Hostname: page-component-7dd5485656-gs9qr Total loading time: 0 Render date: 2025-10-31T12:07:02.321Z Has data issue: false hasContentIssue false
  • English
  • Français

Examining Interindividual Differences in Cyclicity of Pleasant and Unpleasant Affects Using Spectral Analysis and Item Response Modeling

Published online by Cambridge University Press:  01 January 2025

Nilam Ram ,
Sy-Miin Chow ,
Ryan P. Bowles ,
Lijuan Wang ,
Kevin Grimm ,
Frank Fujita  and
John R. Nesselroade
Nilam Ram*
Affiliation:
University of Virginia
Sy-Miin Chow
Affiliation:
University of Notre Dame
Ryan P. Bowles
Affiliation:
University of Virginia
Lijuan Wang
Affiliation:
University of Virginia
Kevin Grimm
Affiliation:
University of Virginia
Frank Fujita
Affiliation:
University of Indiana, South Bend
John R. Nesselroade
Affiliation:
University of Virginia
*
Requests for reprints should be sent to Nilam Ram, Department of Psychology, PO Box 400400, University of Virginia, Charlottesville, VA 22904-4400, USA. E-mail: nilam@virginia.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Weekly cycles in emotion were examined by combining item response modeling and spectral analysis approaches in an analysis of 179 college students' reports of daily emotions experienced over 7 weeks. We addressed the measurement of emotion using an item response model. Spectral analysis and multilevel sinusoidal models were used to identify interindividual differences in intraindividual cyclic change. Simulations and incomplete data designs were used to examine how well this combination of analysis techniques might work when applied to other practical data problems. Empirically, we found systematic individual differences in the extent to which individuals' emotions follow a weekly cycle, and in how such cycles are exhibited. Weekly cycles accounted for very little variance in day to day emotions at the individual level. Analytically, we illustrate how measurement, change, and interindividual difference models from different traditions may be combined in a practical manner to describe some of the complexities of human behavior.

Keywords

non-linearmultilevellongitudinalmissing dataemotion

Information

Type
Application Reviews and Case Studies
Information
Psychometrika , Volume 70 , Issue 4 , December 2005 , pp. 773 - 790
Copyright
Copyright © 2005 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

The authors gratefully acknowledge the support provided by grant T32 AG20500 from the National Institute on Aging in the preparation of this article. Special thanks to those at the Institute for Developmental and Health Research Methodology at the University of Virginia and to Paul De Boeck and the reviewers for helpful comments on earlier versions of this work.

