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24 - Modeling Intensive Longitudinal Data

from Part VI - Intensive Longitudinal Designs

Published online by Cambridge University Press:  23 March 2020

By
Marlies Houben ,
Eva Ceulemans  and
Peter Kuppens
Edited by
Aidan G. C. Wright  and
Michael N. Hallquist
Aidan G. C. Wright
Affiliation:
University of Pittsburgh
Michael N. Hallquist
Affiliation:
Pennsylvania State University
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Summary

The behaviors, thoughts, and feelings related to psychopathology are often not of a static nature, but rather change and fluctuate over time in response to changes in daily life situations. Therefore, clinical psychology research can benefit from focusing on how psychopathological features behave over time, as this can provide new perspectives and insights concerning the phenomenology and mechanisms underlying psychopathology. The collection of intensive longitudinal data, consisting of many repeated measurements from single participants, allows for the investigation of several dynamic properties of single or multiple symptoms (and their interrelations). This chapter presents an overview of some major dynamic properties that can be studied with intensive longitudinal data. First, it focuses on several univariate approaches, allowing the examination of one single feature over time. Then it discusses some methods and models to further examine the dynamic relationships between two or more symptoms. For each approach, information is provided on how to calculate simple indices on a more descriptive level, as well as how to model the dynamic features using more complex models.

Keywords

intensive longitudinal datamodelingunivariatemultivariatecentral tendencyspread measuresdynamic measuresconcurrent relationshipsprospective relationships
Type
Chapter
Information
The Cambridge Handbook of Research Methods in Clinical Psychology , pp. 312 - 326
Publisher: Cambridge University Press
Print publication year: 2020

