Recent developments in theories and data collection methods have made intensive longitudinal data (ILD) increasingly relevant and available for organizational research. New methods for analyzing ILD have emerged under the multilevel modeling framework. In this article, we first delineate features of ILD (including autoregressive relationships, trends, cycles/seasons, and between-subject variability in temporal trends). We discuss the analytic challenges for handling ILD using traditional analytic tools familiar to organizational researchers (e.g., growth models, single-subject time series analyses). We then introduce a statistical approach for handling ILD from the multilevel modeling framework: dynamic structural equation modeling (DSEM). We provide three examples using simulated data sets to demonstrate how to apply DSEM to examine ILD with a software program familiar to organizational researchers (i.e., Mplus). Finally, we discuss issues related to applying DSEM, including centering, missing data, and sample size.
AsparouhovT.MuthénB. (2016). General random effect latent variable modeling: Random subjects, items, contexts, and parameters. In HarringJ. R.StapletonL. M.BeretvasS. N. (Eds.), Advances in multilevel modeling for educational research: Addressing practical issues found in real-world applications (pp. 163–192). Charlotte, NC: Information Age.
5.
AsparouhovT.MuthénB. (2018). Comparison of models for the analysis of intensive longitudinal data (Working paper). Retrieved fromhttp://www.statmodel.com/TimeSeries.shtml
6.
AsparouhovT.MuthénB. (2019). Latent variable centering of predictors and mediators in multilevel and time-series models. Structural Equation Modeling: A Multidisciplinary Journal, 26, 119–142.
7.
BarnesC. M.DangC. T.LeavittK.GuaranaC. L.UhlmannE. L. (2015). Archival data in micro-organizational research: A toolkit for moving to a broader set of topics. Journal of Management, 44, 1453–1478.
8.
BealD. J. (2015). ESM 2.0: State of the art and future potential of experience sampling methods in organizational research. Annual Review of Organizational Psychology and Organizational Behavior, 2, 383–407.
9.
BealD. J.WeissH. M.BarrosE.MacDermidS. M. (2005). An episodic process model of affective influences on performance. Journal of Applied Psychology, 90, 1054–1068.
10.
BettisR. A.HelfatC. E.ShaverJ. M. (2016). The necessity, logic, and forms of replication. Strategic Management Journal, 37, 2193–2203.
11.
BlieseP. D.PloyhartR. E. (2002). Growth modeling using random coefficient models: Model building, testing, and illustrations. Organizational Research Methods, 5, 362–387.
12.
BoxG. E. P.JenkinsG. M.ReinselG. C.LjungG. M. (2016). Time series analysis: Forecasting and control (5th ed.). Hoboken, NJ: Holden-Day.
13.
ChaffinD.HeidlR.HollenbeckJ. R.HoweM.YuA.VoorheesC.CalantoneR. (2015). The promise and perils of wearable sensors in organizational research. Organizational Research Methods, 20, 3–31.
14.
CollinsL. M. (2006). Analysis of longitudinal data: The integration of theoretical model, temporal design, and statistical model. Annual Review of Psychology, 57, 505–528.
15.
CroninM. A.VancouverJ. B. (2019). The only constant is change: Expanding theory by incorporating dynamic properties into one’s models. In HumphreyS. E.LeBretonJ. M. (Eds.), The handbook of multilevel theory, Measurement, and analysis (pp. 89–114). Washington, DC: American Psychological Association.
16.
CurranP. J.BauerD. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583–619.
17.
DassM.ShropshireC. (2012). Introducing functional data analysis to managerial science. Organizational Research Methods, 15, 693–721.
18.
DormannC.GriffinM. A. (2015). Optimal time lags in panel studies. Psychological Methods, 20, 489–505.
19.
FokC. C. T.RamsayJ. O. (2006). Fitting curves with periodic and nonperiodic trends and their interactions with intensive longitudinal data. In SchaferJ. L.WallsT. A. (Eds.), Models for intensive longitudinal data (pp. 109–123). Oxford, UK: Oxford University Press.
20.
