Araştırma Makalesi
BibTex RIS Kaynak Göster

KEŞFEDİCİ VE DOĞRULAYICI FAKTÖR ANALİZİ: KAVRAMSAL BİR ÇALIŞMA

Yıl 2023, Cilt: 9 Sayı: 1, 47 - 63, 22.06.2023
https://doi.org/10.29131/uiibd.1279602

Öz

Ölçek geliştirme, geçerlilik ve güvenirlik çalışmalarında faktör analizleri en önemli kriterlerin başında gelmektedir. Bu doğrultuda çalışmada öncelikle faktör analizlerine genel olarak değinilmiş daha sonrasında keşfedici ve doğrulayıcı faktör analizlerine kritik noktalarına yer verilmiştir. Araştırmacıların analizleri uygulama sırasında hangi noktalara dikkat etmesi gerektiği vurgulanmıştır. Bu kapsamda literatürde araştırmacılara katkı sağlamak ve ilgili analizler noktasında pratik bilgiler sunmak analiz sırasında dikkat edilmesi gereken hususlar üzerinde durmak ve ileride yapılacak araştırmalara bir rehber olması hedeflenmiştir. Bu çalışma ulusal ve uluslararası düzeyde geniş bir literatür taraması yapılarak bir derleme halinde hazırlanmıştır.

