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22 - Dealing with Repeated Measures

Design Decisions and Analytic Strategies for Over-Time Data*

from Part IV - Understanding What Your Data Are Telling You About Psychological Processes

Published online by Cambridge University Press:  12 December 2024

By
Amie M. Gordon  and
Katherine R. Thorson
Edited by
Harry T. Reis ,
Tessa West  and
Charles M. Judd
Harry T. Reis
Affiliation:
University of Rochester, New York
Tessa West
Affiliation:
New York University
Charles M. Judd
Affiliation:
University of Colorado Boulder
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Summary

Over-time, repeated measures, or longitudinal data are terms referring to repeated measurements of the same variables within the same unit (e.g., person, family, team, company). Longitudinal data come from many sources, including self-reports, behaviors, observations, and physiology. Researchers collect repeated measures for a variety of reasons, such as wanting to model change in a process over time or wanting to increase measurement reliability. Whatever the reason for data collection, longitudinal methods pose unique challenges and opportunities. This chapter has three main goals: (1) to help researchers consider design decisions when developing a longitudinal study, (2) to describe the different decisions researchers have to make when analyzing longitudinal data, and (3) to consider the unique properties of longitudinal designs that researchers should be aware of when designing and analyzing longitudinal studies. We aim to provide a comprehensive overview of the major issues that researchers should consider, and we also point to more extensive resources.

Keywords

longitudinal datalongitudinal analysisrepeated measuresanalysis of changenonindependencemultilevel modeling
Type
Chapter
Information
Handbook of Research Methods in Social and Personality Psychology , pp. 532 - 564
Publisher: Cambridge University Press
Print publication year: 2024

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References

Bolger, N., and Laurenceau, J.-P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press.Google Scholar
Fitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2012). Applied Longitudinal Analysis. Wiley.Google Scholar
Little, T. D. (2013). Longitudinal Structural Equation Modeling. Guilford Press.Google Scholar
Mehl, M. R., and Conner, T. S. (eds.) (2012). Handbook of Research Methods for Studying Daily Life. Guilford Press.Google Scholar
O’Connell, A. A., McCoach, D. B., and Bell, B. A. (eds.). (2022). Multilevel Modeling Methods with Introductory and Advanced Applications. Information Age Publishing.Google Scholar