FullerJ. A.StantonJ. M.FisherG. G.SpitzmüllerC.RussellS. S.SmithP. C. (2003). A lengthy look at the daily grind: Time series analysis of events, mood, stress, and satisfaction. Journal of Applied Psychology, 88, 1019–1033.
21.
GabrielA. S.DiefendorffJ. M. (2015). Emotional labor dynamics: A momentary approach. Academy of Management Journal, 58, 1804–1825.
22.
GabrielA. S.DiefendorffJ. M.BennettA. A.SloanM. D. (2016). It’s about time: The promise of continuous rating assessments for the organizational science. Organizational Research Methods, 20, 32–60.
23.
GrandJ. A.BraunM. T.KuljaninG.KozlowskiS. W. J.ChaoG. T. (2016). The dynamics of team cognition: A process-oriented theory of knowledge emergence in teams. Journal of Applied Psychology, 101, 1353–1385.
24.
HamakerE. L. (2005). Conditions for the equivalence of the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances. Sociological Methods & Research, 33, 404–418.
25.
HamakerE. L.AsparouhovT.BroseA.SchmiedekF.MuthénB. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Advance online publication. Multivariate Behavioral Research. doi:10.1080/00273171.2018.1446819
26.
HollenbeckJ. R.WrightP. M. (2017). Harking, sharking, and tharking. Journal of Management, 43, 5–18.
27.
HuY.BokerS.NealeM.KlumpK. L. (2014). Coupled latent differential equation with moderators: Simulation and application. Psychological Methods, 19, 56–71.
28.
JebbA. T.TayL. (2017). Introduction to time series analysis for organizational research: Methods for longitudinal analyses. Organizational Research Methods, 20, 61–94.
29.
JebbA. T.WooS. E. (2015). A Bayesian primer for the organizational sciences: The “two sources” and an introduction to BugsXLA. Organizational Research Methods, 18, 92–132.
30.
JudgeT. A.KlingerR. L.SimonL. S.2010. Time is on my side: Time, general mental ability, human capital, and extrinsic career success. Journal of Applied Psychology, 95, 92–107.
31.
KaplanD. (2014). Bayesian statistics for the social sciences. New York, NY: Guilford Press.
32.
KimY.PloyhartR. E. (2014). The effects of staffing and training on firm productivity and profit growth before, during, and after the Great Recession. Journal of Applied Psychology, 99, 361–389.
33.
KozlowskiS. W.ChaoG. T.GrandJ. A.BraunM. T.KuljaninG. (2013). Advancing multilevel research design capturing the dynamics of emergence. Organizational Research Methods, 16, 581–615.
34.
KruschkeJ. K.AguinisH.JooH. (2012). The time has come: Bayesian methods for data analysis in the organizational sciences. Organizational Research Methods, 15, 722–752.
35.
LaurenceauJ.BolgerN. (2012). Analyzing diary and intensive longitudinal data from dyads. In MehlM.ConnerT. S. (Eds.), Handbook of research methods for studying daily life (pp. 407–422). New York, NY: Guilford.
36.
LiuS.WangM.BambergerP.ShiJ.BacharachS. B. (2015). The dark side of socialization: A longitudinal investigation of newcomer alcohol use. Academy of Management Journal, 58, 334–355.
37.
LiuS.WangM.ZhanY.ShiJ. (2009). Daily work stress and alcohol use: Testing the cross-level moderation effects of neuroticism and job involvement. Personnel Psychology, 62, 575–597.
38.
LiuY.MoS.SongY.WangM. (2016). Longitudinal analysis in occupational health psychology: A review and tutorial of three longitudinal modeling techniques. Applied Psychology: An International Review, 65, 379–411.
39.
LoPilatoA. C.CarterN. T.WangM. (2015). Updating generalizability theory in management research: Bayesian estimation of variance components. Journal of Management, 41, 692–717.
40.
MitchellT. R.JamesL. R. (2001). Building better theory: Time and the specification of when things happen. Academy of Management Review, 26, 530–547.
MuthénL. K.MuthénB. O. (2017). Mplus user’s guide (8th ed.). Los Angeles, CA: Author.
