AN ANALYSİS OF THE ACADEMİC ACHİEVEMENT PROFİLES OF YEAR 8 STUDENTS USİNG LATENT PROFİLE ANALYSİS
DOI:
https://doi.org/10.18623/rvd.v23.6497Keywords:
Latent Profile Analysis, Ordinal Logistic Regression, Academic Achievement ProfilesAbstract
This study examined the academic performance patterns in core subject grades among 8th-grade students attending schools in the province of Antalya and assessed how these patterns varied according to different educational and sociocultural variables. Going beyond approaches that focus solely on average grades, latent profile analysis was used to determine whether students could be grouped into similar performance categories. The sample consisted of 314 students. Grades in Turkish, mathematics, science, social studies, foreign language, and religious culture were included in the model as continuous variables. Model comparisons revealed four distinct profiles, presenting a sequential academic model ranging from low to high performance; mathematics and foreign language courses provided clearer distinctions between the profiles. Ordinal logistic regression analysis was conducted to identify the variables explaining these profiles. The results indicate that academic performance profiles are related not only to cognitive performance but also to the educational and sociocultural resources available to students.
References
Araújo, A. M., Gomes, C. M. A., Almeida, L. S., & Núñez, J. C. (2019). A latent profile analysis of first-year university students' academic expectations. Anales de Psicología, 35(1), 58-67.
Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. John Wiley & Sons.
Engzell, P. (2021). What do books in the home proxy for? A cautionary tale. Sociological Methods & Research, 50(4), 1487-1514.
Fagginger Auer, M. F., Hickendorff, M., Van Putten, C. M., Béguin, A. A., & Heiser, W. J. (2016). Multilevel latent class analysis for large-scale educational assessment data: Exploring the relation between the curriculum and students' mathematical strategies. Applied Measurement in Education, 29(3), 199-220.
Haertel, E. (1984). An application of latent class models to assessment data. Applied Psychological Measurement, 8(3), 333-346.
Hancock, G. R., Harring, J. R., & Macready, G. B. (Eds.). (2019). Advances in latent class analysis: A festschrift in honor of C. Mitchell Dayton. Emerald Group Publishing.
Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
Kim, J. K. (2023). The relationship between course evaluation and academic achievement of university students using latent profile analysis. Journal of Curriculum and Teaching, 12(3), 179-197.
King, K. R., Lembke, E. S., & Reinke, W. M. (2016). Using latent class analysis to identify academic and behavioral risk status in elementary students. School Psychology Quarterly, 31(1), 43.
Lamont, A. E., Markle, R. S., Wright, A., Abraczinskas, M., & Siddall, J. (2017). Innovative methods in evaluation: An application of latent class analysis to assess how teachers adopt educational innovations. American Journal of Evaluation, 39(3), 364-382.
Lanza, S. T., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis. Handbook of psychology, 663-685.
Lee, J. (2021). Latent profile analysis according to the subject selection criteria of general high school students. Journal of Learner-Centered Curriculum and Instruction, 21(24), 213-228.
McMullen, J., Lewis, R. W., & Bailey, D. H. (2020). Latent classes from complex assessments: What do they tell us?. Learning and Individual Differences, 83, 101944.
Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational issues in psychological science, 4(4), 440.
Özberk, E. H., & Türk Kurtça, T. (2021). Profiles of academic procrastination in higher education: A cross-cultural study using latent profile analysis. International Journal of Psychology and Educational Studies, 8(3), 150-160.
Park, H. (2008). Home literacy environments and children's reading performance: A comparative study of 25 countries. Educational Research and Evaluation, 14(6), 489-505.
Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. Sage publications.
Porcu, M., & Giambona, F. (2017). Introduction to latent class analysis with applications. The Journal of Early Adolescence, 37(1), 129-158.
R Core Team. (2023). R: A language and environment for statistical computing (Version 4.3) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/
Rosenberg, J. M., Beymer, P. N., Anderson, D. J., Van Lissa, C. J., & Schmidt, J. A. (2018). tidyLPA: An R package to easily carry out latent profile analysis (LPA) using open-source or commercial software. Journal of Open Source Software, 3(30), Article 978. https://doi.org/10.21105/joss.00978
Shin, Y., & Raudenbush, S. W. (2011). The causal effect of class size on academic achievement: Multivariate instrumental variable estimators with data missing at random. Journal of Educational and Behavioral Statistics, 36(2), 154-185.
Sideridis, G. D., Tsaousis, I., & Al-Harbi, K. (2021). Identifying student subgroups as a function of school level attributes: A multilevel latent class analysis. Frontiers in Psychology, 12, 624221.
Toker, T., & Green, K. (2021). A Comparison of Latent Class Analysis and the Mixture Rasch Model Using 8th Grade Mathematics Data in the Fourth International Mathematics and Science Study (TIMSS-2011). International Journal of Assessment Tools in Education, 8(4), 959-974.
Zhang, Y., Dang, Y., He, Y., Ma, X., & Wang, L. (2021). Is private supplementary tutoring effective? A longitudinally detailed analysis of private tutoring quality in China. Asia Pacific Education Review, 22(2), 239-259.
Zhang, Z., An, N., & Chen, J. (2025). More is worse: the impact of private supplementary tutoring on middle school students’ academic achievement. Applied Economics, 57(1), 106-119.
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