Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes: an application to the effects of stress on the cognitive function of nurses

Citation

Steele F, Clarke P, Leckie G, Allan JL & Johnston D (2017) Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes: an application to the effects of stress on the cognitive function of nurses. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 180 (1), p. 263–283. https://doi.org/10.1111/rssa.12191

Abstract
Ecological momentary assessment is used to measure subjects' mood and behaviour repeatedly over time, leading to intensive longitudinal data. Variability in ecological momentary assessment schedules creates an analytical challenge because predictors are measured more frequently than responses. We consider this problem in a study of the effect of stress on the cognitive function of telephone helpline nurses, where stress is measured for each call and cognitive outcomes are measured at the end of a shift. We propose a flexible structural equation model which can handle multiple levels of clustering, measurement error, time trends and mixed variable types.

Keywords
Ecological momentary assessment; High frequency data; Intensive longitudinal data; Multilevel latent variable model; Occupational stress; Realtime assessment; Simultaneous equation model

Notes
Funded by Chief Scientist Office, Scotland. Grant Number: CZH/4/394 Economic and Social Research Council grant as part of the National Centre for Research Methods. Grant Number: RES-576-25-0032

Journal
Journal of the Royal Statistical Society. Series A, (Statistics in Society): Volume 180, Issue 1