International Association of Educators   |  ISSN: 2834-7919   |  e-ISSN: 1554-5210
International Journal of Progressive Education
International Association of Educators

Original article | International Journal of Progressive Education 2023, Vol. 19(6) 20-32

Latent Transition Modeling for Categorical Latent Variables: An Application Using Longitudinal Resilience Data

Derya Akbaş & Ni̇lüfer Kahraman

pp. 20 - 32   |  DOI: https://doi.org/10.29329/ijpe.2023.615.2   |  Manu. Number: MANU-2212-09-0007

Published online: December 12, 2023  |   Number of Views: 49  |  Number of Download: 467

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Abstract

This study aims to illustrate how the latent transition modeling might be applied to identify qualitative change patterns in longitudinal assessment settings. Using the data collected on three measurement occasions, we examine whether and to what extent resilience latent class memberships of pre-service teachers changed over time. First, latent class models are tested for all time points separately, revealing that a 4-class model is the best fitting model (Resilience, Competence, Maladaptation, and Vulnerability). Next, latent transition model alternatives are tested, leading to the conclusion that the transition model with stationary probabilities provides the best fit. The results show that individuals with the statuses of Vulnerability and Competence have the highest probabilities of maintaining the same status compared to others and that the highest transition probabilities occur from the status of Resilience to Competence and from Maladaptation to Vulnerability. These findings suggest that individuals with sufficient coping skills might have the status of Resilience and move toward Competence, while those lacking coping skills might have the status of Maladaptation and move toward Vulnerability with the absence or decrease of adversity. A discussion is provided highlighting the usefulness of the latent transition modeling when it is suspected that latent class memberships of subjects could be sensitive to change over time.

Keywords: Assessment, Categorical Latent Variable, Latent Transition Analysis, Longitudinal

How to Cite this Article?

APA 6th edition
Akbas, D. & Kahraman, N. (2023). Latent Transition Modeling for Categorical Latent Variables: An Application Using Longitudinal Resilience Data . International Journal of Progressive Education, 19(6), 20-32. doi: 10.29329/ijpe.2023.615.2

Harvard
Akbas, D. and Kahraman, N. (2023). Latent Transition Modeling for Categorical Latent Variables: An Application Using Longitudinal Resilience Data . International Journal of Progressive Education, 19(6), pp. 20-32.

