Latent Trajectories of Subjective Well-Being: An Application of Latent Growth Curve and Latent Class Growth Modeling
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https://doi.org/10.52380/ijcer.2023.10.2.308Keywords:
Dynamic process, Individual differences, Latent growth curve modeling, Latent growth classes, Measurement designAbstract
This study proposed a three-stage measurement model utilizing the Latent Growth Curve Modeling and Latent Class Growth Analysis. The measurement model was illustrated using repeated data collected through a four-week prospective study tracking the subjective well-being of volunteer college students (n=154). Firstly, several unconditional growth models were estimated to define the model providing a better representation of individual growth trajectories. Secondly, several conditional growth models were formulated to test the usefulness of covariate variables hypothesized to explain observed variance in growth factors. Finally, latent class models were estimated to explore different latent trajectory classes further. Results showed that students' subjective well-being changed over time, and the rate of this change, as well as its covariates, were not constant for the entire sample. This study clearly illustrates how a longitudinal measurement approach can enhance the scope of findings and the depth of inferences when repeated measurements are available.
References
Aili, K., Campbell, P., Michaleff, Z. A., Strauss, V. Y., Jordan, K. P., Bremander, A., Croft, P., Bergman, S. (2021). Long-term trajectories of chronic musculoskeletal pain: a 21-year prospective cohort latent class analysis. Pain, 162, 1511-1520. https://doi.org/10.1097/j.pain.0000000000002137 DOI: https://doi.org/10.1097/j.pain.0000000000002137
Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317-332. https://doi.org/10.1007/BF02294359 DOI: https://doi.org/10.1007/BF02294359
Askar, P., & Yurdugul, H. (2009). The using of Latent Growth Models for educational researches. Elementary Education Online, 8(2), 534-555. http://www.ilkogretim-online.org/fulltext/218-1596681446.pdf?1621152578
Bakk, Z., & Kuha, J. (2020). Relating latent class membership to external variables: An overview. British Journal of Mathematical and Statistical Psychology, 74, 340-362. https://doi.org/10.1111/bmsp.12227 DOI: https://doi.org/10.1111/bmsp.12227
Barboza, G. E. (2020). Child maltreatment, delinquent behavior, and school factors as predictors of depressive symptoms from adolescence to adulthood: a Growth Mixture Model. Youth & Society, 52(1), 27-54. https://doi.org/10.1177/0044118X17721803 DOI: https://doi.org/10.1177/0044118X17721803
Berlin, K., Parra, G., & Williams, N. (2014). An introduction to Latent Variable Mixture Modeling (part 2): longitudinal Latent Class Growth Analysis and Growth Mixture Models. Journal of Pediatric Psychology, 39(2), 188-203. https://doi.org/10.1093/jpepsy/jst085 DOI: https://doi.org/10.1093/jpepsy/jst085
Bilir, M. K., Binici, S., & Kamata, A. (2008). Growth Mixture Modeling: application to reading achievement data from a large-scale assessment. Measurement and Evaluation in Counseling and Development, 41(2), 104-119. https://doi.org/10.1080/07481756.2008.11909825 DOI: https://doi.org/10.1080/07481756.2008.11909825
Bollen, K. A., & Curran, P. J. (2004). Autoregressive Latent Trajectory (ALT) models: a synthesis of two traditions. Sociological Methods & Research, 32(3), 336-383. https://doi.org/10.1177/0049124103260222 DOI: https://doi.org/10.1177/0049124103260222
Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford.
Byrne, B. M., Lam, W. W., & Fielding, R. (2008). Measuring patterns of change in personality assessments: an annotated application of Latent Growth Curve Modeling. Journal of Personality Assessment, 90(6), 536-546. https://doi.org/10.1080/00223890802388350 DOI: https://doi.org/10.1080/00223890802388350
Cole, D. A. (2012). Latent trait-state models. In R. H. (Ed.), Handbook of structural equation modeling (s. 585-600). Guilford.
