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Abstract

Research based on intensive longitudinal data (ILD)—consisting of many repeated measures from one or multiple individuals—is rapidly gaining popularity in psychological science. To appreciate the unique potential of ILD research for clinical psychology, this review begins by examining how our three traditional research approaches fall short when the goal is to investigate processes. It then explores how the analysis of ILD can be used to study a process as it unfolds within a specific person over time but also to study average process features or individual differences therein. By emphasizing the alignment between research questions, data collection, and analytical strategies, the potential of ILD research is further highlighted. It is argued that for future progress it is essential to integrate machine learning and causal inference methods with statistical techniques for ILD and to become more explicit about timescales, time frames, and dynamics in psychological theories.

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2025-05-07
2025-06-13
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Literature Cited

  1. Aarts E, Verhage M, Veenvliet JV, Dolan CV, van der Sluis S. 2014.. A solution to dependency: using multilevel analysis to accommodate nested data. . Nat. Neurosci. 17::49196
    [Crossref] [Google Scholar]
  2. Albers CJ, Bringmann LF. 2020.. Inspecting gradual and abrupt changes in emotion dynamics with the time-varying change point autoregressive model. . Eur. J. Psychol. Assess. 36::49299
    [Crossref] [Google Scholar]
  3. Am. Psychiatr. Assoc. 2013.. Diagnostic and Statistical Manual of Mental Disorders. Washington, DC:: Am. Psychiatr. Publ. , 5th ed..
    [Google Scholar]
  4. Ariens S, Ceulemans E, Adolf JK. 2020.. Time series analysis of intensive longitudinal data in psychosomatic research: a methodological overview. . J. Psychosom. Res. 137::110191
    [Crossref] [Google Scholar]
  5. Asendorpf JB. 2021.. Modeling Developmental Processes. London:: Elsevier
    [Google Scholar]
  6. Asparouhov T, Hamaker EL, Muthén B. 2018.. Dynamic structural equation modeling. . Struct. Equ. Model. 25::35988
    [Crossref] [Google Scholar]
  7. Bailey DH, Jung AJ, Beltz AM, Eronen MI, Gische C, et al. 2024.. Causal inference on human behavior. . Nat. Hum. Behav. 8::144859
    [Crossref] [Google Scholar]
  8. Boele S, Nelemans S, Denissen J, Prinzie P, Bülow A, Keijsers L. 2023.. Testing transactional processes between parental support and adolescent depressive symptoms: from a daily to a biennial timescale. . Dev. Psychopathol. 35::165670
    [Crossref] [Google Scholar]
  9. Boker SM, Martin M. 2018.. A conversation between theory, methods, and data. . Multivar. Behav. Res. 53::80619
    [Crossref] [Google Scholar]
  10. Boker SM, Nesselroade JR. 2002.. A method for modeling the intrinsic dynamics of intraindividual variability: recovering parameters of simulated oscillators in multi-wave panel data. . Multivar. Behav. Res. 37::12760
    [Crossref] [Google Scholar]
  11. Bolger N, Laurenceau JP. 2013.. Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. New York:: Guilford
    [Google Scholar]
  12. Bollen KA, Curran P. 2006.. Latent Curve Models: A Structural Equation Approach. Hoboken, NJ:: Wiley
    [Google Scholar]
  13. Bollen KA, Pearl J. 2013.. Eight Myths About Causality and Structural Equation Models. New York:: Springer
    [Google Scholar]
  14. Borsboom D, Cramer AOJ. 2013.. Network analysis: an integrative approach to the structure of psychopathology. . Annu. Rev. Clin. Psychol. 9::91121
    [Crossref] [Google Scholar]
  15. Borsboom D, Haslbeck J. 2024.. Integrating intra- and interindividual phenomena in psychological theories. . Multivar. Behav. Res. 59::1290309
    [Crossref] [Google Scholar]
  16. Box GEP, Jenkins GM, Reinsel GC, Ljung GM. 2016.. Time Series Analysis: Forecasting and Control. Hoboken, NJ:: Holden-Day. , 5th ed..