Chicago 16th edition
Akbas, Derya and Ni̇lufer Kahraman (2023). "Latent Transition Modeling for Categorical Latent Variables: An Application Using Longitudinal Resilience Data ". International Journal of Progressive Education 19 (6):20-32. doi:10.29329/ijpe.2023.615.2.
References
  1. Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52(3), 317-332. [Google Scholar]
  2. Akbas, D. & Kahraman. N. (2019). A primer on applied Latent Class Analysis for modeling qualitative differences: An application on Resilience data. Mediterranean Journal of Educational Research, 13(29), 356-382. https://doi.org/10.29329/mjer.2019.210.19 [Google Scholar] [Crossref] 
  3. Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade, & P. B. Baltes (Eds.), Longitudinal research in the study of behavior and development (pp. 1–39). Academic. [Google Scholar]
  4. Beck, C., McSweeney, J. C., Richards, K. C., Roberson, P. K., Tsai, P. F., & Souder, E. (2010). Challenges in tailored intervention research. Nursing Outlook, 58(2), 104-110. https://doi.org/10.1016/j.outlook.2009.10.004 [Google Scholar] [Crossref] 
  5. Collins, L. M. (1991). The measurement of dynamic latent variables in longitudinal aging research: Quantifying adult development. Experimental Aging Research, 17(1), 13-20. https://doi.org/10.1080/03610739108253882 [Google Scholar] [Crossref] 
  6. Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psycology, 57, 505-528. https://doi.org/10.1146/annurev.psych.57.102904.190146 [Google Scholar] [Crossref] 
  7. Collins, L. M., & Flaherty, B. P. (2002). Latent class models for longitudinal data. In J. A. Hagenaars, & A. L. McCutcheon (Eds.), Applied latent class analysis içinde (pp. 287-303). Cambridge University. [Google Scholar]
  8. Collins, L., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. John Wiley & Sons. [Google Scholar]
  9. Collins, L., Lanza, S. T., Schafer, J. L., & Flaherty, B. P. (2002). WinLTA user’s guide (Version 3.0). The Methodology Center. [Google Scholar]
  10. Collins, L. M., & Wugalter, S. E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behavioral Research, 27(1), 131-157. https://doi.org/10.1207/s15327906mbr2701_8 [Google Scholar] [Crossref] 
  11. Cosco, T. D., Kaushal, A., Hardy, R., Richards, M., Kuh, D., & Stafford, M. (2017). Operationalising resilience in longitudinal studies: A systematic review of methodological approaches. Journal of Epidemiol Community Health, 71(1), 98-104. https://doi.org/10.1136/jech-2015-206980 [Google Scholar] [Crossref] 
  12. IJntema, R. C., Burger, Y. D., & Schaufeli, W. B. (2019). Reviewing the labyrinth of psychological resilience: Establishing criteria for resilience-building programs. Consulting Psychology Journal: Practice and Research, 71(4), 288-304. https://doi.org/10.1037/cpb0000147 [Google Scholar] [Crossref] 
  13. Langeheine, R. (1988). New developments in latent class theory. R. Langeheine & J. Rost (Ed.), Latent trait and latent class models içinde (s. 77-108). Plenum. [Google Scholar]
  14. Lanza, S. T., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis. In J. A. Schinka, W. F. Velicer, & I. B. Weiner (Eds.), Handbook of psychology: Research methods in psychology (pp. 663-685). John Wiley & Sons. [Google Scholar]
  15. Laursen, B., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly, 52(3), 377-389. [Google Scholar]
  16. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Houghton Mifflin. [Google Scholar]
  17. Li, F., Cohen, A., Bottge, B., & Templin, J. (2016). A latent transition analysis model for assessing change in cognitive skills. Educational and Psychological Measurement, 76(2), 181-204. https://doi.org/10.1177/0013164415588946 [Google Scholar] [Crossref] 
  18. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767-778. https://doi.org/10.1093/biomet/88. 3.767 [Google Scholar] [Crossref] 
  19. Magidson, J., & Vermunt, J. K. (2004). Latent class models. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 175-198). Sage. [Google Scholar]
  20. Masten, A. S. (2015). Ordinary magic: Resilience in development. The Guilford. [Google Scholar]
  21. Masten, A. S., Burt, K. B., Roisman, G. I., Obradović, J., Long, J. D., & Tellegen, A. (2004). Resources and resilience in the transition to adulthood: Continuity and change. Development and Psychopathology, 16(4), 1071-1094. https://doi.org/10.1017/S0954579404040143 [Google Scholar] [Crossref] 
  22. Masten, A. S., Hubbard, J. J., Gest, S. D., Tellegen, A., Garmezy, N., & Ramirez, M. (1999). Competence in the context of adversity: Pathways to resilience and maladaptation from childhood to late adolescence. Development and Psychopathology, 11(1), 143-169. https://doi.org/10.1017/S0954579499001996 [Google Scholar] [Crossref] 
  23. Masten, A., & Tellegen, A. (2012). Resilience in developmental psychopathology: Contributions of the Project Competence Longitudinal Study. Development and Psychopathology, 24(2), 345-361. https://doi.org/10.1017/S095457941200003X [Google Scholar] [Crossref] 
  24. Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In T. D. Little (Ed.), The Oxford handbook of quantitative methods (pp. 551-611). Oxford University. [Google Scholar]
  25. Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. https://doi.org/10.1037/1082-989X.12.1.23 [Google Scholar] [Crossref] 
  26. McLachlan, G., & Peel, D. (2000). Finite mixture models. John Wiley & Sons. [Google Scholar]
