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An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups

Yıl 2021, Cilt 12, Sayı 4, 321 - 335, 29.12.2021
https://doi.org/10.21031/epod.984771

Öz

This study aimed to investigate the heterogeneity of the TIMSS 2015 data from Turkey and the USA 8th grade math. Latent Class Analysis (LCA) was used to determine the latent classes that cause heterogeneity in the data by using categorical observed variables. As a result of the LCA, supporting absolute and relative model fit indices through AvePP and entropy values, it was concluded that the data obtained from both countries fit the three-class model. The latent class probabilities and conditional response probabilities were examined for homogeneity and degree of segregation of the classes from each other. Based on the findings, it is recommended that the assumption of homogeneity in international evaluations be evaluated empirically with LCA. With this article, an example of the application of LCA is provided, and it is believed to be useful for researchers in the context of education and psychological evaluation.

Kaynakça

  • Agresti, A. (1990). Categorical data analysis. New York: Wiley.
  • Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332.
  • Baghaei, P., & Carstensen, C. H. (2013). Fitting the mixture Rasch model to a reading comprehension test: Identifying reader types. Practical Assessment, Rese, 18(5), 1–13. doi: 10.7275/n191-pt86
  • Bolt, D. M., Cohen, A. S., & Wollack, J. A. (2002). Item parameter estimation under conditions of test speededness: Application of a mixture Rasch model with ordinal constraints. Journal of Educational Measurement, 39, 331-348. doi: 10.1111/j.1745-3984.2002.tb01146.x
  • Butera, N. M., Lanza, S. T., & Coffman, D. L. (2014). A framework for estimating causal effects in latent class analysis: Is there a causal link between early sex and subsequent profiles of delinquency? Prevention Science, 15(3), 397– 407. doi: 10.1007/s11121-013-0417-3
  • Chung, H., Park, Y., & Lanza, S. T. (2005). Latent transition analysis with covariates: Pubertal timing and substance use behaviours in adolescent females. Statistics in Medicine, 24, 2895-2910. doi: 10.1002/sim.2148
  • Clark, S. L. (2010). Mixture modeling with behavioral data (Unpublished doctoral dissertation). University of California, Los Angeles.
  • Eid, M., Langeheine, R., & Diener, E. (2003). Comparing typological structures across cultures by multigroup latent class analysis: A primer. Journal of Cross-Cultural Psychology, 34(2), 195-210. doi: 10.1177/0022022102250427
  • Clark, S. L., & Muthén, B. O. (2009). Relating latent class analysis results to variables not included in the analysis. 2009 Manuscript submitted for publication. Retrieved from http://www.statmodel.com/download/relatinglca.pdf
  • Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New Jersey: Wiley.
  • De Ayala, R. J., & Santiago, S. Y. (2017). An introduction to mixture item response theory models. Journal of School Psychology, 60, 25-40. doi: 10.1016/j.jsp.2016.01.002
  • DeMars, C. E., & Lau, A. (2011). Differential item functioning detection with latent classes: How accurately detect who is responding differentially? Educational and Psychological Measurement, 71(4), 597–616. doi:10.1177/0013164411404221
  • Embretson, S. E. (2007). Mixture Rasch models for measurement in cognitive psychology. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 235-253). New York: Springer Verlag.
  • Glück, J., & Spiel, C. (2007). Studying development via Item Response Model: A wide range of potential uses. In M. von Davier, & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 281-292). New York: Springer Verlag.