    [Google Scholar]
  17. Bringmann LF. 2021.. Person-specific networks in psychopathology: past, present, and future. . Curr. Opin. Psychol. 41::5964
    [Crossref] [Google Scholar]
  18. Bringmann LF, Hamaker EL, Vigo DE, Aubert A, Borsboom D, Tuerlinckx F. 2017.. Changing dynamics: time-varying autoregressive models using generalized additive modeling. . Psychol. Methods 22::40925
    [Crossref] [Google Scholar]
  19. Bringmann LF, Vissers N, Wichers M, Geschwind N, Kuppens P, et al. 2013.. A network approach to psychopathology: new insights into clinical longitudinal data. . PLOS ONE 8::e60188
    [Crossref] [Google Scholar]
  20. Browne MW, Nesselroade JR. 2005.. Representing Psychological Processes with Dynamic Factor Models: Some Promising Uses and Extensions of ARMA Time Series Models. Mahwah, NJ:: Lawrence Erlbaum
    [Google Scholar]
  21. Bülow A, Boele S, Lougheed JP, Denissen JJA, van Roekel E, Keijsers L. 2025.. A matter of timing? Effects of parent-adolescent conflict on adolescent negative affect and depressive symptoms on six timescales. . J. Psychopathol. Clin. Sci. In press
    [Google Scholar]
  22. Cattell RB. 1952.. The three basic factor-analytical research designs: their interrelations and derivatives. . Psychol. Bull. 49::499520
    [Crossref] [Google Scholar]
  23. Cattell RB. 1978.. The Scientific Use of Factor Analysis in Behavioral and Life Sciences. New York:: Plenum
    [Google Scholar]
  24. Cattell RB, Cattell AKS, Rhymer RD. 1947.. P-technique demonstrated in determining psycho-physiological source traits in a normal individual. . Psychometrika 12:(4):26788
    [Crossref] [Google Scholar]
  25. Chatfield C. 2004.. The Analysis of Time Series: An Introduction. London, UK:: Chapman and Hall. , 6th ed..
    [Google Scholar]
  26. Chow SM, Ferrer E, Nesselroade JR. 2007.. An unscented Kalman filter approach to the estimation of nonlinear dynamical systems models. . Multivar. Behav. Res. 42::283321
    [Crossref] [Google Scholar]
  27. Chow SM, Ram N, Boker SM, Fujita F, Clore G. 2005.. Emotion as a thermostat: representing emotion regulation using a damped oscillator model. . Emotion 5::20825
    [Crossref] [Google Scholar]
  28. Collins LM. 2006.. Analysis of longitudinal data: the integration of theoretical model, temporal design, and statistical model. . Annu. Rev. Psychol. 57::50528
    [Crossref] [Google Scholar]
  29. Conner TS, Tennen H, Fleeson W, Barrett LF. 2009.. Experience sampling methods: a modern idiographic approach to personality research. . Soc. Pers. Psychol. Compass 3::292313
    [Crossref] [Google Scholar]
  30. De Moor EL, Denissen JJA, Emons WHM, Bleidorn W, Luhmann M, et al. 2021.. Self-esteem and satisfaction with social relationships across time. . J. Pers. Soc. Psychol. 120::17391
    [Crossref] [Google Scholar]
  31. Deboeck PR, Preacher KJ. 2016.. No need to be discrete: a method for continuous time mediation analysis. . Struct. Equation Model. 23::6175
    [Crossref] [Google Scholar]
  32. Dorman C, Griffin MA. 2015.. Optimal time lags in panel studies. . Psychol. Methods 20::489505
    [Crossref] [Google Scholar]
  33. Driver CC, Oud JHL, Voelkle MC. 2017.. Continuous time structural equation modeling with R package ctsem. . J. Stat. Softw. 77:(5). https://doi.org/10.18637/jss.v077.i05