References

Arend, M. G., and Schäfer, T. (2019). Statistical power in two-level models: A tutorial based on Monte Carlo simulation. Psychological Methods, 24, 1–19.CrossRefGoogle ScholarPubMed
Arriaga, X. B. (2001). The ups and downs of dating: Fluctuations in satisfaction in newly formed romantic relationships. Journal of Personality and Social Psychology, 80(5), 754–765.CrossRefGoogle ScholarPubMed
Asparouhov, T., Hamaker, E. L., and Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388.CrossRefGoogle Scholar
Barr, D. J., Levy, R., Scheepers, C., and Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255–278.CrossRefGoogle ScholarPubMed
Bauer, D. J., Preacher, K. J., and Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11(2), 142–163.CrossRefGoogle ScholarPubMed
Boker, S. M. (2012). Dynamical systems and differential equation models of change. In APA Handbook of Research Methods in Psychology, vol. 3, Data Analysis and Research PublicationAmerican Psychological Association.Google Scholar
Bolger, N., and Laurenceau, J.-P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press.Google Scholar
Bolger, N., and Shrout, P. E. (2007). Accounting for statistical dependency in longitudinal data on dyads. In Little, T. D., Bovaird, J. A., and Card, N. A. (eds.) Modeling Contextual Effects in Longitudinal Studies. Lawrence Erlbaum Associates Publishers.Google Scholar
Bolger, N., Stadler, G., and Laurenceau, J.-P. (2012). Power analysis for intensive longitudinal studies. In Mehl, M. R. and Conner, T. S. (eds.) Handbook of Research Methods for Studying Daily Life. Guilford Press.Google Scholar
Bolger, N., and Zee, K. S. (2019). Heterogeneity in temporal processes: Implications for theories in health psychology. Applied Psychology: Health and Well-Being, 11(2), 198–201.Google ScholarPubMed
Bolger, N., Zuckerman, A., and Kessler, R. C. (2000). Invisible support and adjustment to stress. Journal of Personality and Social Psychology, 79(6), 953–961.CrossRefGoogle ScholarPubMed
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.Google Scholar
Brock, R. L., and Lawrence, E. (2008). A longitudinal investigation of stress spillover in marriage: Does spousal support adequacy buffer the effects? Journal of Family Psychology, 22(1), 11–20.CrossRefGoogle ScholarPubMed
Butler, E. A., and Barnard, K. J. (2019). Quantifying interpersonal dynamics for studying socio-emotional processes and adverse health behaviors. Psychosomatic Medicine, 81(8), 749–758.CrossRefGoogle ScholarPubMed
Chun, C. A. (2016). The expression of posttraumatic stress symptoms in daily life: A review of experience sampling methodology and daily diary studies. Journal of Psychopathology and Behavioral Assessment, 38(3), 406–420.CrossRefGoogle Scholar
DiGiovanni, A. M., Fagle, T., Vannucci, A., Ohannessian, C. M., and Bolger, N. (2022). Within-person changes in co-rumination and rumination in adolescence: Examining heterogeneity and the moderating roles of gender and time. Journal of Youth and Adolescence, 51(10), 1958–1969.CrossRefGoogle ScholarPubMed
Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F., and Schabenberger, O. (2008). An R2 statistic for fixed effects in the linear mixed model. Statistics in Medicine, 27(29), 6137–6157.CrossRefGoogle ScholarPubMed
Enders, C. K., and Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121–138.CrossRefGoogle Scholar
Foster, K. T., and Beltz, A. M. (2022). Heterogeneity in affective complexity among men and women. Emotion, 22(8), 1815–1827.CrossRefGoogle ScholarPubMed
Fox, J. (2015). Applied Regression Analysis and Generalized Linear Models. Sage.Google Scholar
Frost, D. M., and Forrester, C. (2013). Closeness discrepancies in romantic relationships: Implications for relational well-being, stability, and mental health. Personality and Social Psychology Bulletin, 39(4), 456–469.CrossRefGoogle ScholarPubMed
