A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance

REFERENCES

• Aron, E., & Aron, A. (1997). Sensory-processing sensitivity and its relation to introversion and emotionality. Journal of Personality and Social Psychology, 73, 345–368.
   PubMed Web of Science ®Google Scholar
• Bates, D., Maechler, M., & Bolker, B. (2011). lme4: Linear mixed-effects models using S4 classes [Computer software manual]. Retrieved from http://CRAN.R-project.org/package=lme4 (R package version 0.999375-39).
   Google Scholar
• Bollen, K., & Curran, P. (2004). Autoregressive latent trajectories (ALT) model: A synthesis of two traditions. Sociological Methods and Research, 32, 336–383.
   Web of Science ®Google Scholar
• Bollen, K., & Curran, P. (2006). Latent curve models: A structural equation perspective. New York, NY: Wiley.
   Google Scholar
• Bryk, A., & Raudenbush, S. (1992). Hierarchical linear models. Newbury Park, CA: Sage.
   Google Scholar
• Chatfield, C. (2003). The analysis of time series: An introduction ( 6th ed.). Washington, DC: Chapman & Hall/CRC.
   Google Scholar
• Haan-Rietdijk, S., Gottman, J., Bergeman, C., & Hamaker, E.L. (2014). Get over it! A multilevel threshold autoregressive model for state-dependent affect regulation. Psychometrika. doi: 10.1007/s11336-014-9417-x
   PubMed Web of Science ®Google Scholar
• Enders, C., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121–138.
   PubMed Web of Science ®Google Scholar
• Gelman, A., Carlin, J., Stern, H., & Rubin, D. (2004). Bayesian data analysis. New York, NY: Chapman & Hall.
   Google Scholar
• Gelman, A., Su, Y.-S., Yajima, M., Hill, J., Pittau, M.G., Kerman, J., & Zheng, T. (2011). arm: Data analysis using regression and multilevel/hierarchical models [Computer software manual]. Retrieved from http://CRAN.R-project.org/package=arm (R package version 1.4-13).
   Google Scholar
• Hamaker, E.L. (2012). Why researchers should think within-person: A paradigmatic rationale. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life. New York, NY: Guilford.
   Google Scholar
• Hamaker, E.L., & Grasman, R. P. P. P. (2013). Studying inertia in the multilevel autoregressive model: Should the lagged predictor be cluster mean centered? [Submitted for publication].
   Google Scholar
• Hamaker, E.L., & Klugkist, I. (2011). Bayesian estimation of multilevel models. In K. Roberts & J. Hox (Eds.), Handbook of advanced multilevel analysis (pp. 137–161). New York: Taylor and Francis.
   Google Scholar
• Hamilton, J. (1994). Time series analysis. Princeton, NJ: Princeton University.
   Google Scholar
• Hoijtink, H. (2009). Bayesian data analysis. In A. Millsap & R.E. Maydeu-Olivares (Eds.), The Sage handbook of quantitative methods in psychology (pp. 423–443). London, UK: Sage.
   Google Scholar
• Hox, J. (2002). Multilevel analysis: Techniques and applications ( 2nd ed.). Mahwah, NJ: Erlbaum.
   Google Scholar
• Koval, P., & Kuppens, P. (2012). Changing emotion dynamics: Individual differences in the effect of anticipatory social stress on emotional inertia. Emotion, 12, 256–267.
   PubMed Web of Science ®Google Scholar
• Koval, P., Kuppens, P., Allen, N.B., & Sheeber, L.B. (2012). Getting stuck in depression: The roles of rumination and emotional inertia. Cognition & Emotion, 26, 1412–1427.
   PubMed Web of Science ®Google Scholar
• Kreft, I., de Leeuw, J., & Aiken, L. (1995). The effect of different forms of centering in hierarchical linear models. Multivariate Behavioral Research, 30, 1–21.
   PubMed Web of Science ®Google Scholar
• Kuppens, P., Allen, N., & Sheeber, L. (2010). Emotional inertia and psychological maladjustment. Psychological Science, 21, 984–991.
   PubMed Web of Science ®Google Scholar
• Kuppens, P., Sheeber, L.B., Yap, M.B., Whittle, S., Simmons, J.G., & Allen, N.B. (2012). Emotional inertia prospectively predicts the onset of depressive disorder in adolescence. Emotion, 12, 283–289.