Kaynakça

  • Ab Hamid, M. R., Sami, W., & Sidek, M. M. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. In Journal of Physics: Conference Series. IOP Publishing.
  • Abdi, H., & Williams, L.J. (2010). Principal component analysis. John Wiley & Son s, In c. 433-459.
  • Akgül, A. (2005). Statistical analysis techniques in medical researches SPSS applications. Ankara: Emek Ofset Ltd Sti.
  • Akhtar-Danesh, N. (2017). A comparison between major factor extraction and factor rotation techniques in Q-methodology. Open Journal of Applied Sciences, 7(04), 147-156.
  • Akın, N.K., & Aşçı, F.H. (2021). Beden eğitimi dersinde üçlü yeterlik algılarının değerlendirilmesi: Ölçek uyarlama çalışması. Türkiye Klinikleri Journal of Sports Sciences, 13(2), 302-311.
  • Alavi, M., Visentin, D. C., Thapa, D. K., Hunt, G. E., Watson, R., & Cleary, M. (2020a). Exploratory factor analysis and principal component analysis in clinical studies: Which one should you use. Journal of advanced nursing, 76(8), 1886-1889.
  • Alavi, M., Visentin, D. C., Thapa, D. K., Hunt, G. E., Watson, R., & Cleary, M. L. (2020b). Chi-square for model fit in confirmatory factor analysis. J Adv Nurs., 76, 2209-2211.
  • Albayrak, A.S. (2006). Uygulamalı çok değişkenli istatistik teknikleri. Ankara: Asil Yayın Dağıtım.
  • Arafat, S. Y., Chowdhury, H. R., Qusar, M. M. A. S., & Hafez, M. A. (2016). Cross-cultural adaptation and psychometric validation of research instruments: A methodological review. J Behav Health, 5(3), 129-36.
  • Barendse, M. T., Oort, F. J., & Timmerman, M. E. (2015). Using exploratory factor analysis to determine the dimensionality of discrete responses. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 87-101.
  • Beavers , A. S., Lounsbury , J. W., Richards, J. K., Huck , S. W., & Skolits , G. J. (2013). Practical Consider actical Considerations for Using Explor ations for Using Exploratory Factor Analysis in or Analysis in Educational Research. Practical Assessment, Research, and Evaluation, 18(18), 1-13.
  • Bonett, D. G., & Wright, T. A. (2015). Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of organizational behavior, 36(1), 3-15.
  • Braeken, J., & Van Assen, M. A. (2017). An empirical Kaiser criterion. Psychological methods, 22(3), 450-466.
  • Bujang, M. A., Omar, E. D., & Baharum, N. A. (2018). A review on sample size determination for Cronbach’s alpha test: a simple guide for researchers. The Malaysian journal of medical sciences: MJMS, 25(6), 85-99.
  • Büyüköztürk Ş. (2013). Çok değişkenli istatistikler sosyal bilimler için veri analizi el kitabı. Ankara: Pegem Akademi.
  • Cangur, S., & Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14(1), 152-167.
  • Carpenter, S. (2018). Ten steps in scale development and reporting: A guide for researchers. Communication methods and measures, 12(1), 25-44.
  • Chabo Byaene, A., Mabela, M. M. R., Konde, N. N. J., Muhindo Mavoko, H., & Kayembe, N.N. (2021). Clinical laboratory customers’ loyalty: development and validation of a measuring instrument. J Comm Med and Pub Health Rep, 2(3), 1-11.
  • Chow, J. C. C., Snowden, L. R. ve McConnell, W. (2001). A confirmatory factor analysis of the BASIS-32 in racial and ethnic samples. The Journal of Behavioral Health Services and Research. 28(4), 400-411.
  • Cole, D. A. (1987). Utility of confirmatory factor analysis in test validation research. Journal of Consulting and Clinical Psychology, 55(4), 584-594.
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1-9.
  • Cronbach, L. J. (1951). Coefficient alpha and the interval structure of tests. Psychometrika, 16, 297-334. Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2014). Sosyal bilimler için çok değişkenli istatistik: Spss ve lisrel uygulamaları, Ankara: Pegem Akademi Yayıncılık; 211-275.
  • Çalışkan, A. & Köroğlu, Ö. (2022). Job Performance, Task Performance, Contextual Performance: Development And Validation Of A New Scale. Uluslararası İktisadi ve İdari Bilimler Dergisi, 8 (2), 180-201. DOI: 10.29131/uiibd.1201880
  • Çalışkan, A. (2022). Örgütsel Etik İklimi: Bir Ölçek Geliştirme Çalışması. Uluslararası İktisadi ve İdari Bilimler Dergisi, 8 (1), 34-54. https://doi.org/10.29131/uiibd.1118411
  • Çalışkan, A. (2022). Örgütsel Değişime Açıklık: Bir Ölçek Geliştirme Çalışması. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14 (2), 191-202. https://doi.org/10.52791/aksarayiibd.1069524
  • Davcik, N. (2014). The use and misuse of structural equation modeling in management research: A review and critique. Journal of Advances in Management Research, 11(1), 47-81.
  • Dragan, D., & Topolšek, D. (2014). Introduction to structural equation modeling: review, methodology and practical applications. In The International Conference on Logistics & Sustainable Transport.
  • Draper, Z. A. (2019). Applying modern machine learning to the number of latent variables problem in principal components analysis and principal axis factoring. Doctoral dissertation. University of British Columbia.
  • Dziuban, C. D., & Harris, C. W. (1973). On the extraction of components and the applicability of the factor model. American Educational Research Journal, 10, 93-99. Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. Psychological Bulletin, 81, 358-361.
