Detecting Cohort Effects in Accelerated Longitudinal Designs Using Multilevel Models

References

• Abdolell, M., LeBlanc, M., Stephens, D., & Harrison, R. V. (2002). Binary partitioning for continuous longitudinal data: Categorizing a prognostic variable. Statistics in Medicine, 21(22), 3395–3409. https://doi.org/10.1002/sim.1266
   PubMed Web of Science ®Google Scholar
• Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effect models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v082.i13
   Web of Science ®Google Scholar
• Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth and Brooks.
   Google Scholar
• Cáncer, P. F., Estrada, E., & Ferrer, E. (2022). Modeling cohort effects in autoregressive parameters in accelerated longitudinal designs using latent change score models. [Manuscript submitted for publication] Department of Social Psychology and Methodology, Universidad Autónoma de Madrid.
   Google Scholar
• Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6(4), 330–351. https://doi.org/10.1037/1082-989X.6.4.330
   PubMed Web of Science ®Google Scholar
• Enders, C. K., & Peugh, J. J. (2004). Using an EM covariance matrix to estimate structural equation models with missing data: Choosing an adjusted sample size to improve the accuracy of inferences. Structural Equation Modeling: A Multidisciplinary Journal, 11(1), 1–19. https://doi.org/10.1207/S15328007SEM1101_1
   Web of Science ®Google Scholar
• Estrada, E., Bunge, S. A., & Ferrer, E. (2023). Controlling for cohort effects in accelerated longitudinal designs using continuous- and discrete-time dynamic models. Psychological Methods, 28(2), 359–378. https://doi.org/10.1037/met0000427
   PubMed Web of Science ®Google Scholar
• Estrada, E., & Ferrer, E. (2019). Studying developmental processes in accelerated cohort-sequential designs with discrete- and continuous-time latent change score models. Psychological Methods, 24(6), 708–734. https://doi.org/10.1037/met0000215
   PubMed Web of Science ®Google Scholar
• Fandakova, Y., Selmeczy, D., Leckey, S., Grimm, K. J., Wendelken, C., Bunge, S. A., & Ghetti, S. (2017). Changes in ventromedial prefrontal and insular cortex support the development of metamemory from childhood into adolescence. Proceedings of the National Academy of Sciences, 114(29), 7582–7587. https://doi.org/10.1073/pnas.1703079114
   PubMed Web of Science ®Google Scholar
• Ferrer, E., & McArdle, J. J. (2003). Alternative structural models for multivariate longitudinal data analysis. Structural. Structural Equation Modeling: A Multidisciplinary Journal, 10(4), 493–524. https://doi.org/10.1207/S15328007SEM1004_1
   Web of Science ®Google Scholar
• Ferrer, E., & McArdle, J. J. (2010). Longitudinal modeling of developmental changes in psychological research. Current Directions in Psychological Science, 19(3), 149–154. https://doi.org/10.1177/0963721410370300
   Web of Science ®Google Scholar
• Ferrer, E., O'Hare, E. D., & Bunge, S. A. (2009). Fluid reasoning and the developing brain. Frontiers in Neuroscience, 3(1), 46–51. https://doi.org/10.3389/neuro.01.003.2009
   PubMed Web of Science ®Google Scholar
• Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9(4), 466–491. https://doi.org/10.1037/1082-989X.9.4.466
   PubMed Web of Science ®Google Scholar
• Grimm, K. J., Ram, N., & Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child Development, 82(5), 1357–1371. https://doi.org/10.1111/j.1467-8624.2011.01630.x
   PubMed Web of Science ®Google Scholar
• Hoffman, L. (2012). Considering alternative metrics of time: Does anybody really know what “time” is?. In J. R. Harring & G. R. Hancock (Eds.), Advances in Longitudinal Methods in the Social and Behavioral Sciences. (pp. 255–287) IAP Informaation Age in Publishing.
   Google Scholar
• Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. Routledge/Taylor & Francis.
   Google Scholar
• Hoffman, L., Hofer, S. M., & Sliwinski, M. (2011). On the confounds among retest gains and age-cohort differences in the estimation of within-person change in longitudinal studies: A simulation study. Psychology and Aging, 26(4), 778–791. https://doi.org/10.1037/a0023910
   PubMed Web of Science ®Google Scholar
• Kievit, R. A., Brandmaier, A. M., Ziegler, G., van Harmelen, A.-L., de Mooij, S. M. M., Moutoussis, M., Goodyer, I. M., Bullmore, E., Jones, P. B., Fonagy, P., Lindenberger, U., & Dolan, R. J, NSPN Consortium (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Developmental Cognitive Neuroscience, 33, 99–117. https://doi.org/10.1016/j.dcn.2017.11.007
   PubMed Web of Science ®Google Scholar
• Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(13), 1–26. https://doi.org/10.18637/jss.v082.i13
   Web of Science ®Google Scholar
• Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
   PubMed Web of Science ®Google Scholar
• McArdle, J. J. (2001). latent difference score approach to longitudinal dynamic structural analysis. In R. Cudeck, S. du Toit, & D. Sörbom (Eds.), Structural equation modeling, present and future: A festschrift in honor of Karl Jöreskog. (pp. 7–46) Scientific Software International.
   Google Scholar
• McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60(1), 577–605. https://doi.org/10.1146/annurev.psych.60.110707.163612
   PubMed Web of Science ®Google Scholar
• McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change. (pp. 139–175) American Psychological Association. https://doi.org/10.1037/10409-005
   Google Scholar
• Mehta, P. D., & West, S. G. (2000). Putting the individual back into individual growth curves. Psychological Methods, 5(1), 23–43. https://doi.org/10.1037/10S2-989X.5.I.23
   PubMed Web of Science ®Google Scholar
• Miyazaki, Y., & Raudenbush, S. W. (2000). Tests for linkage of multiple cohorts in an accelerated longitudinal design. Psychological methods, 5(1), 44.
   PubMed Web of Science ®Google Scholar
• Morgan, J. N., & Sonquist, J. A. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association, 58(302), 415–434. https://doi.org/10.1080/01621459.1963.10500855
   Web of Science ®Google Scholar
• Nesselroade, J. R., & Baltes, P. B. (Eds.). (1979). Longitudinal research in the study of behavior and development. Academic Press. https://doi.org/10.1093/sw/24.2.172
   Google Scholar
• R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
   Google Scholar
• Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. SAGE.
   Google Scholar
• Rogosa, D. R., & Willett, J. B. (1985). Understanding correlates of change by modeling individual differences in growth. Psychometrika, 50(2), 203–228. https://doi.org/10.1007/BF02294247
   Web of Science ®Google Scholar
• Schaie, K. W. (1984). Historical time and cohort effects. In K. A. McCluskey (Ed.), Life-span developmental psychology: Historical and generational effects. (pp. 1–15) Academic Press.
   Google Scholar
• Selmeczy, D., Fandakova, Y., Grimm, K. J., Bunge, S. A., & Ghetti, S. (2019). Longitudinal trajectories of hippocampal and prefrontal activations to episodic retrieval: Effects of age and puberty. Developmental Cognitive Neuroscience, 36, 100599. https://doi.org/10.1016/j.dcn.2018.10.003
   PubMed Web of Science ®Google Scholar
• Sliwinski, M., Hoffman, L., & Hofer, S. M. (2010). Evaluating convergence of within-person change and between-person age differences in age-heterogeneous longitudinal studies. Research in Human Development, 7(1), 45–60. https://doi.org/10.1080/15427600903578169
   PubMed Web of Science ®Google Scholar
• Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S. (4th ed.). Springer.
   Google Scholar