Collins, L. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design and statistical model. Annual Review of Psychology, 57, 505-528. https://doi.org/10.1146/annurev.psych.57.102904.190146 DOI: https://doi.org/10.1146/annurev.psych.57.102904.190146
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. DOI: https://doi.org/10.1002/9780470567333
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 DOI: https://doi.org/10.1207/s15327906mbr2701_8
Connell, A. M., & Frye, A. A. (2006). Growth Mixture Modelling in developmental psychology: overview and demonstration of heterogeneity in developmental trajectories of adolescent antisocial behaviour. Infant and Child Development, 15(6), 609-621. https://doi.org/10.1002/icd.481 DOI: https://doi.org/10.1002/icd.481
Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281-302. https://doi.org/10.1037/h0040957 DOI: https://doi.org/10.1037/h0040957
Curran, P. J., & Willoughby, M. T. (2003). Implications of latent trajectory models for the study of developmental psychopathology. Development and Psychopathology, 15(3), 581-612. https://doi.org/10.1017/S0954579403000300 DOI: https://doi.org/10.1017/S0954579403000300
Curran, P. J., & Wirth, R. J. (2004). Interindividual differences in intraindividual variation: balancing internal and external validity. Measurement ( Mahwah N J), 2(4), 219-247. https://doi.org/10.1207/s15366359mea0204_2 DOI: https://doi.org/10.1207/s15366359mea0204_2
Diallo, T. M., Morin, A. J., & Lu, H. Z. (2017). The impact of total and partial inclusion or exclusion of active and inactive time invariant covariates in Growth Mixture Models. Psychological Methods, 22(1), 166-190. https://doi.org/10.1037/met0000084. DOI: https://doi.org/10.1037/met0000084
Diener, E., Oishi, S., & Tay, L. (2018). Advances in subjective well-being research. Nature Human Behaviour, 2, 253-260. https://doi.org/10.1038/s41562-018-0307-6 DOI: https://doi.org/10.1038/s41562-018-0307-6
Duncan, T. E., & Duncan, S. C. (2009). The ABC’s of LGM: an introductory guide to Latent Variable Growth Curve Modeling. Social Personality Psychology Compass, 3(6), 979-991. https://doi.org/10.1111/j.1751-9004.2009.00224.x DOI: https://doi.org/10.1111/j.1751-9004.2009.00224.x
Elahi Shirvan, M., Taherian, T., Shahnama, M., & Yazdanmehr, E. (2021). A longitudinal study of foreign language enjoyment and L2 grit: a Latent Growth Curve Modeling. Front. Psychol., 12. https://doi.org/10.3389/fpsyg.2021.720326 DOI: https://doi.org/10.3389/fpsyg.2021.720326
Feingold, A. (2021). Effect of parameterization on statistical power and effect size estimation in Latent Growth Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 28(4), 609-621. https://doi.org/10.1080/10705511.2021.1878895 DOI: https://doi.org/10.1080/10705511.2021.1878895
Fernandez-Rio, J., Cecchini, J. A., Mendez-Gimenez, A., & Carriedo, A. (2021). Mental well-being profiles and physical activity in times of social isolation by the COVID-19: a latent class analysis. International Journal of Sport and Exercise Psychology. https://doi.org/10.1080/1612197X.2021.1877328 DOI: https://doi.org/10.1080/1612197X.2021.1877328
Fukkink, R. G., & van Verseveld, M. (2020). Inclusive early childhood education and and care: a longitudinal study into the growth of interprofessional collaboration. Journal of Interprofessional Care, 34(3), 362-372. https://doi.org/10.1080/13561820.2019.1650731 DOI: https://doi.org/10.1080/13561820.2019.1650731
