IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/64893.html
   My bibliography  Save this paper

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

Author

Listed:
  • Steele, Fiona
  • Clarke, Paul
  • Leckie, George
  • Allan, Julia
  • Johnston, Derek

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.

Suggested Citation

  • Steele, Fiona & Clarke, Paul & Leckie, George & Allan, Julia & 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," LSE Research Online Documents on Economics 64893, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:64893
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/64893/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. George Leckie & Robert French & Chris Charlton & William Browne, 2014. "Modeling Heterogeneous Variance–Covariance Components in Two-Level Models," Journal of Educational and Behavioral Statistics, , vol. 39(5), pages 307-332, October.
    2. Harvey Goldstein & Daphne Kounali, 2009. "Multilevel multivariate modelling of childhood growth, numbers of growth measurements and adult characteristics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 599-613, June.
    3. Li C. Liu & Donald Hedeker, 2006. "A Mixed-Effects Regression Model for Longitudinal Multivariate Ordinal Data," Biometrics, The International Biometric Society, vol. 62(1), pages 261-268, March.
    4. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    5. Fiona Steele & Harvey Goldstein, 2006. "A Multilevel Factor Model for Mixed Binary and Ordinal Indicators of Women's Status," Sociological Methods & Research, , vol. 35(1), pages 137-153, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fiona Steele & Paul Clarke & George Leckie & Julia Allan & Derek Johnston, 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, Royal Statistical Society, vol. 180(1), pages 263-283, January.
    2. Pilar Lopez-Llompart & G. Mathias Kondolf, 2016. "Encroachments in floodways of the Mississippi River and Tributaries Project," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 513-542, March.
    3. Michelle Sheran Sylvester, 2007. "The Career and Family Choices of Women: A Dynamic Analysis of Labor Force Participation, Schooling, Marriage and Fertility Decisions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(3), pages 367-399, July.
    4. DAVID M. BLAU & WILBERT van der KLAAUW, 2013. "What Determines Family Structure?," Economic Inquiry, Western Economic Association International, vol. 51(1), pages 579-604, January.
    5. Afanasyev, Dmitriy O. & Fedorova, Elena A. & Popov, Viktor U., 2015. "Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis," Energy Economics, Elsevier, vol. 51(C), pages 215-226.
    6. Peter Viggo Jakobsen, 2009. "Small States, Big Influence: The Overlooked Nordic Influence on the Civilian ESDP," Journal of Common Market Studies, Wiley Blackwell, vol. 47(1), pages 81-102, January.
    7. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2016. "Efficient Gibbs sampling for Markov switching GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 37-57.
    8. Jan Babecký & Fabrizio Coricelli & Roman Horváth, 2009. "Assessing Inflation Persistence: Micro Evidence on an Inflation Targeting Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(2), pages 102-127, June.
    9. Lloyd, S. P., 2017. "Unconventional Monetary Policy and the Interest Rate Channel: Signalling and Portfolio Rebalancing," Cambridge Working Papers in Economics 1735, Faculty of Economics, University of Cambridge.
    10. Ichiro Fukunaga, 2007. "Imperfect Common Knowledge, Staggered Price Setting, and the Effects of Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1711-1739, October.
    11. Albertazzi, Ugo & Gambacorta, Leonardo, 2009. "Bank profitability and the business cycle," Journal of Financial Stability, Elsevier, vol. 5(4), pages 393-409, December.
    12. Beck, Thorsten & Demirgüç-Kunt, Asli & Merrouche, Ouarda, 2013. "Islamic vs. conventional banking: Business model, efficiency and stability," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 433-447.
    13. Jinho Bae & Chang-Jin Kim & Dong Kim, 2012. "The evolution of the monetary policy regimes in the U.S," Empirical Economics, Springer, vol. 43(2), pages 617-649, October.
    14. McMahon, Rob, 2020. "Co-developing digital inclusion policy and programming with indigenous partners: Interventions from Canada," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 9(2), pages 1-26.
    15. George W. Evans & Seppo Honkapohja, 2009. "Robust Learning Stability with Operational Monetary Policy Rules," Central Banking, Analysis, and Economic Policies Book Series, in: Klaus Schmidt-Hebbel & Carl E. Walsh & Norman Loayza (Series Editor) & Klaus Schmidt-Hebbel (Series (ed.),Monetary Policy under Uncertainty and Learning, edition 1, volume 13, chapter 5, pages 145-170, Central Bank of Chile.
    16. Lehtonen, Heikki & Kujala, Sanna, 2007. "Climate change impacts on crop risks and agricultural production in Finland," 101st Seminar, July 5-6, 2007, Berlin Germany 9259, European Association of Agricultural Economists.
    17. Michael Pomerleano, 2011. "Developing Regional Financial Markets – the Case of East Asia," Chapters, in: Ulrich Volz (ed.), Regional Integration, Economic Development and Global Governance, chapter 9, Edward Elgar Publishing.
    18. Gary Charness & Francesco Feri & Miguel A. Meléndez-Jiménez & Matthias Sutter, 2023. "An Experimental Study on the Effects of Communication, Credibility, and Clustering in Network Games," The Review of Economics and Statistics, MIT Press, vol. 105(6), pages 1530-1543, November.
    19. Kitsul, Yuriy & Wright, Jonathan H., 2013. "The economics of options-implied inflation probability density functions," Journal of Financial Economics, Elsevier, vol. 110(3), pages 696-711.
    20. Dieter Balkenborg & Rosemarie Nagel, 2016. "An Experiment on Forward vs. Backward Induction: How Fairness and Level k Reasoning Matter," German Economic Review, Verein für Socialpolitik, vol. 17(3), pages 378-408, August.

    More about this item

    Keywords

    ecological momentary assessment; high frequency data; intensive longitudinal data; multilevel latent variable model; occupational stress; realtime assessment; simultaneous equation model;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:64893. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.