kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.ORCID iD: 0000-0001-7957-5103
Institute of Hearing Technology and Audiology, Jade University of Applied Sciences, Oldenburg, Germany.
Institute of Hearing Technology and Audiology, Jade University of Applied Sciences, Oldenburg, Germany.
KTH, School of Electrical Engineering and Computer Science (EECS).
Show others and affiliations
2023 (English)In: Frontiers in Digital Health, E-ISSN 2673-253X, Vol. 5, article id 1100705Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Frontiers Media SA , 2023. Vol. 5, article id 1100705
Keywords [en]
ambulatory assessment, Bayesian inference, Ecological Momentary Assessment, EMA, experience sampling, nominal data, ordinal data
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-331101DOI: 10.3389/fdgth.2023.1100705ISI: 001030200300001PubMedID: 36874366Scopus ID: 2-s2.0-85149934062OAI: oai:DiVA.org:kth-331101DiVA, id: diva2:1780225
Note

QC 20230705

Available from: 2023-07-05 Created: 2023-07-05 Last updated: 2024-01-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records

Leijon, ArneTaghia, Jalil

Search in DiVA

By author/editor
Leijon, ArneTaghia, Jalil
By organisation
Speech, Music and Hearing, TMHSchool of Electrical Engineering and Computer Science (EECS)
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 35 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf