Research Article
BibTex RIS Cite

Latent Class Analysis and DIF Testing in Mathematics Achievement: A Comparative Study of Korea and Türkiye Using MIMIC Modeling

Year 2025, Volume: 16 Issue: 1, 13 - 29, 31.03.2025

Abstract

This study examines the latent classes of mathematics achievement and investigates measurement invariance (differential item functioning) between Korea and Turkey. Moreover, it explores the influence of the country on the latent classes of mathematics achievement. To achieve this, data from eighth-grade students in TIMSS 2019 were analyzed using Latent Class MIMIC Modeling. The findings uncovered diverse latent classes of math achievement and detected both uniform and non-uniform DIF between Korea and Turkey. Furthermore, the country was found to significantly affect the latent class membership of math achievement. This study highlights the necessity of verifying measurement invariance of indicator variables in latent class analysis (LCA). It also sheds light on areas where students performed favorably or unfavorably in mathematics achievement tests across these countries by investigating DIF. These findings have important implications for mathematics education in Korea and Turkey.

Ethical Statement

The authors confirm that there are no conflicts of interest related to this research, and that no unethical behavior occurred during the process of data collection, analysis, and reporting of the results.

References

  • Arıkan, S., Van de Vijver, F., & Yagmur, K. (2016). Factors contributing to mathematics achievement differences of Turkish and Australian students in TIMSS 2007 and 2011. EURASIA Journal of Mathematics, Science and Technology Education, 12(8), 2039-2059. https://doi.org/10.12973/eurasia.2016.1268a
  • Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using M plus. Structural equation modeling: A multidisciplinary Journal, 21(3), 329-341. https://doi.org/10.1080/10705511.2014.915181
  • Badri, M. (2019). School Emphasis on Academic Success and TIMSS Science/Math Achievements. International Journal of Research in Education and Science, 5(1), 176-189. https://eric.ed.gov/?id=EJ1197994
  • Clark, S. L., & Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis. https://www.statmodel.com/download/relatinglca.pdf
  • Demirus, K. B., & Pektas, S. (2022). Investigation of Timss 2015 science test items in terms of differential item functioning according to language and culture. International Journal of Education Technology & Scientific Researches, 7(18), 1166-1178. http://dx.doi.org/10.35826/ijetsar.499
  • De Ayala, R. J., Kim, S. H., Stapleton, L. M., & Dayton, C. M. (2002). Differential item functioning: A mixture distribution conceptualization. International Journal of Testing, 2(3-4), 243-276. https://doi.org/10.1080/15305058.2002.9669495
  • Dittrich, K., & Neuhaus, D. A. (2023). Korea’s ‘education fever’ from the late nineteenth to the early twenty-first century. History of Education, 52(4), 539-552. https://doi.org/10.1080/0046760X.2022.2098391
  • Dorans, N. J., & Holland, P. W. (1992). DIF detection and description: Mantel‐Haenszel and standardization 1, 2. ETS Research Report Series, 1992(1), i-40. https://doi.org/10.1002/j.2333-8504.1992.tb01440.x
  • Geesa, R. L., Izci, B., Song, H., & Chen, S. (2019). Exploring Factors of Home Resources and Attitudes Towards Mathematics in Mathematics Achievement in South Korea, Turkey, and the United States. Eurasia Journal of Mathematics, Science and Technology Education, 15(9), eM_1751. https://doi.org/10.29333/ejmste/108487