  • Effendi, M., Matore, E. M., Khairani, A. Z., & Adnan, R. (2019). Exploratory factor analysis (EFA) for adversity quotient (AQ) instrument among youth. Journal of Critical Reviews, 6(6), 234-242.
  • Ernest, O. N., King, B. D., Esther, A., Kwadwo, A. N., & AY, A. R. (2019). The effect of lean operations in manufacturing on firm performance: the case of manufacturing firms in ACCRA. International Journal of Engineering Research and Reviews, 7(2), 1-7.
  • Fabrigar, L. R., & Wegener, D. T. (2012). Understanding statistics: Exploratory factor analysis. New York, NY: Oxford University.
  • Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299.
  • Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecological Processes, 5, 1-12.
  • Ford, J. K., MacCullum, R. C., & Tait, M. (1986). The application of exploratory factor analysis in applied psychology: A critical review and analysis. Personnel Psychology, 39, 291-314.
  • Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Ghozali, I. (2014). Structural equation modeling, metode alternatif dengan partial least square (PLS). Semarang : Badan Penerbit Universitas Diponegoro.
  • Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology, 40, 3510-3521.
  • Grieder, S., & Steiner, M. D. (2022). Algorithmic jingle jungle: A comparison of implementations of principal axis factoring and promax rotation in R and SPSS. Behavior Research Methods, 54(1), 54-74.
  • Haig, B. D. (2005). Exploratory factor analysis, theory generation, and scientific method. Multivariate Behavioral Research, 40(3), 303-329.
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2013). Multivariate data analysis. Pearson Education Limited.
  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). Applications of partial least squares path modeling in management journals: A review of past practices and recommendations for future applications. Long Range Planning, 45(5-6), 320-340.
  • Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2014). Exploratory factor analysis. Multivariate data analysis. Prentice Hall.
  • Hair, JR, J.F., Ringle, C.M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
  • Henrica C.W. de Vet, H. C. D., Adèr, H. J., Terwee, C. B., & Pouwer, F. (2005). Are factor analytical techniques used appropriately in the validation of health status questionnaires? A systematic review on the quality of factor analysis of the SF-36. Quality of Life Research, 14, 1203-1218.
  • Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393-416.
  • Hooper, D., Coughlan, J., & Mullen, R. M. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Howard, M. C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve?. International Journal of Human-Computer Interaction, 32(1), 51-62.
  • Hu LT, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling 6(1), 1-55.
  • Izquierdo, I., Olea, J., & Abad, F. J. (2014). Exploratory factor analysis in validation studies: Uses and recommendations. Psicothema, 26, 395-400.
  • Jin, S., Moustaki, I., & Yang-Wallentin, F. (2018). Approximated penalized maximum likelihood for exploratory factor analysis: An orthogonal case. Psychometrika, 83, 628-649.
  • Kääriäinen, M., Kanste, O., Elo, S., Pölkki, T., Miettunen, J., & Kyngäs, H. (2011). Testing and verifying nursing theory by confirmatory factor analysis. Journal of Advanced Nursing, 67(5), 1163–1172.
  • Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36.
  • Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200.
  • Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling. New York: The Guilford Press
  • Kline, R. B. (2013). Exploratory and confirmatory factor analysis. In Y. Petscher, C. Schatschneider, & D. L. Compton (Eds.), Applied quantitative analysis education and the social sciences. USA: Routledge.
  • Kozak M. (2017). Veri analizi. [Scientific research: design, writing and publishing techniques]. Bilimsel araştırma: tasarım, yazım ve yayım teknikleri. Ankara: Detay Yayıncılık.
  • Kwon, Y. ve Marzec, M. L. (2016). Does worksite culture of health (CoH) matter to employees? Empirical evidence using job-related metrics. Journal of Occupational and Environmental Medicine, 58(5), 448-454.
  • Kyriazos, T. A. (2018). Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology, 9(08), 2207-2230.
  • Ledesma, R. D., Valero-Mora, P., & Macbeth, G. (2015). The scree test and the number of factors: a dynamic graphics approach. The Spanish Journal of Psychology, 18, 1-10.
  • Leech, N.L., Barret, K.C., & Morgan, G.A. (2015). IBMSPSS for Intermediate Statistics: Use and Interpretation. Fifth Edition. New Jersey: Lawrence Erlbaum Associates, Inc.
  • Lloret, S., Ferreres, A., Hernandez, A., & Tomas, I. (2017). The exploratory factor analysis of items: Guided analysis based on empirical data and software. Anales de Psicologia, 33, 417-432.
  • Luo, L., Arizmendi, C., & Gates, K. M. (2019). Exploratory factor analysis (EFA) programs in R. Structural Equation Modeling: A Multidisciplinary Journal, 26(5), 819-826.
  • Marofi, Z., Bandari, R., Heravi-Karimooi, M., Rejeh, N., & Montazeri, A. (2020). Cultural adoption, and validation of the Persian version of the coronary artery disease education questionnaire (CADE-Q): a second-order confirmatory factor analysis. BMC Cardiovascular Disorders, 20, 1-9.
  • Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralising Hu & Bentler’s (1999) findings. Structural Equation Modelling, 11, 320-341.
  • Maydeu-Olivares, A. (2017). Assessing the size of model misfit in structural equation models. Psychometrika, 82(3), 533-558.
  • McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61-88.
  • Meydan, C. H., & Şeşen, H. (2015). Yapısal eşitlik modellemesi AMOS uygulamaları.). Ankara: Detay Yayıncılık. Mukaka, M.M. (2012). Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71.
  • Mulaik, S. A. (1987). A brief history of the philosophical foundations of exploratory factor analysis. Multivariate Behavioral Research, 22, 267-305.
  • Myers, N. D., Ahn, S., & Jin, Y. (2011). Sample size and power estimates for a confirmatory factor analytic model in exercise and sport: A Monte Carlo approach. Research Quarterly for Exercise and Sport, 82(3), 412-423.
  • Nikkhah, M., Heravi-Karimooi, M., Montazeri, A., Rejeh, N., & Sharif Nia, H. (2018). Psychometric properties the Iranian version of older People’s quality of life questionnaire (OPQOL). Health and Quality of Life Outcomes, 16, 1-10.
  • Olivares, M.A., & Forero, G.C. (2010). Goodness-of-fit testing. International Encyclopedia of Education, 7, 190-196.
  • Pallant, J. (2010). SPSS survival manual: a step by step guide to data analysis using SPSS. Open University Press/Mc Graw-Hill, Maidenhead.
  • Pituch, K. A. and Stevens, J., Applied multivariate statistics for the social sciences: Analyses with SAS and IBM’s SPSS. Taylor & Francis. New York.
  • Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2(1), 13–43.
  • Prudon, P. (2015). Confirmatory factor analysis as a tool in research using questionnaires: a critique. Comprehensive Psychology, 4, 1-19.
  • Purwanto, A., & Sudargini, Y. (2021). Partial least squares structural squation modeling (PLS-SEM) analysis for social and management research: a literature review. Journal of Industrial Engineering & Management Research, 2(4), 114-123.
  • Ramjit, S. (2022). Primary Care Assessment Tool-adult edition (PCAT-AE) and the assessment of the primary care in South-West Trinidad. Doctoral dissertation. The University of the West Indies.
  • Raykov, T., Gabler, S., & Dimitrov, D. M. (2016). Maximal reliability and composite reliability: Examining their difference for multicomponent measuring instruments using latent variable modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23(3), 384-391.
  • Sarmento, R. P., & Costa, V. (2019). Confirmatory factor analysis--a case study. arXiv preprint arXiv:1905.05598.
  • Sarmento, R., & Costa, V. (2017). Factor Analysis. In Comparative Approaches to Using R and Python for Statistical Data Analysis; 148–178.
  • Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-338.
  • Sharif Nia, H., Pahlevan Sharif, S., Koocher, G. P., Yaghoobzadeh, A., Haghdoost, A. A., Mar Win, M. T., & Soleimani, M. A. (2020). Psychometric properties of the death anxiety scale-extended among patients with end-stage renal disease. OMEGA-Journal of Death and Dying, 80(3), 380-396.
  • Shrestha, N. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39-42.
  • Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4-11.
  • Singh, F., & Kaur, M. (2016). Why exporting SMEs switch banks?. Global Business Review, 16(4), 652-664.
  • Streiner, D.L., Norman, G.R., & Cairney, J. (2015). Health measurement scales: a practical guide to their development and use. Inglaterra: Oxford University Press.
  • Suhr, D. (2006). Exploratory or Confirmatory Factor Analysis? Statistics and Data Analysis, 1-17.
  • Sürücü, L., Şeşen, H., & Maşlakçı, A. (2021). SPSS, AMOS ve PROCESS Macro ile ilişkisel, aracı/düzenleyici ve yapısal eşitlik modellemesi (uygulamalı analizler). Ankara: Detay Yayıncılık.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Allyn ve Bacon/Pearson Education.
  • Tucker, L. R. & MacCallum, R. C. (1997). Exploratory factor analysis. Unpublished manuscript, Ohio State University, Columbus.
  • Verma, J. P., & Abdel-Salam, A. S. G. (2019). Testing statistical assumptions in research. John Wiley & Sons.
  • Wang, K., Xu, Y., Wang, C., Tan, M., & Chen, P. (2020). A Corrected Goodness-of-Fit Index (CGFI) for model evaluation in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 27(5), 735-749.
  • Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219-246.
  • Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84-136.
  • Worthington, R. L., & Whittaker, T. A. (2006). Scale development research. A content analysis for recommendations for best practices. The Counseling Psychologist, 34(6), 806-838.
  • Yamin, S., & Kurniawan, H. (2011). Generasi baru mengolah data penelitian dengan partial least square path modeling: Aplikasi dengan Software XLSTAT, SmartPLS dan Visual PLS. Jakarta: Salemba Infotek.
  • Yaşlıoğlu, M. M. (2017). Sosyal bilimlerde faktör analizi ve geçerlilik: Keşfedici ve doğrulayıcı faktör analizlerinin kullanılması. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 46, 74-85.
  • Yirci, R. (2014). Devlet ve vakıf üniversitelerindeki öğretim elemanlarının algılanan örgütsel destek, örgütsel bağlılık düzeyleri ile yükseköğretimde özelleştirmeye ilişkin görüşlerinin karşılaştırılması. Doktora Tezi. Fırat Üniversitesi Eğitim Bilimleri Enstitüsü. Elazığ.

