TY - JOUR
T1 - The use of growth mixture modeling for studying resilience to major life stressors in adulthood and old age
T2 - Lessons for class size and identification and model selection
AU - Infurna, Frank
AU - Grimm, Kevin
N1 - Funding Information:
The Health and Retirement Study (HRS) was supported by a cooperative agreement (Grant U01 AG09740) between the National Institute on Aging and the University of Michigan. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Publisher Copyright:
© The Author(s) 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Objectives: Growth mixture modeling (GMM) combines latent growth curve and mixture modeling approaches and is typically used to identify discrete trajectories following major life stressors (MLS). However, GMM is often applied to data that does not meet the statistical assumptions of the model (e.g., within-class normality) and researchers often do not test additional model constraints (e.g., homogeneity of variance across classes), which can lead to incorrect conclusions regarding the number and nature of the trajectories. We evaluate how these methodological assumptions influence trajectory size and identification in the study of resilience to MLS. Method: We use data on changes in subjective well-being and depressive symptoms following spousal loss from the HILDA and HRS. Results: Findings drastically differ when constraining the variances to be homogenous versus heterogeneous across trajectories, with overextraction being more common when constraining the variances to be homogeneous across trajectories. In instances, when the data are non-normally distributed, assuming normally distributed data increases the extraction of latent classes. Discussion: Our findings showcase that the assumptions typically underlying GMM are not tenable, influencing trajectory size and identification and most importantly, misinforming conceptual models of resilience. The discussion focuses on how GMM can be leveraged to effectively examine trajectories of adaptation following MLS and avenues for future research.
AB - Objectives: Growth mixture modeling (GMM) combines latent growth curve and mixture modeling approaches and is typically used to identify discrete trajectories following major life stressors (MLS). However, GMM is often applied to data that does not meet the statistical assumptions of the model (e.g., within-class normality) and researchers often do not test additional model constraints (e.g., homogeneity of variance across classes), which can lead to incorrect conclusions regarding the number and nature of the trajectories. We evaluate how these methodological assumptions influence trajectory size and identification in the study of resilience to MLS. Method: We use data on changes in subjective well-being and depressive symptoms following spousal loss from the HILDA and HRS. Results: Findings drastically differ when constraining the variances to be homogenous versus heterogeneous across trajectories, with overextraction being more common when constraining the variances to be homogeneous across trajectories. In instances, when the data are non-normally distributed, assuming normally distributed data increases the extraction of latent classes. Discussion: Our findings showcase that the assumptions typically underlying GMM are not tenable, influencing trajectory size and identification and most importantly, misinforming conceptual models of resilience. The discussion focuses on how GMM can be leveraged to effectively examine trajectories of adaptation following MLS and avenues for future research.
KW - Adult development and aging
KW - Growth mixture modeling
KW - Longitudinal panel surveys
KW - Longitudinal research methodology
KW - Resilience
KW - Structural equation modeling
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U2 - 10.1093/geronb/gbx019
DO - 10.1093/geronb/gbx019
M3 - Article
C2 - 28329850
AN - SCOPUS:85046266054
SN - 1079-5014
VL - 73
SP - 148
EP - 159
JO - Journals of Gerontology - Series B Psychological Sciences and Social Sciences
JF - Journals of Gerontology - Series B Psychological Sciences and Social Sciences
IS - 1
ER -