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

Steele, Fiona ORCID: 0000-0001-6417-7444, Clarke, Paul, Leckie, George, Allan, Julia and Johnston, Derek (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). 263 - 283. ISSN 0964-1998

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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.

Item Type:	Article
Official URL:	https://rss.onlinelibrary.wiley.com/journal/146798...
Additional Information:	© 2016 Royal Statistical Society
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	08 Jan 2016 15:54
Last Modified:	12 Dec 2024 01:20
Projects:	CZH/4/394; RES-576-25-0032, CZH/4/394, ES-576-25-0032
Funders:	Chief Scientist Office, Economic and Social Research Council, Chief Scientist Office, Economic and Social Research Council
URI:	http://eprints.lse.ac.uk/id/eprint/64893

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