  • Geesa, R. L., Izci, B., Chen, S., & Song, H. S. (2020). The Role of Gender and Attitudes toward Science in Fourth and Eighth Graders' Science Achievement in South Korea, Turkey, and the United States. Journal of Research in Education, 29(2), 54-87. https://eric.ed.gov/?id=EJ1274027
  • Guhl, P. (2019). The impact of early math and numeracy skills on academic achievement in elementary school.
  • Hambleton, R. K. (2001). The next generation of the ITC test translation and adaptation guidelines. European journal of psychological assessment, 17(3), 164-172. https://doi.org/10.1027/1015-5759.17.3.164
  • Huang, J. (2020). Assessing robustness of the Rasch mixture model to detect differential item functioning: A Monte Carlo simulation study (Doctoral dissertation, University of Denver). https://digitalcommons.du.edu/etd/1784/
  • Hwang, H. J., & Hwang, H. K. (2008). An Effect of the Constructivist Discussion on Learning Attitude in Mathematics and Children's Mathematics Achievement. Education of Primary School Mathematics, 11(1), 59-74. https://koreascience.kr/article/JAKO200801440607130.page
  • Im, S., & Park, H. J. (2010). A comparison of US and Korean students' mathematics skills using a cognitive diagnostic testing method: linkage to instruction. Educational Research and Evaluation, 16(3), 287-301. https://doi.org/10.1080/13803611.2010.523294
  • Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity, Marketing Science 16(1), 39-59. https://doi.org/10.1287/mksc.16.1.39
  • Kalaycioğlu, D. B., & Berberoğlu, G. (2011). Differential item functioning analysis of the science and mathematics items in the university entrance examinations in Turkey. Journal of Psychoeducational Assessment, 29(5), 467-478. https://doi.org/10.1177/0734282910391623
  • Klieme, E., & Baumert, J. (2001). Identifying national cultures of mathematics education: Analysis of cognitive demands and differential item functioning in TIMSS. European journal of psychology of education, 16, 385-402. https://link.springer.com/article/10.1007/BF03173189
  • Ko, D. H., & Jung, H. S. (2020). Analysis on the Effect of Mathematics Class Characteristics and Mathematical Confidence on Mathematical Academic Achievement: Applying Hierarchical Linear Model. School Mathematics, 22(2), 313-332. https://doi.org/10.29275/sm.2020.06.22.2.313
  • Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and Individual Differences, 65, 50-64. https://doi.org/10.1016/j.lindif.2018.05.009
  • Lubinski, D., Benbow, C. P., & Kell, H. J. (2014). Life paths and accomplishments of mathematically precocious males and females four decades later. Psychological Science, 25(12), 2217-2232. https://doi.org/10.1177/0956797614551371
  • Lyons-Thomas, J., Sandilands, D., & Ercikan, K. (2014). Gender Differential Item Functioning in Mathematics in Four International Jurisdictions. Education & Science/Egitim ve Bilim, 39, 20-32.
  • Masyn, K. E. (2017). Measurement invariance and differential item functioning in latent class analysis with stepwise multiple indicator multiple cause modeling. Structural Equation Modeling: A Multidisciplinary Journal, 24(2), 180-197. https://doi.org/10.1080/10705511.2016.1254049
  • Mullis, I. V., & Martin, M. O. (2017). TIMSS 2019 Assessment Frameworks. International Association for the Evaluation of Educational Achievement. https://eric.ed.gov/?id=ed596167
  • Mullis, I. V., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (2020). TIMSS 2019 international results in mathematics and science. https://www.skolporten.se/app/uploads/2020/12/timss-2019-highlights-1.pdf
  • Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science, 4(4), 440-461. https://doi.org/10.1037/tps0000176