Gable, S. L., and Reis, H. T. (1999). Now and then, them and us, this and that: Studying relationships across time, partner, context, and person. Personal Relationships, 6(4), 415–432.CrossRefGoogle Scholar
Garson, G. D. (2019). Multilevel Modeling. SAGE Publications, Inc.Google Scholar
Gates, K. M., and Molenaar, P. C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1), 310–319.CrossRefGoogle ScholarPubMed
Girme, Y. U. (2020). Step out of line: Modeling nonlinear effects and dynamics in close-relationships research. Current Directions in Psychological Science, 29(4), 351–357.CrossRefGoogle Scholar
Goldring, M. R., and Bolger, N. (2021). Physical effects of daily stressors are psychologically mediated, heterogeneous, and bidirectional. Journal of Personality and Social Psychology, 121, 722–746.CrossRefGoogle ScholarPubMed
Gordon, A. M. (2023). Within-person variance in daily conflict and relationship satisfaction. Unpublished data.Google Scholar
Gordon, A. M., and Chen, S. (2014). The role of sleep in interpersonal conflict: Do sleepless nights mean worse fights? Social Psychological and Personality Science, 5, 168–175.CrossRefGoogle Scholar
Gordon, A. M., Cross, E., Ascigil, E., Balzarini, R., Luerssen, A., and Muise, A. (2022). Feeling appreciated buffers against the negative effects of unequal division of household labor on relationship satisfaction. Psychological Science, 33(8), 1313–1327.CrossRefGoogle ScholarPubMed
Greene, W. H. (2008). Econometric Analysis, 6th ed. Pearson/Prentice Hall.Google Scholar
Hamaker, E. L., and Muthén, B. (2020). The fixed versus random effects debate and how it relates to centering in multilevel modeling. Psychological Methods, 25, 365–379.CrossRefGoogle ScholarPubMed
Harris, P. E., Gordon, A. M., Dover, T. L., Small, P. A., Collins, N. L., and Major, B. (2022). Sleep, emotions, and sense of belonging: A daily experience study. Affective Science, 3(3), DOI:10.1007/s42761-021-00088-0.CrossRefGoogle ScholarPubMed
Hayes, A. M., Laurenceau, J.-P., Feldman, G., Strauss, J. L., and Cardaciotto, L. (2007). Change is not always linear: The study of nonlinear and discontinuous patterns of change in psychotherapy. Clinical Psychology Review, 27(6), 715–723.CrossRefGoogle ScholarPubMed
Hox, J., Moerbeek, M., and van de Schoot, R. (2018). Multilevel Analysis: Techniques and Applications, 3rd ed. Routledge.Google Scholar
Iida, M., Shrout, P. E., Laurenceau, J.-P., and Bolger, N. (2012). Using diary methods in psychological research. In Cooper, H. et al. (eds.) APA Handbook of Research Methods In Psychology, vol. 1, Foundations, Planning, Measures, and Psychometrics. American Psychological Association.Google Scholar
Karney, B. R., and Bradbury, T. N. (1997). Neuroticism, marital interaction, and the trajectory of marital satisfaction. Journal of Personality and Social Psychology, 72(5), 1075–1092.CrossRefGoogle ScholarPubMed
Kashdan, T., and Steger, M. F. (2006). Expanding the topography of social anxiety: An experience-sampling assessment of positive emotions, positive events, and emotion suppression. Psychological Science, 17(2), 120–128.CrossRefGoogle ScholarPubMed
Kashy, D. A., and Donnellan, M. B. (2008). Comparing MLM and SEM approaches to analyzing developmental dyadic data: Growth curve models of hostility in families. In Card, N. A., Selig, J. P., and Little, T. D. (eds.) Modeling Dyadic and Interdependent Data in the Developmental and Behavioral Sciences. Routledge.Google Scholar
Kenny, D. A., Kashy, D. A., and Bolger, N. (1998). Data analysis in social psychology. In Gilbert, D. T., Fiske, S. T., and Lindzey, G. (eds.) The Handbook of Social Psychology, vol. 1. Oxford University Press.Google Scholar
Kenny, D. A., Kashy, D. A., and Cook, W. L. (2006). Dyadic Data Analysis. Guilford Press.Google Scholar
Killip, S., Mahfoud, Z., and Pearce, K. (2004). What is an intracluster correlation coefficient? Crucial concepts for primary care researchers. Annals of Family Medicine, 2(3), 204–208.CrossRefGoogle ScholarPubMed
