Latent Profile Analysis: Comparison of Achievement versus Ability-Derived Subgroups of Mathematical Skills

References

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.
2. Akogul, S., & Erisoglu, M. (2017). An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis. Entropy, 19(9), 452. MDPI AG. http://dx.doi.org/10.3390/e19090452.
3. Banfield, J.D., & Raftery, A.E (1993). Model-based Gaussian and non-Gaussian clustering. Biometrics, 49, 803–821.
4. Biernacki, C., & Govaert, G. (1997). Using the classification likelihood to choose the number of clusters. Computing Science and Statistics, 29, 451–457.
5. Carriedo, N., Rodríguez-Villagra, O. A., Pérez, L., & Iglesias-Sarmiento, V. (2024). Executive functioning profiles and mathematical and reading achievement in Grades 2, 6, and 10. Journal of School Psychology, 106, 101353. https://doi.org/10.1016/j.jsp.2024.101353
6. Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54, 1–22.
7. Cattell, R. B. (1987). Intelligence: Its structure, growth, and action. Amsterdam: North-Holland.
8. Cavanaugh, J. E. (1999). A large-sample model selection criterion based on Kullback's symmetric divergence. Statistics & Probability Letters, 42(4), 333–343. https://doi.org/10.1016/S0167-7152(98)00200-4.
9. Curriculum Associates, LLC. (2020). Understanding Student Needs: Early Results from Fall Assessments. Curriculum Associates Research Report No. 2020-40. North Billerica, MA: Author.
10. Darling-Hammond, L., & Cook-Harvey, C. M. (2018). Educating the whole child: Improving school climate to support student success. Retrieved from Learning Policy Institute https://learningpolicyinstitute.org/media/547/download?inline&file=Educating_Whole_Child_REPORT.pdf
11. Dunbar, S. B., & Welch, C. J. (2015). Iowa Assessments, Form E. Riverside Insights: Itasca, IL.
12. Green, C. T., Bunge, S. A., Chiongbian, V. B., Barrow, M., & Ferrer, E. (2017). Fluid reasoning predicts future mathematical performance among children and adolescents. Journal of Experimental Child Psychology, 157, 125–143. https://doi.org/10.1016/j.jecp.2016.12.005
13. Hwang, J., Hand, B., & French, B. F. (2023). Critical thinking skills and science achievement: A latent profile analysis. Thinking Skills and Creativity, 49, 101349. https://doi.org/10.1016/j.tsc.2023.101349
14. Kuhfeld, M., Tarasawa, B., Johnson, A., Ruzek, E., & Lewis, K. (2020). Learning during COVID-19: initial findings on students’ reading and math achievement and growth. NWEA.
15. Laursen, B., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly (1982-), 377–389.
16. Lubke, G., & Neale, M. C. (2006). Distinguishing Between Latent Classes and Continuous Factors: Resolution by Maximum Likelihood? Multivariate Behavioral Research, 41(4), 499–532. https://doi.org/10.1207/s15327906mbr4104_4.
17. Lohman, D. F. (2013) CogAT Form 7 Score Interpretation Guide: Part 3 Adapting Instruction to Student’s Needs and Abilities. Riverside Insights.
18. Lohman, D. F., & Lakin, J. M. (2017). Cognitive Abilities Test (CogAT), Form 7. Riverside Insights.
19. Litkowski, E. C., Finders, J. K., Borriello, G. A., Purpura, D. J., & Schmitt, S. A. (2020). Patterns of heterogeneity in kindergarten children's executive function: Profile associations with third grade achievement. Learning and Individual Differences, 80, 101846. https://doi.org/10.1016/j.lindif.2020.101846
20. Mahatmya, D., Assouline, S., Foley-Nicpon, M., Ali, S. R., McGinnis, D., & Teriba, A. (2023). Patterns of high ability and underrepresented students’ subject-specific psychosocial strengths: A latent profile analysis. High Ability Studies, 34(2), 229–248. https://doi.org/10.1080/13598139.2023.2176293
21. Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person-and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling, 16(2), 191–225.
22. McLachlan, G., & Peel, D. (2000). Finite mixture models. John Wiley & Sons. https://doi.org/10.1002/0471721182
23. Morin, A., Meyer, J., Creusier, J., & Biétry, F. (2016). Multiple-group analysis of similarity in latent profile solutions. Organizational Research Methods, 19(2), 231–254. https://doi.org/10.1177/1094428115621148
24. Moses, R. P., & Cobb, C. E. Jr., (2001). Radical equations: Civil rights from Mississippi to the Algebra Project. Boston: Beacon
25. National Center for Education Statistics. (2019). NAEP, Reading Report Card for the Nation and the States. U.S. Department of Education.
26. Nickerson, R. (2011). Developing intelligence through instruction. In R. J. Sternberg, & S. B. Kaufman (Eds.), The Cambridge handbook of intelligence (pp. 107–129). New York: Cambridge University Press.
27. R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org
28. Renaissance Learning (2021). How kids are performing: Tracking the school-year impact of COVID-19 on reading and mathematics achievement. Special Report Series, Spring 2021 edition.
29. Riverside Insights. (2012). Forms E and F Score Interpretation Guide. Itasca, IL: The University of Iowa (Iowa Testing Program).
30. Rosenberg, J. M., van Lissa, C. J., Beymer, P. N., Anderson, D. J., Schell, M. J. & Schmidt, J. A.
31. (2019). tidyLPA: Easily carry out Latent Profile Analysis (LPA) using open-source or
32. commercial software [R package]. https://data-edu.github.io/tidyLPA/
33. Schneider, W. (2013). Principles of assessment of aptitude and achievement. In D. H. Saklofske, V. L. Schwean, & C. R. Reynolds (Eds.), The Oxford handbook of child psychological assessment (pp. 286–330). New York: Oxford
34. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.
35. Soares, D. L., Lemos, G. C., Primi, R., & Almeida, L. S. (2015). The relationship between intelligence and academic achievement throughout middle school: The role of students' prior academic performance. Learning and Individual Differences, 41, 73–78. https://doi.org/10.1016/j.lindif.2015.02.005
36. Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. https://doi.org/10.1016/j.jvb.2020.103445
37. Younger, J. W., Schaerlaeken, S., Anguera, J. A., & Gazzaley, A. (2024). The whole is greater than the sum of its parts: Using cognitive profiles to predict academic achievement. Trends in Neuroscience and Education, 36, 100237. https://doi.org/10.1016/j.tine.2024.100237

Keywords

Mathematical Skills, Ability Assessment, Achievement Assessment, Latent Profile Analysis, CogAT

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