Solutions for latent growth modeling following COVID-19-related discontinuities in change and disruptions in longitudinal data collection

Abstract

Following the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, daily life significantly changed for the population. Accordingly, researchers interested in examining patterns of change over time may now face discontinuities around the pandemic. Researchers collecting in-person longitudinal data also had to cancel or delay data collection waves, further complicating analyses. Accordingly, the purpose of this article is to aid researchers aiming to examine latent growth models (LGM) in analyzing their data following COVID-19. An overview of basic LGM notions, LGMs with discontinuities, and solutions for studies that had to cancel or delay data collection waves are discussed and exemplified using simulated data. Syntax for R and Mplus is available to readers in online supplemental materials.

Keywords

Growth curve discontinuity piecewise analysis missing data SARS-COV-2

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References

Bernal

J. L.

Cummins

Gasparrini

(2017). Interrupted time series regression for the evaluation of public health interventions: A tutorial. International Journal of Epidemiology, 46(1), 348–355. https://doi.org/10.1093/ije/dyw098

Bollen

K. A.

Curran

P. J.

(2006). Latent curve models: A structural equation perspective. Wiley.

Chou

C. P.

Yang

D. Y.

Pentz

M. A.

Hser

Y. I.

(2004). Piecewise growth curve modeling approach for longitudinal prevention study. Computational Statistics & Data Analysis, 46(2), 213–225. https://doi.org/10.1016/s0167-9473(03)00149-x

Coulombe

Selig

J. P.

Delaney

H. D.

(2016). Ignoring individual differences in times of assessment in growth curve modeling. International Journal of Behavioral Development, 40(1), 76–86. https://doi.org/10.1177/0165025415577684

Enders

C. K.

(2008). A note on the use of missing auxiliary variables in full information maximum likelihood-based structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 15(3), 434–448. https://doi.org/10.1080/10705510802154307

Enders

C. K.

(2010). Applied missing data analysis. The Guildford Press.

Flora

D. B.

(2008). Specifying piecewise latent trajectory models for longitudinal data. Structural Equation Modeling: A Multidisciplinary Journal, 15(3), 513–533. https://doi.org/10.1080/10705510802154349

Grimm

K. J.

Ram

Estabrook

. (2016). Growth modeling: Structural equation and multilevel modeling approaches. The Guilford Press.

Grimm

Marcoulides

(2016). Individual change and the timing and onset of important life events: Methods, models, and assumptions. International Journal of Behavioral Development, 40(1), 87–96. https://doi.org/10.1177/0165025415580806

10.

Gupta

Jawanda

M. K.

(2020). The impacts of COVID-19 on children. Acta Paediatrica, 109(11), 2181–2183. https://doi.org/10.1111/apa.15484

11.

Howard

W. J.

Rhemtulla

Little

T. D.

(2015). Using principal components as auxiliary variables in missing data estimation. Multivariate Behavioral Research, 50(3), 285–299. https://doi.org/10.1080/00273171.2014.999267

12.

Isiordia

Ferrer

(2018). Curve of factors model: A latent growth modeling approach for educational research. Educational and Psychological Measurement, 78(2), 203–231. https://doi.org/10.1177/0013164416677143

13.

Kohli

Harring

J. R.

(2013). Modeling growth in latent variables using a piecewise function. Multivariate Behavioral Research, 48(3), 370–397. https://doi.org/10.1080/00273171.2013.778191

14.

Lang

K. M.

Little

T. D.

(2018). Principled missing data treatments. Prevention Science, 19(3), 284–294. https://doi.org/10.1007/s11121-016-0644-5

15.

Lange

S. J.

Ritchey

M. D.

Goodman

A. B.

Dias

Twentyman

Fuld

Schieve

L. A.

Imperatore

Benoit

S. R.

Kite-Powell

Stein

Peacock

Dowling

N. F.

Briss

P. A.

Hacker

Gundlapalli

A. V.

Yang

Q. H.

(2020). Potential indirect effects of the covid-19 pandemic on use of emergency departments for acute life-threatening conditions – United States, January-May 2020. Morbidity and Mortality Weekly Report, 69(25), 795–800. https://doi.org/10.15585/mmwr.mm6925e2

16.

Little

T. D.

(2013). Longitudinal structural equation modeling. The Guildford Press.

17.

