Circumplex Models with Multivariate Time Series: An Idiographic Approach

References

• APA dictionary of psychology. (n.d.). Retrieved from https://dictionary.apa.org/idiographic
   Google Scholar
• Becker, W. C., & Krug, R. S. (1964). A circumplex model for social behavior in children. Child Development, 35, 371–396. https://doi.org/10.2307/1126703
   PubMed Web of Science ®Google Scholar
• Blozis, S. A. (2004). Structured latent curve models for the study of change in multivariate repeated measures. Psychological Methods, 9, 334–353. https://doi.org/10.1037/1082-989X.9.3.334
   PubMed Web of Science ®Google Scholar
• Borkenau, P., & Ostendorf, F. (1998). The big five as states: How useful is the five factor model to describe intraindividual variations over time? Journal of Research in Personality, 32, 202–221. https://doi.org/10.1006/jrpe.1997.2206
   Web of Science ®Google Scholar
• Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799. https://doi.org/10.1080/01621459.1976.10480949
   Web of Science ®Google Scholar
• Brockwell, P. J., & Davis, R. A. (1991). Time series: Theory and methods (2nd ed.). Springer-Verlag.
   Google Scholar
• Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. The British Journal of Mathematical and Statistical Psychology, 37, 62–83. https://doi.org/10.1111/j.2044-8317.1984.tb00789.x
   PubMed Web of Science ®Google Scholar
• Browne, M. W. (1992). Circumplex models for correlation matrices. Psychometrika, 57, 469–497. https://doi.org/10.1007/BF02294416
   Web of Science ®Google Scholar
• Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage.
   Google Scholar
• Browne, M. W., & Shapiro, A. (1986). The asymptotic covariance matrix of sample correlation coefficients under general conditions. Linear Algebra and Its Applications, 82, 169–176. https://doi.org/10.1016/0024-3795(86)90150-3
   Web of Science ®Google Scholar
• Browne, M. W., & Zhang, G. (2007). Repeated time series models for learning data. In S. M. Boker & M. J. Wenger (Eds.), Data analytic techniques for dynamical systems in the social and behavioral sciences. Lawrence Erlbaum Associates.
   Google Scholar
• Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147–158. https://doi.org/10.1037/0033-2909.101.1.147
   Web of Science ®Google Scholar
• Conte, H. R., & Plutchik, R. (1981). A circumplex model for interpersonal personality traits. Journal of Personality and Social Psychology, 40, 701–711. https://doi.org/10.1037/0022-3514.40.4.701
   Web of Science ®Google Scholar
• Evmenenko, A., & Teixeira, D. S. (2022). The circumplex model of affect in physical activity contexts: a systematic review. International Journal of Sport and Exercise Psychology, 20, 168–201. https://doi.org/10.1080/1612197X.2020.1854818
   Web of Science ®Google Scholar
• Fournier, M. A., Moskowitz, D. S., & Zuroff, D. C. (2011). Origins and applications of the interpersonal circumplex. In L. M. Horowitz & S. Strack (Eds.), Handbook of interpersonal psychology: Theory, research, assessment, and therapeutic interventions (pp. 57–73). Wiley.
   Google Scholar
• Goldstein, H. (1995). Multilevel statistical models (2nd ed.). Halsted.
   Google Scholar
• Green, A. S., Rafaeli, E., Bolger, N., Shrout, P. E., & Reis, H. T. (2006). Paper or plastic? data equivalence in paper and electronic diaries. Psychological Methods, 11, 87–105. https://doi.org/10.1037/1082-989X.11.1.87
   PubMed Web of Science ®Google Scholar
• Green, D. P., Goldman, S. L., & Salovey, P. (1993). Measurement error masks bipolarity in affect ratings. Journal of Personality and Social Psychology, 64, 1029–1041. https://doi.org/10.1037//0022-3514.64.6.1029
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20, 102–116. https://doi.org/10.1037/a0038889
   PubMed Web of Science ®Google Scholar
• Hamilton, J. D. (2010). Regime switching models. In Macroeconometrics and time series analysis (pp. 202–209). Springer.
