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Nicel Araştırmalarda Açıklayıcı Faktör Analizi (EFA) ve Pratik Hususlar

Yıl 2024, , 947 - 965, 29.06.2024
https://doi.org/10.37989/gumussagbil.1183271

Öz

Açıklayıcı faktör analizi (EFA), nicel araştırmalarda sıklıkla kullanılan çok değişkenli istatistiksel bir yöntem olup, sosyal bilimler, sağlık bilimleri ve ekonomi gibi birçok alanda kullanılmaya başlamıştır. EFA yardımıyla önemsiz olabilecek çok fazla öğeyi dikkate almak yerine, yapıyı açıklayan daha az sayıdaki öğeye odaklanır ve bu öğeleri anlamlı kategorilere (faktör) yerleştirerek çalışmalarını yürütürler. Bununla birlikte altmış yılı aşkın bir süredir birçok araştırmacı EFA’nın ne zaman ve nasıl kullanılacağına dair birbirinden farklı tavsiyelerde bulunmaktadır. Yapılan tartışma konularının başında örneklem büyüklüğü, madde sayısı, madde çıkarma yöntemleri, faktör tutma kriterleri, döndürme yöntemleri ve uygulanan prosedürlerin genel uygulanabilirliği konuları yer almaktadır. Literatürde yaşanan bu tartışmalar ve görüşlerin bolluğu, araştırmacıların EFA’da hangi prosedürleri izleyeceğine karar vermesini zorlaştırmaktadır. Bu nedenle EFA’nın kullanımındaki genel prosedürlere (örneklem sayısı, döndürme yöntemleri vb.) ait farklı bilgilerin bir araya getirilmesi araştırmacılar için faydalı olacaktır. Bu çalışmanın amacı; EFA uygulanırken hangi prosedürlerinin izleneceği konusunda okuyuculara genel bir bakış açısı sunmak ve EFA sürecindeki metodolojik kararlarla ilgili en son gelişmeler hakkında okuyuculara pratik bilgiler paylaşmaktır. Çalışmanın en güncel bilgileri toplu bir şekilde sunması yönü ile EFA kullanımında net karar yollarının geliştirilmesinde okuyuculara önemli bir kılavuz olacağı değerlendirilmektedir.

