FARKLI ÇOKLU VERİ ATAMA TEKNİKLERİNİN DOĞRULAYICI FAKTÖR ANALİZİ MODEL UYUMU ÜZERİNDEKİ ETKİSİ

Abstract

Bugüne kadar kayıp verilerin istatistiksel analizler üzerindeki erkilerini incelemek için birçok araştırma gerçekleştirilmiştir ve bu durumla başa çıkabilmek için farklı yöntemler geliştirilmiştir. Kayıp verinin silinmesini içeren yöntemler örneklem büyüklüğünün önemli miktarda azalmasına sebep olmakta ve analizlerin istatistiksel gücünü düşürmektedir. Bu duruma bir alternatif olarak önerilen kayıp veri kestirimine dayalı yöntemler araştırmacıların yoğun ilgisini çekmektedir. Bu yöntemler içerisinde çoklu veri atama teknikleri göreceli olarak daha yakın bir geçmişe sahiptir ve daha iyi kestirimler sağlamaktadır. Çoklu veri atama tekniklerinin üstünlüğü düşünüldüğünde, gerçekleştirilen bu çalışmanın amacı farklı çoklu veri atama tekniklerinin doğrulayıcı faktör analizi model uyumu üzerideki etkisinin değerlendirilmesidir. Bu amaç doğrultusunda örneklem büyüklüğü, kayıp veri mekanizması, kayıp veri yüzdesi, madde sayısı ve kayıp veri atama tekniğini kontrol edilerek tek boyutlu yapıya sahip veri setleri üretilmiştir. Kayıp veri tekniklerinin etkileri tam veri setleri ve veri ataması gerçekleştirilmiş veri setleri için elde edilmiş 𝜒² model uyum istatistikleri arasındaki fark ile değerlendirilmiştir. Elde edilen sonuçlar çoklu veri atama tekniklerinin geleneksel regresyon temelli veri atama tekniklerine kıyasla daha iyi sonuçlar sağladığını göstermiştir. Bu bulgular daha sonrasında tartışılarak daha iyi test uygulmaları için bir takım önerilerde bulunulmuştur.

Keywords

• kayıp veri
• çoklu veri atama
• benzetim

EFFECTS OF DIFFERENT MULTIPLE IMPTUTATION TECHNIQUES ON THE MODEL FIT OF CONFIRMATORY FACTOR ANALYSIS

Abstract

So far, many researches have been conducted to investigate the impact of missing data on statistical analysis and various methods have been developed to deal with the problem. The methods based on removing observations with missing values from the dataset cause the sample size to drop dramatically and the statistical power of the analyzes to be decreased. Therefore, as an alternative solution, the estimation of missing values seized intensive attention of researchers. Among these methods, multiple imputation techniques are relatively more recent and provide better estimations. Considering the superiority of multiple imputation techniques, the aim of the current study is to investigate the effects of different multiple imptutation techniques on the model fit of confirmatory factor analysis. For this aim, datasets with the unidimensional structure were simulated to manipulate sample size, missing data mechanism, percentage of missing data, number of items and missing data imputation technique. The effect of multiple imputation techniqes was evaluated based on the difference of 𝜒² model fit statistics for complete datasets and imputed datasets. The results showed that, multiple impuation techniques provided better results than conventional regression based imputation. Those finding were discussed later and some recommendations were given for better testing applications.

Keywords

• missing data
• data imputation
• simulation

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