    [Crossref] [Google Scholar]
  34. Epskamp S, Waldorp LJ, Mõttus R, Borsboom D. 2018.. The Gaussian graphical model in cross-sectional and time-series data. . Multivar. Behav. Res. 53::45380
    [Crossref] [Google Scholar]
  35. Ernst AF, Albers CJ, Jeronimus BF, Timmerman ME. 2020.. Inter-individual differences in multivariate time-series. . Eur. J. Psychol. Assess. 36::48291
    [Crossref] [Google Scholar]
  36. Fisher AJ, Medaglia JD, Jeronimus BF. 2018.. Lack of group-to-individual generalizability is a threat to human subjects research. . PNAS 115::E610615
    [Google Scholar]
  37. Fulcher BD, Little MA, Jones NS. 2013.. Highly comparative time-series analysis: the empirical structure of time series and their methods. . J. R. Soc. Interface 10::20130048
    [Crossref] [Google Scholar]
  38. Gates KM, Molenaar PCM. 2012.. Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. . NeuroImage 65::31019
    [Crossref] [Google Scholar]
  39. Geschwind N, Peeters F, Drukker M, van Os J, Wichers M. 2011.. Mindfulness training increases momentary positive emotions and reward experience in adults vulnerable to depression: a randomized controlled trial. . J. Consult. Clin. Psychol. 79::61828
    [Crossref] [Google Scholar]
  40. Gollob HF, Reichardt CS. 1987.. Taking account of time lags in causal models. . Child Dev. 58::8092
    [Crossref] [Google Scholar]
  41. Grice JW. 2004.. Bridging the idiographic-nomothetic divide in ratings of self and others. . J. Personal. 72::20341
    [Crossref] [Google Scholar]
  42. Grice JW, Medellin E, Jones I, Horvath S, McDaniel H, et al. 2020.. Persons as effect sizes. . Adv. Methods Pract. Psychol. Sci. 3::44355
    [Crossref] [Google Scholar]
  43. Hamaker EL. 2012.. Why researchers should think “within-person”: a paradigmatic rationale. . In Handbook of Research Methods for Studying Daily Life, ed. MR Mehl, TS Conner , pp. 4361. New York:: Guilford
    [Google Scholar]
  44. Hamaker EL. 2023.. The within-between dispute in cross-lagged panel research and how to move forward. . Psychol. Methods. https://doi.org/10.1037/met0000600
    [Google Scholar]
  45. Hamaker EL. 2024.. The curious case of the cross-sectional correlation. . Multivar. Behav. Res. 59::111122
    [Crossref] [Google Scholar]
  46. Hamaker EL, Asparouhov T, Muthén B. 2023.. Dynamic structural equation modeling as a combination of time series modeling, multilevel modeling, and structural equation modeling. . In Handbook of Structural Equation Modeling, ed. RH Hoyle , pp. 57696. New York:: Guilford. , 2nd ed..
    [Google Scholar]
  47. Hamaker EL, Ceulemans E, Grasman RPPP, Tuerlinckx F. 2015.. Modeling affect dynamics: state of the art and future challenges. . Emot. Rev. 7::31622
    [Crossref] [Google Scholar]
  48. Hamaker EL, Dolan CV. 2009.. Idiographic data analysis: quantitative methods—from simple to advanced. . In Dynamic Process Methodology in the Social and Developmental Sciences, ed. J Valsiner, PCM Molenaar, MCDP Lyra, N Chaudhary , pp. 191216. New York:: Springer
    [Google Scholar]
  49. Hamaker EL, Grasman RPPP, Kamphuis JH. 2010.. Regime-switching models to study psychological processes. . In Individual Pathways of Change: Statistical Models for Analyzing Learning and Development, ed. PCM Molenaar, KM Newell , pp. 15568. Washington, DC:: Am. Psychol. Assoc.
