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FARKLI ÇOKLU VERİ ATAMA TEKNİKLERİNİN DOĞRULAYICI FAKTÖR ANALİZİ MODEL UYUMU ÜZERİNDEKİ ETKİSİ

Year 2021, Volume: 11 Issue: 3, 1227 - 1238, 27.09.2021

Akif Avcu

https://doi.org/10.24315/tred.789832

Abstract

Bugüne kadar kayıp verilerin istatistiksel analizler üzerindeki erkilerini incelemek için birçok araştırma gerçekleştirilmiştir ve bu durumla başa çıkabilmek için farklı yöntemler geliştirilmiştir. Kayıp verinin silinmesini içeren yöntemler örneklem büyüklüğünün önemli miktarda azalmasına sebep olmakta ve analizlerin istatistiksel gücünü düşürmektedir. Bu duruma bir alternatif olarak önerilen kayıp veri kestirimine dayalı yöntemler araştırmacıların yoğun ilgisini çekmektedir. Bu yöntemler içerisinde çoklu veri atama teknikleri göreceli olarak daha yakın bir geçmişe sahiptir ve daha iyi kestirimler sağlamaktadır. Çoklu veri atama tekniklerinin üstünlüğü düşünüldüğünde, gerçekleştirilen bu çalışmanın amacı farklı çoklu veri atama tekniklerinin doğrulayıcı faktör analizi model uyumu üzerideki etkisinin değerlendirilmesidir. Bu amaç doğrultusunda örneklem büyüklüğü, kayıp veri mekanizması, kayıp veri yüzdesi, madde sayısı ve kayıp veri atama tekniğini kontrol edilerek tek boyutlu yapıya sahip veri setleri üretilmiştir. Kayıp veri tekniklerinin etkileri tam veri setleri ve veri ataması gerçekleştirilmiş veri setleri için elde edilmiş 𝜒² model uyum istatistikleri arasındaki fark ile değerlendirilmiştir. Elde edilen sonuçlar çoklu veri atama tekniklerinin geleneksel regresyon temelli veri atama tekniklerine kıyasla daha iyi sonuçlar sağladığını göstermiştir. Bu bulgular daha sonrasında tartışılarak daha iyi test uygulmaları için bir takım önerilerde bulunulmuştur.