   PubMed Web of Science ®Google Scholar
• Laurenceau, J.P., Barrett, L., & Rovine, M.J. (2005). The interpersonal process model of intimacy in marriage: A daily-diary and multilevel modeling approach. Journal of Family Psychology, 19, 314–323.
   PubMed Web of Science ®Google Scholar
• Lunn, D.J., Thomas, A., Best, N., & Spiegelhalter, D. (2000). Winbugs, a Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing, 10, 325–337.
   Web of Science ®Google Scholar
• Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122.
   Web of Science ®Google Scholar
• Mood, A.M., Graybill, F.A., & Boes, D.C. (1985). Introduction to the theory of statistics. London, UK: McGraw-Hill.
   Google Scholar
• Nesselroade, J.R. (1991). Interindividual differences in intraindividual change. In L.M. Collins & J.L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions. Washington, DC: American Psychological Association.
   Google Scholar
• O’Hagan, A. (1995). Fractional Bayes factors for model comparison. Journal of the Royal Statistical Society Series B (Methodological), 57, 99–138.
   Web of Science ®Google Scholar
• Rasbash, J., Charlton, C., Browne, W., Healy, M., & Cameron, B. (2009). MLwiN Version 2.1 [Computer software manual]. Centre for Multilevel Modelling, University of Bristol.
   Google Scholar
• Rottenberg, J. (2005). Mood and emotion in major depression. Current Directions in Psychological Science, 14, 167–170.
   Web of Science ®Google Scholar
• Rovine, M.J., & Walls, T.A. (2006). A multilevel autoregressive model to describe interindividual differences in the stability of a process. In J.L. Schafer & T.A. Walls (Eds.), Models for intensive longitudinal data. New York, NY: Oxford University Press.
   Google Scholar
• Schmittmann, V.D., Dolan, C.V., Maas, H. L.J., & Neale, M.C. (2005). Discrete latent Markov models for normally distributed response data. Multivariate Behavioral Research, 40, 461–488.
   PubMed Web of Science ®Google Scholar
• Sturtz, S., Ligges, U., & Gelman, A. (2005). R2WinBUGS: A package for running WinBUGS from R. Journal of Statistical Software, 12, 1–16. Retrieved from http://www.jstatsoft.org
   Web of Science ®Google Scholar
• Suls, J., Green, P., & Hillis, S. (1998). Emotional reactivity to everyday problems, affective inertia, and neuroticism. Personality and Social Psychology Bulletin, 24, 127–136.
   Web of Science ®Google Scholar
• Wang, L.P., Hamaker, E.L., & Bergeman, C.S. (2012). Investigating inter-individual differences in short-term intra-individual variability. Psychological Methods, 17, 567–581. doi: 10.1037/a0029317
   PubMed Web of Science ®Google Scholar
• Watson, D.&, Clark, L.A. (1999). The PANAS-X: Manual for the Positive and Negative Affect Schedule - Expanded Form [Computer software manual].
   Google Scholar
• Wichers, M., Barge-Schaapman, D. Q. C. M., Nicolson, N.A., Peeters, F., de Vries, M., Mengelers, R., & van Os, J. (2009). Reduced stress-sensitivity or increased reward experience: The psychological mechanism of response to antidepressant medication. Neuropsychopharmacology, 34, 923–931.
   PubMed Web of Science ®Google Scholar
• Wichers, M., Lothmann, C., Simons, C. J.P., Nicolson, N.A., & Peeters, F. (2012). The dynamic interplay between negative and positive emotions in daily life predicts response to treatment in depression: A momentary assessment study. British Journal of Clinical Psychology, 51, 206–222.
   PubMed Web of Science ®Google Scholar
• Wichers, M., Peeters, F., Geschwind, N., Jacobs, N., Simons, C. J.P., Derom, C., … van Os, J. (2010). Unveiling patterns of affective responses in daily life may improve outcome prediction in depression: A momentary assessment study. Journal of Affective Disorders, 124, 191–195.
   PubMed Web of Science ®Google Scholar
• Wichers, M., Peeters, F., Rutten, B. P.F., Jacobs, N., Derom, C., Thiery, E., … van Os, J. (2012). A time-lagged momentary assessment study on daily life physical activity and affect. Health Psychology, 31, 135–144.
   PubMed Web of Science ®Google Scholar