  • Nylund-Gibson, K., & Masyn, K. E. (2016). Covariates and mixture modeling: Results of a simulation study exploring the impact of misspecified effects on class enumeration. Structural Equation Modeling: A Multidisciplinary Journal, 23(6), 782-797. https://doi.org/10.1080/10705511.2016.1221313
  • Ram, N., & Grimm, K. J. (2009). Methods and measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International journal of behavioral development, 33(6), 565-576. https://doi.org/10.1177/0165025409343765
  • Saatcioglu, F. M. (2022). Differential item functioning across gender with MIMIC modeling: PISA 2018 financial literacy items. International Journal of Assessment Tools in Education, 9(3), 631-653. https://doi.org/10.21449/ijate.1076464
  • Samuelsen, K. M. (2008). Examining differential item functioning from a latent mixture perspective. Advances in latent variable mixture models, 177-197.
  • Şen, S., & Arıkan, M. (2015). A diagnostic comparison of Turkish and Korean students’ mathematics performances on the TIMSS 2011 assessment. Journal of Measurement and Evaluation in Education and Psychology, 6(2). https://doi.org/10.21031/epod.65266
  • Shin, K., Jahng, K. E., & Kim, D. (2019). Stories of South Korean mothers’ education fever for their children’s education. Asia Pacific Journal of Education, 39(3), 338-356. https://doi.org/10.1080/02188791.2019.1607720
  • Sohn, W. S. (2010). Exploring Potential Sources of DIF for PISA 2006 Mathematics Literacy Items: Application of Logistic Regression Analysis. Journal of Educational Evaluation, 23(2), 371-390. https://scholar-kyobobook-co-kr-ssl.ca.skku.edu/article/detail/4010023071385
  • Song, H. S., Kim, H. C., & Jung, H. S. (2023). The Effect of Participation in Private Education on Math Affective Attitudes: Measurement Invariance and Differential Item Functioning in Latent Class MIMIC Model. Journal of Educational Evaluation, 36(4), 687-709. http://dx.doi.org/10.31158/JEEV.2023.36.4.687
  • Tsaousis, I., Sideridis, G. D., & AlGhamdi, H. M. (2020). Measurement invariance and differential item functioning across gender within a latent class analysis framework: Evidence from a high-stakes test for university admission in Saudi Arabia. Frontiers in Psychology, 11, 622. https://doi.org/10.3389/fpsyg.2020.00622
  • Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political analysis, 18(4), 450-469. https://doi.org/10.1093/pan/mpq025
  • Wang, X. S., Perry, L. B., Malpique, A., & Ide, T. (2023). Factors predicting mathematics achievement in PISA: a systematic review. Large-scale Assessments in Education, 11(1), 24. https://doi.org/10.1186/s40536-023-00174-8
  • Wiberg, M. (2019). The relationship between TIMSS mathematics achievements, grades, and national test scores. Education Inquiry, 10(4), 328-343. https://doi.org/10.1080/20004508.2019.1579626
  • Woo, H., & Hodges, N. N. (2015). Education fever: Exploring private education consumption motivations among Korean parents of preschool children. Family and Consumer Sciences Research Journal, 44(2), 127-142. https://doi.org/10.1111/fcsr.12131
  • Yildirim, H. H. (2006). The DIF analysis of mathematics items in the international assessment programs. https://www.proquest.com/openview/37bc178a79e82e2c13ca84983403a39a/1?cbl=2026366&diss=y&pq-origsite=gscholar
  • Yoon, J. Y., & Lee, Y. S. (2013). A Study of DIF Analyses using TIMSS (2007) Mathematics Test Across South Korea, U. S., and Singapore. Journal of Educational Evaluation, 26(2), 415-439. https://scholar-kyobobook-co-kr-ssl.ca.skku.edu/article/detail/4010023595562
Year 2025, Volume: 16 Issue: 1, 13 - 29, 31.03.2025