Keywords

kayıp veri çoklu veri atama benzetim

References

  • Akın Arıkan, C., & Soysal, S. (2018). Investigation of Reliability Coefficients According to Missing Data Imputation Methods. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 33(2), 316-336.
  • Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications.
  • Alpar, R. (2003). Uygulamalı Çok Değişkenli İstatistiksel Yöntemlere Giriş 1, 2. Basım, Ankara: Nobel Yayın Evi.
  • Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum Associates, Inc
  • Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of school psychology, 48(1), 5 37.
  • Bennett D. A. (2001). How can I deal with missing data in my study?. Australian and New Zealand journal of public health, 25(5), 464-469.
  • Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., (1984). Classification and Regression Trees. Wadsworth, Pacific Grove, CA.
  • Chalmers, R.P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29.
  • Chhabra, G., Vashisht, V., & Ranjan, J. (2017). A comparison of multiple imputation methods for data with missing values. Indian Journal of Science and Technology, 10(19), 1-7.
  • Çüm, S, Gelbal, S. (2015). Kayıp Veriler Yerine Yaklaşık Değer Atamada Kullanılan Farklı Yöntemlerin Model Veri Uyumu Üzerindeki Etkisi. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, 1(35) , 87-111.
  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39, 1–38.
  • Doove, L. L., Van Buuren, S., & Dusseldorp, E. (2014). Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics & Data Analysis, 72, 92-104.
  • Durant, G.B. (2005). Imputation Techniques to handling item-nonresponse in the social sciences: A methodical Review, National Centre for Research Methods Working Paper Series, ESRC National Centre for Research Methods and Southampton Statistical Sciences Research Institute (SRI), University of Southampton.
  • Enders, C.K. (2010). Applied missing data analysis. Guilford press.
  • 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(3), 272–299.
  • Guan, N. C., & Yusoff, M. S. B. (2011). Missing values in data analysis: Ignore or Impute?. Education in Medicine Journal, 3(1): e6-e11
  • Hayes, T., Usami, S., Jacobucci, R., & McArdle, J. J. (2015). Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations. Psychology and aging, 30(4), 911-929.
  • Hohensinn, C., & Kubinger, K. (2011). On the impact of missing values on item fit and model validness of the
  • Rasch model. Psychological Test and Assessment Modeling, 53, 380-393.
  • Howell, D. C. (2007) The analysis of missing data. In: Outhwaite, W. & Turner, S. (Eds.) Handbook of Social Science Methodology. London: Sage.
  • Jöreskog, K.G. (1977), Structural Equation Models in the Social Sciences: Specification, Estimation and Testing. In: Krishnaiah, P. R. (Eds), Applications of Statistics,. Amsterdam: North-Holland Publishing Co., pp. 265-387.
  • Kalkan, Ö., Kara, Y., Keleci̇oğlu, H. (2018). Evaluating Performance of Missing Data Imputation Methods in IRT Analyses. International Journal of Assessment Tools in Education, 5(3) , 403-416 .
  • Kim, J., & Curry, J. (1977). The Treatment of Missing Data in Multivariate Analysis. Sociological Methods & Research, 6, 215-240.
  • Kros, J. F., & Brown, M. L. (2003). Data mining and the impact of missing data. Industrial Management & Data System, 103(8), 611-621.
  • Little R.J.A. (1988). Missing data adjustments in large surveys (with discussion). Journal of Business Economics and Statistics, 6, 287-301
  • Little, R.J.A. & Rubin, D.B. (2002). Statistical analysis with missing data. (Second Edition.) New York: Wiley.
  • Marsh, H. W. (1998). Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling: A Multidisciplinary Journal, 20(1), 22-36.
  • Misztal, M. (2012). Imputation of Missing Data Using R Package. Acta Universitatis Lodziensis Folia Oeconomica, 269, 132-144.
  • Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52(3), 431-462.
  • Nassiri, V., Lovik, A., Molenberghs, G. et al. (2018). On using multiple imputation for exploratory factor analysis of incomplete data. Behavioral Research Methods, 50, 501-517.
  • Nie, W. H., Hull, C. H., Jenkins, J. G., Steinbrenner, K. and Bent, D. H. (1975). SPSS Statistical package for the social sciences. McGraw-Hill, New York.
  • Oğuzlar, A. (2001). Alan Araştırmalarında Kayıp Değer Problemi ve Çözüm Önerileri, 5. Ulusal Ekonometri ve İstatistik Sempozyumu, Adana: Çukurova Üniversitesi, 20-22 Eylül 2001, s.1-28.
  • Özberk, E.H., Kabasakal, K.A., & Öztürk, N.B. (2017). Investigating the Factors Affecting Turkish Students’ PISA 2012 Mathematics Achievement Using Hierarchical Linear. Hacettepe University Journal of Education. 32, 554-559.
  • Peng, C., Harwell, M., Liou, S., Ehman, L. (2006). Advances in missing data methods and implications for educational research. In Sawilowsky, S. S. (Eds.), Real data analysis (pp. 31–78). Charlotte, NC: New Information Age.
  • Peugh, J.L. and Enders, C.K. (2004). Missing data in educational research. Review of educational research. 74(4), 525-556.
  • R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
  • Rahman M.M., Davis D.N. (2013) Machine Learning-Based Missing Value Imputation Method for Clinical Datasets. In: Yang GC., Ao S., Gelman L. (Eds.), IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 229. Springer, Dordrecht
  • Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.
  • Roth, P. L. (1994). Missing data: A conceptual review for applied psychologists. Personnel Psychology, 47, 537-560.
  • Rubin D.B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business Economics and Statistics, 4, 87-94
  • Rubin, D.B. (1976). Inference and missing data. Biometrika, 63, 581-592.
  • Rubin, D.B. (1987). Multiple Imputationfor Nonresponse in Surveys, New York: John Wiley.
  • Schafer, J. L. (1999). Multiple imputation: a primer. Statistical methods in medical research, 8(1), 3-15.
  • Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147-177.
  • van Buuren S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification. Statistical methods in medical research, 16(3), 219-242.
  • van Buuren, S, Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67.
  • van Praag, B.M.S., T. K. Dijkstra & J. van Velzen (1985) Least squares theory based on general distributional assumptions with an application to the incomplete observations problem. Psychometrika, 50, 25-36.
  • Wisniewski S.R., Leon A.C., Otto M.W. ve Trivedi M.H., (2006). Prevention of missing data in clinical research studies. Biological Psychiatry, 59(11), 997-1000
  • Wothke, W. (2000). Longitudinal and multi-group modeling with missing data. In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multiple group data: Practical issues, applied approaches and specific examples (pp. 219–240). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
  • Xu, C., Baines, P. D., & Wang, J. L. (2014). Standard error estimation using the EM algorithm for the joint modeling of survival and longitudinal data. Biostatistics (Oxford, England), 15(4), 731-744.
  • Yuan, K.-H., & Bentler, P. M. (2000). 5. Three Likelihood-Based Methods for Mean and Covariance Structure Analysis with Nonnormal Missing Data. Sociological Methodology, 30(1), 165–200.