  • Goodman, L. A. (2002). Latent class analysis: The empirical study of latent types, latent variables, and latent structures. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis. New York: Cambridge University Press.
  • Güngör Culha, D. & Korkmaz, M. (2011). Örtük sınıf analizi ile bir örnek uygulama. Eğitimde ve Psikolojide Ölçme Değerlendirme Dergisi, 2(2), 191-199.
  • Güngör, Culha, D., Korkmaz, M. & Somer, O. (2013). Çoklu-grup örtük sınıf analizi ve ölçme eşdeğerliği. Türk Psikoloji Dergisi, 28(72), 48-57.
  • Hagenaars, J. A., & McCutcheon, A. (2002). Applied latent class analysis. Cambridge, UK: Cambridge University Press.
  • Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Newbury Park, CA: Sage.
  • Jeon, M. (2018). A constrained confirmatory mixture IRT model: Extensions and estimation of the Saltus model using Mplus. The Quantitative Methods for Psychology, 14, 120–136. doi: 10.20982/tqmp.14.2.p120
  • Jung, T., & Wickrama, K. A. S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317. doi: 10.1111/j.1751-9004.2007.00054.x
  • Kankaraš, M., Vermunt, J. K., & Moors, G. (2011). Measurement equivalence of ordinal items: A comparison of factor analytic, Item Response Theory, and latent class approaches. Sociological Methods & Research, 40, 279–310. doi: 10.1177/0049124111405301
  • Kreiner, S., & Christensen, K. B. (2007). Validity and objectivity in health-related scales: Analysis by graphical loglinear Rasch models. In M. von Davier & C. H. Carstensen (Eds.). Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 329-346). New York: Springer Verlag. doi:10.1007/s11336-013-9347-z
  • Lanza, S. T., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis. In J. A. Schinka & W. F. Velicer (Eds.), Handbook of psychology: Vol. 2, research methods in psychology (pp. 663-685). Hoboken, NJ: Wiley.
  • Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.
  • Leech, R. M., McNaughton, S. A., & Timperio, A. (2014). The clustering of diet, physical activity and sedentary behavior in children and adolescents: A review. International Journal of Behavioral Nutrition and Physical Activity, 11, 4. doi: 10.1186/1479-5868-11-4
  • Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767-778. doi: 10.1093/biomet/88.3.767
  • Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21–39. doi: 10.1037/1082-989X.10.1.21
  • Lubke, G., & Neale, M. C. (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood? Multivariate Behavioral Research, 41, 499–532. doi: 10.1207/s15327906mbr4104_4
  • Magidson, J., & Vermunt, J. K. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing, 20(1), 36–43.
  • Martin, M. O., Mullis, I. V. S., & Hooper, M. (2016). Methods and procedures in TIMSS 2015. Chestnut Hill, MA: Boston College, TIMSS & PIRLS International Study Center. Zugriff am (Vol. 21).
  • Masyn, K. (2017). Measurement invariance and differential item functioning in latent class analysis with stepwise multiple indicator multiple cause modeling. Structural Equation Modeling, 24, 180-197. doi: 10.1080/10705511.2016.1254049
  • McLachlan, G. J., & Peel, D. (2004). Finite mixture models. New York: Wiley.
  • Morin, A. J., Meyer, J. P., Creusier, J., & Biétry, F. (2016). Multiple-group analysis of similarity in latent profile solutions. Organizational Research Methods, 19(2), 231–254. doi: 10.1177/1094428115621148
  • Mullis, I. V., Martin, M. O., Foy, P., & Hooper, M. (2016). TIMSS 2015 international results in mathematics. Amsterdam, The Netherlands: International Association for the Evaluation of Educational Achievement.
  • Messick, S. (1994). The interplay of evidence and consequences in the validation of performance assessments. Educational Researcher, 23, 13–23. doi: 10.3102/0013189X023002013