    [Google Scholar]
  50. Hamaker EL, Grasman RPPP, Kamphuis JH. 2016.. Modeling BAS dysregulation in bipolar disorder: illustrating the potential of time series analysis. . Assessment 23::43646
    [Crossref] [Google Scholar]
  51. Hamaker EL, Mulder JD, van IJzendoorn MH. 2020.. Description, prediction and causation: methodological challenges of studying child and adolescent development. . Dev. Cogn. Neurosci. 46::100867
    [Crossref] [Google Scholar]
  52. Hamaker EL, Muthén B. 2020.. The fixed versus random effects debate and how it relates to centering in multilevel modeling. . Psychol. Methods 25::36579
    [Crossref] [Google Scholar]
  53. Hamaker EL, Wichers M. 2017.. No time like the present: discovering the hidden dynamics in intensive longitudinal data. . Curr. Direct. Psychol. Sci. 26::1015
    [Crossref] [Google Scholar]
  54. Hamilton JD. 1989.. A new approach to the economic analysis of nonstationary time series and the business cycle. . Econometrica 57::35784
    [Crossref] [Google Scholar]
  55. Hamilton JD. 1994.. Time Series Analysis. Princeton, NJ:: Princeton Univ. Press
    [Google Scholar]
  56. Harvey AC. 1989.. Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
  57. Harvey AC. 1993.. Time Series Models. New York:: Harvester Wheatsheaf. , 2nd ed..
    [Google Scholar]
  58. Haslbeck JMB, Bringmann LF, Waldorp LJ. 2021.. A tutorial on estimating time-varying vector autoregressive models. . Multivar. Behav. Res. 56::12049
    [Crossref] [Google Scholar]
  59. Hasselman F. 2022.. Early warning signals in phase space: geometric resilience loss indicators from multiplex cumulative recurrence networks. . Front. Physiol. 13::859127
    [Crossref] [Google Scholar]
  60. Heise DR. 1970.. Causal inference from panel data. . Sociol. Methodol. 2::327
    [Crossref] [Google Scholar]
  61. Helmich MA, Smit AC, Bringmann LF, Schreuder MJ, Oldehinkel AJ, et al. 2023.. Detecting impending symptom transitions using early-warning signals in individuals receiving treatment for depression. . Clin. Psychol. Sci. 11::9941010
    [Crossref] [Google Scholar]
  62. Hernán MA, Hsu J, Healy B. 2019.. A second chance to get causal inference right: a classification of data science tasks. . CHANCE 32::4249
    [Crossref] [Google Scholar]
  63. Hernán MA, Robins JM. 2020.. Causal Inference: What If. Boca Raton, FL:: Chapman & Hall/CRC
    [Google Scholar]
  64. Hoffman L. 2014.. Longitudinal Analysis: Modeling Within-Person Fluctuations and Change. New York:: Taylor & Francis
    [Google Scholar]
  65. Holland PW. 1986.. Statistics and causal inference. . J. Am. Stat. Assoc. 81::94560
    [Crossref] [Google Scholar]
  66. Hollenstein T. 2013.. State Space Grids: Depicting Dynamics Across Development. New York:: Springer
    [Google Scholar]
  67. Hopwood CJ, Bleidorn W, Wright A. 2022.. Connecting theory to methods in longitudinal research. . Perspect. Psychol. Sci. 17::88494
    [Crossref] [Google Scholar]
  68. Huntington-Klein N. 2022.. The Effect: An Introduction to Research Design and Causality. Boca Raton, FL:: Chapman and Hall/CRC
    [Google Scholar]