Destekleyen Kurum

Yoktur

Proje Numarası

Yoktur

Teşekkür

Yoktur

Kaynakça

  • 1. Child, D. (2006). “The essentials of factor analysis. (3rd ed.)”. New York, NY: Continuum International Publishing Group.
  • 2. Bartholomew, D, Knotts, M. and Moustaki, I. (2011). “Latent variable models and factor analysis: A unified approach. (3rd ed.)”. West Sussex, UK: John Wiley & Sons.
  • 3. Tabachnick, B. G. and Fidell, L. S. (2007). “Using multivariate statistics (5th ed.)”. Boston, MA: Allyn & Bacon.
  • 4. Beavers, A. S, Lounsbury, J. W, Richards, J. K, Huck, S. W, Skolits, G. J. and Esquivel, S. L. (2013). “Practical considerations for using exploratory factor analysis in educational research”. Practical Assessment, Research, and Evaluation, 18 (1), 6–16.
  • 5. Fabrigar, L. R, Wegener, D. T, MacCallum, R. C. and Strahan, E. J. (1999). “Evaluating the use of exploratory factor analysis in psychological research”. Psychological Methods, 4 (3), 272–299.
  • 6. Goretzko, D, Pham, T. T. H. and Bühner, M. (2021). “Exploratory factor analysis: Current use, methodological developments and recommendations for good practice”. Current Psychology, 40 (7), 3510–3521.
  • 7. Yong, A. G. and Pearce, S. (2013). “A beginner’s guide to factor analysis: Focusing on exploratory factor analysis”. Tutorials in Quantitative Methods for Psychology, 9 (2), 79–94.
  • 8. Rummel, R. J. (1970). “Applied factor analysis. Evanston”, IL: Northwestern University Press.
  • 9. Field, A. (2009). “Discovering statistics using SPSS: Introducing statistical method (3rd ed.)”. Thousand Oaks, CA: Sage Publications.
  • 10. Hogarty, K. Y, Hines, C. V, Kromrey, J. D, Ferron, J. M. and Mumford, K. R. (2005). “The quality of factor solutions in exploratory factor analysis: The influence of sample size, communality, and overdetermination”. Educational and Psychological Measurement, 65 (2), 202–226.
  • 11. Tabachnick, B. G, Fidell, L. S. and Ullman, J. B. (2007). “Using multivariate statistics (Vol. 5, pp. 481–498)”. Boston, MA: Pearson.
  • 12. Kline, P. (1994). “An easy guide to factor analysis”. New York, NY: Routledge.
  • 13. Baraldi, A. N. and Enders, C. K. (2010). “An introduction to modern missing data analyses”. Journal of School Psychology, 48 (1), 5–37.
  • 14. Schumacker, R. E. (2015). “Learning statistics using R”. Thousand Oaks, CA: Sage.
  • 15. Sürücü, L, Şeşen, H. and Maslakçı, A. (2023). “Regression, Mediation/ Moderation, and Structural Equation Modeling with SPSS, AMOS, and PROCESS Macro”. France, Livre de Lyon.
  • 16. Mundfrom, D. J, Shaw, D. G. and Ke, T. L. (2005). “Minimum sample size recommendations for conducting factor analyses”. International Journal of Testing, 5 (1), 159–168.
  • 17. Winter, J. C, Dodou, A. and Wieringa, P. A. (2009). “Exploratory factor analysis with small sample sizes”. Multivariate behavioral research, 44 (2), 147–181.
  • 18. Comrey, A. L. (1973). “A first course in factor analysis”. New York: Academic.
  • 19. Pett, M. A, Lackey, N. R. and Sullivan J.J. (2003).“ Making Sense of Factor Analysis: The use of factor analysis for instrument development in health care research”. California: Sage Publications Inc.
  • 20. Lawley, D. N. and Maxwell, A. E. (1971). Factor analysis as a statistical method. Butterworths: United Kingdom.
  • 21. Hair, J, Anderson, R. E, Tatham, R. L. and Black, W.C. (1995). “Multivariate data analysis”. New Jersey: Prentice-Hall Inc.
  • 22. Cattell, R. B. (1973). “Factor analysis”. Westport, CT: Greenwood Press.
  • 23. Suhr, D. (2006). “Exploratory or Confirmatory Factor Analysis”. SAS Users Group International Conference (pp. 1 - 17). Cary: SAS Institute, Inc.
  • 24. Bryant, F. B. and Yarnold, P. R. (1995). “Principal-components analysis and exploratory and confirmatory factor analysis”. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate statistics (pp. 99–136). American Psychological Association.
  • 25. Rouquette, A. and Falissard, B. (2011). “Sample size requirements for the internal validation of psychiatric scales”. International Journal of Methods in Psychiatric Research, 20 (4), 235–249.
  • 26. MacCallum, R. C, Widaman, K. F, Zhang, S. and Hong S. (1999). “Sample size in factor analysis”. Psychological Methods, 4 (1), 84–99.