44.
NewmanD. A. (2014). Missing data: Five practical guidelines. Organizational Research Methods, 17, 372–411.
45.
NickellS. (1981). Biases in dynamic models with fixed effects. Econometrica: Journal of the Econometric Society, 49, 1417–1426.
46.
PloyhartR. E.MoliternoT. P. (2011). Emergence of the human capital resource: A multilevel model. Academy of Management Review, 36, 127–150.
47.
PloyhartR. E.VandenbergR. J. (2010). Longitudinal research: The theory, design, and analysis of change. Journal of Management, 36, 94–120.
48.
RovineM. J.WallsT. A. (2006). Multilevel autoregressive modeling of interindividual differences in the stability of a process. In SchaferJ. L.WallsT. A. (Eds.), Models for intensive longitudinal data (pp. 124–147). Oxford, UK: Oxford University Press.
49.
SadlerP.GunnG. R.EthierN.DuongD.WoodyE. (2009). Are we on the same wavelength? Interpersonal complementarity as shared cyclical patterns during interactions. Journal of Personality & Social Psychology, 97, 1005–1020.
50.
SchaferJ. L.WallsT. A. (2006). Models for intensive longitudinal data. Oxford, UK: Oxford University Press.
51.
SchultzbergM.MuthénB. (2018). Number of subjects and time points needed for multilevel time-series analysis: A simulation study of dynamic structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 25, 495–515.
52.
ShumwayR. H.StofferD. S. (2017). Time series analysis and its applications: With R examples (4th ed.). New York, NY: Springer.
53.
SimmonsJ. P.NelsonL. D.SimonsohnU. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22, 1359–1366.
54.
SongY.LiuY.ShiJ.WangM. (2017). Use of proactive socialization tactics and socialization outcomes: A latent growth modeling approach to understanding newcomer socialization process. Academy of Management Discoveries, 3, 42–63.
55.
SteeleJ. S.FerrerE. (2011). Latent differential equation modeling of self-regulatory and coregulatory affective processes. Multivariate Behavioral Research, 46, 956–984.
56.
VancouverJ. B.WeinhardtJ. M.SchmidtA. M. (2010). A formal, computational theory of multiple-goal pursuit: Integrating goal-choice and goal-striving processes. Journal of Applied Psychology, 95, 985–1008.
57.
WallsT. A.JungH.SchwartzJ. E. (2006). Multilevel models for intensive longitudinal data. In SchaferJ. L.WallsT. A. (Eds.), Models for intensive longitudinal data (pp. 3–37). Oxford, UK: Oxford University Press.
58.
WangM. (2007). Profiling retirees in the retirement transition and adjustment process: Examining the longitudinal change patterns of retirees’ psychological well-being. Journal of Applied Psychology, 92, 455–474.
59.
WangM.LiuS.ZhanY.ShiJ. (2010). Daily work-family conflict and alcohol use: Testing the cross-level moderation effects of peer drinking norms and social support. Journal of Applied Psychology, 95, 377–386.
60.
WangM.ZhouL.ZhangZ. (2016). Dynamic modeling. Annual Review of Organizational Psychology and Organizational Behavior, 3, 241–266.
61.
ZhouL.SongY.AltermanV.LiuY.WangM. (2019). Introduction to data collection in multilevel research. In HumphreyS. E.LeBretonJ. M. (Eds.), The handbook of multilevel theory, measurement, and analysis (pp. 225–252). Washington, DC: American Psychological Association.
62.
ZhouL.WangM.ChangD.LiuS.ZhanY.ShiJ. (2017). Commuting stress process and self-regulation at work: Moderating roles of daily task significance, family interference with work, and commuting means efficacy. Personnel Psychology, 70, 891–922.
63.
ZhouL.WangM.VancouverJ. B. (in press). A formal model of leadership goal striving: Development of core process mechanisms and extensions to action team context. Journal of Applied Psychology.
64.
ZyphurM. J.OswaldF. L. (2015). Bayesian estimation and inference: A user’s guide. Journal of Management, 41, 390–420.
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