  69. Hyndman RJ, Athanasopoulos G. 2021.. Forecasting: Principles and Practice. Melbourne, Aust:.: OTexts. , 3rd ed..
    [Google Scholar]
  70. Imbens GW, Rubin DB. 2015.. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
  71. Jahng S, Wood PK, Trull TJ. 2008.. Analysis of affective instability in ecological momentary assessment: indices using successive difference and group comparison via multilevel modeling. . Psychol. Methods 13::35475
    [Crossref] [Google Scholar]
  72. Kievit RA, Frankenhuis WE, Waldorp LJ, Borsboom D. 2013.. Simpson's paradox in psychological science: a practical guide. . Front. Psychol. 4::513
    [Crossref] [Google Scholar]
  73. Killingsworth MA, Gilbert DT. 2010.. A wandering mind is an unhappy mind. . Science 330::932
    [Crossref] [Google Scholar]
  74. Kim CJ, Nelson CR. 1999.. State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. Cambridge, MA:: MIT Press
    [Google Scholar]
  75. Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, et al. 2015.. Micro-randomized trials: an experimental design for developing just-in-time adaptive interventions. . Health Psychol. 34::122028
    [Crossref] [Google Scholar]
  76. Kreft IGG, de Leeuw J, Aiken LS. 1995.. The effect of different forms of centering in hierarchical linear models. . Multivar. Behav. Res. 30::121
    [Crossref] [Google Scholar]
  77. Kunicki ZJ, Smith ML, Murray EJ. 2023.. A primer on structural equation model diagrams and directed acyclic graphs: when and how to use each in psychological and epidemiological research. . Adv. Methods Pract. Psychol. Sci. 6:. https://doi.org/10.1177/25152459231156085
    [Google Scholar]
  78. Kuper N, Andresen PK, Beck ED, Costantini G, Hamaker EL, et al. 2024.. From persons to general principles: methodological decisions for idiographic and nomothetic research. . Eur. J. Personal. In press. https://doi.org/10.1177/08902070241278020
    [Google Scholar]
  79. Kuppens P, Sheeber LB, Yap MBH, Whittle S, Simmons J, Allen NB. 2012.. Emotional inertia prospectively predicts the onset of depression in adolescence. . Emotion 12::28389
    [Crossref] [Google Scholar]
  80. Lamiell JT. 1998.. ‘ Nomothetic’ and ‘idiographic’: contrasting Windelband's understanding with contemporary usage. . Theory Psychol. 8::2338
    [Crossref] [Google Scholar]
  81. Larsen RJ, Augustine AA, Prizmic Z. 2009.. Quantifying idiodynamics: a process approach to personality psychology. . Cogn. Emot. 23::140726
    [Crossref] [Google Scholar]
  82. Liu Y, West SG. 2016.. Weekly cycles in daily report data: an overlooked issue. . J. Personal. 84::56079
    [Crossref] [Google Scholar]
  83. Mansueto AC, Wiers RW, van Weert J, Schouten BC, Epskamp S. 2022.. Investigating the feasibility of idiographic network models. . Psychol. Methods 28::105268
    [Crossref] [Google Scholar]
  84. Maxwell SE, Cole DA. 2007.. Bias in cross-sectional analyses of longitudinal mediation. . Psychol. Methods 12::2344
    [Crossref] [Google Scholar]
  85. McCrae RR, John OP. 1992.. An introduction to the five-factor model and its applications. . J. Personal. 60::175215
    [Crossref] [Google Scholar]
  86. McNeish D, Hamaker EL. 2020.. A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. . Psychol. Methods 25::61035
    [Crossref] [Google Scholar]
  87. Mehl MR, Conner TS, eds. 2012.. Handbook of Research Methods for Studying Daily Life. New York:: Guilford
    [Google Scholar]
  88. Mehl MR, Eid M, Wrzus C, Harari GM, Ebner-Priemer UW. 2024.. Mobile Sensing in Psychology: Methods and Applications. New York:: Guilford
    [Google Scholar]
  89. Molenaar PCM. 1985.. A dynamic factor model for the analysis of multivariate time series. . Psychometrika 50::181202
    [Crossref] [Google Scholar]
  90. Molenaar PCM. 1987.. Dynamic assessment and adaptive optimization of the psychotherapeutic process. . Behav. Assess. 9::389416
    [Google Scholar]
  91. Molenaar PCM. 2004.. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology—this time forever. . Measurement 2::20118
    [Google Scholar]
  92. Molenaar PCM, Huizenga HM, Nesselroade JR. 2003.. The relationship between the structure of interindividual and intraindividual variability: a theoretical and empirical vindication of developmental systems theory. . In Understanding Human Development: Dialogues with Lifespan Psychology, ed. UM Staudinger, U Lindenberger , pp. 33960. Norwell, MA:: Kluwer Acad.