Goswami, H., Fox, C., & Pollock, G. (2016). The current evidence base and future needs in ımproving children’s well-being across Europe: is there a case for a comparative longitudinal survey? Child Indicators Research, 9, 371-388. https://doi.org/10.1007/s12187-015-9323-5 DOI: https://doi.org/10.1007/s12187-015-9323-5
Gottfried, A. E., Nylund-Gibson, K., Gottfried, A. W., Morovati, D., & Gonzalez, A. M. (2016). Trajectories from academic intrinsic motivation to need for cognition and educational attainment. The Journal of Educational Research, 110(6), 642-652. https://doi.org/10.1080/00220671.2016.1171199 DOI: https://doi.org/10.1080/00220671.2016.1171199
Hertzog, C., & Nesselroade, J. R. (2003). Assessing psychological change in adulthood: an overview of methodological issues. Psychology and Aging, 18(4), 639-657. https://doi.org/10.1037/0882-7974.18.4.639 DOI: https://doi.org/10.1037/0882-7974.18.4.639
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118 DOI: https://doi.org/10.1080/10705519909540118
Jung, T., & Wickrama, K. A. (2008). An introduction to Latent Class Growth Analysis and Growth Mixture Modeling. Social and Personality Psychology Compass, 2(1), 302-317. https://doi.org/10.1111/j.1751-9004.2007.00054.x DOI: https://doi.org/10.1111/j.1751-9004.2007.00054.x
Kane, M. T. (2013). Validating the interpretations and uses of test scores. Journal of Educational Measurement, 50(1), 1-73. https://doi.org/10.1111/jedm.12000 DOI: https://doi.org/10.1111/jedm.12000
Kim, Y., Kang, M., Tacon, A., & Morrow, J. (2016). Longitudinal trajectories of physical activity in women using Latent Class Growth Analysis: the WIN study. Journal of Sport and Health Science, 5(4), 410-416. https://doi.org/10.1016/j.jshs.2015.04.007 DOI: https://doi.org/10.1016/j.jshs.2015.04.007
Lee, J. (2020). Trajectories of mental health across baby boomers: Latent Growth Curve Modeling for depression. Social Work in Mental Health, 18(1), 96-120. https://doi.org/10.1080/15332985.2019.1683674 DOI: https://doi.org/10.1080/15332985.2019.1683674
Li, M., & Harring, J. R. (2017). Investigating approaches to estimating covariate effects in Growth Mixture Modeling: A simulation study. Educational and Psychological Measurement, 77(5), 766-791. https://doi.org/10.1177/0013164416653789 DOI: https://doi.org/10.1177/0013164416653789
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 DOI: https://doi.org/10.1093/biomet/88.3.767
Lorenz, F. O., Wickrama, K. A., & Conger, R. D. (2004). Modeling continuity and change in family relations with panel data. In R. D. Conger, F. O. Lorenz, & K. A. Wickrama, Continuity and change in family relations: Theory, methods, and empirical findings (s. 15-62). Erlbaum.
McLachlan, G., & Peel, D. (2000). Finite mixture modeling. Wiley. DOI: https://doi.org/10.1002/0471721182
Menard, S. (2008). Handbook of longitudinal research: Design, measurement, and analysis. Academic.
Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55(1), 107-122. https://doi.org/10.1007/BF02294746 DOI: https://doi.org/10.1007/BF02294746
Muthén, B. (2007). Latent variables hybrids: Overview of old and new models. In G. R. Hancock, & K. M. Samuelsen, Advances in latent variable mixture models (s. 1-24). Information Age.