  • Mislevy, R., & Huang, C. W. (2007). Measurement models as narrative structures. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications. New York: Springer Verlag.
  • Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th Edition). Los Angeles, CA: Muthén & Muthén.
  • Nagin, D. (2005). Group-based modeling of development. London: Harvard University.
  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535-569. doi: 10.1080/10705510701575396
  • Oliveri, M. E., Ercikan, K., Zumbo, B. D., & Lawless, R. (2014). Uncovering substantive patterns in student responses in international large-scale assessments—Comparing a latent class to a manifest DIF approach. International Journal of Testing, 14(3), 265-287. doi: 10.1080/15305058.2014.891223
  • Oliveri, M. E., & von Davier, M. (2011). Investigation of model fit and score scale comparability in international assessments. Psychological Test and Assessment Modeling, 53(3), 315–333.
  • Olson J. S., Hummer A. K., & Harris K. M. (2017). Gender and health behavior clustering among U.S. Young Adults, Biodemography and Social Biology, 63(1), 3-20. doi: 10.1080/19485565.2016.1262238
  • Park, Y. S., Lee, Y.-S., & Xing, K. (2016). Investigating the impact of item parameter drift for item response theory models with mixture distributions. Frontiers in Psychology, 7, 255. doi: 10.3389/fpsyg.2016.00255
  • Rindskopf, D. (2003). Mixture or homogeneous? Comment on Bauer and Curran (2003). Psychological Methods, 8(3), 364–368. doi: 10.1037/1082-989X.8.3.364
  • Rutkowski, L., & Rutkowski, D. (2018). Improving the comparability and local usefulness of international assessments: A look back and a way forward. Scandinavian Journal of Educational Research, 62(3), 354–367. doi: 10.1080/00313831.2016.1261044
  • Samuelsen, K. (2005). Examining differential item functioning from a latent class perspective. Doctoral Dissertation. Available from ProQuest Dissertations and Theses database. (UMI No. 3175148).
  • Schwarz, G. (1978). Estimating the dimension of a model. The Annuals of Statistics, 6(2), 461-464.
  • Sen, S. (2016) Applying the Mixture Rasch Model to the Runco Ideational Behavior Scale. Creativity Research Journal, 28(4), 426-434. doi: 10.1080/10400419.2016.1229985
  • Thomson, S., Wernert, N., O’Grady, E., & Rodrigues, S. (2017). TIMSS 2015: Reporting Australia’s results. Melbourne, Australia: Australian Council for Educational Research.
  • Toker, T. (2016). A comparison latent class analysis and the mixture Rasch model: A cross-cultural comparison of 8th grade mathematics achievement in the fourth international mathematics and science study (TIMSS-2011). Doctoral Dissertation, The Faculty of the Morgridge College of Education University of Denver, USA.
  • Uyar, Ş. (2015). Gözlenen gruplara ve örtük sınıflara göre belirlenen değişen madde fonksiyonunun karşılaştırılması. Yayınlanmamış Doktora Tezi. Hacettepe Üniversitesi, Eğitim Bilimleri Ensitüsü. Ankara.
  • Vermunt, J. K., & Magidson, J. (2004). Latent class analysis. The Sage Encyclopedia of Social Sciences Research Methods (pp. 549-553).
  • Vermunt, J. K., & Magidson, J. (2020). How to perform three-step latent class analysis in the presence of measurement non-invariance or differential item functioning. Structural Equation Modeling: A Multidisciplinary Journal. doi: 10.1080/10705511.2020.1818084
  • Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. Hoboken, NJ: John Wiley & Sons.
  • Yandı, A., Köse, İ. A. & Uysal, Ö. (2017). Farklı yöntemlerle ölçme değişmezliğinin incelenmesi: PISA 2012 örneği. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 13(1), 243-253. doi: 10.17860/mersinefd.305952