Abstract

References

  • Arıkan, S., Van de Vijver, F., & Yagmur, K. (2016). Factors contributing to mathematics achievement differences of Turkish and Australian students in TIMSS 2007 and 2011. EURASIA Journal of Mathematics, Science and Technology Education, 12(8), 2039-2059. https://doi.org/10.12973/eurasia.2016.1268a
  • Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using M plus. Structural equation modeling: A multidisciplinary Journal, 21(3), 329-341. https://doi.org/10.1080/10705511.2014.915181
  • Badri, M. (2019). School Emphasis on Academic Success and TIMSS Science/Math Achievements. International Journal of Research in Education and Science, 5(1), 176-189. https://eric.ed.gov/?id=EJ1197994
  • Clark, S. L., & Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis. https://www.statmodel.com/download/relatinglca.pdf
  • Demirus, K. B., & Pektas, S. (2022). Investigation of Timss 2015 science test items in terms of differential item functioning according to language and culture. International Journal of Education Technology & Scientific Researches, 7(18), 1166-1178. http://dx.doi.org/10.35826/ijetsar.499
  • De Ayala, R. J., Kim, S. H., Stapleton, L. M., & Dayton, C. M. (2002). Differential item functioning: A mixture distribution conceptualization. International Journal of Testing, 2(3-4), 243-276. https://doi.org/10.1080/15305058.2002.9669495
  • Dittrich, K., & Neuhaus, D. A. (2023). Korea’s ‘education fever’ from the late nineteenth to the early twenty-first century. History of Education, 52(4), 539-552. https://doi.org/10.1080/0046760X.2022.2098391
  • Dorans, N. J., & Holland, P. W. (1992). DIF detection and description: Mantel‐Haenszel and standardization 1, 2. ETS Research Report Series, 1992(1), i-40. https://doi.org/10.1002/j.2333-8504.1992.tb01440.x
  • Geesa, R. L., Izci, B., Song, H., & Chen, S. (2019). Exploring Factors of Home Resources and Attitudes Towards Mathematics in Mathematics Achievement in South Korea, Turkey, and the United States. Eurasia Journal of Mathematics, Science and Technology Education, 15(9), eM_1751. https://doi.org/10.29333/ejmste/108487
  • Geesa, R. L., Izci, B., Chen, S., & Song, H. S. (2020). The Role of Gender and Attitudes toward Science in Fourth and Eighth Graders' Science Achievement in South Korea, Turkey, and the United States. Journal of Research in Education, 29(2), 54-87. https://eric.ed.gov/?id=EJ1274027
  • Guhl, P. (2019). The impact of early math and numeracy skills on academic achievement in elementary school.
  • Hambleton, R. K. (2001). The next generation of the ITC test translation and adaptation guidelines. European journal of psychological assessment, 17(3), 164-172. https://doi.org/10.1027/1015-5759.17.3.164
  • Huang, J. (2020). Assessing robustness of the Rasch mixture model to detect differential item functioning: A Monte Carlo simulation study (Doctoral dissertation, University of Denver). https://digitalcommons.du.edu/etd/1784/
  • Hwang, H. J., & Hwang, H. K. (2008). An Effect of the Constructivist Discussion on Learning Attitude in Mathematics and Children's Mathematics Achievement. Education of Primary School Mathematics, 11(1), 59-74. https://koreascience.kr/article/JAKO200801440607130.page
  • Im, S., & Park, H. J. (2010). A comparison of US and Korean students' mathematics skills using a cognitive diagnostic testing method: linkage to instruction. Educational Research and Evaluation, 16(3), 287-301. https://doi.org/10.1080/13803611.2010.523294
  • Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity, Marketing Science 16(1), 39-59. https://doi.org/10.1287/mksc.16.1.39
  • Kalaycioğlu, D. B., & Berberoğlu, G. (2011). Differential item functioning analysis of the science and mathematics items in the university entrance examinations in Turkey. Journal of Psychoeducational Assessment, 29(5), 467-478. https://doi.org/10.1177/0734282910391623
  • Klieme, E., & Baumert, J. (2001). Identifying national cultures of mathematics education: Analysis of cognitive demands and differential item functioning in TIMSS. European journal of psychology of education, 16, 385-402. https://link.springer.com/article/10.1007/BF03173189
  • Ko, D. H., & Jung, H. S. (2020). Analysis on the Effect of Mathematics Class Characteristics and Mathematical Confidence on Mathematical Academic Achievement: Applying Hierarchical Linear Model. School Mathematics, 22(2), 313-332. https://doi.org/10.29275/sm.2020.06.22.2.313
  • Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and Individual Differences, 65, 50-64. https://doi.org/10.1016/j.lindif.2018.05.009
  • Lubinski, D., Benbow, C. P., & Kell, H. J. (2014). Life paths and accomplishments of mathematically precocious males and females four decades later. Psychological Science, 25(12), 2217-2232. https://doi.org/10.1177/0956797614551371
  • Lyons-Thomas, J., Sandilands, D., & Ercikan, K. (2014). Gender Differential Item Functioning in Mathematics in Four International Jurisdictions. Education & Science/Egitim ve Bilim, 39, 20-32.
