@article{Leijon2023,
 author = {Leijon, A. and von Gablenz, P. and Holube, I. and Taghia, J. and Smeds, K.},
 title = {Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation},
 abstract = {This paper presents a new Bayesian method for analyzing Ecological Momentary Assessment (EMA) data and applies this method in a re-analysis of data from a previous EMA study. The analysis method has been implemented as a freely available Python package EmaCalc, RRID:SCR 022943. The analysis model can use EMA input data including nominal categories in one or more situation dimensions, and ordinal ratings of several perceptual attributes. The analysis uses a variant of ordinal regression to estimate the statistical relation between these variables. The Bayesian method has no requirements related to the number of participants or the number of assessments by each participant. Instead, the method automatically includes measures of the statistical credibility of all analysis results, for the given amount of data. For the previously collected EMA data, the analysis results demonstrate how the new tool can handle heavily skewed, scarce, and clustered data that were collected on ordinal scales, and present results on interval scales. The new method revealed results for the population mean that were similar to those obtained in the previous analysis by an advanced regression model. The Bayesian approach automatically estimated the inter-individual variability in the population, based on the study sample, and could show some statistically credible intervention results also for an unseen random individual in the population. Such results may be interesting, for example, if the EMA methodology is used by a hearing-aid manufacturer in a study to predict the success of a new signal-processing method among future potential customers.},
 keywords = {Bayesian inference, EMA, Ecological Momentary Assessment, ambulatory assessment, experience sampling, nominal data, ordinal data},
 publisher = {Frontiers Media},
 year = {2023},
 volume = {5},
 journal = {Frontiers in Digital Health},
 doi = {10.3389/fdgth.2023.1100705},
 url = {https://doi.org/10.3389/fdgth.2023.1100705},
 address = {Lausanne},
 language = {eng},
 eissn = {2673-253X},
}