Yıl 2021, Cilt 12, Sayı 4, 321 - 335, 29.12.2021
https://doi.org/10.21031/epod.984771

Öz

Kaynakça

  • Agresti, A. (1990). Categorical data analysis. New York: Wiley.
  • Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332.
  • Baghaei, P., & Carstensen, C. H. (2013). Fitting the mixture Rasch model to a reading comprehension test: Identifying reader types. Practical Assessment, Rese, 18(5), 1–13. doi: 10.7275/n191-pt86
  • Bolt, D. M., Cohen, A. S., & Wollack, J. A. (2002). Item parameter estimation under conditions of test speededness: Application of a mixture Rasch model with ordinal constraints. Journal of Educational Measurement, 39, 331-348. doi: 10.1111/j.1745-3984.2002.tb01146.x
  • Butera, N. M., Lanza, S. T., & Coffman, D. L. (2014). A framework for estimating causal effects in latent class analysis: Is there a causal link between early sex and subsequent profiles of delinquency? Prevention Science, 15(3), 397– 407. doi: 10.1007/s11121-013-0417-3
  • Chung, H., Park, Y., & Lanza, S. T. (2005). Latent transition analysis with covariates: Pubertal timing and substance use behaviours in adolescent females. Statistics in Medicine, 24, 2895-2910. doi: 10.1002/sim.2148
  • Clark, S. L. (2010). Mixture modeling with behavioral data (Unpublished doctoral dissertation). University of California, Los Angeles.
  • Eid, M., Langeheine, R., & Diener, E. (2003). Comparing typological structures across cultures by multigroup latent class analysis: A primer. Journal of Cross-Cultural Psychology, 34(2), 195-210. doi: 10.1177/0022022102250427
  • Clark, S. L., & Muthén, B. O. (2009). Relating latent class analysis results to variables not included in the analysis. 2009 Manuscript submitted for publication. Retrieved from http://www.statmodel.com/download/relatinglca.pdf
  • Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New Jersey: Wiley.
  • De Ayala, R. J., & Santiago, S. Y. (2017). An introduction to mixture item response theory models. Journal of School Psychology, 60, 25-40. doi: 10.1016/j.jsp.2016.01.002
  • DeMars, C. E., & Lau, A. (2011). Differential item functioning detection with latent classes: How accurately detect who is responding differentially? Educational and Psychological Measurement, 71(4), 597–616. doi:10.1177/0013164411404221
  • Embretson, S. E. (2007). Mixture Rasch models for measurement in cognitive psychology. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 235-253). New York: Springer Verlag.
  • Glück, J., & Spiel, C. (2007). Studying development via Item Response Model: A wide range of potential uses. In M. von Davier, & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 281-292). New York: Springer Verlag.
  • Goodman, L. A. (2002). Latent class analysis: The empirical study of latent types, latent variables, and latent structures. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis. New York: Cambridge University Press.
  • Güngör Culha, D. & Korkmaz, M. (2011). Örtük sınıf analizi ile bir örnek uygulama. Eğitimde ve Psikolojide Ölçme Değerlendirme Dergisi, 2(2), 191-199.
  • Güngör, Culha, D., Korkmaz, M. & Somer, O. (2013). Çoklu-grup örtük sınıf analizi ve ölçme eşdeğerliği. Türk Psikoloji Dergisi, 28(72), 48-57.
  • Hagenaars, J. A., & McCutcheon, A. (2002). Applied latent class analysis. Cambridge, UK: Cambridge University Press.
  • Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Newbury Park, CA: Sage.
  • Jeon, M. (2018). A constrained confirmatory mixture IRT model: Extensions and estimation of the Saltus model using Mplus. The Quantitative Methods for Psychology, 14, 120–136. doi: 10.20982/tqmp.14.2.p120
  • Jung, T., & Wickrama, K. A. S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317. doi: 10.1111/j.1751-9004.2007.00054.x
  • Kankaraš, M., Vermunt, J. K., & Moors, G. (2011). Measurement equivalence of ordinal items: A comparison of factor analytic, Item Response Theory, and latent class approaches. Sociological Methods & Research, 40, 279–310. doi: 10.1177/0049124111405301
  • Kreiner, S., & Christensen, K. B. (2007). Validity and objectivity in health-related scales: Analysis by graphical loglinear Rasch models. In M. von Davier & C. H. Carstensen (Eds.). Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 329-346). New York: Springer Verlag. doi:10.1007/s11336-013-9347-z
  • Lanza, S. T., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis. In J. A. Schinka & W. F. Velicer (Eds.), Handbook of psychology: Vol. 2, research methods in psychology (pp. 663-685). Hoboken, NJ: Wiley.
  • Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.
  • Leech, R. M., McNaughton, S. A., & Timperio, A. (2014). The clustering of diet, physical activity and sedentary behavior in children and adolescents: A review. International Journal of Behavioral Nutrition and Physical Activity, 11, 4. doi: 10.1186/1479-5868-11-4
  • Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767-778. doi: 10.1093/biomet/88.3.767
  • Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21–39. doi: 10.1037/1082-989X.10.1.21
