Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus

References

• Aknin, L. B., De Neve, J.-E., Dunn, E. W., Fancourt, D. E., Goldberg, E., Helliwell, J. F., Jones, S. P., Karam, E., Layard, R., Lyubomirsky, S., Rzepa, A., Saxena, S., Thornton, E. M., VanderWeele, T. J., Whillans, A. V., Zaki, J., Karadag, O., & Ben Amor, Y. (2022). Mental health during the first year of the COVID-19 pandemic: A review and recommendations for moving forward. Perspectives on Psychological Science, 17(4), 915–936. https://doi.org/10.1177/17456916211029964
   Web of Science ®Google Scholar
• Aristodemou, M., Rommelse, N., & Kievit, R. (2022). Attentiveness modulates reaction-time variability: Findings from a population-based sample of 1032 children. PsyArXiv. https://doi.org/10.31234/osf.io/j2n5w
   Google Scholar
• Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling, 25(3), 359–388. https://doi.org/10.1080/10705511.2017.1406803
   Web of Science ®Google Scholar
• Asparouhov, T., & Muthen, B. (2019). Latent variable interactions using maximum-likelihood and Bayesian estimation for single- and two-level models. Mplus Web Notes.
   Google Scholar
• Asparouhov, T., & Muthén, B. (2020a). Bayesian estimation of single and multilevel models with latent variable interactions. Structural Equation Modeling: A Multidisciplinary Journal, 28(2), 314–328. https://doi.org/10.1080/10705511.2020.1761808
   Web of Science ®Google Scholar
• Asparouhov, T., & Muthén, B. (2020b). Comparison of models for the analysis of intensive longitudinal data. Structural Equation Modeling: A Multidisciplinary Journal, 27(2), 275–297. https://doi.org/10.1080/10705511.2019.1626733
   Web of Science ®Google Scholar
• Barton, J., & Rogerson, M. (2017). The importance of greenspace for mental health. BJPsych International, 14(4), 79–81. https://doi.org/10.1192/S2056474000002051
   PubMedGoogle Scholar
• Blanke, E. S., Neubauer, A. B., Houben, M., Erbas, Y., & Brose, A. (2021). Why do my thoughts feel so bad? Getting at the reciprocal effects of rumination and negative affect using dynamic structural equation modeling. Emotion (Washington, DC), 22(8), 1773–1786. https://doi.org/10.1037/EMO0000946
   PubMed Web of Science ®Google Scholar
• Brown, R., Speyer, L. G., Eisner, M., Ribeaud, D., & Murray, A. L. (2022). Exploring the effect of ADHD traits on the moment-to-moment interplay between provocation and aggression: Evidence from dynamic structural equation modelling. 49(5), 469–479. https://doi.org/10.1002/ab.22081
   Google Scholar
• Cortina, J. M., Markell-Goldstein, H. M., Green, J. P., & Chang, Y. (2021). How are we testing interactions in latent variable models? Surging forward or fighting shy? Organizational Research Methods, 24(1), 26–54. https://doi.org/10.1177/1094428119872531
   Web of Science ®Google Scholar
• Cox, K., & Kelcey, B. (2021). Croon’s bias-corrected estimation of latent interactions. Structural Equation Modeling, 28(6), 863–874. https://doi.org/10.1080/10705511.2021.1922283
   Web of Science ®Google Scholar
• Erzen, E., & Çikrikci, Ö. (2018). The effect of loneliness on depression: A meta-analysis. The International Journal of Social Psychiatry, 64(5), 427–435. https://doi.org/10.1177/0020764018776349
   PubMed Web of Science ®Google Scholar
• Evans, S. W., Pelham, W. E., Smith, B. H., Bukstein, O., Gnagy, E. M., Greiner, A. R., Altenderfer, L., & Baron-Myak, C. (2001). Dose–response effects of methylphenidate on ecologically valid measures of academic performance and classroom behavior in adolescents with ADHD. Experimental and Clinical Psychopharmacology, 9(2), 163–175. https://doi.org/10.1037/1064-1297.9.2.163
   PubMed Web of Science ®Google Scholar
• Fried, E. I., Papanikolaou, F., & Epskamp, S. (2021). Mental health and social contact during the COVID-19 pandemic: An ecological momentary assessment study. Clinical Psychological Science, 10(2), 340–354. https://doi.org/10.1177/21677026211017839
   Web of Science ®Google Scholar
• Gassen, J., Nowak, T. J., Henderson, A. D., & Muehlenbein, M. P. (2022). Dynamics of temperature change during experimental respiratory virus challenge: Relationships with symptoms, stress hormones, and inflammation. Brain, Behavior, and Immunity, 99, 157–165. https://doi.org/10.1016/j.bbi.2021.10.001
   PubMed Web of Science ®Google Scholar
• Gnam, J.-P., Loeffler, S.-N., Haertel, S., Engel, F., Hey, S., Boes, K., Woll, A., & Strahler, J. (2019). On the relationship between physical activity, physical fitness, and stress reactivity to a real-life mental stressor. International Journal of Stress Management, 26(4), 344–355. https://doi.org/10.1037/str0000113
   Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26(1), 10–15. https://doi.org/10.1177/0963721416666518
   Web of Science ®Google Scholar
• Hodgson, S., Watts, I., Fraser, S., Roderick, P., & Dambha-Miller, H. (2020). Loneliness, social isolation, cardiovascular disease and mortality: A of the literature and conceptual framework. Journal of the Royal Society of Medicine, 113(5), 185–192. https://doi.org/10.1177/0141076820918236
   PubMed Web of Science ®Google Scholar
• Jebb, A. T., Tay, L., Wang, W., & Huang, Q. (2015). Time series analysis for psychological research: Examining and forecasting change. Frontiers in Psychology, 6(Jun), 727. https://doi.org/10.3389/FPSYG.2015.00727/ABSTRACT
   PubMedGoogle Scholar
• Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13(3), 203–229. https://doi.org/10.1037/A0012869
   PubMed Web of Science ®Google Scholar
• McCormick, E. M., Cambridge Centre for Ageing and Neuroscience, & Kievit, R. A. (2023). Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort. The Journal of neuroscience : the official journal of the Society for Neuroscience, 43(19), 3557–3566. https://doi.org/10.1523/JNEUROSCI.1042-22.2023
   PubMed Web of Science ®Google Scholar
• McNeish, D. (2016). On using Bayesian methods to address small sample problems. Structural Equation Modeling, 23(5), 750–773. https://doi.org/10.1080/10705511.2016.1186549
   Web of Science ®Google Scholar
• McNeish, D., & Hamaker, E. L. (2019). 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
   PubMed Web of Science ®Google Scholar
• McNeish, D., & Mackinnon, D. P. (2022). Intensive longitudinal mediation in Mplus. Psychological methods. Advance online publication. https://doi.org/10.1037/met0000536
   Web of Science ®Google Scholar
• Metcalf, O., Little, J., Cowlishaw, S., Varker, T., Arjmand, H.-A., O'Donnell, M., Phelps, A., Hinton, M., Bryant, R., Hopwood, M., McFarlane, A., & Forbes, D. (2021). Modelling the relationship between poor sleep and problem anger in veterans: A dynamic structural equation modelling approach. Journal of Psychosomatic Research, 150, 110615. https://doi.org/10.1016/j.jpsychores.2021.110615
   PubMed Web of Science ®Google Scholar
• Muthen, B., Asparouhov, T. (2023). Can cross-lagged panel modeling be relied on to establish cross-lagged effects? The case of contemporaneous and reciprocal effects. http://www.statmodel.com/download/ReciprocalV3.pdf
   Google Scholar
• Muthén, L. K., & Muthén, B. (1998–2017). Mplus user’s guide (8th ed.). Muthen & Muthen.
   Google Scholar
• Neubauer, A. B., Brose, A., & Schmiedek, F. (2022). How within-person effects shape between-person differences: A multilevel structural equation modeling perspective. Psychological Methods, 28(5), 1069–1086. https://doi.org/10.1037/met0000481
   PubMed Web of Science ®Google Scholar
• Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49(6), 1417. https://doi.org/10.2307/1911408
   Web of Science ®Google Scholar
• Olsson, U. H., Foss, T., Troye, S. V., & Howell, R. D. (2000). The performance of ML, GLS, and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural Equation Modeling, 7(4), 557–595. https://doi.org/10.1207/S15328007SEM0704_3
   Web of Science ®Google Scholar
• Palmer, C. A., & Alfano, C. A. (2017). Sleep and emotion regulation: An organizing, integrative review. Sleep Medicine Reviews, 31, 6–16. https://doi.org/10.1016/j.smrv.2015.12.006
   PubMed Web of Science ®Google Scholar
• Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315
   PubMed Web of Science ®Google Scholar
• Rosseel, Y., & Loh, W. W. (2022). A structural after measurement approach to structural equation modeling. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000503
   PubMed Web of Science ®Google Scholar
• Ryu, E. (2015). The role of centering for interaction of level 1 variables in multilevel structural equation models. Structural Equation Modeling, 22(4), 617–630. https://doi.org/10.1080/10705511.2014.936491
   Web of Science ®Google Scholar
• Sardeshmukh, S. R., & Vandenberg, R. J. (2017). Integrating moderation and mediation: A structural equation modeling approach. Organizational Research Methods, 20(4), 721–745. https://doi.org/10.1177/1094428115621609
   Web of Science ®Google Scholar
• Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8(2), 23–74.
   Google Scholar
• Shieh, G. (2010). Clarifying the role of mean centring in multicollinearity of interaction effects. Multivariate Behavioral Research, 45(3), 483–507. https://doi.org/10.1080/00273171.2010.483393
   PubMed Web of Science ®Google Scholar
• Shieh, G. (2011). Clarifying the role of mean centring in multicollinearity of interaction effects. The British Journal of Mathematical and Statistical Psychology, 64(3), 462–477. https://doi.org/10.1111/j.2044-8317.2010.02002.x
   PubMed Web of Science ®Google Scholar
• Shiyko, M., Naab, P., Shiffman, S., & Li, R. (2014). Modeling complexity of EMA data: Time-varying lagged effects of negative affect on smoking urges for subgroups of nicotine addiction. Nicotine & Tobacco Research, 16 (Suppl 2), S144–S150. https://doi.org/10.1093/ntr/ntt109
   PubMed Web of Science ®Google Scholar
• Speyer, L. G., Brown, R. H., Ribeaud, D., Eisner, M., & Murray, A. L. (2022). The role of moment-to-moment dynamics of perceived stress and negative affect in co-occurring ADHD and internalising symptoms. Journal of Autism and Developmental Disorders, 53(3), 1213–1223. https://doi.org/10.1007/S10803-022-05624-W/TABLES/2
   PubMed Web of Science ®Google Scholar
• Ziedzor, R. (2022). Dynamic structural equation modeling with Gaussian processes [PhD]. Southern Illinois University. https://www.proquest.com/docview/2681078493/abstract/97233B62180346ADPQ/1
   Google Scholar
• Zyphur, M. J., Zhang, Z., Preacher, K. J., & Bird, L. J. (2019). Moderated mediation in multilevel structural equation models: Decomposing effects of race on math achievement within versus between high schools in the United States. In The handbook of multilevel theory, measurement, and analysis (pp. 473–494) American Psychological Association. https://doi.org/10.1037/0000115-021
   Google Scholar