    [Google Scholar]
  93. Moraffah R, Sheth P, Karami M, Bhattacharya A, Wang Q, et al. 2021.. Causal inference for time series analysis: problems, methods and evaluation. . Knowl. Inform. Syst. 63::304185
    [Crossref] [Google Scholar]
  94. Morgan SL, Winship C. 2015.. Counterfactuals and Causal Inference: Methods and Principles for Social Research. New York:: Cambridge Univ. Press. , 2nd ed..
    [Google Scholar]
  95. Myin-Germeys I, Kuppens P. 2022.. The Open Handbook of Experience Sampling Methodology: A Step-by-Step Guide to Designing, Conducting, and Analyzing ESM Studies. Leuven, Neth:.: Cent. Res. Exp. Sampl. Ambul. Methods. , 2nd ed..
    [Google Scholar]
  96. Myin-Germeys I, van Os J, Schwartz JE, Stone AA, Delespaul PA. 2001.. Emotional reactivity to daily life stress in psychosis. . Arch. Gen. Psychiatry 58::113744
    [Crossref] [Google Scholar]
  97. Nesselroade JR. 1991.. Interindividual differences in intraindividual change. . In Best Methods for the Analysis of Change: Recent Advances, Unanswered Questions, Future Directions, ed. LM Collins, JL Horn , pp. 92105. Washington, DC:: Am. Psychol. Assoc.
    [Google Scholar]
  98. Nesselroade JR, McArdle JJ, Aggen SH, Meyers JM. 2002.. Dynamic Factor Analysis Models for Representing Process in Multivariate Time-Series. Mahwah, NJ:: Lawrence Erlbaum
    [Google Scholar]
  99. Neubauer AB, Koval P, Zyphur MJ, Hamaker EL. 2025.. Experiments in daily life: when causal within-person effects do (not) translate into between-person differences. . Psychol. Methods. https://doi.org/10.1037/met0000741
    [Google Scholar]
  100. Onghena P, Edgington ES. 2005.. Customization of pain treatments: single-case design and analysis. . Clin. J. Pain 21::5672
    [Crossref] [Google Scholar]
  101. Oravecz Z, Tuerlinckx F, Vandekerckhove J. 2011.. A hierarchical latent stochastic difference equation model for affective dynamics. . Psychol. Methods 16::46890
    [Crossref] [Google Scholar]
  102. Ou L, Hunter M, Chow SM. 2019.. What's for dynr: a package for linear and nonlinear dynamic modeling in R. . R J. 11::91111
    [Crossref] [Google Scholar]
  103. Pargent F, Schoedel R, Stachl C. 2023.. Best practices in supervised machine learning: a tutorial for psychologists. . Adv. Methods Pract. Psychol. Sci. 6:(3). https://doi.org/10.1177/25152459231162559
    [Google Scholar]
  104. Park K, Waldorp LJ, Ryan O. 2024.. Discovering cyclic causal models in psychological research. . Adv. Psychol. 2::e72425
    [Google Scholar]
  105. Qian T, Klasnja P, Murphy SA. 2020.. Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study. . Stat. Sci. 35::37590
    [Google Scholar]
  106. Ram N, Chow SM, Bowles RP, Wang L, Grimm K, et al. 2005.. Examining interindividual differences in cyclicity of pleasant and unpleasant affects using spectral analysis and item response modeling. . Psychometrika 70::77390
    [Crossref] [Google Scholar]