Muthén, B. O. (2002). Beyond SEM: General Latent Variable Modeling. Behaviormetrika, 29(1), 81-117. https://doi.org/10.2333/bhmk.29.81 DOI: https://doi.org/10.2333/bhmk.29.81
Muthén, B. O., & Muthén, L. K. (2020, March). Chi-square difference testing using the Satorra–Bentler scaled chi-square. http://www.statmodel.com/chidiff.shtml
Muthén, B., & Brown, H. C. (2009). Estimating drug effects in the presence of placebo response: causal inference using Growth Mixture Modeling. Statistics in Medicine, 28(27), 3363-3385. https://doi.org/10.1002/sim.3721 DOI: https://doi.org/10.1002/sim.3721
Muthén, B., & Shedden, K. (1999). Finite Mixture Modeling with mixture outcomes using the EM algorithm. Biometrics, 55(2), 463-469. https://doi.org/10.1111/j.0006-341x.1999.00463.x DOI: https://doi.org/10.1111/j.0006-341X.1999.00463.x
Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th Edition). Muthén & Muthén.
Nagin, D. (1999). Analyzing developmental trajectories: a semi-parametric, group-based approach. Psychological Methods, 4(2), 139-157. https://doi.org/10.1037/1082-989X.4.2.139 DOI: https://doi.org/10.1037/1082-989X.4.2.139
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 DOI: https://doi.org/10.1080/10705510701575396
Pierce, M., Mcmanus, S., Hope, H., Hotopf, M., Ford, T., Hatch, S. L., . . . Abel, K. M. (2021). Mental health responses to the COVID-19 pandemic: a latent class trajectory analysis using longitudinal UK data. Lancet Psychiatry, 8, 610-619. https://doi.org/10.1016/S2215-0366(21)00151-6 DOI: https://doi.org/10.1016/S2215-0366(21)00151-6
Ployhart, R. E., & Vanderberg, R. J. (2010). Longitudinal research: the theory, design, and analysis of change. Journal of Management, 36(1), 94-120. https://doi.org/10.1177/0149206309352110 DOI: https://doi.org/10.1177/0149206309352110
Ram, N., & Grimm, K. J. (2009). 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 DOI: https://doi.org/10.1177/0165025409343765
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). https://doi.org/10.1111/j.1467-8721.2008.00575.x DOI: https://doi.org/10.1111/j.1467-8721.2008.00575.x
Schwarz, G. (1978). Estimating the dimension of a model. The Annuals of Statistics, 6(2), 461-464. https://doi.org/10.1214/aos/1176344136 DOI: https://doi.org/10.1214/aos/1176344136
Sclove, L. S. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333-343. https://doi.org/10.1007/BF02294360 DOI: https://doi.org/10.1007/BF02294360
Scott, P. W. (2021). Accounting for Time-Varying Inter-Individual Differences in Trajectories when Assessing Cross-Lagged Models. Structural Equation Modeling: A Multidisciplinary Journal, 28(3), 365-375. https://doi.org/10.1080/10705511.2020.1819815 DOI: https://doi.org/10.1080/10705511.2020.1819815
Shi, S., DiStefano, C., Zheng, X., Liu, R., & Jiang, Z. (2021). Fitting latent growth models with small sample sizes and non-normal missing data. International Journal of Behavioral Development, 45(2), 179-192. https://doi.org/10.1177/0165025420979365 DOI: https://doi.org/10.1177/0165025420979365
Tofighi, D., & Enders, C. (2007). Identifying the correct number of classes in a Growth Mixture Model. In G. R. Hancock, Mixture models in latent variable research (s. 317-341). Information Age.
Wang, M., & Bodner, T. E. (2007). Growth Mixture Modeling: identifying and predicting unobserved subpopulations with longitudinal data. Organizational Research Methods, 10(4), 635-656. https://doi.org/10.1177/1094428106289397 DOI: https://doi.org/10.1177/1094428106289397
Wickrama, K. A., Lorenz, F. O., & Conger, R. D. (1997). Parental support and adolescent physical health status: a Latent Growth-Curve Analysis. Journal of Health and Social Behavior, 38(2), 149-163. https://doi.org/10.2307/2955422 DOI: https://doi.org/10.2307/2955422
Willett, J., & Sayer, A. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116(2), 363-381. https://doi.org/10.1037/0033-2909.116.2.363 DOI: https://doi.org/10.1037/0033-2909.116.2.363
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