  • 27. Sapnas, K. and Zeller, R. A. (2002). “Minimizing sample size when using exploratory factor analysis for measurement”. Journal of Nursing Measurement, 10 (2), 135–53.
  • 28. Guadagnoli, E. and Velicer, W. F. (1988). “Relation to sample size to the stability of component patterns”. Psychological Bulletin, 103 (2), 265–275
  • 29. Preacher, K. J. and MacCallum, R. C. (2002). “Exploratory factor analysis in behavior genetics research: Factor recovery with small sample sizes”. Behavior Genetics, 32 (1), 153–161.
  • 30. Costello, A. B. and Osborne, J. (2005). “Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis”. Practical Assessment, Research, and Evaluation, 10 (1), 7–27.
  • 31. Haitovsky, Y. (1969). “Multicollinearity in regression analysis: A comment”. Review of Economics and Statistics, 51 (4), 486–489.
  • 32. Conway, J. M. and Huffcutt, A. I. (2003). “A review and evaluation of exploratory factor analysis practices in organizational research”. Organizational Research Methods, 6 (2), 147–168.
  • 33. Byrne, B. M. (2001). “Structural equation modeling with AMOS - Basic concepts, applications, and programming”. LEA, ISBN 0- 8058-4104-0
  • 34. Fava, J. L. and Velicer, W. F. (1992). “The effects of overextraction on factor and component analysis”. Multivariate Behavioral Research, 27 (1), 387–415.
  • 35. Gorsuch, R. L. (1997). “Exploratory factor analysis: Its role in item analysis”. Journal of Personality Assessment, 68 (3), 532–560.
  • 36. DeWinter, J. C. F. and Dodou, D. (2012). “Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size”. Journal of Applied Statistics, 39 (4), 695–710.
  • 37. Widaman, K. F. (2012). “Exploratory factor analysis and confirmatory factor analysis”. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology, Vol 3: Data analysis and research publication (pp. 361–389). Washington, DC: American Psychological Association.
  • 38. Zwick, W. R. and Velicer, W. F. (1986). “Comparison of five rules for determining the number of components to retain”. Psychological Bulletin, 99 (3), 432–442. 39. Kaiser, H. F. (1960). “The application of electronic computers to factor analysis”. Educational and Psychological Measurement, 20 (1), 141–151.
  • 40. Cattell, R. B. (1966). “The scree test for the number of factors”. Multivariate Behavioral Research, 1 (1), 245–276.
  • 41. Bartlett, M. S. (1951). “A further note on tests of significance in factor analysis”. British Journal of Psychology, 4 (1), 1–2.
  • 42. Velicer, W. F. (1976). “Determining the number of components from the matrix of partial correlations”. Psychometrika, 41 (1), 321–327.
  • 43. Thompson, B. (2004). “Exploratory and confirmatory factor analysis”. Washington, DC: American Psychological Association.
  • 44. Jolliffe, I. T. (1972). “Discarding variables in a principal component analysis, I: Artificial data”. Applied Statistics, 21 (1), 160–173.
  • 45. Schonrock-Adema, J, Heijne-Penninga, M, Van Hell, E. A. and Cohen-Schotanus, J. (2009). “Necessary steps in factor analysis: enhancing validation studies of educational instruments”. Medical Teacher, 31 (1), 226–232.
  • 46. Baglin, J. (2014). “Improving your exploratory factor analysis for ordinal data: A demonstration using FACTOR”. Practical Assessment, Research & Evaluation, 19 (1), 5–10.
  • 47. Browne, M. W. (2001). “An overview of analytic rotation in exploratory factor analysis”. Multivariate Behavioral Research, 36 (1), 111–150.
  • 48. Stevens, J. P. (2002). “Applied multivariate statistics for the social sciences (4th ed.)”. Hillsdale, NS: Erlbaum.
  • 49. Gorsuch, R. L. (1983). “Factor analysis (2nd ed.)”. Hillside, NJ: Lawrence Erlbaum Associates.
  • 50. Norris, M. and Lecavalier, L. (2010). “Evaluating the use of exploratory factor analysis in developmental disability psychological research”. Journal of Autism and Developmental Disorders, 40 (1), 8–20.
  • 51. Watkins, M. W. (2018). “Exploratory factor analysis: A guide to best practice”. Journal of Black Psychology, 44 (3), 219–246.
  • 52. Baraldi, A. N. and Enders, C. K. (2010). “An introduction to modern missing data analyses”. Journal of School Psychology, 48 (1), 5–37.

Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations

Yıl 2024, , 947 - 965, 29.06.2024
https://doi.org/10.37989/gumussagbil.1183271

Öz

Explanatory factor analysis (EFA) is a multivariate statistical method frequently used in quantitative research and has begun to be used in many fields such as social sciences, health sciences and economics. With EFA, researchers focus on fewer items that explain the structure, instead of considering too many items that may be unimportant and carry out their studies by placing these items into meaningful categories (factors). However, for over sixty years, many researchers have made different recommendations about when and how to use EFA. Differences in these recommendations confuse the use of EFA. The main topics of discussion are sample size, number of items, item extraction methods, factor retention criteria, rotation methods and general applicability of the applied procedures. The abundance of these discussions and opinions in the literature makes it difficult for researchers to decide which procedures to follow in EFA. For this reason, it would be beneficial for researchers to gather different information about the general procedures (sample number, rotation methods, etc.) in the use of EFA. This paper aims to provide readers with an overview of what procedures to follow when implementing EFA and share practical information about the latest developments in methodological decisions in the EFA process. It is considered that the study will be an important guide for the researchers in the development of clear decision paths in the use of EFA, with the aspect of presenting the most up-to-date information collectively.

Proje Numarası

Yoktur

Kaynakça

  • 1. Child, D. (2006). “The essentials of factor analysis. (3rd ed.)”. New York, NY: Continuum International Publishing Group.
  • 2. Bartholomew, D, Knotts, M. and Moustaki, I. (2011). “Latent variable models and factor analysis: A unified approach. (3rd ed.)”. West Sussex, UK: John Wiley & Sons.
  • 3. Tabachnick, B. G. and Fidell, L. S. (2007). “Using multivariate statistics (5th ed.)”. Boston, MA: Allyn & Bacon.
  • 4. Beavers, A. S, Lounsbury, J. W, Richards, J. K, Huck, S. W, Skolits, G. J. and Esquivel, S. L. (2013). “Practical considerations for using exploratory factor analysis in educational research”. Practical Assessment, Research, and Evaluation, 18 (1), 6–16.
  • 5. Fabrigar, L. R, Wegener, D. T, MacCallum, R. C. and Strahan, E. J. (1999). “Evaluating the use of exploratory factor analysis in psychological research”. Psychological Methods, 4 (3), 272–299.
  • 6. Goretzko, D, Pham, T. T. H. and Bühner, M. (2021). “Exploratory factor analysis: Current use, methodological developments and recommendations for good practice”. Current Psychology, 40 (7), 3510–3521.
  • 7. Yong, A. G. and Pearce, S. (2013). “A beginner’s guide to factor analysis: Focusing on exploratory factor analysis”. Tutorials in Quantitative Methods for Psychology, 9 (2), 79–94.
  • 8. Rummel, R. J. (1970). “Applied factor analysis. Evanston”, IL: Northwestern University Press.
  • 9. Field, A. (2009). “Discovering statistics using SPSS: Introducing statistical method (3rd ed.)”. Thousand Oaks, CA: Sage Publications.
  • 10. Hogarty, K. Y, Hines, C. V, Kromrey, J. D, Ferron, J. M. and Mumford, K. R. (2005). “The quality of factor solutions in exploratory factor analysis: The influence of sample size, communality, and overdetermination”. Educational and Psychological Measurement, 65 (2), 202–226.
  • 11. Tabachnick, B. G, Fidell, L. S. and Ullman, J. B. (2007). “Using multivariate statistics (Vol. 5, pp. 481–498)”. Boston, MA: Pearson.
  • 12. Kline, P. (1994). “An easy guide to factor analysis”. New York, NY: Routledge.
  • 13. Baraldi, A. N. and Enders, C. K. (2010). “An introduction to modern missing data analyses”. Journal of School Psychology, 48 (1), 5–37.
  • 14. Schumacker, R. E. (2015). “Learning statistics using R”. Thousand Oaks, CA: Sage.
  • 15. Sürücü, L, Şeşen, H. and Maslakçı, A. (2023). “Regression, Mediation/ Moderation, and Structural Equation Modeling with SPSS, AMOS, and PROCESS Macro”. France, Livre de Lyon.
  • 16. Mundfrom, D. J, Shaw, D. G. and Ke, T. L. (2005). “Minimum sample size recommendations for conducting factor analyses”. International Journal of Testing, 5 (1), 159–168.
  • 17. Winter, J. C, Dodou, A. and Wieringa, P. A. (2009). “Exploratory factor analysis with small sample sizes”. Multivariate behavioral research, 44 (2), 147–181.
  • 18. Comrey, A. L. (1973). “A first course in factor analysis”. New York: Academic.
  • 19. Pett, M. A, Lackey, N. R. and Sullivan J.J. (2003).“ Making Sense of Factor Analysis: The use of factor analysis for instrument development in health care research”. California: Sage Publications Inc.
  • 20. Lawley, D. N. and Maxwell, A. E. (1971). Factor analysis as a statistical method. Butterworths: United Kingdom.
  • 21. Hair, J, Anderson, R. E, Tatham, R. L. and Black, W.C. (1995). “Multivariate data analysis”. New Jersey: Prentice-Hall Inc.