  107. Raudenbush SW, Bryk AS. 2002.. Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA:: Sage. , 2nd ed..
    [Google Scholar]
  108. Roefs A, Fried EI, Kindt M, Martijn C, Elzinga B, et al. 2022.. A new science of mental disorders: using personalised, transdiagnostic, dynamical systems to understand, model, diagnose and treat psychopathology. . Behav. Res. Ther. 153::104096
    [Crossref] [Google Scholar]
  109. Rohrer JM. 2018.. Thinking clearly about correlations and causation: graphical causal models for observational data. . Adv. Methods Pract. Psychol. Sci. 1::2742
    [Crossref] [Google Scholar]
  110. Rohrer JM, Murayama K. 2023.. These are not the effects you are looking for: causality and the within-/between-person distinction in longitudinal data analysis. . Adv. Methods Pract. Psychol. Sci. 6:(1). https://doi.org/10.1177/25152459221140842
    [Google Scholar]
  111. Rosenzweig S. 1958.. The place of the individual and of idiodynamics in psychology: a dialogue. . J. Individ. Psychol. 14::321
    [Google Scholar]
  112. Rovine MJ, Walls TA. 2006.. Multilevel autoregressive modeling of interindividual differences in the stability of a process. . In Models for Intensive Longitudinal Data, ed. TA Walls, JL Schafer , pp. 12447. New York:: Oxford Univ. Press
    [Google Scholar]
  113. Rubin DB. 2005.. Causal inference using potential outcomes. . J. Am. Stat. Assoc. 100::32231
    [Crossref] [Google Scholar]
  114. Rude SS, Gortner EM, Pennebaker JW. 2004.. Language use of depressed and depression-vulnerable college students. . Cogn. Emot. 18::112133
    [Crossref] [Google Scholar]
  115. Ryan O, Bringmann LF, Schuurman NK. 2022.. The challenge of generating causal hypotheses using network models. . Struct. Equ. Model. 29::95370
    [Crossref] [Google Scholar]
  116. Ryan O, Kuiper RM, Hamaker EL. 2018.. A continuous-time approach to intensive longitudinal data: what, why and how?. In Continuous Time Modeling in the Behavioral and Related Sciences, ed. K van Montfort, JHL Oud, MC Voelkle , pp. 2754. Cham, Switz:.: Springer
    [Google Scholar]
  117. Schmitz B, Skinner E. 1993.. Perceived control, effort, and academic performance: interindividual, intraindividual, and multivariate time-series analyses. . J. Pers. Soc. Psychol. 64::101028
    [Crossref] [Google Scholar]
  118. Schultzberg M, Muthén B. 2017.. Number of subjects and time points needed for multilevel time series analysis: a simulation study of dynamic structural equation modeling. . Struct. Equ. Model. 25::495515
    [Crossref] [Google Scholar]
  119. Schuurman NK, Hamaker EL. 2019.. Measurement error and person-specific reliability in multilevel autoregressive models. . Psychol. Methods 24::7091
    [Crossref] [Google Scholar]
  120. Shmueli G. 2010.. To explain or to predict?. Stat. Sci. 25::289310
    [Crossref] [Google Scholar]
  121. Singer JD, Willett JB. 2003.. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York:: Oxford Univ. Press
    [Google Scholar]
  122. Stirman SW, Pennebaker JW. 2001.. Word use in the poetry of suicidal and nonsuicidal poets. . Psychosomat. Med. 63::51722
    [Crossref] [Google Scholar]
  123. Stone AA, Schneider S, Smyth JM. 2023.. Evaluation of pressing issues in ecological momentary assessment. . Annu. Rev. Clin. Psychol. 19::10731
    [Crossref] [Google Scholar]
  124. Suls J, Green P, Hillis S. 1998.. Emotional reactivity to everyday problems, affective inertia, and neuroticism. . Personal. Soc. Psychol. Bull. 24::12736
    [Crossref] [Google Scholar]
  125. Tong H, Lim KS. 1980.. Threshold autoregression, limit cycles and cyclical data. . J. R. Stat. Soc. B 42::24592
    [Crossref] [Google Scholar]
  126. Trull TJ, Ebner-Priemer U. 2013.. Ambulatory assessment. . Annu. Rev. Clin. Psychol. 9::15176
