KaydedilenlerDerginizi ekleyin

Dergiler / International Journal of Assessment Tools in Education / 2020 / Cilt: 7 - Sayı: 3

Comparison of Confirmatory Factor Analysis Estimation Methods on Binary Data

İbrahim UYSAL
Burcu ATAR
Abdullah Faruk KILIÇ
Sayfa
451–487
Yayın Tarihi
2022-10-19
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

According to the findings of the study, although there is not the most accurate method in all conditions, it can be recommended to use ULSMV method because it performs adequately in more conditions.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure.
📊
Temel Bulgular & Veri:Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance adjusted unweighted least squares (ULSMV), mean and variance adjusted weighted least squares (WLSMV), and Bayesian estimators.
💡
Sonuç & Katkı:According to the findings of the study, although there is not the most accurate method in all conditions, it can be recommended to use ULSMV method because it performs adequately in more conditions.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Confirmatory factor analysis · Estimation methods · Binary data

Özet

This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance adjusted unweighted least squares (ULSMV), mean and variance adjusted weighted least squares (WLSMV), and Bayesian estimators. As a result of the study, it was revealed that increased average factor loading and sample size had a positive effect on the performance of the estimation methods. According to the research findings, it can be said that the methods are sufficient to estimate average factor loading and interfactor correlations, regardless of the estimation methods, in most of the conditions where the average factor loading is 0.7. In small sample sizes particularly, the interfactor correlation was underestimated for skewed indicator conditions. According to the findings of the study, although there is not the most accurate method in all conditions, it can be recommended to use ULSMV method because it performs adequately in more conditions.

Abstract

This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance adjusted unweighted least squares (ULSMV), mean and variance adjusted weighted least squares (WLSMV), and Bayesian estimators. As a result of the study, it was revealed that increased average factor loading and sample size had a positive effect on the performance of the estimation methods. According to the research findings, it can be said that the methods are sufficient to estimate average factor loading and interfactor correlations, regardless of the estimation methods, in most of the conditions where the average factor loading is 0.7. In small sample sizes particularly, the interfactor correlation was underestimated for skewed indicator conditions. According to the findings of the study, although there is not the most accurate method in all conditions, it can be recommended to use ULSMV method because it performs adequately in more conditions.

🔖

Bu Makaleye Atıf Yap

UYSAL, İ., ATAR, B., KILIÇ, A. F. (2022). Comparison of Confirmatory Factor Analysis Estimation Methods on Binary Data. International Journal of Assessment Tools in Education, Cilt: 7 - Sayı: 3, 451–487. https://ajindex.com/articles/540141

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (8)

  1. Acar-Güvendir, M., & Özer-Özkan, Y. (2015). Türkiye’deki eğitim alanında yayımlanan bilimsel dergilerde ölçek geliştirme ve uyarlama konulu makalelerin incelenmesi [The examination of scale development and scale adaptation articles published in Turkish academic journals on education]. Electronic Journal of Social Sciences, 14(52), 23–33. https://doi.org/10.17755/esosder.54872
  2. AERA, APA, & NCME. (2014). Standards for educational and psychological testing. American Educational Research Association. Anıl, D., Güzeller, C. O., Çokluk, Ö., & Şekercioǧlu, G. (2010). Level determination exam (SBS-2008) the determination of the validity and reliability of 7th grade mathematics sub- test. Procedia Social and Behavioral Sciences, 2(2), 5292 5298. https://doi.org/10.1016/j.sbspro.2010.03.863
  3. Babakus, E., Ferguson, C. E., & Jöreskog, K. G. (1987). The sensitivity of confirmatory maximum likelihood factor analysis to violations of measurement scale and distributional assumptions. Journal of Marketing Research, 24(2), 222 228. https://doi.org/10.2307/3151512
  4. Beauducel, A., & Herzberg, P. Y. (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling: A Multidisciplinary Journal, 13(2), 186 203. https://doi.org/10.1207/s15328007sem1302_2
  5. Bollen, K. A. (1989). Structural equations with latent variables. John Wiley & Sons, Inc. https://doi.org/10.1002/9781118619179
  6. Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in lisrel maximum likelihood estimation. Psychometrika, 50(2), 229 242. https://doi.org/10.1007/BF02294248
  7. Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford. Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge. Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6(4), 330–351. https://doi.org/10.1037/1082-989X.6.4.330
  8. Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Lawrence Erlbaum Associates. Crocker, L., & Algina, J. (2008). Introduction of classical and modern test theory. Cengage Learning. Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29.