EXPLORATORY AND CONFIRMATORY FACTOR ANALYSIS: A CONCEPTUAL STUDY

Yıl 2023, Cilt: 9 Sayı: 1, 47 - 63, 22.06.2023
https://doi.org/10.29131/uiibd.1279602

Öz

Factor analyses are one of the most important criteria in scale development, validity and reliability studies. In this direction, firstly, factor analyses were mentioned in general and then the critical points of exploratory and confirmatory factor analyses were given. It is emphasised which points the researchers should pay attention to during the application of the analyses. In this context, it is aimed to contribute to the researchers in the literature and to provide practical information on the relevant analyses, to focus on the issues to be considered during the analysis and to be a guide for future research. This study has been prepared as a compilation by making a wide literature review at national and international level.

Kaynakça

  • Ab Hamid, M. R., Sami, W., & Sidek, M. M. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. In Journal of Physics: Conference Series. IOP Publishing.
  • Abdi, H., & Williams, L.J. (2010). Principal component analysis. John Wiley & Son s, In c. 433-459.
  • Akgül, A. (2005). Statistical analysis techniques in medical researches SPSS applications. Ankara: Emek Ofset Ltd Sti.
  • Akhtar-Danesh, N. (2017). A comparison between major factor extraction and factor rotation techniques in Q-methodology. Open Journal of Applied Sciences, 7(04), 147-156.
  • Akın, N.K., & Aşçı, F.H. (2021). Beden eğitimi dersinde üçlü yeterlik algılarının değerlendirilmesi: Ölçek uyarlama çalışması. Türkiye Klinikleri Journal of Sports Sciences, 13(2), 302-311.
  • Alavi, M., Visentin, D. C., Thapa, D. K., Hunt, G. E., Watson, R., & Cleary, M. (2020a). Exploratory factor analysis and principal component analysis in clinical studies: Which one should you use. Journal of advanced nursing, 76(8), 1886-1889.
  • Alavi, M., Visentin, D. C., Thapa, D. K., Hunt, G. E., Watson, R., & Cleary, M. L. (2020b). Chi-square for model fit in confirmatory factor analysis. J Adv Nurs., 76, 2209-2211.
  • Albayrak, A.S. (2006). Uygulamalı çok değişkenli istatistik teknikleri. Ankara: Asil Yayın Dağıtım.
  • Arafat, S. Y., Chowdhury, H. R., Qusar, M. M. A. S., & Hafez, M. A. (2016). Cross-cultural adaptation and psychometric validation of research instruments: A methodological review. J Behav Health, 5(3), 129-36.
  • Barendse, M. T., Oort, F. J., & Timmerman, M. E. (2015). Using exploratory factor analysis to determine the dimensionality of discrete responses. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 87-101.
  • Beavers , A. S., Lounsbury , J. W., Richards, J. K., Huck , S. W., & Skolits , G. J. (2013). Practical Consider actical Considerations for Using Explor ations for Using Exploratory Factor Analysis in or Analysis in Educational Research. Practical Assessment, Research, and Evaluation, 18(18), 1-13.
  • Bonett, D. G., & Wright, T. A. (2015). Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of organizational behavior, 36(1), 3-15.
  • Braeken, J., & Van Assen, M. A. (2017). An empirical Kaiser criterion. Psychological methods, 22(3), 450-466.
  • Bujang, M. A., Omar, E. D., & Baharum, N. A. (2018). A review on sample size determination for Cronbach’s alpha test: a simple guide for researchers. The Malaysian journal of medical sciences: MJMS, 25(6), 85-99.
  • Büyüköztürk Ş. (2013). Çok değişkenli istatistikler sosyal bilimler için veri analizi el kitabı. Ankara: Pegem Akademi.
  • Cangur, S., & Ercan, I. (2015). Comparison of model fit indices used in structural equation modeling under multivariate normality. Journal of Modern Applied Statistical Methods, 14(1), 152-167.
  • Carpenter, S. (2018). Ten steps in scale development and reporting: A guide for researchers. Communication methods and measures, 12(1), 25-44.
  • Chabo Byaene, A., Mabela, M. M. R., Konde, N. N. J., Muhindo Mavoko, H., & Kayembe, N.N. (2021). Clinical laboratory customers’ loyalty: development and validation of a measuring instrument. J Comm Med and Pub Health Rep, 2(3), 1-11.
  • Chow, J. C. C., Snowden, L. R. ve McConnell, W. (2001). A confirmatory factor analysis of the BASIS-32 in racial and ethnic samples. The Journal of Behavioral Health Services and Research. 28(4), 400-411.
  • Cole, D. A. (1987). Utility of confirmatory factor analysis in test validation research. Journal of Consulting and Clinical Psychology, 55(4), 584-594.
  • Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1-9.
  • Cronbach, L. J. (1951). Coefficient alpha and the interval structure of tests. Psychometrika, 16, 297-334. Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2014). Sosyal bilimler için çok değişkenli istatistik: Spss ve lisrel uygulamaları, Ankara: Pegem Akademi Yayıncılık; 211-275.
  • Çalışkan, A. & Köroğlu, Ö. (2022). Job Performance, Task Performance, Contextual Performance: Development And Validation Of A New Scale. Uluslararası İktisadi ve İdari Bilimler Dergisi, 8 (2), 180-201. DOI: 10.29131/uiibd.1201880
  • Çalışkan, A. (2022). Örgütsel Etik İklimi: Bir Ölçek Geliştirme Çalışması. Uluslararası İktisadi ve İdari Bilimler Dergisi, 8 (1), 34-54. https://doi.org/10.29131/uiibd.1118411
  • Çalışkan, A. (2022). Örgütsel Değişime Açıklık: Bir Ölçek Geliştirme Çalışması. Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14 (2), 191-202. https://doi.org/10.52791/aksarayiibd.1069524
  • Davcik, N. (2014). The use and misuse of structural equation modeling in management research: A review and critique. Journal of Advances in Management Research, 11(1), 47-81.
  • Dragan, D., & Topolšek, D. (2014). Introduction to structural equation modeling: review, methodology and practical applications. In The International Conference on Logistics & Sustainable Transport.
