There has been a strong increase in the number of studies based on intensive longitudinal data, such as those obtained with experience sampling and daily diaries. These data contain a wealth of information regarding the dynamics of processes as they unfold within individuals over time. In this article, we discuss how combining intensive longitudinal data with either time-series analysis, which consists of modeling the temporal dependencies in the data for a single individual, or dynamic multilevel modeling, which consists of using a time-series model at Level 1 to describe the within-person process while allowing for individual differences in the parameters of these processes at Level 2, has led to new insights in clinical psychology. In addition, we discuss several methodological and statistical challenges that researchers face when they are interested in studying the dynamics of psychological processes using intensive longitudinal data.
HamakerE. L.CeulemansE.GrasmanR. P. P. P.TuerlinckxF. (2015). (See References). A brief overview of the diverse statistical techniques available for modeling (affective) dynamics.
2.
HoubenM.Van den NoortgateW.KuppensP. (2015). (See References). Provides a meta-analysis of the patterns of short-term emotion dynamics (including inertia and variability) in psychological well-being and psychopathology.
3.
MehlM. R.ConnerT. S. (2012). (See References). A true handbook for studying daily life as it unfolds.
4.
SchuurmanN. K.FerrerE.de Boer-SonnenscheinM.HamakerE. L. (2016). (See References). A thorough discussion of how to standardize parameters in a multilevel VAR model in order to allow for meaningful comparisons between the cross-lagged relationships per individual.
5.
WichersM. (2014). (See References). Presents the cascade model as a way to understand how micro-level processes may spill over into macro-level processes such as the onset and course of psychopathology.
6.
BolgerN.LaurenceauJ.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York, NY: Guilford Press.
7.
BringmannL. F.VissersN.WichersM.GeschwindN.KuppensP.PeetersF.. . . TuerlinckxF. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS ONE, 8, e60188. doi:10.1371/journal.pone.0060188
8.
ConnerT. S.LehmanB. (2012). Getting started: Launching a study in daily life. In MehlM.ConnerT. S. (Eds.), Handbook of research methods for studying daily life (pp. 89–107). New York, NY: Guilford Press.
9.
ConnerT. S.ReidK. A. (2012). Effects of intensive mobile happiness reporting in daily life. Social Psychological and Personality Science, 3, 315–323. doi:10.1177/1948550611419677
10.
DeboeckP. R.PreacherK. J. (2016). No need to be discrete: A method for continuous time mediation analysis. Structural Equation Modeling, 23, 61–75. doi:10.1080/10705511.2014.973960
11.
DelespaulP. (1995). Assessing schizophrenia in daily life: The experience sampling method. Maastricht, The Netherlands: University of Limburg.
12.
DormanC.GriffinM. A. (2015). Optimal time lags in panel studies. Psychological Methods, 20, 487–505. doi:10.1037/met0000041
13.
EichlerM. (2012). Causal inference in time series analysis. In BerzuiniC.DawidP.BernardinelliL. (Eds.), Causality: Statistical perspectives and applications (pp. 327–354). Chichester, England: John Wiley & Sons. doi:10.1002/9781119945710.ch22
14.
GeschwindN.PeetersF.JacobsN.DelespaulP.DeromC.ThieryE.. . . WichersM. (2010). Meeting risk with resilience: High daily life reward experience preserves mental health. Acta Psychiatrica Scandinavica, 122, 129–138. doi:ACP1525 [pii] 10.1111/j.1600-0447.2009.01525.x
15.
GollobH. F.ReichardtC. S. (1987). Taking account of time lags in causal models. Child Development, 58, 80–92.
16.
HamakerE. L.CeulemansE.GrasmanR. P. P. P.TuerlinckxF. (2015). Modeling affect dynamics: State-of-the-art and future challenges. Emotion Review, 7, 316–322. doi:10.1177/1754073915590619
17.
HamakerE. L.DolanC. V. (2009). Idiographic data analysis: Quantitative methods—from simple to advanced. In ValsinerJ.MolenaarP. C. M.LyraM. C. D. P.ChaudharyN. (Eds.), Dynamic process methodology in the social and developmental sciences (pp. 191–216). New York, NY: Springer-Verlag.
18.
HamakerE. L.GrasmanR. P. P. P.KamphuisJ. H. (2016). Modeling BAS dysregulation in bipolar disorder: Illustrating the potential of time series analysis. Assessment. Advance online publication. doi:10.1177/1073191116632339
19.
