Journals / Business and Management Studies: An International Journal / 2020 / Cilt: 8 - Sayı: 1

NORMALITY ASSUMPTION IN THE EXPLORATORY FACTOR ANALYSIS WITH LIKERT SCALE DATA AND TESTING ITS EFFECT ON FACTOR EXTRACTION

LİKERT VERİLERİNİN KULLANILDIĞI KEŞFEDİCİ FAKTÖR ANALİZLERİNDE NORMALLİK VARSAYIMI VE FAKTÖR ÇIKARMA ÜZERİNDEKİ ETKİSİNİN SPSS, FACTOR VE PRELIS YAZILIMLARIYLA SINANMASI

Pages
640–687
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the findings of the research, it was understood that the most healthy factorial structures could be obtained with software such as Lisrel-Prelis and Factor.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this article, in which situations the normality assumptions will be brought up to make the Exploratory Factor Analysis (EFA) with Likert data evaluated in the ordered category, what kind of EFA methods are concerned, how to make normality evaluation with SPSS, PRELIS and FACTOR software, how to make the factorial structures of different EFA methods and how the factor structures will be affected if the non-conformity EFA method is applied in cases where the normality condition is not met and how the results of the normality analysis will be reported.
📊
Key Findings & Data:While the study has an tutorial quality in one aspect, it questions the factorial structures that the data which do not show normal distribution characteristics can be revealed in different statistical software.
💡
Conclusion & Impact:According to the findings of the research, it was understood that the most healthy factorial structures could be obtained with software such as Lisrel-Prelis and Factor.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this article, in which situations the normality assumptions will be brought up to make the Exploratory Factor Analysis (EFA) with Likert data evaluated in the ordered category, what kind of EFA methods are concerned, how to make normality evaluation with SPSS, PRELIS and FACTOR software, how to make the factorial structures of different EFA methods and how the factor structures will be affected if the non-conformity EFA method is applied in cases where the normality condition is not met and how the results of the normality analysis will be reported. While the study has an tutorial quality in one aspect, it questions the factorial structures that the data which do not show normal distribution characteristics can be revealed in different statistical software. According to the findings of the research, it was understood that the most healthy factorial structures could be obtained with software such as Lisrel-Prelis and Factor.

Özet

Bu yazıda sıralı kategorisinde değerlendirilen Likert verileriyle Keşfedici Faktör Analizi (KFA) yapmak için normallik varsayımının hangi durumlarda gündeme geleceği, hangi tür KFA yöntemlerinin söz konusu olduğu, SPSS, PRELIS ve FACTOR yazılımlarıyla normallik değerlemesinin nasıl yapılacağı, değişik KFA yöntemlerinin faktöriyel yapıları nasıl ortaya çıkardığı, normallik varsayımının sağlanmadığı durumlarda uygun olmayan KFA yöntemi uygulanırsa faktör yapılarının bundan nasıl etkileneceği ve normallik analizi sonuçlarının nasıl raporlanacağı konuları üzerinde durulmuştur. Çalışma bir yönden eğitsel bir niteliğe sahipken diğer taraftan normal dağılım özelliği göstermeyen verilerin değişik istatistiki yazılımlarda ortaya koyabileceği faktöriyel yapıları sorgulamaktadır. Araştırma bulgularından sıralı ölçek verilerinin kullanıldığı çalışmalarda en sağlıklı faktöriyel yapıların Lisrel-Prelis ve Factor gibi yazılımlarla elde edilebileceği anlaşılmıştır.

🔖

Cite This Article

ŞENCAN, H., FİDAN, Y. (2020). NORMALITY ASSUMPTION IN THE EXPLORATORY FACTOR ANALYSIS WITH LIKERT SCALE DATA AND TESTING ITS EFFECT ON FACTOR EXTRACTION. Business and Management Studies: An International Journal, Cilt: 8 - Sayı: 1, 640–687. https://ajindex.com/articles/142146

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (8)

  1. "Lab notes.". (t.y.). Lab notes: Examples of PRELIS runs. Retrieved Şubat 17, 2020, from Faculty of Washington: http://faculty.washington.edu/matsueda/courses/529
  2. Abraham, M. (2018). Construction and validation of the adolescent perceived risks and benefits of exposure to music from personal music players questionnaire. Psykhe, 27(2), pp. 1-16. doi:https://doi.org/10.7764/psykhe.27.2.1065
  3. AFLMC. (2008). Air force journal of logistics, 32. cilt. Gunter Air Force Base: Air Force Logistics Management Center. Alemayehu, D., Cappelleri, J. C., Emir, B., & Zou, K. H. (2017). Statistical topics in health economics and outcomes research. Newyork: CRC Press.
  4. Aletras, V. H., Kostarelis, A., Tsitouridou, M., & Nicolaou, D. N. (2010, 5 5). Development and preliminary validation of a questionnaire to measure satisfaction with home care in Greece: an exploratory factor analysis of polychoric correlations. BMC Health Services Research, pp. 1-14. Retrieved 1 20, 2020, from https://bmchealthservres.biomedcentral.com/track/pdf/10.1186/1472-6963-10-189 Asún, R. A., Rdz-Navarro, K., & Alvarado, J. M. (2015). Developing multidimensional likert scales using item factor analysis the case of four-point items. Sociological Methods and Research, 2015, pp. 1-20.
  5. Baglin, J. (2014). Improving your exploratory factor analysis for ordinal data: A demonstration using FACTOR. Practical Assessment, Research & Evaluation, 19(5), pp. 1-15.
  6. Brace, N., Snelgar, R., & Kemp, R. (2016). SPSS for psychologists: and everybody else. Newyork: Macmillan International Higher. Brislin, R. W., Lonner, W. J., & Thorndike, R. M. (1973). Theoretical and methodological issues in cross cultural research in psychology. Toronto: John Wiley & Sons. Büyüköztürk, Ş. (2002). Faktör analizi: Temel kavramlar ve ölçek geliştirmede kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, Güz(32), pp. 470-482.
  7. Cain, M. K., Zhang, Z., & Yuan, K.-H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), pp. 1716–1735.
  8. Catalano, A. J. (2018). Measurements in distance education: a compendium of ınstruments, scales, and measures of evualuating online learning. London: Taylor & Francis Ltd. Comrey, A. L., & Lee, H. B. (1992). A First Course in Factor Analysis. Broadway: Lawrance. Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Pra