The Use of Growth Mixture Modeling for Studying Resilience to Major Life Stressors in Adulthood and Old Age: Lessons for Class Size and Identification and Model Selection

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)148-159
Number of pages12
JournalThe journals of gerontology. Series B, Psychological sciences and social sciences
Volume73
Issue number1
DOIs
StatePublished - Dec 15 2017

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old age
adulthood
resilience
Growth
normality
Statistical Models
well-being
Research Personnel
Depression

Keywords

  • Adult development and aging
  • Growth mixture modeling
  • Longitudinal panel surveys
  • Longitudinal research methodology
  • Resilience
  • Structural equation modeling

ASJC Scopus subject areas

  • Health(social science)
  • Social Psychology
  • Clinical Psychology
  • Sociology and Political Science
  • Gerontology
  • Geriatrics and Gerontology
  • Life-span and Life-course Studies

Cite this

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abstract = "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.",
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author = "Frank Infurna and Kevin Grimm",
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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.

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