HamakerE. L.KuiperR. M.GrasmanR. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20, 102–116. doi:10.1037/a0038889
20.
HoubenM.Van den NoortgateW.KuppensP. (2015). The relation between short-term emotion dynamics and psychological well-being: A meta-analysis. Psychological Bulletin, 141, 901–930. doi:10.1037/a0038822
21.
KramerI.SimonsC. J.WigmanJ. T.CollipD.JacobsN.DeromC.. . . WichersM. (2014). Time-lagged moment-to-moment interplay between negative affect and paranoia: New insights in the affective pathway to psychosis. Schizophrenia Bulletin, 40, 278–286. doi:10.1093/schbul/sbs194
22.
KuppensP.SheeberL. B.YapM. B. H.WhittleS.SimmonsJ. G.AllenN. B. (2012). Emotional inertia prospectively predicts the onset of depressive disorder in adolescence. Emotion, 12, 283–289. doi:10.1037/a0025046
23.
LiuY.WestS. G. (2015). Weekly cycles in daily report data: An overlooked issue. Journal of Personality, 84, 546–579. doi:10.1111/jopy.12182
24.
LütkepohlH. (1982). Non-causality due to omitted variables. Journal of Econometrics, 19, 367–378. doi:10.1016/0304-4076(82)90011-2
25.
MehlM. R.ConnerT. S. (2012). Handbook of research methods for studying daily life. New York, NY: Guilford Press.
26.
OraveczZ.TuerlinckxF.VandekerckhoveJ. (2011). A hierarchical latent stochastic differential equation model for affective dynamics. Psychological Methods, 16, 468–490. doi:10.1037/a0024375
27.
RaudenbushS. W.BrykA. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
28.
RubyF. J. M.SmallwoodJ.EngenH.SingerT. (2013). How self-generated thought shapes mood—The relation between mind-wandering and mood depends on the socio-temporal content of thoughts. PLoS ONE, 8, e77554. doi:10.1371/journal.pone.0077554
29.
SchuurmanN. K.FerrerE.de Boer-SonnenscheinM.HamakerE. L. (2016). How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods, 21, 206–221. doi:10.1037/met0000062
30.
StavrakakisN.BooijS. H.RoestA. M.de JongeP.OldehinkelA. J.BosE. H. (2015). Temporal dynamics of physical activity and affect in depressed and nondepressed individuals. Health Psychology, 34, 1268–1277. doi:10.1037/hea0000303
31.
StoneA. A.BroderickJ. E.SchwartzJ. E.ShiffmanS.Litcher-KellyL.CalvaneseaP. (2003). Intensive momentary reporting of pain with an electronic diary: Reactivity, compliance, and patient satisfaction. Pain, 104, 343–351. doi:10.1016/S0304-3959(03)00040-X
32.
TrullT. J.Ebner-PriemerU. (2014). The role of ambulatory assessment in psychological science. Current Directions in Psychological Science, 23, 466–470. doi:10.1177/0963721414550706
33.
VoelkleM. C.OudJ. H. L.DavidovE.SchmidtP. (2012). An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia. Psychological Methods, 17, 176–192. doi:10.1037/a0027543
34.
WangL. P.MaxwellS. E. (2015). On disaggregating between-person and within-person effects with longitudinal data using multilevel models. Psychological Methods, 20, 63–83. doi:10.1037/met0000030
35.
WichersM. (2014). The dynamic nature of depression: A new micro-level perspective of mental disorders that meets current challenges. Psychological Medicine, 44, 1349–1360. doi:10.1017/S0033291713001979
36.
WichersM.Barge-SchaapveldD. Q.NicolsonN. A.PeetersF.de VriesM.MengelersR.van OsJ. (2009). Reduced stress-sensitivity or increased reward experience: The psychological mechanism of response to antidepressant medication. Neuropsychopharmacology, 34, 923–931. doi:10.1038/npp.2008.66
37.
WichersM.GrootP. C., & Psychosystems, ESM Group, EWS Group. (2016). Critical slowing down as a personalized early warning signal for depression. Psychotherapy and Psychosomatics, 85, 114–116. doi:10.1159/000441458
38.
WichersM.PeetersF.RuttenB. P.JacobsN.DeromC.ThieryE.. . . van OsJ. (2012). A time-lagged momentary assessment study on daily life physical activity and affect. Health Psychology, 31, 135–144. doi:10.1037/a0025688
