Deciding The Number Of Dimensions In Explanatory Factor Analysis: A Brief Overview Of The Methods

Abstract

Exploratory factor analysis (EFA) finds its place in many scientific fields (e.g. education, health science or economics). With this analysis, information about the nature and structure of the measured feature can be obtained. It is possible to have information about the nature of the measured feature by fulfilling the requirements of this analysis. Correctly deciding on the number of dimensions in EFA can also be challenging for researchers. For this reason, this study presents information on the theoretical background of the factor retention methods used when deciding on the number of dimensions in EFA. In addition, it has been given information about which software is available for these methods. Moreover, there is information about which method gives more accurate results in the simulation studies. As a result, the number of dimensions can be decided by using traditional methods such as optimal parallel analysis, comparative data, or minimum average partial, as well as making use of machine learning methods (random forest or extreme gradient augmentation), which have found new uses in the literature, to researchers who will perform EFA.

Keywords

• parallel analysis
• minimum average partial
• exploratory graph analysis
• random forest
• xgboost

AÇIMLAYICI FAKTÖR ANALİZİNDE BOYUT SAYISINA KARAR VERME: YÖNTEMLERE KISA BİR BAKIŞ

Abstract

Açımlayıcı faktör analizi (AFA), bilimsel çalışma alanlarının birçoğunda (eğitim, sağlık, iktisat gibi) kendine yer bulmaktadır. Bu analizle ölçülen özelliğin doğası ve yapısı hakkında bilgi sahibi olunabilmektedir. Ölçülen özelliğin doğası hakkında bilgi sahibi olmak ise bu analizin gerekliliklerini yerine getirerek mümkündür. Açımlayıcı faktör analizinde boyut sayısına doğru bir şekilde karar verilmesi de araştırmacılar için zorlayıcı olabilmektedir. Bu nedenle bu çalışmada, açımlayıcı faktör analizinde boyut sayısına karar verirken kullanılabilecek yöntemlerin hem teorik alt yapısına hem de bu yöntemlerin hangi yazılımlarda bulunduğuna yönelik bilgiler sunulmuştur. Dahası gerçekleştirilen çalışmalarda hangi yöntemin daha uygun sonuçlar verdiği ve hangi yöntemlerin güncel çalışmalarda kullanılabileceğine yer verilmiştir. Sonuç olarak geleneksel yöntemlerden optimal paralel analiz, karşılaştırmalı veriler ya da kısmi korelasyonların ortalaması yöntemleriyle boyut sayısına karar verilebileceği gibi literatürde kendine daha yeni kullanım alanı bulmuş makine öğrenmesi yöntemlerinden de (rastgele orman ya da aşırı gradyan arttırma) yararlanılması AFA gerçekleştirecek araştırmacılara önerilebilir.

Keywords

• paralel analiz
• kısmi korelasyonların ortalaması
• açımlayıcı grafik analizi
• rastgele orman
• aşırı gradyan artırma

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