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ISSN (Print): 1574-8871
ISSN (Online): 1876-1038

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Research Article

Temporal Pattern of Co-Development of Internalizing and Externalizing Problem Behaviors: An Application of Bivariate Mixed-Effects Models

Author(s): Guang Zeng, Zhengyi Chen and Pingfu Fu*

Volume 15, Issue 1, 2020

Page: [60 - 69] Pages: 10

DOI: 10.2174/1574887114666191028115245

Price: $65

Abstract

Background: Although previous research has shown that internalizing and externalizing behavior problems often co-occur, the relationship between the developmental trajectories of these two types of behavior problems is understudied. The co-occurring evolutions of developmental trajectories of two behaviors has two components: 1) the correlation between the slopes of two behavior profiles (termed the association of the evolutions); and 2) the marginal correlation of two development trajectory profiles, which is the development of correlation between internalizing and externalizing behavior over time (termed the evolution of the association). The association of the evolutions and the evolution of the association have not been fully explored in the context of the development of internalizing and externalizing behavior problems among kindergarteners in the United States.

Methods: The random-effects approach for joint modeling of multivariate longitudinal profiles was used to evaluate the co-development and its temporal pattern of internalizing and externalizing behavior problems on a nationally representative sample of 9791 kindergarteners from the Early Childhood Longitudinal Study-Kindergarten Class of 1998-99 (ECLS-K).

Results: There was a moderate positive association between the evolutions of the two behavior problems with correlation coefficient of 0.319. The evolution of association between the two behaviors was increasing over time with the correlation coefficient from 0.195 at the Fall of kindergarten to 0.291 by the time of fifth grade in general. Race and age groups act differently on the evolution of association. The associations were getting stronger for the Asian group and older groups than their peer groups.

Conclusion: This investigation of the association of evolutions and the evolution of association between the internalizing and externalizing behaviors show that the two problem behaviors reciprocally reinforce each other and lead to increases in the other in a moderate strength and the strength is increasing over time.

Keywords: Evolution of association, externalizing problems, internalizing problems, joint modeling of bivariate longitudinal data, kindergarteners, bivariate mixed-effects models.

