209
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

A Mixed-Effects Model in Which the Parameters of the Autocorrelated Error Structure Can Differ between Individuals

ORCID Icon

References

  • Asparouhov, T., Hamaker, E. L., & Muthén, B. O. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388. https://doi.org/10.1080/10705511.2017.1406803
  • Baird, B. M., Le, K., & Lucas, R. E. (2006). On the nature of intraindividual personality variability: Reliability, validity, and associations with well-being. Journal of Personality and Social Psychology, 90(3), 512–527. https://doi.org/10.1037/0022-3514.90.3.512
  • Blozis, S. A. (2022). A latent variable mixed-effects location scale model with an application to daily diary data. Psychometrika, 87(4), 1548–1570. https://doi.org/10.1007/s11336-022-09864-8
  • Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. Guilford Press.
  • Booth, J. G., & Hobert, J. P. (1999). Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society Series B: Statistical Methodology, 61(1), 265–285. https://doi.org/10.1111/1467-9868.00176
  • Casella, G., & Berger, R. (2001). Statistical inference. Duxbury Resource Center.
  • Conner, T. S., Tennen, H., Fleeson, W., & Barrett, L. F. (2009). Experience sampling methods: A modern idiographic approach to personality research. Social and Personality Psychology Compass, 3(3), 292–313. https://doi.org/10.1111/j.1751-9004.2009.00170.x
  • Crowther, M. J. (2017). Extended multivariate generalised linear and non-linear mixed effects models. arXiv. https://doi.org/10.48550/ARXIV.1710.02223
  • De Haan-Rietdijk, S., Voelkle, M., Keijsers, L., & Hamaker, E. L. (2017). Discrete- vs. continuous-time modeling of unequally spaced experience sampling method data. Frontiers in Psychology, 8, 1849. https://doi.org/10.3389/fpsyg.2017.01849
  • Gadosey, C., Schnettler, T., Scheunemann, A., Fries, S., & Grunschel, G. (2021). The intraindividual co-occurrence of anxiety and hope in procrastination episodes during exam preparations: An experience sampling study. Learning and Individual Differences, 88, 102013. https://doi.org/10.1016/j.lindif.2021.102013
  • Gasimova, F., Robitzsch, A., Wilhelm, O., & Hülür, G. (2014). A hierarchical Bayesian model with correlated residuals for investigating stability and change in intensive longitudinal data settings. Methodology, 10(4), 126–137. https://doi.org/10.1027/1614-2241/a000083
  • Geukes, K., Nestler, S., Hutteman, R., Dufner, M., Küfner, A. C. P., Egloff, B., Denissen, J. J. A., & Back, M. D. (2017). Puffed up but shaky selves: State self-esteem level and variability in narcissists. Journal of Personality and Social Psychology, 112(5), 769–786. https://doi.org/10.1037/pspp0000093
  • González, J., Tuerlinckx, F., De Boeck, P., & Cools, R. (2006). Numerical integration in logistic-normal models. Computational Statistics & Data Analysis, 51(3), 1535–1548. https://doi.org/10.1016/j.csda.2006.05.003
  • Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth modeling: Structural equation and multilevel modeling approaches. Guilford Press.
  • Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the cogito study. Multivariate Behavioral Research, 53(6), 820–841. https://doi.org/10.1080/00273171.2018.1446819
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.
  • Hedeker, D., Demirtas, H., & Mermelstein, R. J. (2009). A mixed ordinal location scale model for analysis of Ecological Momentary Assessment (EMA) data. Statistics and Its Interface, 2(4), 391–401. https://doi.org/10.4310/sii.2009.v2.n4.a1
  • Hedeker, D., & Gibbons, R. D. (2006). Longitudinal data analysis. John Wiley & Sons.
  • Hedeker, D., Mermelstein, R. J., Berbaum, M. L., & Campbell, R. T. (2009). Modeling mood variation associated with smoking: An application of a heterogeneous mixed-effects model for analysis of ecological momentary assessment (EMA) data. Addiction (Abingdon, England), 104(2), 297–307. https://doi.org/10.1111/j.1360-0443.2008.02435.x
  • Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2008). An application of a mixed-effects location scale model for analysis of ecological momentary assessment (EMA) data. Biometrics, 64(2), 627–634. https://doi.org/10.1111/j.1541-0420.2007.00924.x
  • Hedeker, D., & Nordgren, R. (2013). MIXREGLS: A program for mixed-effects location scale analysis. Journal of Statistical Software, 52(12), 1–38. https://doi.org/10.18637/jss.v052.i12
  • Huh, D., Kaysen, D. L., & Atkins, D. C. (2015). Modeling cyclical patterns in daily college drinking data with many zeroes. Multivariate Behavioral Research, 50(2), 184–196. https://doi.org/10.1080/00273171.2014.977433
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice. OTexts.
  • Jahng, S., Wood, P. K., & Trull, T. J. (2008). Analysis of affective instability in ecological momentary assessment: Indices using successive difference and group comparison via multilevel modeling. Psychological Methods, 13(4), 354–375. https://doi.org/10.1037/a0014173
  • Kapur, K., Li, X., Blood, E. A., & Hedeker, D. (2015). Bayesian mixed-effects location and scale models for multivariate longitudinal outcomes: An application to ecological momentary assessment data. Statistics in Medicine, 34(4), 630–651. https://doi.org/10.1002/sim.6345
  • Li, X., & Hedeker, D. (2012). A three-level mixed-effects location scale model with an application to ecological momentary assessment data. Statistics in Medicine, 31(26), 3192–3210. https://doi.org/10.1002/sim.5393
  • Lin, X., Mermelstein, R. J., & Hedeker, D. (2018). A three level bayesian mixed effects location scale model with an application to Ecological Momentary Assessment data. Statistics in Medicine, 37(13), 2108–2119. https://doi.org/10.1002/sim.7627
  • McCulloch, C. E., Searle, S. R., & Neuhaus, J. M. (2009). Generalized, linear, and mixed models. John Wiley/& Sons.
  • McNeish, D. M., & Hamaker, E. L. (2020). A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. Psychological Methods, 25(5), 610–635. https://doi.org/10.1037/met0000250
  • Mund, M., Johnson, M., & Nestler, S. (2021). Changes in size and interpretation of parameter estimates in within-person models in the presence of time-invariant and time-varying covariates. Frontiers in Psychology, 12, 3663. https://doi.org/10.3389/fpsyg.2021.666928
  • Nestler, S. (2020). Modeling interindividual differences in latent within-person variation: The confirmatory factor level variability model. The British Journal of Mathematical and Statistical Psychology, 73(3), 452–473. https://doi.org/10.1111/bmsp.12196
  • Nestler, S. (2021). Modeling intraindividual variability in growth with measurement burst designs. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 28–39. https://doi.org/10.1080/10705511.2020.1757455
  • Nestler, S. (2022). An extension of the mixed-effects growth model that considers between-person differences in the within-subject variance and the autocorrelation. Statistics in Medicine, 41(3), 471–482. https://doi.org/10.1002/sim.9280
  • Nestler, S., Geukes, K., & Back, M. D. (2018). Modeling intraindividual variability in three-level models. Methodology, 14(3), 95–108. https://doi.org/10.1027/1614-2241/a000150
  • Nestler, S., Geukes, K., Zaun, T., & Eckes, T. (2021). On the role of response styles in the study of intraindividual variability. Collabra: Psychology, 7(1), 29929. https://doi.org/10.1525/collabra.29929
  • Nestler, S., & Humberg, S. (2022). A lasso and a regression tree mixed-effect model with random effects for the level, the residual variance, and the autocorrelation. Psychometrika, 87(2), 506–532. https://doi.org/10.1027/1614-2241/a000150