  • Lubke, G., & Neale, M. C. (2006). Distinguishing between latent classes and continuous factors: Resolution by maximum likelihood? Multivariate Behavioral Research, 41, 499–532. doi: 10.1207/s15327906mbr4104_4
  • Magidson, J., & Vermunt, J. K. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing, 20(1), 36–43.
  • Martin, M. O., Mullis, I. V. S., & Hooper, M. (2016). Methods and procedures in TIMSS 2015. Chestnut Hill, MA: Boston College, TIMSS & PIRLS International Study Center. Zugriff am (Vol. 21).
  • Masyn, K. (2017). Measurement invariance and differential item functioning in latent class analysis with stepwise multiple indicator multiple cause modeling. Structural Equation Modeling, 24, 180-197. doi: 10.1080/10705511.2016.1254049
  • McLachlan, G. J., & Peel, D. (2004). Finite mixture models. New York: Wiley.
  • Morin, A. J., Meyer, J. P., Creusier, J., & Biétry, F. (2016). Multiple-group analysis of similarity in latent profile solutions. Organizational Research Methods, 19(2), 231–254. doi: 10.1177/1094428115621148
  • Mullis, I. V., Martin, M. O., Foy, P., & Hooper, M. (2016). TIMSS 2015 international results in mathematics. Amsterdam, The Netherlands: International Association for the Evaluation of Educational Achievement.
  • Messick, S. (1994). The interplay of evidence and consequences in the validation of performance assessments. Educational Researcher, 23, 13–23. doi: 10.3102/0013189X023002013
  • Mislevy, R., & Huang, C. W. (2007). Measurement models as narrative structures. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications. New York: Springer Verlag.
  • Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th Edition). Los Angeles, CA: Muthén & Muthén.
  • Nagin, D. (2005). Group-based modeling of development. London: Harvard University.
  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535-569. doi: 10.1080/10705510701575396
  • Oliveri, M. E., Ercikan, K., Zumbo, B. D., & Lawless, R. (2014). Uncovering substantive patterns in student responses in international large-scale assessments—Comparing a latent class to a manifest DIF approach. International Journal of Testing, 14(3), 265-287. doi: 10.1080/15305058.2014.891223
  • Oliveri, M. E., & von Davier, M. (2011). Investigation of model fit and score scale comparability in international assessments. Psychological Test and Assessment Modeling, 53(3), 315–333.
  • Olson J. S., Hummer A. K., & Harris K. M. (2017). Gender and health behavior clustering among U.S. Young Adults, Biodemography and Social Biology, 63(1), 3-20. doi: 10.1080/19485565.2016.1262238
  • Park, Y. S., Lee, Y.-S., & Xing, K. (2016). Investigating the impact of item parameter drift for item response theory models with mixture distributions. Frontiers in Psychology, 7, 255. doi: 10.3389/fpsyg.2016.00255
  • Rindskopf, D. (2003). Mixture or homogeneous? Comment on Bauer and Curran (2003). Psychological Methods, 8(3), 364–368. doi: 10.1037/1082-989X.8.3.364
  • Rutkowski, L., & Rutkowski, D. (2018). Improving the comparability and local usefulness of international assessments: A look back and a way forward. Scandinavian Journal of Educational Research, 62(3), 354–367. doi: 10.1080/00313831.2016.1261044
  • Samuelsen, K. (2005). Examining differential item functioning from a latent class perspective. Doctoral Dissertation. Available from ProQuest Dissertations and Theses database. (UMI No. 3175148).
  • Schwarz, G. (1978). Estimating the dimension of a model. The Annuals of Statistics, 6(2), 461-464.
  • Sen, S. (2016) Applying the Mixture Rasch Model to the Runco Ideational Behavior Scale. Creativity Research Journal, 28(4), 426-434. doi: 10.1080/10400419.2016.1229985
  • Thomson, S., Wernert, N., O’Grady, E., & Rodrigues, S. (2017). TIMSS 2015: Reporting Australia’s results. Melbourne, Australia: Australian Council for Educational Research.
  • Toker, T. (2016). A comparison latent class analysis and the mixture Rasch model: A cross-cultural comparison of 8th grade mathematics achievement in the fourth international mathematics and science study (TIMSS-2011). Doctoral Dissertation, The Faculty of the Morgridge College of Education University of Denver, USA.
  • Uyar, Ş. (2015). Gözlenen gruplara ve örtük sınıflara göre belirlenen değişen madde fonksiyonunun karşılaştırılması. Yayınlanmamış Doktora Tezi. Hacettepe Üniversitesi, Eğitim Bilimleri Ensitüsü. Ankara.
  • Vermunt, J. K., & Magidson, J. (2004). Latent class analysis. The Sage Encyclopedia of Social Sciences Research Methods (pp. 549-553).
  • Vermunt, J. K., & Magidson, J. (2020). How to perform three-step latent class analysis in the presence of measurement non-invariance or differential item functioning. Structural Equation Modeling: A Multidisciplinary Journal. doi: 10.1080/10705511.2020.1818084
  • Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. Hoboken, NJ: John Wiley & Sons.
  • Yandı, A., Köse, İ. A. & Uysal, Ö. (2017). Farklı yöntemlerle ölçme değişmezliğinin incelenmesi: PISA 2012 örneği. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 13(1), 243-253. doi: 10.17860/mersinefd.305952

