setinde tamamen rastgele kayıp mekanizmasında kayıp veri olması durumunda çoklu atama ve beklenti maksimizasyon algoritması yöntemlerinin kayıp veri oranına da bağlı olarak performansının iyi olduğu sonucuna ulaşılmıştır. Tüm örneklem büyüklüklerinde kayıp veri oranı %2.00 olduğunda her iki yöntemin de en iyi performansı sergilediği, kayıp veri oranı arttıkça referans değerden uzaklaşıldığı görülmektedir. Buna karşın, örneklem büyüklüğü 3000 olduğunda kayıp veri oranı yüksek de olsa referans değere daha yakın kesitimler sundukları sonucuna ulaşılmıştır. Rastgele kayıp veri mekanizmasında ise kayıp veri oranı düşük olduğunda her iki yöntemin de şans parametresi üzerinde iyi performans gösterdiği ancak kayıp veri oranı arttıkça bu performansta önemli düşüşlerin olduğu sonucuna ulaşılmıştır. Çoklu atama ve beklenti maksimizasyon algoritması ile atama yöntemlerinin her ikisi de rastgele kayıp veri mekanizmasında şans parametresi üzerinde iyi performans göstermemektedir

The Effects of Model Based Missing Data Methods on Guessing Parameter in Case of Ignorable Missing Data

The present study aims to investigate the effects of model based missing data methods on guessing parameter in case of ignorable missing data. For this purpose, data based on Item Response Theory with 3 parameters logistic model were created in sample sizes of 500, 1000 and 3000; and then, missing values at random and missing values at completely random were created in ratios of 2.00%, 5.00% and 10.00%. These missing values were completed using expectation–maximization (EM) algorithm and multiple imputation methods. It was concluded that the performance of EM algorithm and multiple imputation methods was efficient depending on the rate of missing values on the data sets with missing values completely at random. When the missing value rate was 2.00%, both methods performed well in all sample sizes; however, they moved away from reference point as the number of missing values increased. On the other hand, it was also found that when the sample size was 3000, the cuts were closer to reference point even when the number of missing values was high. As for missing values at random mechanism, it was observed that both methods performed efficiently on guessing parameter when the number of missing values was low. Yet, this performance deteriorated considerably as the number of missing values increased. Both EM algorithm and multiple imputation methods did not perform effectively on guessing parameter in missing values at random mechanism

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