  • Draper, Z. A. (2019). Applying modern machine learning to the number of latent variables problem in principal components analysis and principal axis factoring. Doctoral dissertation. University of British Columbia.
  • Dziuban, C. D., & Harris, C. W. (1973). On the extraction of components and the applicability of the factor model. American Educational Research Journal, 10, 93-99. Dziuban, C. D., & Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. Psychological Bulletin, 81, 358-361.
  • Effendi, M., Matore, E. M., Khairani, A. Z., & Adnan, R. (2019). Exploratory factor analysis (EFA) for adversity quotient (AQ) instrument among youth. Journal of Critical Reviews, 6(6), 234-242.
  • Ernest, O. N., King, B. D., Esther, A., Kwadwo, A. N., & AY, A. R. (2019). The effect of lean operations in manufacturing on firm performance: the case of manufacturing firms in ACCRA. International Journal of Engineering Research and Reviews, 7(2), 1-7.
  • Fabrigar, L. R., & Wegener, D. T. (2012). Understanding statistics: Exploratory factor analysis. New York, NY: Oxford University.
  • Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299.
  • Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecological Processes, 5, 1-12.
  • Ford, J. K., MacCullum, R. C., & Tait, M. (1986). The application of exploratory factor analysis in applied psychology: A critical review and analysis. Personnel Psychology, 39, 291-314.
  • Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Ghozali, I. (2014). Structural equation modeling, metode alternatif dengan partial least square (PLS). Semarang : Badan Penerbit Universitas Diponegoro.
  • Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology, 40, 3510-3521.
  • Grieder, S., & Steiner, M. D. (2022). Algorithmic jingle jungle: A comparison of implementations of principal axis factoring and promax rotation in R and SPSS. Behavior Research Methods, 54(1), 54-74.
  • Haig, B. D. (2005). Exploratory factor analysis, theory generation, and scientific method. Multivariate Behavioral Research, 40(3), 303-329.
  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2013). Multivariate data analysis. Pearson Education Limited.
  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). Applications of partial least squares path modeling in management journals: A review of past practices and recommendations for future applications. Long Range Planning, 45(5-6), 320-340.
  • Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2014). Exploratory factor analysis. Multivariate data analysis. Prentice Hall.
  • Hair, JR, J.F., Ringle, C.M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-151.
  • Henrica C.W. de Vet, H. C. D., Adèr, H. J., Terwee, C. B., & Pouwer, F. (2005). Are factor analytical techniques used appropriately in the validation of health status questionnaires? A systematic review on the quality of factor analysis of the SF-36. Quality of Life Research, 14, 1203-1218.
  • Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393-416.
  • Hooper, D., Coughlan, J., & Mullen, R. M. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Howard, M. C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve?. International Journal of Human-Computer Interaction, 32(1), 51-62.
  • Hu LT, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling 6(1), 1-55.
  • Izquierdo, I., Olea, J., & Abad, F. J. (2014). Exploratory factor analysis in validation studies: Uses and recommendations. Psicothema, 26, 395-400.
  • Jin, S., Moustaki, I., & Yang-Wallentin, F. (2018). Approximated penalized maximum likelihood for exploratory factor analysis: An orthogonal case. Psychometrika, 83, 628-649.
  • Kääriäinen, M., Kanste, O., Elo, S., Pölkki, T., Miettunen, J., & Kyngäs, H. (2011). Testing and verifying nursing theory by confirmatory factor analysis. Journal of Advanced Nursing, 67(5), 1163–1172.
  • Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36.
  • Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200.
  • Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling. New York: The Guilford Press
  • Kline, R. B. (2013). Exploratory and confirmatory factor analysis. In Y. Petscher, C. Schatschneider, & D. L. Compton (Eds.), Applied quantitative analysis education and the social sciences. USA: Routledge.
  • Kozak M. (2017). Veri analizi. [Scientific research: design, writing and publishing techniques]. Bilimsel araştırma: tasarım, yazım ve yayım teknikleri. Ankara: Detay Yayıncılık.
  • Kwon, Y. ve Marzec, M. L. (2016). Does worksite culture of health (CoH) matter to employees? Empirical evidence using job-related metrics. Journal of Occupational and Environmental Medicine, 58(5), 448-454.
  • Kyriazos, T. A. (2018). Applied psychometrics: sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology, 9(08), 2207-2230.
  • Ledesma, R. D., Valero-Mora, P., & Macbeth, G. (2015). The scree test and the number of factors: a dynamic graphics approach. The Spanish Journal of Psychology, 18, 1-10.
  • Leech, N.L., Barret, K.C., & Morgan, G.A. (2015). IBMSPSS for Intermediate Statistics: Use and Interpretation. Fifth Edition. New Jersey: Lawrence Erlbaum Associates, Inc.
  • Lloret, S., Ferreres, A., Hernandez, A., & Tomas, I. (2017). The exploratory factor analysis of items: Guided analysis based on empirical data and software. Anales de Psicologia, 33, 417-432.
  • Luo, L., Arizmendi, C., & Gates, K. M. (2019). Exploratory factor analysis (EFA) programs in R. Structural Equation Modeling: A Multidisciplinary Journal, 26(5), 819-826.