  27. Menard, S. (2008). Introduction: Longitudinal research design and analysis. In S. Menard (Ed.), Handbook of longitudinal research: design, measurement, and analysis içinde (pp. 3-12). Elsevier. [Google Scholar]
  28. Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology - This time forever. Measurement, 2(4), 201-218. https://doi.org/10.1207/s15366359mea0204_1 [Google Scholar] [Crossref] 
  29. Muthén, B. (2007). Latent variable hybrids: Overview of old and new methods. In G. R. Hancock, & K. M. Samuelsen (Eds.), Advances in latent variable mixture modeling (pp. 1-24). Information Age. [Google Scholar]
  30. Muthén, L., & Muthén, B. (1998-2012). Mplus user’s guide (Seventh edition). Muthén & Muthén. [Google Scholar]
  31. Muthén, B., & Muthén, L. K. (2000). Integrating person‐centered and variable‐centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24(6), 882-891. https://doi.org/10.1111/j.1530-0277.2000.tb02070.x [Google Scholar] [Crossref] 
  32. Nylund, K. L. (2007). Latent transition analysis: Modeling extensions and an application to peer victimization. [Doctoral Dissertation, University of California].  [Google Scholar]
  33. 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: A Multidisciplinary Journal, 14(4), 535-569. https://doi.org/10.1080/10705510701575396 [Google Scholar] [Crossref] 
  34. 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 [Google Scholar] [Crossref] 
  35. Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014). A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 439-454. https://doi.org/10.1080/10705511.2014.915375 [Google Scholar] [Crossref] 
  36. Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The theory, design, and analysis of change. Journal of Management, 36(1), 94-120. https://doi.org/10.11 77/0149206309352110 [Google Scholar] [Crossref] 
  37. Raufelder, D., Jagenow, D., Hoferichter, F., & Drury, K. M. (2013). The person-oriented approach in the field of educational psychology. Problems of Psychology in the 21st Century, 5, 79-88. [Google Scholar]
  38. Ruscio, J., & Ruscio, A. M. (2008). Categories and dimensions: Advancing psychological science through the study of latent structure. Current Directions in Psychological Science, 17(3), 203-207. https://doi.org/10.1111/j.1467-8721.2008.00575.x [Google Scholar] [Crossref] 
  39. Ryoo, J. H., Wang, C., Swearer, S. M., Hull, M., & Shi, D. (2018). Longitudinal model buiding using latent transition analysis: An example using school bullying data. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.00675 [Google Scholar] [Crossref] 
  40. Schoenberg, R. (2008). Dynamic models and cross-sectional data: The consequences of dynamic misspecification. In S. Menard (Ed.), Handbook of longitudinal research: Design, measurement, and analysis (pp. 249-258). Elsevier. [Google Scholar]
  41. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. http://www.jstor.org/stable/2958889  [Google Scholar]
  42. Shaffer, D. R., & Kipp, K. (2013). Developmental psychology: Childhood and adolescence. Cengage Learning. [Google Scholar]
  43. Sorgente, A., Lanz, M., Serido, J., Tagliabue, S., & Shim, S. (2019). Latent transition analysis: Guidelines and an application to emerging adults' social development. TPM: Testing, Psychometrics, Methodology in Applied Psychology, 26(1), 39-72. https://doi.org/10.4473/TPM26.1.3 [Google Scholar] [Crossref] 
  44. Timmons, A. C., & Preacher, K. J. (2015). The importance of temporal design: How do measurement intervals affect the accuracy and efficiency of parameter estimates in longitudinal research? Multivariate Behavioral Research, 50(1), 41-55. https://doi.org/10.1080/00273171.2014.961056 [Google Scholar] [Crossref] 
  45. Tusaie, K., & Dyer, J. (2004). Resilience: A historical review of the construct. Holistic Nursing Practice, 18(1), 3-10. [Google Scholar]
  46. Vella, S. L. C., & Pai, N. B. (2019). A theoretical review of psychological resilience: Defining resilience and resilience research over the decades. Archives of Medicine and Health Sciences, 7(2), 233-239. https://doi.org/10.4103/amhs.amhs_119_19 [Google Scholar] [Crossref] 
  47. Vuong, Q. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307-333. https://www.jstor.org/stable/1912557  [Google Scholar]
  48. Wang, M., Beal, D. J., Chan, D., Newman, D. A., Vancouver, J. B., & Vandenberg, R. J. (2017). Longitudinal research: A panel discussion on conceptual issues, research design, and statistical techniques. Work, Aging and Retirement, 3(1), 1-24. https://doi.org/10.1093/workar/waw033 [Google Scholar] [Crossref] 
  49. Wang, M., & Chan, D. (2011). Mixture latent Markov modeling: Identifying and predicting unobserved heterogeneity in longitudinal qualitative status change. Organizational Research Methods, 14(3), 411-431. https://doi.org/10.1177/1094428109357107 [Google Scholar] [Crossref] 
  50. Wang, J., & Wang, X. (2012). Structural equation modeling: Applications using Mplus. John Wiley & Sons. [Google Scholar]
  51. Wu, W., Selig, J. P., & Little, T. D. (2013). Longitudinal data analysis. In T. D. Little (Ed.), The Oxford handbook of quantitative methods (pp. 387-410). Oxford University. [Google Scholar]
  52. Yu, H. T. (2013). Models with discrete latent variables for analysis of categorical data: A framework and a MATLAB MDLV toolbox. Behavior Research Methods, 45, 1036-1047. https://doi.org/10.3758/s13428-013-0335- [Google Scholar] [Crossref] 
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