Planned missing data designs for spline growth models in salivary cortisol research

Candace M. Hogue, Sunthud Pornprasertmanit, Mary D. Fry, Mijke Rhemtulla, Todd D. Little

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Salivary cortisol is often used as an index of physiological and psychological stress in exercise science and psychoneuroendocrine research. A primary concern when designing research studies examining cortisol stems from the high cost of analysis. Planned missing data designs involve intentionally omitting a random subset of observations from data collection, reducing both the cost of data collection and participant burden. These designs have the potential to result in more efficient, cost-effective analyses with minimal power loss. Using salivary cortisol data from a previous study (Hogue, Fry, Fry, & Pressman, 2013), this article examines statistical power and estimated costs of six different planned missing data designs using growth curve modeling. Results indicate that using a planned missing data design would have provided the same results at a lower cost relative to the traditional, complete data analysis of salivary cortisol.

Original languageEnglish
Pages (from-to)310-325
Number of pages16
JournalMeasurement in Physical Education and Exercise Science
Volume17
Issue number4
DOIs
StatePublished - Oct 1 2013

Keywords

  • growth curve modeling
  • measurement efficiency
  • missing data
  • planned missing data design
  • salivary cortisol

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