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A latent Gaussian process model for analysing intensive longitudinal data

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  • Chen, Yunxiao
  • Zhang, Siliang

Abstract

Intensive longitudinal studies are becoming progressively more prevalent across many social science areas, and especially in psychology. New technologies such as smart-phones, fitness trackers, and the Internet of Things make it much easier than in the past to collect data for intensive longitudinal studies, providing an opportunity to look deep into the underlying characteristics of individuals under a high temporal resolution. In this paper we introduce a new modelling framework for latent curve analysis that is more suitable for the analysis of intensive longitudinal data than existing latent curve models. Specifically, through the modelling of an individual-specific continuous-time latent process, some unique features of intensive longitudinal data are better captured, including intensive measurements in time and unequally spaced time points of observations. Technically, the continuous-time latent process is modelled by a Gaussian process model. This model can be regarded as a semi-parametric extension of the classical latent curve models and falls under the framework of structural equation modelling. Procedures for parameter estimation and statistical inference are provided under an empirical Bayes framework and evaluated by simulation studies. We illustrate the use of the proposed model though the analysis of an ecological momentary assessment data set.

Suggested Citation

  • Chen, Yunxiao & Zhang, Siliang, 2020. "A latent Gaussian process model for analysing intensive longitudinal data," LSE Research Online Documents on Economics 101121, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:101121
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    File URL: http://eprints.lse.ac.uk/101121/
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    References listed on IDEAS

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    1. Johan Oud & Robert Jansen, 2000. "Continuous time state space modeling of panel data by means of sem," Psychometrika, Springer;The Psychometric Society, vol. 65(2), pages 199-215, June.
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    Cited by:

    1. Siliang Zhang & Yunxiao Chen, 2022. "Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1473-1502, December.
    2. Zhang, Siliang & Chen, Yunxiao, 2022. "Computation for latent variable model estimation: a unified stochastic proximal framework," LSE Research Online Documents on Economics 114489, London School of Economics and Political Science, LSE Library.

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    More about this item

    Keywords

    Gaussian process; latent curve analysis; structural equation modelling; intensive longitudinal data; ecological momentary assessment; time‐varying latent trait;
    All these keywords.

    JEL classification:

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

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