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[1]
Zeng G, Fu P, May H, et al. America’s youngest kindergarteners’ elevated levels of internalizing problems at school entry and beyond: Evidence from the early childhood longitudinal study. School Ment Health 2012; 4(3): 129-42.
[http://dx.doi.org/10.1007/s12310-012-9077-x]
[2]
Beyers JM, Loeber R. Untangling developmental relations between depressed mood and delinquency in male adolescents. J Abnorm Child Psychol 2003; 31(3): 247-66.
[http://dx.doi.org/10.1023/A:1023225428957] [PMID: 12774859]
[3]
Lee EJ, Bukowski WM. Co-development of internalizing and externalizing problem behaviors: Causal direction and common vulnerability. J Adolesc 2012; 35(3): 713-29.
[http://dx.doi.org/10.1016/j.adolescence.2011.10.008] [PMID: 22104758]
[4]
Gilliom M, Shaw DS. Codevelopment of externalizing and internalizing problems in early childhood. Dev Psychopathol 2004; 16(2): 313-33.
[http://dx.doi.org/10.1017/S0954579404044530] [PMID: 15487598]
[5]
Overbeek G, Biesecker G. Kerr, et al.Co-occurrence of depressive moods and delinquency in early adolescence: The role of failure expectations, manipulativeness, and social contexts. Int J Behav Dev 2006; 30(5): 433-43.
[http://dx.doi.org/10.1177/0165025406071491]
[6]
Gueorguieva RV, Agresti A. A correlated probit model for joint modeling of clustered binary and continuous responses. J Am Stat Assoc 2001; 96(455): 1102-12.
[http://dx.doi.org/10.1198/016214501753208762]
[7]
Tsiatis AA, Degruttola V, Wulfsohn MS. Modeling the relationship of survival to longitudinal data measured with error - Applications to survival and Cd4 counts in patients with AIDS. J Am Stat Assoc 1995; 90(429): 27-37.
[http://dx.doi.org/10.1080/01621459.1995.10476485]
[8]
Schluchter MD. Estimating correlation between alternative measures of disease progression in a longitudinal study. Modification of Diet in Renal Disease Study. Stat Med 1990; 9(10): 1175-88.
[http://dx.doi.org/10.1002/sim.4780091007] [PMID: 2247718]
[9]
Catalano PJ, Ryan LM. Bivariate latent variable models for clustered discrete and continuous outcomes. J Am Stat Assoc 1992; 87(419): 651-8.
[http://dx.doi.org/10.1080/01621459.1992.10475264]
[10]
Fieuws S, Verbeke G. Joint modelling of multivariate longitudinal profiles: Pitfalls of the random-effects approach. Stat Med 2004; 23(20): 3093-104.
[http://dx.doi.org/10.1002/sim.1885] [PMID: 15449333]
[11]
Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics 1982; 38(4): 963-74.
[http://dx.doi.org/10.2307/2529876] [PMID: 7168798]
[12]
Bollen KA, Curran PJ. Latent curve models: A Structural Equation Perspective 2006. 1-2: 93.
[13]
Michael PC. Dynamic Factor Models. Oxford University Press: Oxford;. 2011.
[14]
Aigner DJ, Goldberger AS. Latent variables in socio-economic models Elsevier: North-Holland Pub. Co.,. 1977.
[15]
Stock J, Watson MW. Testing for Common Trend. J Am Stat Assoc 1988; 83: 1097-107.
[16]
Song H, Zhang Z. Analyzing multiple multivariate time series data using multilevel dynamic factor models. Multivariate Behav Res 2014; 49(1): 67-77.
[http://dx.doi.org/10.1080/00273171.2013.851018] [PMID: 26745674]
[17]
Chou CP, Bentler PM, Pentz MA. Comparisons of two statistical approaches to study growth curves: The multilevel model and the latent curve analysis. Struct Equ Modeling 1998; 5(3): 247-66.
[http://dx.doi.org/10.1080/10705519809540104]
[18]
Verbeke G, Fieuws S, Molenberghs G, Davidian M. The analysis of multivariate longitudinal data: A review. Stat Methods Med Res 2014; 23(1): 42-59.
[http://dx.doi.org/10.1177/0962280212445834] [PMID: 22523185]
[19]
Ghisletta P, Lindenberger U. Static and dynamic longitudinal structural analyses of cognitive changes in old age. Gerontology 2004; 50(1): 12-6.
[http://dx.doi.org/10.1159/000074383] [PMID: 14654721]
[20]
Thum YM. Hierarchical linear models for multivariate outcomes. J Educ Behav Stat 1997; 22(1): 77-108.
[http://dx.doi.org/10.3102/10769986022001077]
[21]
Zucker DM, Zerbe GO, Wu MC. Inference for the association between coefficients in a multivariate growth curve model. Biometrics 1995; 51(2): 413-24.
[http://dx.doi.org/10.2307/2532930] [PMID: 7662834]
[22]
Carroll CD. Opportunities and challenges at the National Center for Education Statistics. In: Kelly TK, Butz WP, Carroll S, Adamson DM, Bloom G, edsThe US scientific and technical workforce: Improving data for decisionmaking. Santa Monica: Rand;. 2004; pp. 93-6.
[23]
McArdle JJ. Dynamic but structural equation modeling of repeated measures data Handbook of multivariate experimental psychology. 2nd ed. New York, NY, US: Plenum Press 1988; pp. 561-614.
[http://dx.doi.org/10.1007/978-1-4613-0893-5_17]
[24]
Duncan TE. An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers 1999.
[25]
Thiébaut R, Jacqmin-Gadda H, Chêne G, Leport C, Commenges D. Bivariate linear mixed models using SAS proc MIXED. Comput Methods Programs Biomed 2002; 69(3): 249-56.
[http://dx.doi.org/10.1016/S0169-2607(02)00017-2] [PMID: 12204452]
[26]
Agnes Brunnekreef J, De Sonneville LM, Althaus M, et al. Information processing profiles of internalizing and externalizing behavior problems: Evidence from a population-based sample of preadolescents. J Child Psychol Psychiatry 2007; 48(2): 185-93.
[http://dx.doi.org/10.1111/j.1469-7610.2006.01695.x] [PMID: 17300557]
[27]
Sheidow AJ. The relation of antisocial behavior patterns and changes in internalizing symptoms for a sample of inner-city youth: Comorbidity within a developmental framework. J Youth Adolesc 2008; 37(7): 821-9.
[http://dx.doi.org/10.1007/s10964-007-9265-4]
[28]
Cosgrove VE, Rhee SH, Gelhorn HL, et al. Structure and etiology of co-occurring internalizing and externalizing disorders in adolescents. J Abnorm Child Psychol 2011; 39(1): 109-23.
[http://dx.doi.org/10.1007/s10802-010-9444-8] [PMID: 20683651]
[29]
Lee JJ. Correlation and causation in the study of personality. Eur J Pers 2012; 26(4): 372-90.
[http://dx.doi.org/10.1002/per.1863]
[30]
Loeber R, Burke JD. Developmental pathways in juvenile externalizing and internalizing problems. J Res Adolesc 2011; 21(1): 34-46.
[http://dx.doi.org/10.1111/j.1532-7795.2010.00713.x] [PMID: 22468115]
[31]
Stone LL, Mares SH, Otten R, Engels RC, Janssens JM. The co-development of parenting stress and childhood internalizing and externalizing problems. J Psychopathol Behav Assess 2016; 38: 76-86.
[http://dx.doi.org/10.1007/s10862-015-9500-3] [PMID: 27069304]

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