EFFECTS OF DIFFERENT MULTIPLE IMPTUTATION TECHNIQUES ON THE MODEL FIT OF CONFIRMATORY FACTOR ANALYSIS

Year 2021, Volume: 11 Issue: 3, 1227 - 1238, 27.09.2021

Akif Avcu

https://doi.org/10.24315/tred.789832

Abstract

So far, many researches have been conducted to investigate the impact of missing data on statistical analysis and various methods have been developed to deal with the problem. The methods based on removing observations with missing values from the dataset cause the sample size to drop dramatically and the statistical power of the analyzes to be decreased. Therefore, as an alternative solution, the estimation of missing values seized intensive attention of researchers. Among these methods, multiple imputation techniques are relatively more recent and provide better estimations. Considering the superiority of multiple imputation techniques, the aim of the current study is to investigate the effects of different multiple imptutation techniques on the model fit of confirmatory factor analysis. For this aim, datasets with the unidimensional structure were simulated to manipulate sample size, missing data mechanism, percentage of missing data, number of items and missing data imputation technique. The effect of multiple imputation techniqes was evaluated based on the difference of 𝜒² model fit statistics for complete datasets and imputed datasets. The results showed that, multiple impuation techniques provided better results than conventional regression based imputation. Those finding were discussed later and some recommendations were given for better testing applications.