  • Masyn, K. E. (2017). Measurement invariance and differential item functioning in latent class analysis with stepwise multiple indicator multiple cause modeling. Structural Equation Modeling: A Multidisciplinary Journal, 24(2), 180-197. https://doi.org/10.1080/10705511.2016.1254049
  • Mullis, I. V., & Martin, M. O. (2017). TIMSS 2019 Assessment Frameworks. International Association for the Evaluation of Educational Achievement. https://eric.ed.gov/?id=ed596167
  • Mullis, I. V., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (2020). TIMSS 2019 international results in mathematics and science. https://www.skolporten.se/app/uploads/2020/12/timss-2019-highlights-1.pdf
  • Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science, 4(4), 440-461. https://doi.org/10.1037/tps0000176
  • Nylund-Gibson, K., & Masyn, K. E. (2016). Covariates and mixture modeling: Results of a simulation study exploring the impact of misspecified effects on class enumeration. Structural Equation Modeling: A Multidisciplinary Journal, 23(6), 782-797. https://doi.org/10.1080/10705511.2016.1221313
  • Ram, N., & Grimm, K. J. (2009). Methods and measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International journal of behavioral development, 33(6), 565-576. https://doi.org/10.1177/0165025409343765
  • Saatcioglu, F. M. (2022). Differential item functioning across gender with MIMIC modeling: PISA 2018 financial literacy items. International Journal of Assessment Tools in Education, 9(3), 631-653. https://doi.org/10.21449/ijate.1076464
  • Samuelsen, K. M. (2008). Examining differential item functioning from a latent mixture perspective. Advances in latent variable mixture models, 177-197.
  • Şen, S., & Arıkan, M. (2015). A diagnostic comparison of Turkish and Korean students’ mathematics performances on the TIMSS 2011 assessment. Journal of Measurement and Evaluation in Education and Psychology, 6(2). https://doi.org/10.21031/epod.65266
  • Shin, K., Jahng, K. E., & Kim, D. (2019). Stories of South Korean mothers’ education fever for their children’s education. Asia Pacific Journal of Education, 39(3), 338-356. https://doi.org/10.1080/02188791.2019.1607720
  • Sohn, W. S. (2010). Exploring Potential Sources of DIF for PISA 2006 Mathematics Literacy Items: Application of Logistic Regression Analysis. Journal of Educational Evaluation, 23(2), 371-390. https://scholar-kyobobook-co-kr-ssl.ca.skku.edu/article/detail/4010023071385
  • Song, H. S., Kim, H. C., & Jung, H. S. (2023). The Effect of Participation in Private Education on Math Affective Attitudes: Measurement Invariance and Differential Item Functioning in Latent Class MIMIC Model. Journal of Educational Evaluation, 36(4), 687-709. http://dx.doi.org/10.31158/JEEV.2023.36.4.687
  • Tsaousis, I., Sideridis, G. D., & AlGhamdi, H. M. (2020). Measurement invariance and differential item functioning across gender within a latent class analysis framework: Evidence from a high-stakes test for university admission in Saudi Arabia. Frontiers in Psychology, 11, 622. https://doi.org/10.3389/fpsyg.2020.00622
  • Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political analysis, 18(4), 450-469. https://doi.org/10.1093/pan/mpq025
  • Wang, X. S., Perry, L. B., Malpique, A., & Ide, T. (2023). Factors predicting mathematics achievement in PISA: a systematic review. Large-scale Assessments in Education, 11(1), 24. https://doi.org/10.1186/s40536-023-00174-8
  • Wiberg, M. (2019). The relationship between TIMSS mathematics achievements, grades, and national test scores. Education Inquiry, 10(4), 328-343. https://doi.org/10.1080/20004508.2019.1579626
  • Woo, H., & Hodges, N. N. (2015). Education fever: Exploring private education consumption motivations among Korean parents of preschool children. Family and Consumer Sciences Research Journal, 44(2), 127-142. https://doi.org/10.1111/fcsr.12131
  • Yildirim, H. H. (2006). The DIF analysis of mathematics items in the international assessment programs. https://www.proquest.com/openview/37bc178a79e82e2c13ca84983403a39a/1?cbl=2026366&diss=y&pq-origsite=gscholar
  • Yoon, J. Y., & Lee, Y. S. (2013). A Study of DIF Analyses using TIMSS (2007) Mathematics Test Across South Korea, U. S., and Singapore. Journal of Educational Evaluation, 26(2), 415-439. https://scholar-kyobobook-co-kr-ssl.ca.skku.edu/article/detail/4010023595562
There are 41 citations in total.

Details

Primary Language English
Subjects Modelling
Journal Section Articles
Authors

Hee Sun Jung 0000-0003-0093-2193

Hyo Seob Song This is me 0000-0001-7554-2849

Publication Date March 31, 2025
Submission Date August 28, 2024
Acceptance Date March 7, 2025
Published in Issue Year 2025 Volume: 16 Issue: 1

Cite

APA Jung, H. S., & Song, H. S. (2025). Latent Class Analysis and DIF Testing in Mathematics Achievement: A Comparative Study of Korea and Türkiye Using MIMIC Modeling. Journal of Measurement and Evaluation in Education and Psychology, 16(1), 13-29. https://doi.org/10.21031/epod.1539828