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References

American Psychiatric Association (APA) (Ed.). (2013). Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (5th edn.). Washington, DC: American Psychiatric Association.Google Scholar
Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day.Google Scholar
Brandt, P. T., & Williams, J. T. (2007). Multiple Time Series Models. Thousand Oaks, CA: Sage.Google Scholar
Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., … Tuerlinckx, F. (2013). A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data. PLOS ONE, 8(4), e60188.CrossRefGoogle ScholarPubMed
Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., … Kuppens, P. (2016). Assessing Temporal Emotion Dynamics Using Networks. Assessment, 23(4), 425–435.Google Scholar
Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., & Tuerlinckx, F. (2017). Changing Dynamics: Time-Varying Autoregressive Models Using Generalized Additive Modeling. Psychological Methods, 22(3), 409–425.Google Scholar
Bringmann, L. F., Ferrer, E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2018). Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model. Multivariate Behavioral Research, 53, 293–314.Google Scholar
Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., ... Snippe, E. (2019). What Do Centrality Measures Measure in Psychological Networks? Journal of Abnormal Psychology. Retrieved from https://doi.org/10.1037/abn0000446Google Scholar
Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Using Raw VAR Regression Coefficients to Build Networks can be Misleading. Multivariate Behavioral Research, 51(2–3), 330–344.CrossRefGoogle ScholarPubMed
Bulteel, K., Mestdagh, M., Tuerlinckx, F., & Ceulemans, E. (2018). VAR(1) Based Models Do not Always Outpredict AR(1) Models in Typical Psychological Applications. Psychological Methods, 23, 740–756.Google Scholar
Bylsma, L. M., Taylor-Clift, A., & Rottenberg, J. (2011). Emotional Reactivity to Daily Events in Major and Minor Depression. Journal of Abnormal Psychology, 120(1), 155–167.CrossRefGoogle ScholarPubMed
Cabrieto, J., Tuerlinckx, F., Kuppens, P., Grassmann, M., & Ceulemans, E. (2017). Detecting Correlation Changes in Multivariate Time Series: A Comparison of Four Non-Parametric Change Point Detection Methods. Behavior Research Methods, 49(3), 988–1005.Google Scholar
Cabrieto, J., Tuerlinckx, F., Kuppens, P., Hunyadi, B., & Ceulemans, E. (2018). Testing for the Presence of Correlation Changes in a Multivariate Time Series: A Permutation Based Approach. Scientific Reports, 8(1), 769.Google Scholar
Cattell, R. B. (1952). The Three Basic Factor-Analytic Research Designs: Their Interrelations and Derivatives. Psychological Bulletin, 49, 499–520.Google Scholar
Ceulemans, E., Wilderjans, T. F., Kiers, H. A. L., & Timmerman, M. E. (2016). MultiLevel Simultaneous Component Analysis: A Computational Shortcut and Software Package. Behavior Research Methods, 48, 1008–1020.Google Scholar
Dejonckheere, E., Mestdagh, M., Houben, M., Rutten, I., Sels, L., Kuppens, P., & Tuerlinckx, F. (2019). Complex Affect Dynamics Add Limited Information to the Prediction of Psychological Well-Being. Nature Human Behaviour, 3, 478–491.CrossRefGoogle Scholar
Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous Time Structural Equation Modeling with R Package ctsem. Journal of Statistical Software, 77(5), 1–35.CrossRefGoogle Scholar
Ebner-Priemer, U. W., Houben, M., Santangelo, P., Kleindienst, N., Tuerlinckx, F., Oravecz, Z., … Kuppens, P. (2015). Unraveling Affective Dysregulation in Borderline Personality Disorder: A Theoretical Model and Empirical Evidence. Journal of Abnormal Psychology, 124(1), 186–198.CrossRefGoogle ScholarPubMed
Erbas, Y., Ceulemans, E., Pe, M. L., Koval, P., & Kuppens, P. (2014). Negative Emotion Differentiation: Its Personality and Well-Being Correlates and a Comparison of Different Assessment Methods. Cognition and Emotion, 28(7), 1196–1213.Google Scholar
First, M. B., Williams, J. B. W., Karg, R. S., & Spitzer, R. L. (2015). Structured Clinical Interview for DSM-5 ‒ Research Version (SCID-5 for DSM-5, Research Version; SCID-5-RV). Arlington, VA: American Psychiatric Association.Google Scholar
Hedeker, D., Mermelstein, R. J., & Demirtas, H. (2012). Modeling Between-Subject and Within-Subject Variances in Ecological Momentary Assessment Data Using Mixed-Effects Location Scale Models. Statistics in Medicine, 31(27), 3328–3336.CrossRefGoogle ScholarPubMed
Hepp, J., Carpenter, R. W., Lane, S. P., & Trull, T. J. (2016). Momentary Symptoms of Borderline Personality Disorder as a Product of Trait Personality and Social Context. Personality Disorders, 7(4), 384–393.Google Scholar
Heylen, J., Verduyn, P., Van Mechelen, I., & Ceulemans, E. (2015). Variability in Anger Intensity Profiles: Structure and Predictive Basis. Cognition and Emotion, 29(1), 168–177.CrossRefGoogle ScholarPubMed