Lafit, G., Adolf, J. K., Dejonckheere, E., Myin-Germeys, I., Viechtbauer, W., and Ceulemans, E. (2021). Selection of the number of participants in intensive longitudinal studies: A user-friendly shiny app and tutorial for performing power analysis in multilevel regression models that account for temporal dependencies. Advances in Methods and Practices in Psychological Science, 4(1), https://doi.org/10.1177/2515245920978738.CrossRefGoogle Scholar
Lane, S. P., and Hennes, E. P. (2018). Power struggles: Estimating sample size for multilevel relationships research. Journal of Social and Personal Relationships, 35(1), 7–31.CrossRefGoogle Scholar
Lavner, J. A., and Bradbury, T. N. (2010). Patterns of change in marital satisfaction over the newlywed years. Journal of Marriage and Family, 72(5), 1171–1187.CrossRefGoogle ScholarPubMed
Ledermann, T., and Kenny, D. A. (2017). Analyzing dyadic data with multilevel modeling versus structural equation modeling: A tale of two methods. Journal of Family Psychology, 31, 442–452.CrossRefGoogle ScholarPubMed
Liang, M., Koslovsky, M. D., Hébert, E. T., Kendzor, D. E., Businelle, M. S., and Vannucci, M. (2021). Bayesian continuous-time hidden Markov models with covariate selection for intensive longitudinal data with measurement error. Psychological Methods, 28(4), 880–894.CrossRefGoogle ScholarPubMed
McArdle, J. J., and Nesselroade, J. R. (2014). Longitudinal Data Analysis Using Structural Equation Models. American Psychological Association.CrossRefGoogle Scholar
McClelland, G. H. (2000). Nasty data: Unruly, ill-mannered observations can ruin your analysis. In Reis, H. T. and Judd, C. M. (eds.) Handbook of Research Methods in Social and Personality Psychology, 1st ed. Cambridge University Press.Google Scholar
McNeish, D. (2017). Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward–Roger correction. Multivariate Behavioral Research, 52(5), 661–670.CrossRefGoogle ScholarPubMed
McNeish, D., and Hamaker, E. L. (2020). A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. Psychological Methods, 25, 610–635.CrossRefGoogle ScholarPubMed
McNeish, D., and Matta, T. (2018). Differentiating between mixed-effects and latent-curve approaches to growth modeling. Behavior Research Methods, 50(4), 1398–1414.CrossRefGoogle ScholarPubMed
McNeish, D., Stapleton, L. M., and Silverman, R. D. (2017). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22(1), 114–140.CrossRefGoogle ScholarPubMed
Matthews, R. A., Wayne, J. H., and Ford, M. T. (2014). A work–family conflict/subjective well-being process model: A test of competing theories of longitudinal effects. Journal of Applied Psychology, 99(6), 1173–1187.CrossRefGoogle Scholar
Moller, A. C., Deci, E. L., and Elliot, A. J. (2010). Person-level relatedness and the incremental value of relating. Personality and Social Psychology Bulletin, 36(6), 754–767.CrossRefGoogle ScholarPubMed
Nezlek, J. B. (2012). Multilevel modeling analyses of diary-style data. In Mehl, M. R. and Conner, T. S. (eds.) Handbook of Research Methods for Studying Daily Life. Guilford Press.Google Scholar
Orth, U., Clark, D. A., Donnellan, M. B., and Robins, R. W. (2021). Testing prospective effects in longitudinal research: Comparing seven competing cross-lagged models. Journal of Personality and Social Psychology, 120(4), 1013–1034.CrossRefGoogle ScholarPubMed
Raudenbush, S. W., and Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed. Sage Publications.Google Scholar
Reis, H. T., and Wheeler, L. (1991). Studying social interaction with the Rochester interaction record. In Zanna, M. P. (ed.) Advances in Experimental Social Psychology, vol. 24. Academic Press.Google Scholar
Reynolds, B. M., Robles, T. F., and Repetti, R. L. (2016). Measurement reactivity and fatigue effects in daily diary research with families. Developmental Psychology, 52, 442–456.CrossRefGoogle ScholarPubMed
Rights, J. D., and Sterba, S. K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24, 309–338.CrossRefGoogle ScholarPubMed
