EmaCalc 0.5

Statistical Analysis of Ecological Momentary Assessment (EMA) Data

Package EmaCalc implements probabilistic Bayesian analysis of Ecological Momentary Assessment (EMA) data. The EMA methodology is used, for example, to evaluate the subjective performance of hearing aids or other equipment, or the effect of any other kind of psycho-socio-medical intervention, in the everyday life of the user or client.

EMA Experiments

In an EMA study, each participant is requested to respond to a questionnaire during normal everyday life, typically several times per day. Some questions may address the current real-life Scenario, i.e., the physical environment and the user's activity and intentions in that situation. The participant may also be asked to rate, e.g., the pleasantness of aided sound or the ease of speech understanding in the current scenario, or any other perceptual Attribute of interest in the study. Typically, many records are collected from each participant, but the number of records may vary a lot among respondents.

Thus, EMA data usually include both nominal and ordinal results. The analysis model estimates Attribute values numerically on an objective interval scale, although the raw input data are subjective and indicate only ordinal judgments for each Attribute.

This package does not include functions to handle the data collection; it can only use existing files with data recorded earlier. The package can analyze data from simple or rather complex experimental designs, including the following features:

1. The complete EMA study may include one or more Test Stages, for example, before and after some kind of intervention.

2. Each EMA record may characterize the current situation in one or more pre-defined Scenario Dimensions. For example, one scenario dimension may be specified by the Common Sound Scenarios (CoSS) (Wolters et al., 2016), which is a list of broad categories of acoustic environments. Other dimensions may specify the Importance of the situation, and/or the Hearing-Aid Program currently used.

3. Each EMA record may also include discrete ratings for one or more perceptual Attributes. For example, one Attribute may be Speech Understanding, with ordinal grades Bad, Fair, Good, Perfect. Another attribute may be Comfort, with grades Bad, Good.

4. For each Scenario Dimension, a list of allowed Scenario Categories must be pre-defined. An assessment event is defined by a combination of exactly one selected Category from each Dimension.

5. For each perceptual Attribute, a list of discrete ordinal Attribute Grades must be pre-defined.

6. An EMA study may involve one or more distinct Sub-populations, from which separate groups of participants are recruited.

7. Sub-populations are distinguished by a combination of categories from one or more Grouping Factors. For example, one factor may be Age, with categories Young, Middle, or Old. Another group factor may be, e.g., Gender, with categories Female, Male, or Undefined.

8. The analysis model does not require anything about the number of participants from each sub-population, or the number of assessments by each participant. Of course, the reliability is improved if there are many participants from each sub-population, each reporting a large number of EMA records.

EMA Data Analysis

The analysis model uses the recorded data to learn a probabilistic model, representing the statistically most relevant aspects of the data. The analysis includes a regression model to show how the Attribute values are affected by Scenarios.

1. The analysis results will show predictive Scenario Profiles for each sub-population, credible differences between scenario probabilities within each sub-population, and credible differences between sub-populations.

2. The analysis results will also show perceptual Attribute Values for each sub-population, credible differences between Attribute Values in separate scenarios, and credible Attribute Differences between sub-populations.

The Bayesian analysis automatically estimates the statistical credibility of all analysis results, given the amount of collected data. The Bayesian model is hierarchical. The package can estimate results for

• a random unseen individual in the (sub-)population from which the participants were recruited,
• the (sub-)population mean,
• each individual participant.

Package Documentation

General information and version history is given in the package doc-string that may be accessed by commands import EmaCalc, help(EmaCalc) in an interactive Python environment.

Specific information about the organization and accepted formats of input data files is presented in the doc-string of module cp_data, accessible by commands import EmaCalc.ema_data, help(EmaCalc.ema_data).

After running an analysis, the logging output briefly explains the analysis results presented in figures and tables.

Usage

1. Install the most recent package version: python3 -m pip install --upgrade EmaCalc

2. For an introduction to the analysis results and the xlsx input format, study, (edit,) and run the included simulation script: python3 run_sim.py

3. Copy the template script run_ema.py, rename it, and edit the copy as suggested in the template, to specify

   • your experimental layout,
   • the top input data directory,
   • a directory where all output result files will be stored.

4. Run your edited script: python3 run_my_ema.py

5. In the planning phase, complete analysis results may also be calculated for synthetic data generated from simulated experiments. Simulated experiments allow the same design variants as real experiments. Copy the template script run_sim.py, rename it, edit the copy, and run your own EMA simulation.

Requirements

This package requires Python 3.9 or newer, with recent versions of Numpy, Scipy, and Matplotlib, as well as a support package samppy, and openpyxl for reading xlsx files. The pip installer will check and install the required packages if needed.

References

A. Leijon (2021). Bayesian Analysis of Ecological Momentary Assessment (EMA) Data for Hearing Aid Evaluations. Technical Report with all math details. Contact the author for information.

F. Wolters, K. Smeds, E. Schmidt, and C. Norup (2016). Common sound scenarios: A context-driven categorization of everyday sound environments for application in hearing-device research. J Amer Acad Audiol, 27(7):527–540. download

K. Smeds, F. Wolters, J. Larsson, P. Herrlin, and M. Dahlquist (2018). Ecological momentary assessments for evaluation of hearing-aid preference. J Acoust Soc Amer 143(3):1742–1742. download