ÇARPIK DAĞILIMLARDA FAKTÖR SAYISI BELİRLEME YÖNTEMLERİNİN PERFORMANSLARININ İNCELENMESİ

Year 2023, Volume: 5, 288 - 312, 20.10.2023

Gül Güler , Abdullah Faruk Kılıç

https://doi.org/10.48166/ejaes.1357828

Abstract

Bu araştırmanın amacı faktör sayısı belirleme yöntemlerinin çeşitli simülasyon koşulları altında performanslarını değerlendirmektir. Bu amaç doğrultusunda boyutluluk belirleme yöntemlerinden optimal paralel analiz, MAP, HULL, EGA (TMFG) kestirimi, EGA (Glasso) kestirimi ve comparison data forest yöntemi karşılaştırılmıştır. Çalışmada simülasyon koşulları olarak dağılımın türü, örneklem büyüklüğü, faktör başına düşen madde sayısı, kategori sayısı ve ölçme modeli belirlenmiştir. Çalışmada her bir koşul için 100 replikasyon yapılmıştır. Çalışmada tamamen çaprazlanmış simülasyon deseni kullanılmıştır. Çarpık dağılımlarda faktör sayısı belirleme yöntemlerinin performanslarının incelendiği bu çalışma sonucunda tüm koşulların accuracy değerlerinin ortalaması dikkate alındığında en yüksek ortalamaya HULL yönteminin sahip olduğu görülmüştür. Aynı zamanda en düşük RB ortalaması da HULL yöntemindedir. Ancak tüm koşullarda yeterli performansı gösteren bir yöntemin olmadığı söylenebilir. Diğer bir deyişle her koşulda doğru sonucu verecek bir yöntem bulunmamaktadır. Bu çalışmada tek faktörlü, faktörler arası korelasyonu 0.00 ve 0.30 olan iki faktörlü yapılar incelenmiştir. Eğitimde ve psikolojide ikiden fazla faktör sayısına sahip yapılar göz önünde bulundurulduğunda gelecekteki araştırmalarda çarpık dağılım gösteren verilerde daha fazla faktör ve madde sayısıyla çalışılarak yöntemlerin performansları karşılaştırılabilir.

Keywords

Faktör sayısı belirleme, MAP, HULL, karşılaştırmalı faktör forest, EGA

PERFORMANCE OF FACTOR RETENTION METHODS IN SKEWED DISTRIBUTIONS

Abstract

This research aims to evaluate the performance of dimensionality determination methods under various simulation conditions. Therefore, dimensionality determination methods were compared, including optimal parallel analysis, MAP, HULL, EGA (TMFG) estimation, EGA (glasso) estimation, and comparison data forest method. The type of distribution, sample size, number of items per factor, number of categories, and measurement model were specified as simulation conditions in the study. For each condition, 100 replications were conducted. A fully crossed simulation design was employed in the study. The results of this study, which examined the performance of factor determination methods under skewed distributions, indicated that the HULL method had the highest average considering the average accuracy values of all conditions. Meanwhile, the HULL method had the lowest RB average. However, no method demonstrated adequate performance under all conditions. This study examined one-factor and two-factor structures with interfactor correlations of 0.00 and 0.30. Considering structures with more than two factors in education and psychology, future research could focus on working with data exhibiting skewed distributions involving more factors and items to compare the performance of methods.

Keywords

factor retention, MAP, HULL, comparison factor forest, EGA

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