Rigorous methods for the analysis, reporting and evaluation of ESM style data

Student thesis: Phd


Experience sampling methodology (ESM) is a real-time data capture method that can be used to monitor symptoms and behaviours as they occur during everyday life. With measures completed multiple times a day, over several days, this intensive longitudinal data collection method results in multilevel data with observations nested within days, nested within subjects. The aim of this thesis was to investigate the optimal use of multilevel models for ESM in the design, reporting and analysis of ESM data, and apply these models to a study in people with psychosis.A methodological systematic review was conducted to identify design, analysis and statistical reporting practices in current ESM studies. Seventy four studies from 2012 were reviewed, and together with the analysis of a motivating example, four significant areas of interest were identified: power and sample size, missing data, momentary variation and predicting momentary change. Appropriate multilevel methods were sought for each of these areas, and were evaluated in the three-level context of ESM.Missing data was found to be both underreported and rarely considered when choosing analysis methods in practice. This work has introduced a more detailed understanding of nonresponse in ESM studies and has discussed appropriate statistical methods in the presence of missing data. This thesis has extended two-level statistical methodology for data analysis to accommodate the three-level structure of ESM. Novel applications of time trends have been developed, were time can be measured at two separate levels. The suitability of predicting momentary change in ESM data has been questioned; it is argued that the first-difference and joint modelling methods that are claimed in the literature to remove bias possibly induce more in this context. Finally, Monte Carlo simulations were shown to be a flexible option for estimating empirical power under varying sample sizes at levels 3, 2 and 1, with recommendations made for conservative power estimates when a priori parameter estimates are unknown. In summary, this work demonstrates how multilevel models can be used to examine the rich data structure of ESM and fully utilize the variation in measures captured at all levels.
Date of Award31 Dec 2016
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorChristopher Roberts (Supervisor) & Richard Emsley (Supervisor)


  • Experience sampling
  • Multilevel
  • Intensive longitudinal data

Cite this