  • Marofi, Z., Bandari, R., Heravi-Karimooi, M., Rejeh, N., & Montazeri, A. (2020). Cultural adoption, and validation of the Persian version of the coronary artery disease education questionnaire (CADE-Q): a second-order confirmatory factor analysis. BMC Cardiovascular Disorders, 20, 1-9.
  • Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralising Hu & Bentler’s (1999) findings. Structural Equation Modelling, 11, 320-341.
  • Maydeu-Olivares, A. (2017). Assessing the size of model misfit in structural equation models. Psychometrika, 82(3), 533-558.
  • McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61-88.
  • Meydan, C. H., & Şeşen, H. (2015). Yapısal eşitlik modellemesi AMOS uygulamaları.). Ankara: Detay Yayıncılık. Mukaka, M.M. (2012). Statistics Corner: A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal, 24(3), 69-71.
  • Mulaik, S. A. (1987). A brief history of the philosophical foundations of exploratory factor analysis. Multivariate Behavioral Research, 22, 267-305.
  • Myers, N. D., Ahn, S., & Jin, Y. (2011). Sample size and power estimates for a confirmatory factor analytic model in exercise and sport: A Monte Carlo approach. Research Quarterly for Exercise and Sport, 82(3), 412-423.
  • Nikkhah, M., Heravi-Karimooi, M., Montazeri, A., Rejeh, N., & Sharif Nia, H. (2018). Psychometric properties the Iranian version of older People’s quality of life questionnaire (OPQOL). Health and Quality of Life Outcomes, 16, 1-10.
  • Olivares, M.A., & Forero, G.C. (2010). Goodness-of-fit testing. International Encyclopedia of Education, 7, 190-196.
  • Pallant, J. (2010). SPSS survival manual: a step by step guide to data analysis using SPSS. Open University Press/Mc Graw-Hill, Maidenhead.
  • Pituch, K. A. and Stevens, J., Applied multivariate statistics for the social sciences: Analyses with SAS and IBM’s SPSS. Taylor & Francis. New York.
  • Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2(1), 13–43.
  • Prudon, P. (2015). Confirmatory factor analysis as a tool in research using questionnaires: a critique. Comprehensive Psychology, 4, 1-19.
  • Purwanto, A., & Sudargini, Y. (2021). Partial least squares structural squation modeling (PLS-SEM) analysis for social and management research: a literature review. Journal of Industrial Engineering & Management Research, 2(4), 114-123.
  • Ramjit, S. (2022). Primary Care Assessment Tool-adult edition (PCAT-AE) and the assessment of the primary care in South-West Trinidad. Doctoral dissertation. The University of the West Indies.
  • Raykov, T., Gabler, S., & Dimitrov, D. M. (2016). Maximal reliability and composite reliability: Examining their difference for multicomponent measuring instruments using latent variable modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23(3), 384-391.
  • Sarmento, R. P., & Costa, V. (2019). Confirmatory factor analysis--a case study. arXiv preprint arXiv:1905.05598.
  • Sarmento, R., & Costa, V. (2017). Factor Analysis. In Comparative Approaches to Using R and Python for Statistical Data Analysis; 148–178.
  • Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-338.
  • Sharif Nia, H., Pahlevan Sharif, S., Koocher, G. P., Yaghoobzadeh, A., Haghdoost, A. A., Mar Win, M. T., & Soleimani, M. A. (2020). Psychometric properties of the death anxiety scale-extended among patients with end-stage renal disease. OMEGA-Journal of Death and Dying, 80(3), 380-396.
  • Shrestha, N. (2020). Detecting multicollinearity in regression analysis. American Journal of Applied Mathematics and Statistics, 8(2), 39-42.
  • Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American Journal of Applied Mathematics and Statistics, 9(1), 4-11.
  • Singh, F., & Kaur, M. (2016). Why exporting SMEs switch banks?. Global Business Review, 16(4), 652-664.
  • Streiner, D.L., Norman, G.R., & Cairney, J. (2015). Health measurement scales: a practical guide to their development and use. Inglaterra: Oxford University Press.
  • Suhr, D. (2006). Exploratory or Confirmatory Factor Analysis? Statistics and Data Analysis, 1-17.
  • Sürücü, L., Şeşen, H., & Maşlakçı, A. (2021). SPSS, AMOS ve PROCESS Macro ile ilişkisel, aracı/düzenleyici ve yapısal eşitlik modellemesi (uygulamalı analizler). Ankara: Detay Yayıncılık.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Allyn ve Bacon/Pearson Education.
  • Tucker, L. R. & MacCallum, R. C. (1997). Exploratory factor analysis. Unpublished manuscript, Ohio State University, Columbus.
  • Verma, J. P., & Abdel-Salam, A. S. G. (2019). Testing statistical assumptions in research. John Wiley & Sons.
  • Wang, K., Xu, Y., Wang, C., Tan, M., & Chen, P. (2020). A Corrected Goodness-of-Fit Index (CGFI) for model evaluation in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 27(5), 735-749.
  • Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219-246.
  • Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84-136.
  • Worthington, R. L., & Whittaker, T. A. (2006). Scale development research. A content analysis for recommendations for best practices. The Counseling Psychologist, 34(6), 806-838.
  • Yamin, S., & Kurniawan, H. (2011). Generasi baru mengolah data penelitian dengan partial least square path modeling: Aplikasi dengan Software XLSTAT, SmartPLS dan Visual PLS. Jakarta: Salemba Infotek.
  • Yaşlıoğlu, M. M. (2017). Sosyal bilimlerde faktör analizi ve geçerlilik: Keşfedici ve doğrulayıcı faktör analizlerinin kullanılması. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 46, 74-85.
  • Yirci, R. (2014). Devlet ve vakıf üniversitelerindeki öğretim elemanlarının algılanan örgütsel destek, örgütsel bağlılık düzeyleri ile yükseköğretimde özelleştirmeye ilişkin görüşlerinin karşılaştırılması. Doktora Tezi. Fırat Üniversitesi Eğitim Bilimleri Enstitüsü. Elazığ.
Toplam 99 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Mesut Karaman 0000-0002-7584-0800