   Google Scholar
• Jonathan, P., Russell, J. A., & S, P. B. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17, 715–734. https://doi.org/10.1017/S0954579405050340
   PubMed Web of Science ®Google Scholar
• Lebo, M. A., & Nesselroade, J. R. (1978). Intraindividual difference dimensions of mood change during pregancy identified in five P-technique factor analyses. Journal of Research in Personality, 12, 205–224. https://doi.org/10.1016/0092-6566(78)90098-3
   Web of Science ®Google Scholar
• Lee, D. (2022). Circumo [Computer software manual]. Retrieved from https://github.com/Dayoung-L/CircumO (R package version 1.0)
   Google Scholar
• Lee, D., & Zhang, G. (2022). Circumplex models with ordinal data. Structural Equation Modeling: A Multidisciplinary Journal, 29, 854–871. https://doi.org/10.1080/10705511.2022.2060231
   Web of Science ®Google Scholar
• MacCallum, R. C. (2003). Working with imperfect models. Multivariate Behavioral Research, 38, 113–139. https://doi.org/10.1207/S15327906MBR3801_5
   PubMed Web of Science ®Google Scholar
• MacCallum, R. C., Kim, C., Malarkey, W. B., & Kiecolt-Glaser, J. K. (1997). Studying multivariate change using multilevel models and latent curve models. Multivariate Behavioral Research, 32, 215–253. https://doi.org/10.1207/s15327906mbr3203_1
   PubMed Web of Science ®Google Scholar
• MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4, 84–99. https://doi.org/10.1037/1082-989X.4.1.84
   Web of Science ®Google Scholar
• Molenaar, P. C. M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50, 181–202. https://doi.org/10.1007/BF02294246
   Web of Science ®Google Scholar
• Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research & Perspective, 2, 201–218. https://doi.org/10.1207/s15366359mea0204_1
   Google Scholar
• Molenaar, P. C. M., & Nesselroade, J. R. (1998). A comparison of pseudo-maximum likelihood and asumptotically distribution-free dynamic factor analysis parameter estimation in fitting covariance-structure models to block-Toeplitz matrices representing single subject multivariate time series. Multivariate Behavioral Research, 33, 313–342. https://doi.org/10.1207/s15327906mbr3303_1
   PubMed Web of Science ®Google Scholar
• Nel, D. G. (1985). A matrix derivation of the asymptotic covariance matrix of correlation coefficients. Linear Algebra and Its Applications, 67, 137–145. https://doi.org/10.1016/0024-3795(85)90191-0
   Web of Science ®Google Scholar
• Nesselroade, J. R., McArdle, J. J., Aggen, S. H., & Meyers, J. M. (2002). Dynamic factor analysis models for representing process in multivariate time-series. In D. Moskowitz & S. L. Hershberger (Eds.), Modeling intraindividual variability with repeated measures data: methods and applications (pp. 235–265). Erlbaum.
   Google Scholar
• Olson, D. H. (2000). Circumplex model of marital and family systems. Journal of Family Therapy, 22, 144–167. https://doi.org/10.1111/1467-6427.00144
   Web of Science ®Google Scholar
• Ram, N., Yang, X., Cho, M.-J., Brinberg, M., Muirhead, F., Reeves, B., & Robinson, T. N. (2020). Screenomics: a new appoach for observing and studying individuals’ digital lives. Journal of Adolescent Research, 35, 16–50. https://doi.org/10.1177/0743558419883362
   PubMed Web of Science ®Google Scholar
• Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161–1178. https://doi.org/10.1037/h0077714
   Web of Science ®Google Scholar
• Schaefer, E. S. (1959). A circumplex model for maternal behavior. Journal of Abnormal and Social Psychology, 59, 226–235. https://doi.org/10.1037/h0041114
   PubMed Web of Science ®Google Scholar
• Stanisławski, K. (2019). The coping circumplex model: an integrative model of the structure of coping with stress. Frontiers in Psychology, 10, 694. https://doi.org/10.3389/fpsyg.2019.00694
   PubMed Web of Science ®Google Scholar
• Steiger, J. H., & Hakstian, A. R. (1982). The asymptotic distribution of elements of a correlation matrix: theory and application. British Journal of Mathematical and Statistical Psychology, 35, 208–215. https://doi.org/10.1111/j.2044-8317.1982.tb00653.x
   Web of Science ®Google Scholar
• Steiger, J. H., & Lind, J. C. (1980). Statistically based tests for the number of common factors. Paper presented at the annual meeting of the Psychometric Society.
   Google Scholar
• Trichtinger, L. A., & Zhang, G. (2020). Quantifying model error in p-technique factor analysis. Multiviariate Behavioral Research.
   Google Scholar
• Tucker, L. R., Koopman, R. F., & Linn, R. L. (1969). Evaluation of factor analytic research procedures by means of simulated correlation matrices. Psychometrika, 34, 421–459. https://doi.org/10.1007/BF02290601
   Web of Science ®Google Scholar
• Watson, D., & Clark, L. A. (1997). Measurement and mismeasurement of mood: Recurrent and emergent issues. Journal of Personality Assessment, 68, 267–296. https://doi.org/10.1207/s15327752jpa6802_4
   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. Psychology Publications: University of Iowa.
   Google Scholar
• Watson, D., Wiese, D., Vaidya, J., & Tellegen, A. (1999). The two general activation systems of affect: structural findings, evolutionary considerations, and psychobiological evidence. Journal of Personality and Social Psychology, 76, 820–838. https://doi.org/10.1037/0022-3514.76.5.820
   Web of Science ®Google Scholar
• Wiggins, J. S. (1979). A psychological taxonomy of trait-descriptive terms: The interpersonal domain. Journal of Personality and Social Psychology, 37, 395–412. https://doi.org/10.1037/0022-3514.37.3.395
   Web of Science ®Google Scholar
• Wiggins, J. S. (2003). Paradigms of personality assessment. Guilford Press.
   Google Scholar
• Zhang, G., Chow, S., & Ong, A. (2011). A sandwich-type standard error estimator of SEM models with multivariate time series. Psychometrika, 76, 77–96. https://doi.org/10.1007/s11336-010-9189-x
   Web of Science ®Google Scholar