References

Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561–573.CrossRefGoogle Scholar
Bond, T.G., Fox, C. (2001). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Earlbaum.CrossRefGoogle Scholar
Box, G.E.P., & Jenkins, G.M. (1976). Time series analysis: Forecasting and control (rev. ed.). San Francisco, CA: Holden-Day.Google Scholar
Browne, M.W., & Nesselroade, J.R. (2005). Representing psychological processes with dynamic factor models: Some promising uses and extensions of ARMA time series models. In Maydeu-Olivares, A., & McArdle, J.J. (Eds.), Psychometrics: A festschrift to Roderick P. McDonald. Mahwah, NJ: Lawrence Earlbaum.Google Scholar
Cattell, R.B. (1952). The three basic factor analytic research designs—Their interrelations and derivatives. Psychological Bulletin, 49, 499–520.CrossRefGoogle ScholarPubMed
Chow, S., Ram, N., Boker, S.M., Fujita, F., Clore, G. (2005). Capturing weekly fluctuation in emotion using a latent differential structural approach. Emotion, 5, 208–225.CrossRefGoogle Scholar
Croft, G.P., Walker, A.E. (2001). Are the Monday blues all in the mind? The role of expectancy in the subjective experience of mood. Journal of Applied Social Psychology, 31, 1133–1145.CrossRefGoogle Scholar
Dawson, T.L. (2000). Moral and evaluative reasoning across the life-span. Journal of Applied Measurement, 1, 346–371.Google ScholarPubMed
Diener, E., Smith, H., Fujita, F. (1995). The personality structure of affect. Journal of Personality and Social Psychology, 69(1), 130–141.CrossRefGoogle Scholar
Egloff, B., Tausch, A., Kohlmann, C.W., Krohne, H.W. (1995). Relationships between time of day, day of the week, and positive mood: Exploring the role of the mood measure. Motivation & Emotion, 19, 99–110.CrossRefGoogle Scholar
Eid, M., Diener, E. (1999). Intraindividual variability in affect: Reliability, validity and personality correlates. Journal of Personality and Social Psychology, 76(4), 662–676.CrossRefGoogle Scholar
Estes, W. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53(2), 134–140.CrossRefGoogle ScholarPubMed
Fahrmeir, L., Tutz, G. (2001). Multivariate statistical modelling based on generalized linear models (2nd ed.). New York: Springer Verlag.CrossRefGoogle Scholar
Fox, J., Glas, C.A.W. (2001). Bayesian estimation of a multilevel IRT model using GIBBS sampling. Psychometrika, 66, 271–288.CrossRefGoogle Scholar
Kennedy-Moore, E., Greenberg, M.A., Newman, M.G., Stone, A.A. (1992). The relationship between daily events and mood: The mood measure may matter. Motivation & Emotion, 16, 143–155.CrossRefGoogle Scholar
Kolen, M.J., Brennan, R.L. (1995). Test equating: Methods and practices. New York: Springer-Verlag.CrossRefGoogle Scholar
Koopmans, L.H. (1995). The spectral analysis of time series (2nd ed.) San Diego, CA: Academic Press.Google Scholar
Larsen, R.J., Kasimatis, M. (1990). Individual differences in entrainment of mood to the weekly calendar. Journal of Personality and Social Psychology, 58(1), 164–171.CrossRefGoogle Scholar
Lee, O.K. (2003). Rasch simultaneous vertical equating for measuring reading growth. Journal of Applied Measurement, 4, 10–23.Google ScholarPubMed
Linacre, J.M. (2003). Winsteps (Version 3) [computer program]. Chicago, IL: Winsteps.com.Google Scholar
Little, R., Rubin, D. (1987). Statistical analysis with missing data. New York: Wiley.Google Scholar
McArdle, J.J., Grimm, K.J., Hamagami, F., Bowles, R.P., & Meredith, W. (2005). Modeling non-repeated measures using longitudinal structural equations. In preparation.Google Scholar
McArdle, J.J., Hamagami, F. (2004). Methods for dynamic change hypotheses. In van Montfort, K., Oud, J., Satorra, A. (Eds.), Recent developments on structural equation models: Theory and applications (pp. 295–335). Dordrecht: Kluwer Academic.CrossRefGoogle Scholar
McFarlane, J.M., Martin, C.L., Williams, T.M. (1988). Mood fluctuations: Women versus men and menstrual versus other cycles. Psychology of Women Quarterly, 12, 201–223.CrossRefGoogle 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 and Perspectives, 2, 201–218.Google Scholar
Murray, G., Allen, N.B., Trinder, J., Burgess, H. (2002). Is weakened circadian rhymicity a characteristic of neuroticism?. Journal of Affective Disorders, 72(3), 281–289.CrossRefGoogle Scholar
Reid, S., Towell, A.D., Golding, J.F. (2000). Seasonality, social zeitgebers and mood variability in entrainment of mood: Implications for seasonal affective disorder. Journal of Affective Disorders, 59(1), 47–54.CrossRefGoogle ScholarPubMed
Rost, J. (2001). The growing family of Rasch models. In Boomsma, A., van Duijn, M.A.J., Snijders, T.A.B. (Eds.), Essays on item response theory (pp. 2–42). New York: Springer Verlag.Google Scholar
Rusting, C.L., Larsen, R.J. (1998). Diurnal patterns of unpleasant mood: Associations with neuroticism, depression, and anxiety. Journal of Personality, 66(1), 85–103.CrossRefGoogle ScholarPubMed
Spiegelhalter, D.J., Thomas, A., Best, N.G., & Lunn, D. (2003). WinBUGS version 1.4.1 User Manual. Cambridge: Medical Research Council Biostatistics Unit. (Available from. http://www.mrc-bsu.cam.ac.uk/bugs.)Google Scholar
Stone, A.A., Hedges, S.N., Neale, J.M., Satin, M.S. (1985). Prospective and cross-sectional mood reports offer no evidence of a “Blue Monday” phenomenon. Journal of Personality and Social Psychology, 49, 129–134.CrossRefGoogle Scholar
Warner, R.M. (1998). Spectral analysis of time-series data. New York: Guilford Press.Google Scholar
Wright, B.D. (1984). Despair and hope for educational measurement. Contemporary Education Review, 1, 281–288.Google Scholar
Wright, B.D. (1997). A history of social science measurement. Educational Measurement: Issues and Practice, 16, 33–52.CrossRefGoogle Scholar
Wright, B.D., Linacre, J.M. (1994). Reasonable mean-square fit statistics. Rasch Measurement Transactions, 8, 370.Google Scholar
Wright, B.D., Masters, G.N. (1982). Rating scale analysis. Chicago: MESA Press.Google Scholar