Yayımlanma Tarihi 22 Haziran 2023
Gönderilme Tarihi 8 Nisan 2023
Kabul Tarihi 22 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 9 Sayı: 1

Kaynak Göster

APA Karaman, M. (2023). KEŞFEDİCİ VE DOĞRULAYICI FAKTÖR ANALİZİ: KAVRAMSAL BİR ÇALIŞMA. Uluslararası İktisadi Ve İdari Bilimler Dergisi, 9(1), 47-63. https://doi.org/10.29131/uiibd.1279602

Cited By






Hemşirelikte Gelecek Kaygısının Kariyer İyimserliği Üzerindeki Etkisi
Bandırma Onyedi Eylül Üniversitesi Sağlık Bilimleri ve Araştırmaları Dergisi
https://doi.org/10.46413/boneyusbad.1334028




ARAŞTIRMA VE YAYIN ETİĞİNE İLİŞKİN DERGİ POLİTİKAMIZ

Etik Kurul izni gerektiren çalışmalar (anket ya da ölçek uygulamayı gerektiren, görüşme ve gözlem içeren; doküman, resim, anket vb. diğerleri tarafından geliştirilen ve kullanım izni gerektiren çalışmalar) için etik kurullardan ya da komisyonlardan gerekli izinlerin araştırma yapılmadan önce alınmış olması, bunların makale içeriğinde belirtilmesi ya da ek olarak sunulması gerekmektedir. Bu izinlerin olmaması durumunda yayın ön inceleme safhasında yazara iade edilir.  

Diğer yandan, Araştırma Makalelerinde verisi 2020 yılından önce toplanmış makaleler için ETİK KURUL İZNİ istenmemektedir. Ancak yine bu makalelerde de yöntem kısmında verilerin toplanma tarihlerine ilişkin bilgilere yer verilmesi gerekmektedir.

Hakem değerlendirmelerinde olan araştırmalara ilişkin ham verilerin hakemler tarafından talep edildiğinde sunulması şarttır. Verilerin makalenin yayımı sonrasında da gerektiğinde sağlanması zorunludur. 

MAKALE BENZERLİK RAPORU VE BENZERLİK ORANI HAKKINDA DERGİ POLİTİKAMIZ


Aday makaleler akademik intihal engelleme programından (Örneğin: Turnitin, Ithenticate, intihal.net vb.) alınmış orijinallik raporu ile birlikte gönderilmelidir. Bu oran %20'ye kadar kabul edilmektedir. %20'nin üzerinde benzerlik oranı olan çalışmalar ön kontrol aşamasında iade edilecektir.

ISSN: 2149 - 5823