Heylen, J., Van Mechelen, I., Verduyn, P., & Ceulemans, E. (2016). KSC-N: Clustering of Hierarchical Time Profile Data. Psychometrika, 81(2), 411–433.Google Scholar
Hollenstein, T., & Lewis, M. D. (2006). A State Space Analysis of Emotion and Flexibility in Parent-Child Interactions. Emotion, 6(4), 656–662.CrossRefGoogle ScholarPubMed
Houben, M., Van Den Noortgate, W., & Kuppens, P. (2015). The Relation between Short-Term Emotion Dynamics and Psychological Well-Being: A Meta-Analysis. Psychological Bulletin, 141(4), 901–930.CrossRefGoogle ScholarPubMed
Houben, M., Vansteelandt, K., Claes, L., Sienaert, P., Berens, A., Sleuwaegen, E., & Kuppens, P. (2016). Emotional Switching in Borderline Personality Disorder: A Daily Life Study. Personality Disorders: Theory, Research, and Treatment, 7(1), 50–60.Google Scholar
Houben, M., Claes, L., Vansteelandt, K., Berens, A., Sleuwaegen, E., & Kuppens, P. (2017). The Emotion Regulation Function of Nonsuicidal Self-Injury: A Momentary Assessment Study in Inpatients with Borderline Personality Disorder Features. Journal of Abnormal Psychology, 126(1), 89–95.Google Scholar
Hox, J. J., Moerbeek, M., & van de Schoot, R. (2010). Multilevel Analysis: Techniques and Applications (2nd edn.). New York: Routledge.Google Scholar
Jahng, S., Wood, P. K., & Trull, T. J. (2008). Analysis of Affective Instability in Ecological Momentary Assessment: Indices Using Successive Difference and Group Comparison via Multilevel Modeling. Psychological Methods, 13(4), 354–375.CrossRefGoogle ScholarPubMed
Jones, C. J., & Nesselroade, J. R. (1990). Multivariate, Replicated, Single-Subject, Repeated Measures Designs and P-Technique Factor Analysis: A Review of Intraindividual Change Studies. Experimental Aging Research, 16, 171–183.Google ScholarPubMed
Koval, P., Ogrinz, B., Kuppens, P., den Bergh, O. V., Tuerlinckx, F., & Sütterlin, S. (2013a). Affective Instability in Daily Life Is Predicted by Resting Heart Rate Variability. PLOS ONE, 8(11), e81536.Google Scholar
Koval, P., Pe, M. L., Meers, K., & Kuppens, P. (2013b). Affect Dynamics in Relation to Depressive Symptoms: Variable, Unstable or Inert? Emotion, 13(6), 1132–1141.CrossRefGoogle ScholarPubMed
Krone, T., Albers, C. J., & Timmerman, M. E. (2016). Comparison of Estimation Procedures for Multilevel AR(1) Models. Frontiers in Psychology, 7, 486.Google Scholar
Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010a). Emotional Inertia and Psychological Maladjustment. Psychological Science, 21(7), 984–991.Google Scholar
Kuppens, P., Oravecz, Z., & Tuerlinckx, F. (2010b). Feelings Change: Accounting for Individual Differences in the Temporal Dynamics of Affect. Journal of Personality and Social Psychology, 99(6), 1042–1060.Google Scholar
Lamey, A., Hollenstein, T., Lewis, M. D., & Granic, I. (2004). GridWare (Version 1.1). [Computer software]. Retrieved from http://statespacegrids.orgGoogle Scholar
Lane, S. T., & Gates, K. M. (2017). Automated Selection of Robust Individual-Level Structural Equation Models for Time Series Data. Structural Equation Modeling: A Multidisciplinary Journal, 24(5), 1–15.CrossRefGoogle Scholar
Lane, S. T., Gates, K. M., Pike, H. K., Beltz, A. M., & Wright, A. G. C. (2019). Uncovering General, Shared, and Unique Temporal Patterns in Ambulatory Assessment Data. Psychological Methods, 24, 54–69.Google Scholar
Linehan, M. (1993). Cognitive-Behavioral Treatment of Borderline Personality Disorder. New York: Guilford Press.Google Scholar
Liu, S. (2017). Person-Specific versus Multilevel Autoregressive Models: Accuracy in Parameter Estimates at the Population and Individual Levels. British Journal of Mathematical and Statistical Psychology, 70, 480–498.CrossRefGoogle ScholarPubMed
Lougheed, J. P., & Hollenstein, T. (2016). Socioemotional Flexibility in Mother-Daughter Dyads: Riding the Emotional Rollercoaster across Positive and Negative Contexts. Emotion, 16(5), 620–633.CrossRefGoogle ScholarPubMed
Manolov, R., & Onghena, P. (2018). Analyzing Data from Single-Case Alternating Treatments Designs. Psychological Methods, 23, 480–504.Google Scholar
McArdle, J. J., & Nesselroade, J. R. (2003). Growth Curve Analysis in Contemporary Psychological Research. In Schinka, J. A. & Velicer, W. F. (Eds.), Handbook of Psychology: Research Methods in Psychology (Vol. 2, pp. 447–480). New York: Wiley.Google Scholar
Mestdagh, M., Pe, M. L., Pestman, W., Verdonck, S., Kuppens, P., & Tuerlinckx, F. (2018). Sidelining the Mean: The Relative Variability Index as a Generic Mean-Corrected Variability Measure for Bounded Variables. Psychological Methods, 23, 690–707.CrossRefGoogle ScholarPubMed
Myin-Germeys, I., Oorschot, M., Collip, D., Lataster, J., Delespaul, P., & van Os, J. (2009). Experience Sampling Research in Psychopathology: Opening the Black Box of Daily Life. Psychological Medicine, 39(9), 1533–1547.Google Scholar