Marchetti

Fontanesi

Mazza

Di Giandomenico

Roma

Verrocchio

M. C.

(2020). Parenting-related exhaustion during the Italian COVID-19 lockdown. Journal of Pediatric Psychology, 45(10), 1114–1123. https://doi.org/10.1093/jpepsy/jsaa093

18.

Masonbrink

A. R.

Hurley

(2020). Advocating for children during the COVID-19 school closures. Pediatrics, 146(3), Article e20201440. https://doi.org/10.1542/peds.2020-1440

19.

Mehta

P. D.

West

S. G.

(2000). Putting the individual back into individual growth curves. Psychological Methods, 5(1), 23–43. https://doi.org/10.1037//1082-989x.5.1.23

20.

Muthén

L. K.

Muthén

B. O.

(1998-2017). Mplus user’s guide (8th ed.). Muthén & Muthén.

21.

Pellegrini

Ponzo

Rosato

Scumaci

Goitre

Benso

Belcastro

Crespi

De Michieli

Ghigo

Broglio

S. M. N.

(2020). Changes in weight and nutritional habits in adults with obesity during the “lockdown” period caused by the covid-19 virus emergency. Nutrients, 12(7), Article 2016. https://doi.org/10.3390/nu12072016

22.

Peugh

J. L.

Beal

S. J.

McGrady

M. E.

Toland

M. D.

Mara

(2020). Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000347

23.

R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing. R-project.org.

24.

Rajkumar

R. P.

(2020). COVID-19 and mental health: A review of the existing literature. Asian Journal of Psychiatry, 52, Article 102066. https://doi.org/10.1016/j.ajp.2020.102066

25.

Raykov

(2005). Analysis of longitudinal studies with missing data using covariance structure modeling with full-information maximum likelihood. Structural Equation Modeling: A Multidisciplinary Journal, 12(3), 493–505. https://doi.org/10.1207/s15328007sem1203_8

26.

Rioux

Lewin

Odejimi

O. A.

Little

T. D.

(2020). Reflection on modern methods: Planned missing data designs for epidemiological research. International Journal of Epidemiology, 49(5), 1702–1711. https://doi.org/10.1093/ije/dyaa042

27.

Rioux

Little

T. D.

(2021). Missing data treatments in intervention studies: What was, what is, and what should be. International Journal of Behavioral Development, 45(1), 51–58. https://doi.org/10.1177/0165025419880609

28.

Roos

L. E.

Salisbury

Penner-Goeke

Cameron

E. E.

Protudjer

J. L. P.

Giuliano

Afifi

Reynolds

(2021). Supporting families to protect child health: Parenting quality and household needs during the COVID-19 pandemic. Plos One, 16(5), Article e0251720. https://doi.org/10.1371/journal.pone.0251720

29.

Rosseel

(2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

30.

RStudio Team. (2020). RStudio: Integrated development environment for R. RStudio, Inc. https://rstudio.com

31.

Seaman

Galati

Jackson

Carlin

(2013). What is meant by “missing at random”? Statistical Science, 28(2), 257–268. https://doi.org/10.1214/13-sts415

32.

Singer

J. D.

Willett

J. B.

(2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press.

33.

Skibbe

L. E.

Grimm

K. J.

Bowles

R. P.

Morrison

F. J.

(2012). Literacy growth in the academic year versus summer from preschool through second grade: Differential effects of schooling across four skills. Scientific Studies of Reading, 16(2), 141–165. https://doi.org/10.1080/10888438.2010.543446

34.

Tomfohr-Madsen

L. M.

Racine

Giesbrecht

G. F.

Lebel

Madigan

(2021). Depression and anxiety in pregnancy during COVID-19: A rapid review and meta-analysis. Psychiatry Research, 300, article 113912. https://doi.org/10.1016/j.psychres.2021.113912

35.

van Buuren

(2018). Flexible imputation of missing data (2nd ed.). CRC Press.

36.

van de Schoot

Schmidt

De Beuckelaer

Lek

Zondervan-Zwijnenburg

(2015). Editorial: Measurement invariance. Frontiers in Psychology, 6(1064). https://doi.org/10.3389/fpsyg.2015.01064

37.

Widaman

K. F.

Ferrer

Conger

R. D.

(2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 10–18. https://doi.org/10.1111/j.1750-8606.2009.00110.x

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