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Cited by 303Stress Responses and CortisolStatistical Methods and Bayesian InferenceMental Health Research Topics

Estimating between- and within-individual variation in cortisol levels using multilevel models

Hruschka, Daniel J., Kohrt, Brandon A., Worthman, Carol M.·2005·Psychoneuroendocrinology
DOI 10.1016/j.psyneuen.2005.03.002
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Abstract

Cortisol measures often are used to examine variation in hypothalamic-pituitary-adrenal axis (HPA) activity as well as broader patterns of differential health. However, substantial within-individual variation renders single cortisol measurements unreliable as estimates for probing differences between individuals and groups. A standard practice to clarify between-individual differences involves collecting multiple samples from each participant and then deriving person-specific averages. By ignoring information about variation at between- and within-individual levels, this technique impedes cross-study comparison of results, ignores data useful for future study design, and hinders the analysis of cross-level interactions. This report describes how multilevel approaches can simultaneously model between- and within-individual variation in diurnal cortisol levels without using crude averages. We apply these models to data from children in Nepal (n=29, 11-15 samples per child), Mongolia (n=47, 8-12 samples per child) and the US (n=1269, 1-6 samples per child). Using the Nepal data, we show how an analysis of crude time-adjusted aggregates does not detect an association between aggressive behavior and cortisol levels, while a multilevel analysis does. More importantly, we argue that the 'roadmap' to variation generated by these multilevel models provides meaningful information about the predictive accuracy--not just statistical significance--of relationships between cortisol levels and individual-level variables, such as psychopathology, age, and gender. The 'roadmap' also facilitates comparison between the results from different studies and estimation of the necessary number of cortisol measurements for future investigations.

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  • Growth curve models are better than repeated measures ANOVA for analyzing cortisol data because they do not require complete data for every participant or equally spaced observations.

    “Growth curve models are better than repeated measures analysis of variance (ANOVA), which has traditionally been used to analyze cortisol data, because the former do not require complete data for every participant or that observations be equally spaced in time (31).”

    From Cortisol response to stress in female youths exposed to childhood maltreatment: results of the youth mood project (2009) · cites this paper as [31]

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Authors
Hruschka, D. J., Kohrt, B. A., & Worthman, C. M.
Affiliations
Emory University (US)
Corresponding author
Hruschka, Daniel J.
ORCID
Venue
Psychoneuroendocrinology
Publisher
Elsevier BV
Published
2005-08
Volume
30
Issue
7
Pages
698-714
ISSN
0306-4530 (ISSN-L)
DOI
10.1016/j.psyneuen.2005.03.002
OpenAlex
W2131578123
Cited by
303
References
85
Citations by year
2026: 2 · 2025: 9 · 2024: 10 · 2023: 7 · 2022: 11 · 2021: 20 · 2020: 12 · 2019: 10 · 2018: 13 · 2017: 14 · 2016: 19 · 2015: 28 · 2014: 24 · 2013: 23 · 2012: 18
FWCI
4.73
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Psychoneuroendocrinology (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Institute of Mental Health (MH57761, P30MH057761)
Topics
  • Life Sciences › Neuroscience › Behavioral Neuroscience › Stress Responses and Cortisol
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords
cortisol, within-person variability, between-person variation, multilevel models, diurnal cortisol rhythm, hypothalamic-pituitary-adrenal axis, aggression, psychopathology, children, sample size determination, cross-study comparison
MeSH terms
Adolescent, Algorithms, Child, Child, Preschool, Circadian Rhythm, Data Interpretation, Statistical, Humans, Hydrocortisone, Male, Population, Research Design, Saliva, Reproducibility of Results, Models, Statistical, Cluster Analysis, Linear Models, White People, Asian People

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