Nezlek, J. B. (2008). An Introduction to Multilevel Modeling for Social and Personality Psychology. Social and Personality Psychology Compass, 2(2), 842–860.Google Scholar
Nezlek, J. B., & Plesko, R. M. (2001). Day-to-Day Relationships among Self-Concept Clarity, Self-Esteem, Daily Events, and Mood. Personality and Social Psychology Bulletin, 27(2), 201–211.Google Scholar
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2011). A Hierarchical Latent Stochastic Differential Equation Model for Affective Dynamics. Psychological Methods, 16(4), 468–490.Google Scholar
Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J. (2012). BHOUM: Bayesian Hierarchical Ornstein-Uhlenbeck Modeling [Computer Software]. Retrieved from www.zitaoravecz.net/Google Scholar
Pe, M. L., Kircanski, K., Thompson, R. J., Bringmann, L. F., Tuerlinckx, F., Mestdagh, M., … Gotlib, I. H. (2015). Emotion-Network Density in Major Depressive Disorder. Clinical Psychological Science, 3(2), 292–300.Google Scholar
Peeters, F., Berkhof, J., Delespaul, P., Rottenberg, J., & Nicolson, N. A. (2006). Diurnal Mood Variation in Major Depressive Disorder. Emotion, 6(3), 383–391.Google Scholar
Radloff, L. S. (1977). The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Applied Psychological Measurement, 1(3), 385–401.Google Scholar
Rovine, M., & Walls, T. (2006). A Multilevel Autoregressive Model to Describe Interindividual Differences in the Stability of a Process. In Schafer, J. and Walls, T. (Eds.), Models for Intensive Longitudinal Data (pp. 124–147). New York: Oxford University Press.Google Scholar
Santangelo, P. S., Limberger, M. F., Stiglmayr, C., Houben, M., Coosemans, J., Verleysen, G., … Ebner-Priemer, U. W. (2016). Analyzing Subcomponents of Affective Dysregulation in Borderline Personality Disorder in Comparison to Other Clinical Groups Using Multiple e-Diary Datasets. Borderline Personality Disorder and Emotion Dysregulation, 3(1), 5.Google Scholar
Sbarra, D. A. (2006). Predicting the Onset of Emotional Recovery Following Nonmarital Relationship Dissolution: Survival Analyses of Sadness and Anger. Personality & Social Psychology Bulletin, 32(3), 298–312.Google Scholar
Shrout, P. E., & Fleiss, J. L. (1979). Intraclass Correlations: Uses in Assessing Rater Reliability. Psychological Bulletin, 86, 420–428.Google Scholar
Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Gotlib, I. H. (2012). The Everyday Emotional Experience of Adults with Major Depressive Disorder: Examining Emotional Instability, Inertia, and Reactivity. Journal of Abnormal Psychology, 121(4), 819–829.CrossRefGoogle ScholarPubMed
Timmerman, M. E. (2006). Multilevel Component Analysis. British Journal of Mathematical and Statistical Psychology, 59(2), 301–320.CrossRefGoogle ScholarPubMed
Timmerman, M. E., & Kiers, H. A. L. (2003). Four Simultaneous Component Models for the Analysis of Multivariate Time Series from More than One Subject to Model Intraindividual and Interindividual Differences. Psychometrika, 68(1), 105–121.Google Scholar
Tomko, R. L., Lane, S. P., Pronove, L. M., Treloar, H. R., Brown, W. C., Solhan, M. B., … Trull, T. J. (2015). Undifferentiated Negative Affect and Impulsivity in Borderline Personality and Depressive Disorders: A Momentary Perspective. Journal of Abnormal Psychology, 124(3), 740–753.Google Scholar
Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory Assessment. Annual Review of Clinical Psychology, 9, 151–176.Google Scholar
Trull, T. J., Solhan, M. B., Tragesser, S. L., Jahng, S., Wood, P. K., Piasecki, T. M., & Watson, D. (2008). Affective Instability: Measuring a Core Feature of Borderline Personality Disorder with Ecological Momentary Assessment. Journal of Abnormal Psychology, 117, 647–661.Google Scholar
Verduyn, P., Delvaux, E., Van Coillie, H., Tuerlinckx, F., & Van Mechelen, I. (2009). Predicting the Duration of Emotional Experience: Two Experience Sampling Studies. Emotion, 9(1), 83–91.Google Scholar
Wichers, M., Groot, P. C., & Psychosystems, ESM Group, EWS Group. (2016). Critical Slowing Down as a Personalized Early Warning Signal for Depression. Psychotherapy and Psychosomatics, 85(2), 114–116.Google Scholar
Wright, A. G. C., Gates, K. M., Arizmendi, C., Lane, S. T., Woods, W. C., & Edershile, E. A. (2019). Focusing Personality Assessment on the Person: Modeling General, Shared, and Person Specific Processes in Personality and Psychopathology. Psychological Assessment, 31, 502–515.Google Scholar
Yang, J., & Leskovec, J. (2011). Patterns of Temporal Variation in Online Media. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (pp. 177–186). New York: ACM. Retrieved from http://dl.acm.org/citation.cfm?id=1935863Google Scholar

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