  • 22. Cattell, R. B. (1973). “Factor analysis”. Westport, CT: Greenwood Press.
  • 23. Suhr, D. (2006). “Exploratory or Confirmatory Factor Analysis”. SAS Users Group International Conference (pp. 1 - 17). Cary: SAS Institute, Inc.
  • 24. Bryant, F. B. and Yarnold, P. R. (1995). “Principal-components analysis and exploratory and confirmatory factor analysis”. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate statistics (pp. 99–136). American Psychological Association.
  • 25. Rouquette, A. and Falissard, B. (2011). “Sample size requirements for the internal validation of psychiatric scales”. International Journal of Methods in Psychiatric Research, 20 (4), 235–249.
  • 26. MacCallum, R. C, Widaman, K. F, Zhang, S. and Hong S. (1999). “Sample size in factor analysis”. Psychological Methods, 4 (1), 84–99.
  • 27. Sapnas, K. and Zeller, R. A. (2002). “Minimizing sample size when using exploratory factor analysis for measurement”. Journal of Nursing Measurement, 10 (2), 135–53.
  • 28. Guadagnoli, E. and Velicer, W. F. (1988). “Relation to sample size to the stability of component patterns”. Psychological Bulletin, 103 (2), 265–275
  • 29. Preacher, K. J. and MacCallum, R. C. (2002). “Exploratory factor analysis in behavior genetics research: Factor recovery with small sample sizes”. Behavior Genetics, 32 (1), 153–161.
  • 30. Costello, A. B. and Osborne, J. (2005). “Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis”. Practical Assessment, Research, and Evaluation, 10 (1), 7–27.
  • 31. Haitovsky, Y. (1969). “Multicollinearity in regression analysis: A comment”. Review of Economics and Statistics, 51 (4), 486–489.
  • 32. Conway, J. M. and Huffcutt, A. I. (2003). “A review and evaluation of exploratory factor analysis practices in organizational research”. Organizational Research Methods, 6 (2), 147–168.
  • 33. Byrne, B. M. (2001). “Structural equation modeling with AMOS - Basic concepts, applications, and programming”. LEA, ISBN 0- 8058-4104-0
  • 34. Fava, J. L. and Velicer, W. F. (1992). “The effects of overextraction on factor and component analysis”. Multivariate Behavioral Research, 27 (1), 387–415.
  • 35. Gorsuch, R. L. (1997). “Exploratory factor analysis: Its role in item analysis”. Journal of Personality Assessment, 68 (3), 532–560.
  • 36. DeWinter, J. C. F. and Dodou, D. (2012). “Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size”. Journal of Applied Statistics, 39 (4), 695–710.
  • 37. Widaman, K. F. (2012). “Exploratory factor analysis and confirmatory factor analysis”. In H. Cooper, P. M. Camic, D. L. Long, A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology, Vol 3: Data analysis and research publication (pp. 361–389). Washington, DC: American Psychological Association.
  • 38. Zwick, W. R. and Velicer, W. F. (1986). “Comparison of five rules for determining the number of components to retain”. Psychological Bulletin, 99 (3), 432–442. 39. Kaiser, H. F. (1960). “The application of electronic computers to factor analysis”. Educational and Psychological Measurement, 20 (1), 141–151.
  • 40. Cattell, R. B. (1966). “The scree test for the number of factors”. Multivariate Behavioral Research, 1 (1), 245–276.
  • 41. Bartlett, M. S. (1951). “A further note on tests of significance in factor analysis”. British Journal of Psychology, 4 (1), 1–2.
  • 42. Velicer, W. F. (1976). “Determining the number of components from the matrix of partial correlations”. Psychometrika, 41 (1), 321–327.
  • 43. Thompson, B. (2004). “Exploratory and confirmatory factor analysis”. Washington, DC: American Psychological Association.
  • 44. Jolliffe, I. T. (1972). “Discarding variables in a principal component analysis, I: Artificial data”. Applied Statistics, 21 (1), 160–173.
  • 45. Schonrock-Adema, J, Heijne-Penninga, M, Van Hell, E. A. and Cohen-Schotanus, J. (2009). “Necessary steps in factor analysis: enhancing validation studies of educational instruments”. Medical Teacher, 31 (1), 226–232.
  • 46. Baglin, J. (2014). “Improving your exploratory factor analysis for ordinal data: A demonstration using FACTOR”. Practical Assessment, Research & Evaluation, 19 (1), 5–10.
  • 47. Browne, M. W. (2001). “An overview of analytic rotation in exploratory factor analysis”. Multivariate Behavioral Research, 36 (1), 111–150.
  • 48. Stevens, J. P. (2002). “Applied multivariate statistics for the social sciences (4th ed.)”. Hillsdale, NS: Erlbaum.
  • 49. Gorsuch, R. L. (1983). “Factor analysis (2nd ed.)”. Hillside, NJ: Lawrence Erlbaum Associates.
  • 50. Norris, M. and Lecavalier, L. (2010). “Evaluating the use of exploratory factor analysis in developmental disability psychological research”. Journal of Autism and Developmental Disorders, 40 (1), 8–20.
  • 51. Watkins, M. W. (2018). “Exploratory factor analysis: A guide to best practice”. Journal of Black Psychology, 44 (3), 219–246.
  • 52. Baraldi, A. N. and Enders, C. K. (2010). “An introduction to modern missing data analyses”. Journal of School Psychology, 48 (1), 5–37.
Toplam 51 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sağlık Kurumları Yönetimi
Bölüm Derlemeler
Yazarlar