  • Nestler, S., & Humberg, S. (in press). Univariate autoregressive structural equation models as mixed-effects models. Structural Equation Modeling: A Multidisciplinary Journal.
  • Ormerod, J. T., & Wand, M. P. (2010). Explaining variational approximations. The American Statistician, 64(2), 140–153. https://doi.org/10.1198/tast.2010.09058
  • Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in s and s-plus. Springer.
  • Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2002). Reliable estimation of generalized linear mixed models using adaptive quadrature. The Stata Journal: Promoting Communications on Statistics and Stata, 2(1), 1–21. https://doi.org/10.1177/1536867X0200200101
  • Rast, P., & Ferrer, E. (2018). A mixed-effects location scale model for dyadic interactions. Multivariate Behavioral Research, 53(5), 756–775. https://doi.org/10.1080/00273171.2018.1477577
  • Rast, P., Hofer, S. M., & Sparks, C. (2012). Modeling individual differences in within-person variation of negative and positive affect in a mixed effects location scale model using BUGS/JAGS. Multivariate Behavioral Research, 47(2), 177–200. https://doi.org/10.1080/00273171.2012.658328
  • Robert, C., & Casella, G. (2010). Introducing monte carlo methods with R. Springer.
  • Ruli, E., Sartori, N., & Ventura, L. (2016). Improved Laplace approximation for marginal likelihoods. Electronic Journal of Statistics, 10(2), 3986–4009. https://doi.org/10.1214/16-EJS1218
  • Santangelo, P. S., Ebner-Priemer, U. W., & Trull, T. J. (2013). Experience sampling methods in clinical psychology. In J. S. Comer & P. C. Kendall (Eds.), The oxford handbook of research strategies for clinical psychology. (p. 188–210) Oxford University Press.
  • Scharf, F., & Nestler, S. (2019). Should regularization replace simple structure rotation in exploratory factor analysis? Structural Equation Modeling: A Multidisciplinary Journal, 26(4), 576–590. https://doi.org/10.1080/10705511.2018.1558060
  • Schulte, J., Dietel, F. A., Wilhelm, S., Nestler, S., & Buhlmann, U. (2021). Temporal dynamics of insight in body dysmorphic disorder. Journal of Abnormal Psychology, 130(4), 365–376. https://doi.org/10.1037/abn0000673
  • Schupp, J., & Gerlitz, J. Y. (2008). Das BFI-S: Big Five Inventory-SOEP [the BFI-S: Big five inventory-SOEP]. In A. Gloeckner-Rist (Ed.), Zusammenstellung sozialwissenschaftlicher Items und Skalen. GESIS.
  • Schuurman, N. K., Grasman, R. P. P. P., & Hamaker, E. L. (2016). A comparison of inverse-wishart prior specifications for covariance matrices in multilevel autoregressive models. Multivariate Behavioral Research, 51(2-3), 185–206. https://doi.org/10.1080/00273171.2015.1065398
  • Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis. SAGE Publications.
  • Tuerlinckx, F., Rijmen, F., Verbeke, G., & De Boeck, P. (2006). Statistical inference in generalized linear mixed models: A review. The British Journal of Mathematical and Statistical Psychology, 59(Pt 2), 225–255. https://doi.org/10.1348/000711005X79857
  • Verbeke, G., & Molenberghs, G. (2009). Linear mixed models for longitudinal data analysis. Springer.
  • Verboon, P., & Leontjevas, R. (2018). Analyzing cyclic patterns in psychological data: A tutorial. The Quantitative Methods for Psychology, 14(4), 218–234. https://doi.org/10.20982/tqmp.14.4.p218
  • Wang, L. P., Bergeman, C. S., & Hamaker, E. (2012). Investigating inter-individual differences in short-term intra-individual variability. Psychological Methods, 17(4), 567–581. https://doi.org/10.1037/a0029317
  • Woodward, W. A., Gray, H. L., & Elliot, A. C. (2017). Applied time series analysis with r. CRC Press.
  • Zirkel, S., Garcia, J. A., & Murphy, M. C. (2015). Experience-sampling research methods and their potential for education research. Educational Researcher, 44(1), 7–16. https://doi.org/10.3102/0013189X14566879

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.