A latent Gaussian process model for analysing intensive longitudinal data

Chen, Yunxiao ORCID: 0000-0002-7215-2324 and Zhang, Siliang ORCID: 0000-0002-2641-4944 (2020) A latent Gaussian process model for analysing intensive longitudinal data. British Journal of Mathematical and Statistical Psychology, 73 (2). 237 - 260. ISSN 0007-1102

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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.

Item Type:	Article
Official URL:	https://onlinelibrary.wiley.com/journal/20448317
Additional Information:	© 2019 The British Psychological Society
Divisions:	Statistics
Subjects:	B Philosophy. Psychology. Religion > BF Psychology H Social Sciences > HA Statistics
Date Deposited:	04 Jul 2019 11:36
Last Modified:	17 Oct 2024 17:26
URI:	http://eprints.lse.ac.uk/id/eprint/101121

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