Ayrıntılar

Birincil Dil İngilizce
Konular Sosyal
Bölüm Makaleler
Yazarlar

Fatıma Münevver SAATÇİOĞLU (Sorumlu Yazar)
ANKARA YILDIRIM BEYAZIT ÜNİVERSİTESİ
0000-0003-4797-207X
Türkiye

Yayımlanma Tarihi 29 Aralık 2021
Yayınlandığı Sayı Yıl 2021, Cilt 12, Sayı 4

Kaynak Göster

Bibtex @araştırma makalesi { epod984771, journal = {Journal of Measurement and Evaluation in Education and Psychology}, issn = {1309-6575}, eissn = {1309-6575}, address = {}, publisher = {Eğitimde ve Psikolojide Ölçme ve Değerlendirme Derneği}, year = {2021}, volume = {12}, pages = {321 - 335}, doi = {10.21031/epod.984771}, title = {An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups}, key = {cite}, author = {Saatçioğlu, Fatıma Münevver} }
APA Saatçioğlu, F. M. (2021). An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups . Journal of Measurement and Evaluation in Education and Psychology , 12 (4) , 321-335 . DOI: 10.21031/epod.984771
MLA Saatçioğlu, F. M. "An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups" . Journal of Measurement and Evaluation in Education and Psychology 12 (2021 ): 321-335 <https://dergipark.org.tr/tr/pub/epod/issue/67388/984771>
Chicago Saatçioğlu, F. M. "An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups". Journal of Measurement and Evaluation in Education and Psychology 12 (2021 ): 321-335
RIS TY - JOUR T1 - An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups AU - Fatıma Münevver Saatçioğlu Y1 - 2021 PY - 2021 N1 - doi: 10.21031/epod.984771 DO - 10.21031/epod.984771 T2 - Journal of Measurement and Evaluation in Education and Psychology JF - Journal JO - JOR SP - 321 EP - 335 VL - 12 IS - 4 SN - 1309-6575-1309-6575 M3 - doi: 10.21031/epod.984771 UR - https://doi.org/10.21031/epod.984771 Y2 - 2021 ER -
EndNote %0 Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups %A Fatıma Münevver Saatçioğlu %T An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups %D 2021 %J Journal of Measurement and Evaluation in Education and Psychology %P 1309-6575-1309-6575 %V 12 %N 4 %R doi: 10.21031/epod.984771 %U 10.21031/epod.984771
ISNAD Saatçioğlu, Fatıma Münevver . "An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups". Journal of Measurement and Evaluation in Education and Psychology 12 / 4 (Aralık 2021): 321-335 . https://doi.org/10.21031/epod.984771
AMA Saatçioğlu F. M. An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups. JMEEP. 2021; 12(4): 321-335.
Vancouver Saatçioğlu F. M. An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups. Journal of Measurement and Evaluation in Education and Psychology. 2021; 12(4): 321-335.
IEEE F. M. Saatçioğlu , "An Application of Latent Class Analysis for TIMSS 2015 Data: Detecting Heterogeneous Subgroups", Journal of Measurement and Evaluation in Education and Psychology, c. 12, sayı. 4, ss. 321-335, Ara. 2021, doi:10.21031/epod.984771