Schrader, S. M., Turner, T. W., Breitenstein, M. J., and Simon, S. D. (1988). Longitudinal study of semen quality of unexposed workers: I. Study overview. Reproductive Toxicology, 2(3), 183–190.CrossRefGoogle Scholar
Shrout, P. E., Stadler, G., Lane, S. P., McClure, M. J., Jackson, G. L., Clavél, F. D., Iida, M., Gleason, M. E. J., Xu, J. H., and Bolger, N. (2018). Initial elevation bias in subjective reports. Proceedings of the National Academy of Sciences of the United States of America, 115(1), E15–E23.Google ScholarPubMed
Simonsohn, U. (2018). Two lines: A valid alternative to the invalid testing of U-shaped relationships with quadratic regressions. Advances in Methods and Practices in Psychological Science, 1(4), 538–555.CrossRefGoogle Scholar
Sin, N. L., Graham-Engeland, J. E., Ong, A. D., and Almeida, D. M. (2015). Affective reactivity to daily stressors is associated with elevated inflammation. Health Psychology: Official Journal of the Division of Health Psychology, American Psychological Association, 34(12), 1154–1165.CrossRefGoogle ScholarPubMed
Singer, J. D., and Willett, J. B. (2003). Survival analysis. In Schinka, J. A. and Velicer, W. F. (eds.) Handbook of Psychology, vol. 2,Research Methods in Psychology. John Wiley & Sons Inc.Google Scholar
Snijders, T. A. B., and Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd ed. Sage.Google Scholar
Stadler, G., Snyder, K. A., Horn, A. B., Shrout, P. E., and Bolger, N. P. (2012). Close relationships and health in daily life: A review and empirical data on intimacy and somatic symptoms. Psychosomatic Medicine, 74(4), 398–409.CrossRefGoogle ScholarPubMed
Teague, S., Youssef, G. J., Macdonald, J. A., Sciberras, E., Shatte, A., Fuller-Tyszkiewicz, M., Greenwood, C., McIntosh, J., Olsson, C. A., Hutchinson, D., Bant, S., Barker, S., Booth, A., Capic, T., Di Manno, L., Gulenc, A., Le Bas, G., Letcher, P., Lubotzky, C. A., and the SEED Lifecourse Sciences Theme. (2018). Retention strategies in longitudinal cohort studies: A systematic review and meta-analysis. BMC Medical Research Methodology, 18(1), 151, https://doi.org/10.1186/s12874-018-0586-7.CrossRefGoogle ScholarPubMed
Thorson, K. R., West, T. V., and Mendes, W. B. (2018). Measuring physiological influence in dyads: A guide to designing, implementing, and analyzing dyadic physiological studies. Psychological Methods, 23(4), 595–616.CrossRefGoogle ScholarPubMed
Torre, J. B., and Lieberman, M. D. (2018). Putting feelings into words: Affect labeling as implicit emotion regulation. Emotion Review, 10(2), 116–124.CrossRefGoogle Scholar
Uhlig, S., Meylan, A., and Rudolph, U. (2020). Reliability of short-term measurements of heart rate variability: Findings from a longitudinal study. Biological Psychology, 154, 107905, https://doi.org/10.1016/j.biopsycho.2020.107905.CrossRefGoogle ScholarPubMed
Vajargah, K. F., and Masoomehnikbakht. (2015). Application REML model and determining cut off of ICC by multi-level model based on Markov chains simulation in health. Indian Journal of Fundamental and Applied Life Sciences, 5(S2), 1432–1448.Google Scholar
van Breukelen, G. J. P. (2013). ANCOVA versus CHANGE from baseline in nonrandomized studies: The difference. Multivariate Behavioral Research, 48(6), 895–922.CrossRefGoogle ScholarPubMed
Williamson, H. C., and Lavner, J. A. (2020). Trajectories of marital satisfaction in diverse newlywed couples. Social Psychological and Personality Science, 11(5), 597–604.CrossRefGoogle ScholarPubMed
Wu, W., Selig, J. P, and Little, T. D. (2013). Longitudinal data analysis. In Little, T. D. (ed.) The Oxford Handbook of Quantitative Methods, vol. 2, Statistical Analysis. Oxford University Press.Google ScholarPubMed
Zee, K. S., and Bolger, N. (2022). Physiological coregulation during social support discussions. Emotion, 23(3), 825–843.CrossRefGoogle ScholarPubMed

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