The design and analysis of longitudinal studies of development and psychopathology in context: Statistical models and methodological recommendations
Abstract
The utility and flexibility of recent advances in statistical methods for the quantitative analysis of developmental data—in particular, the methods of individual growth modeling and survival analysis—are unquestioned by methodologists, but have yet to have a major impact on empirical research within the field of developmental psychopathology and elsewhere. In this paper, we show how these new methods provide developmental psychpathologists with powerful ways of answering their research questions about systematic changes over time in individual behavior and about the occurrence and timing of life events. In the first section, we present a descriptive overview of each method by illustrating the types of research questions that each method can address, introducing the statistical models, and commenting on methods of model fitting, estimation, and interpretation. In the following three sections, we offer six concrete recommendations for developmental psychopathologists hoping to use these methods. First, we recommend that when designing studies, investigators should increase the number of waves of data they collect and consider the use of accelerated longitudinal designs. Second, we recommend that when selecting measurement strategies, investigators should strive to collect equatable data prospectively on all time-varying measures and should never standardize their measures before analysis. Third, we recommend that when specifying statistical models, researchers should consider a variety of alternative specifications for the time predictor and should test for interactions among predictors, particularly interactions between substantive predictors and time. Our goal throughout is to show that these methods are essential tools for answering questions about life-span developmental processes in both normal and atypical populations and that their proper use will help developmental psychopathologists and others illuminate how important contextual variables contribute to various pathways of development.
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Growth curve modeling appropriately handles data that violate the assumption of independence.
“Growth curve modeling appropriately handles data that violate the assumption of independence (32).”
From Cortisol response to stress in female youths exposed to childhood maltreatment: results of the youth mood project (2009) · cites this paper as [32]
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- Authors
- WILLETT, J. B., SINGER, J. D., & MARTIN, N. C.
- Affiliations
- Harvard University (US)
- Venue
- Development and Psychopathology
- Publisher
- Cambridge University Press (CUP)
- Published
- 1998-06
- Volume
- 10
- Issue
- 2
- Pages
- 395-426
- ISSN
- 0954-5794 (ISSN-L)
- 1469-2198
- OpenAlex
- W2132873362
- Semantic Scholar
- 51c5ef15e9cd628045d7ee3617a50668780d31b3
- Cited by
- 423
- References
- 11
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 7 · 2023: 9 · 2022: 17 · 2021: 6 · 2020: 11 · 2019: 18 · 2018: 11 · 2017: 15 · 2016: 17 · 2015: 18 · 2014: 16 · 2013: 23 · 2012: 26
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Development and Psychopathology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Clinical Psychology › Child and Adolescent Psychosocial and Emotional Development
- Keywords
- longitudinal study design, developmental psychopathology, survival analysis, accelerated longitudinal design, life events, contextual factors, lifespan development
- MeSH terms
- Adolescent, Mental Disorders, Child, Child Behavior Disorders, Child, Preschool, Data Collection, Female, Humans, Infant, Longitudinal Studies, Male, Personality Development, Psychopathology, Research Design, Social Environment, Models, Statistical, Proportional Hazards Models
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