    [Crossref] [Google Scholar]
  127. Tuarob S, Tucker CS, Kumara S, Giles CL, Pincus AL, et al. 2017.. How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information. . J. Biomed. Informat. 68::119
    [Crossref] [Google Scholar]
  128. Usami S, Murayama K, Hamaker EL. 2019.. A unified framework of cross-lagged models. . Psychol. Methods 24::63757
    [Crossref] [Google Scholar]
  129. Valsiner J. 1986.. Between groups and individuals: psychologists’ and laypersons’ interpretations of correlational findings. . In The Individual Subject and Scientific Psychology, ed. J Valsiner , pp. 11351. New York:: Plenum
    [Google Scholar]
  130. van Emmerik AAP, Hamaker EL. 2017.. Paint it black: using change-point analysis to investigate increasing vulnerability to depression towards the end of Vincent van Gogh's life. . Healthcare 5::53
    [Crossref] [Google Scholar]
  131. Van Geert P, De Ruiter N. 2022.. Towards a Process Approach in Psychology: Stepping into Heraclitus’ River. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
  132. VanderWeele TJ, Jackson JW, Li S. 2016.. Causal inference and longitudinal data: a case study of religion and mental health. . Soc. Psychiatry Psychiatr. Epidemiol. 51::145766
    [Crossref] [Google Scholar]
  133. Vlaeyen JWS, Wicksell RK, Simons LE, Gentili C, De Kumar T, et al. 2020.. From Boulder to Stockholm in 70 years: single case experimental designs in clinical research. . Psychol. Rec. 70::65970
    [Crossref] [Google Scholar]
  134. Vogelsmeier LVDE, Vermunt JK, De Roover K. 2023.. How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa. . Behav. Res. Methods 55::2387422
    [Crossref] [Google Scholar]
  135. Vogelsmeier LVDE, Vermunt JK, Van Roekel E, De Roover K. 2019.. Latent Markov factor analysis for exploring measurement model changes in time-intensive longitudinal studies. . Struct. Equ. Model. 26::55775
    [Crossref] [Google Scholar]
  136. Vroegindeweij A, Houtveen J, Lucassen DA, Van De Putte EM, Wilggraat NM, et al. 2024.. Individual outcomes after tailored versus generic self-management strategies for persistent fatigue in youth with a fatigue syndrome or rheumatic condition: a multiple single-case study. . Br. J. Health Psychol. 29::71230
    [Crossref] [Google Scholar]
  137. Walls TA, Schafer JL, eds. 2006.. Models for Intensive Longitudinal Data. New York:: Oxford Univ. Press
    [Google Scholar]
  138. Wang L, Hamaker EL, Bergman CS. 2012.. Investigating inter-individual difference in short-term intra-individual variability. . Psychol. Methods 17::56781
    [Crossref] [Google Scholar]
  139. Wardenaar KJ, de Jonge P. 2013.. Diagnostic heterogeneity in psychiatry: towards an empirical solution. . BMC Med. 11::201
    [Crossref] [Google Scholar]
  140. Warren K. 2002.. Thresholds and the abstinence violation effect: a nonlinear dynamic model of the behaviors of intellectually disabled sex offenders. . J. Interpers. Violence 17::1198217
    [Crossref] [Google Scholar]
  141. Wood P, Brown D. 1994.. The study of intraindividual differences by means of dynamic factor models: rationale, implementation, and interpretation. . Psychol. Bull. 116::16686
    [Crossref] [Google Scholar]
  142. Wright AGC, Woods WC. 2020.. Personalized models of psychopathology. . Annu. Rev. Clin. Psychol. 16::4974
    [Crossref] [Google Scholar]
  143. Zhang Z, Hamaker EL, Nesselroade JR. 2008.. Comparisons of four methods for estimating a dynamic factor model. . Struct. Equ. Model. 15::377402
    [Crossref] [Google Scholar]
  144. Zyphur MJ, Allison PD, Tay L, Voelkle MC, Preacher KJ, et al. 2019.. From data to causes I: building a general cross-lagged panel model (GCLM). . Organ. Res. Methods 23::65187
    [Crossref] [Google Scholar]
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