Keywords

missing data data imputation simulation

References

  • Akın Arıkan, C., & Soysal, S. (2018). Investigation of Reliability Coefficients According to Missing Data Imputation Methods. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 33(2), 316-336.
  • Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications.
  • Alpar, R. (2003). Uygulamalı Çok Değişkenli İstatistiksel Yöntemlere Giriş 1, 2. Basım, Ankara: Nobel Yayın Evi.
  • Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum Associates, Inc
  • Baraldi, A. N., & Enders, C. K. (2010). An introduction to modern missing data analyses. Journal of school psychology, 48(1), 5 37.
  • Bennett D. A. (2001). How can I deal with missing data in my study?. Australian and New Zealand journal of public health, 25(5), 464-469.
  • Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., (1984). Classification and Regression Trees. Wadsworth, Pacific Grove, CA.
  • Chalmers, R.P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29.
  • Chhabra, G., Vashisht, V., & Ranjan, J. (2017). A comparison of multiple imputation methods for data with missing values. Indian Journal of Science and Technology, 10(19), 1-7.
  • Çüm, S, Gelbal, S. (2015). Kayıp Veriler Yerine Yaklaşık Değer Atamada Kullanılan Farklı Yöntemlerin Model Veri Uyumu Üzerindeki Etkisi. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, 1(35) , 87-111.
  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39, 1–38.
  • Doove, L. L., Van Buuren, S., & Dusseldorp, E. (2014). Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics & Data Analysis, 72, 92-104.
  • Durant, G.B. (2005). Imputation Techniques to handling item-nonresponse in the social sciences: A methodical Review, National Centre for Research Methods Working Paper Series, ESRC National Centre for Research Methods and Southampton Statistical Sciences Research Institute (SRI), University of Southampton.
  • Enders, C.K. (2010). Applied missing data analysis. Guilford press.
  • 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(3), 272–299.
  • Guan, N. C., & Yusoff, M. S. B. (2011). Missing values in data analysis: Ignore or Impute?. Education in Medicine Journal, 3(1): e6-e11
  • Hayes, T., Usami, S., Jacobucci, R., & McArdle, J. J. (2015). Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations. Psychology and aging, 30(4), 911-929.
  • Hohensinn, C., & Kubinger, K. (2011). On the impact of missing values on item fit and model validness of the
  • Rasch model. Psychological Test and Assessment Modeling, 53, 380-393.
  • Howell, D. C. (2007) The analysis of missing data. In: Outhwaite, W. & Turner, S. (Eds.) Handbook of Social Science Methodology. London: Sage.
  • Jöreskog, K.G. (1977), Structural Equation Models in the Social Sciences: Specification, Estimation and Testing. In: Krishnaiah, P. R. (Eds), Applications of Statistics,. Amsterdam: North-Holland Publishing Co., pp. 265-387.
  • Kalkan, Ö., Kara, Y., Keleci̇oğlu, H. (2018). Evaluating Performance of Missing Data Imputation Methods in IRT Analyses. International Journal of Assessment Tools in Education, 5(3) , 403-416 .
  • Kim, J., & Curry, J. (1977). The Treatment of Missing Data in Multivariate Analysis. Sociological Methods & Research, 6, 215-240.
  • Kros, J. F., & Brown, M. L. (2003). Data mining and the impact of missing data. Industrial Management & Data System, 103(8), 611-621.
  • Little R.J.A. (1988). Missing data adjustments in large surveys (with discussion). Journal of Business Economics and Statistics, 6, 287-301
  • Little, R.J.A. & Rubin, D.B. (2002). Statistical analysis with missing data. (Second Edition.) New York: Wiley.
  • Marsh, H. W. (1998). Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling: A Multidisciplinary Journal, 20(1), 22-36.
  • Misztal, M. (2012). Imputation of Missing Data Using R Package. Acta Universitatis Lodziensis Folia Oeconomica, 269, 132-144.
  • Muthén, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52(3), 431-462.
  • Nassiri, V., Lovik, A., Molenberghs, G. et al. (2018). On using multiple imputation for exploratory factor analysis of incomplete data. Behavioral Research Methods, 50, 501-517.
  • Nie, W. H., Hull, C. H., Jenkins, J. G., Steinbrenner, K. and Bent, D. H. (1975). SPSS Statistical package for the social sciences. McGraw-Hill, New York.
  • Oğuzlar, A. (2001). Alan Araştırmalarında Kayıp Değer Problemi ve Çözüm Önerileri, 5. Ulusal Ekonometri ve İstatistik Sempozyumu, Adana: Çukurova Üniversitesi, 20-22 Eylül 2001, s.1-28.
  • Özberk, E.H., Kabasakal, K.A., & Öztürk, N.B. (2017). Investigating the Factors Affecting Turkish Students’ PISA 2012 Mathematics Achievement Using Hierarchical Linear. Hacettepe University Journal of Education. 32, 554-559.
  • Peng, C., Harwell, M., Liou, S., Ehman, L. (2006). Advances in missing data methods and implications for educational research. In Sawilowsky, S. S. (Eds.), Real data analysis (pp. 31–78). Charlotte, NC: New Information Age.
  • Peugh, J.L. and Enders, C.K. (2004). Missing data in educational research. Review of educational research. 74(4), 525-556.
  • R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
  • Rahman M.M., Davis D.N. (2013) Machine Learning-Based Missing Value Imputation Method for Clinical Datasets. In: Yang GC., Ao S., Gelman L. (Eds.), IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 229. Springer, Dordrecht
  • Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.
  • Roth, P. L. (1994). Missing data: A conceptual review for applied psychologists. Personnel Psychology, 47, 537-560.
  • Rubin D.B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business Economics and Statistics, 4, 87-94
  • Rubin, D.B. (1976). Inference and missing data. Biometrika, 63, 581-592.
  • Rubin, D.B. (1987). Multiple Imputationfor Nonresponse in Surveys, New York: John Wiley.
  • Schafer, J. L. (1999). Multiple imputation: a primer. Statistical methods in medical research, 8(1), 3-15.
  • Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147-177.
  • van Buuren S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification. Statistical methods in medical research, 16(3), 219-242.
  • van Buuren, S, Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67.
  • van Praag, B.M.S., T. K. Dijkstra & J. van Velzen (1985) Least squares theory based on general distributional assumptions with an application to the incomplete observations problem. Psychometrika, 50, 25-36.
  • Wisniewski S.R., Leon A.C., Otto M.W. ve Trivedi M.H., (2006). Prevention of missing data in clinical research studies. Biological Psychiatry, 59(11), 997-1000
  • Wothke, W. (2000). Longitudinal and multi-group modeling with missing data. In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multiple group data: Practical issues, applied approaches and specific examples (pp. 219–240). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
  • Xu, C., Baines, P. D., & Wang, J. L. (2014). Standard error estimation using the EM algorithm for the joint modeling of survival and longitudinal data. Biostatistics (Oxford, England), 15(4), 731-744.
  • Yuan, K.-H., & Bentler, P. M. (2000). 5. Three Likelihood-Based Methods for Mean and Covariance Structure Analysis with Nonnormal Missing Data. Sociological Methodology, 30(1), 165–200.
There are 51 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Akif Avcu 0000-0003-1977-7592

Publication Date September 27, 2021
Published in Issue Year 2021 Volume: 11 Issue: 3

Cite

APA Avcu, A. (2021). EFFECTS OF DIFFERENT MULTIPLE IMPTUTATION TECHNIQUES ON THE MODEL FIT OF CONFIRMATORY FACTOR ANALYSIS. Trakya Eğitim Dergisi, 11(3), 1227-1238. https://doi.org/10.24315/tred.789832
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