Lütfi Sürücü 0000-0002-6286-4184

İbrahim Yıkılmaz 0000-0002-1051-0886

Ahmet Maşlakçı 0000-0001-6820-4673

Proje Numarası Yoktur
Yayımlanma Tarihi 29 Haziran 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Sürücü, L., Yıkılmaz, İ., & Maşlakçı, A. (2024). Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, 13(2), 947-965. https://doi.org/10.37989/gumussagbil.1183271
AMA Sürücü L, Yıkılmaz İ, Maşlakçı A. Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations. Gümüşhane Sağlık Bilimleri Dergisi. Haziran 2024;13(2):947-965. doi:10.37989/gumussagbil.1183271
Chicago Sürücü, Lütfi, İbrahim Yıkılmaz, ve Ahmet Maşlakçı. “Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 13, sy. 2 (Haziran 2024): 947-65. https://doi.org/10.37989/gumussagbil.1183271.
EndNote Sürücü L, Yıkılmaz İ, Maşlakçı A (01 Haziran 2024) Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 13 2 947–965.
IEEE L. Sürücü, İ. Yıkılmaz, ve A. Maşlakçı, “Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations”, Gümüşhane Sağlık Bilimleri Dergisi, c. 13, sy. 2, ss. 947–965, 2024, doi: 10.37989/gumussagbil.1183271.
ISNAD Sürücü, Lütfi vd. “Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 13/2 (Haziran 2024), 947-965. https://doi.org/10.37989/gumussagbil.1183271.
JAMA Sürücü L, Yıkılmaz İ, Maşlakçı A. Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations. Gümüşhane Sağlık Bilimleri Dergisi. 2024;13:947–965.
MLA Sürücü, Lütfi vd. “Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations”. Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi, c. 13, sy. 2, 2024, ss. 947-65, doi:10.37989/gumussagbil.1183271.
Vancouver Sürücü L, Yıkılmaz İ, Maşlakçı A. Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations. Gümüşhane Sağlık Bilimleri Dergisi. 2024;13(2):947-65.