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

Fiona Steele; Paul S Clarke; George Leckie; Julia Allan; Derek Johnston

doi:10.1111/rssa.12191

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

Fiona Steele^*, Paul S Clarke, George Leckie, Julia Allan, Derek Johnston

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

7 Citations (Scopus)

321 Downloads (Pure)

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.

Original language	English
Pages (from-to)	263-283
Number of pages	21
Journal	Journal of the Royal Statistical Society: Series A
Volume	180
Issue number	1
Early online date	10 Mar 2016
DOIs	https://doi.org/10.1111/rssa.12191
Publication status	Published - Jan 2017

Keywords

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

Access to Document

10.1111/rssa.12191

Full-text PDF (accepted author manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Wiley at http://onlinelibrary.wiley.com/doi/10.1111/rssa.12191/abstract. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 1.16 MBLicence: CC BY-NC

Handle.net

Persistent link

Cite this

Steele, F., Clarke, P. S., Leckie, G., Allan, J., & 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, 180(1), 263-283. https://doi.org/10.1111/rssa.12191

@article{8e0ad6a4967343fa83ab3ca3a52e31e3,

title = "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",

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, Simultaneous equation model, Realtime assessment, Occupational stress, Multilevel latent variable model, Intensive longitudinal data, High frequency data",

author = "Fiona Steele and Clarke, {Paul S} and George Leckie and Julia Allan and Derek Johnston",

year = "2017",

month = jan,

doi = "10.1111/rssa.12191",

language = "English",

volume = "180",

pages = "263--283",

journal = "Journal of the Royal Statistical Society: Series A",

issn = "0964-1998",

publisher = "Wiley",

number = "1",

}

Steele, F, Clarke, PS, Leckie, G, Allan, J & 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, vol. 180, no. 1, pp. 263-283. https://doi.org/10.1111/rssa.12191

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; Clarke, Paul S; Leckie, George et al.
In: Journal of the Royal Statistical Society: Series A, Vol. 180, No. 1, 01.2017, p. 263-283.

Research output: Contribution to journal › Article (Academic Journal) › peer-review

TY - JOUR

T1 - Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes

T2 - An application to the effects of stress on the cognitive function of nurses

AU - Steele, Fiona

AU - Clarke, Paul S

AU - Leckie, George

AU - Allan, Julia

AU - Johnston, Derek

PY - 2017/1

Y1 - 2017/1

N2 - 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.

AB - 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.

KW - Ecological momentary assessment

KW - Simultaneous equation model

KW - Realtime assessment

KW - Occupational stress

KW - Multilevel latent variable model

KW - Intensive longitudinal data

KW - High frequency data

UR - http://www.scopus.com/inward/record.url?scp=84960540438&partnerID=8YFLogxK

U2 - 10.1111/rssa.12191

DO - 10.1111/rssa.12191

M3 - Article (Academic Journal)

AN - SCOPUS:84960540438

SN - 0964-1998

VL - 180

SP - 263

EP - 283

JO - Journal of the Royal Statistical Society: Series A

JF - Journal of the Royal Statistical Society: Series A

IS - 1

ER -

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

Abstract

Keywords

Access to Document

Handle.net

Other files and links

Fingerprint

Lemma 3

Cite this

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

Abstract

Keywords

Access to Document

Handle.net

Other files and links

Fingerprint

Projects

Lemma 3

Cite this