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Review
. 2022 Jan 12;65(1):344-360.
doi: 10.1044/2021_JSLHR-21-00081. Epub 2021 Dec 15.

Statistical Considerations for Analyzing Ecological Momentary Assessment Data

Jacob J Oleson  1 , Michelle A Jones  1 , Erik J Jorgensen  2 , Yu-Hsiang Wu  2
Affiliations
Review

Statistical Considerations for Analyzing Ecological Momentary Assessment Data

Jacob J Oleson et al. J Speech Lang Hear Res. .

Abstract

Purpose: The analysis of Ecological Momentary Assessment (EMA) data can be difficult to conceptualize due to the complexity of how the data are collected. The goal of this tutorial is to provide an overview of statistical considerations for analyzing observational data arising from EMA studies.

Method: EMA data are collected in a variety of ways, complicating the statistical analysis. We focus on fundamental statistical characteristics of the data and general purpose statistical approaches to analyzing EMA data. We implement those statistical approaches using a recent study involving EMA.

Results: The linear or generalized linear mixed-model statistical approach can adequately capture the challenges resulting from EMA collected data if properly set up. Additionally, while sample size depends on both the number of participants and the number of survey responses per participant, having more participants is more important than the number of responses per participant.

Conclusion: Using modern statistical methods when analyzing EMA data and adequately considering all of the statistical assumptions being used can lead to interesting and important findings when using EMA.

Supplemental material: https://doi.org/10.23641/asha.17155961.

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Figures

Figure 1.
Figure 1.
The dashed line showcases a normal distribution assumption if all observations are allowed to be assumed independent of each other. The solid line shows what happens to the normal distribution if correlation between the observations is included. Note the reduced standard error in the distribution when the correlation, although present, is ignored, which will result in p values that are too small.
Figure 2.
Figure 2.
The top panel shows boxplots of Speech Understanding pre–COVID-19 versus during COVID-19 using aggregated data. The median is represented by the line, and the mean is the dot with the bottom line of the box representing the 25th percentile and the top line of the box representing the 75th percentile. For these boxplots, the lines extend to the minimum and maximum of the data. The bottom panel shows a histogram of the differences of average values pre-COVID and during COVID where the normality assumption can directly be visually assessed.
Figure 3.
Figure 3.
Example of how the random intercept linear mixed model works with the Speech Understanding variable from Ecological Momentary Assessment. The plus signs and dashed lines are from Sample Individual 1, whereas the circles and dotted lines are from Sample Individual 2. The solid line is the average of the entire population.
Figure 4.
Figure 4.
Power and sample size figure demonstrating how the number of participants and the number of surveys per participant are related to each other for a comparison of two independent groups.
Figure 5.
Figure 5.
Power and sample size figure demonstrating how the number of participants and the number of surveys per individual are related to each other for a paired comparison.
See this image and copyright information in PMC

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