Related Experiment Video
Updated: May 31, 2025
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
3.3K
A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized)
Jonas Dora1, Connor J McCabe1, Caspar J van Lissa2
1Department of Psychology, University of Washington, Seattle, Washington.
Advances in Methods and Practices in Psychological Science
|January 24, 2025
Summary
This tutorial introduces Bayesian methods for analyzing ecological momentary assessment (EMA) data in psychological sciences. It demonstrates practical advantages for incorporating prior knowledge and quantifying uncertainty in effect-size estimates.
Area of Science:
- Psychological Sciences
- Statistics
- Data Analysis
Background:
- Ecological Momentary Assessment (EMA) collects real-time data.
- Traditional frequentist methods have limitations for complex EMA data.
- Bayesian statistics offers a flexible alternative for analyzing psychological data.
Purpose of the Study:
- Introduce Bayesian generalized linear mixed-effects models for EMA data analysis.
- Highlight practical and conceptual advantages of the Bayesian approach.
- Provide a reproducible workflow for EMA data analysis using Bayesian methods.
Main Methods:
- Application of Bayesian generalized linear mixed-effects models.
- Demonstration using EMA data to predict alcohol outcomes.
- Comparison of Bayesian versus frequentist approaches.
Main Results:
- Bayesian methods facilitate incorporation of prior knowledge.
- Bayesian models accommodate diverse outcome distributions.
- Quantification of effect-size uncertainty and evidence for hypotheses is enabled.
Conclusions:
- Bayesian workflow enhances EMA data analysis in psychological sciences.
- Researchers can adopt these methods for robust data interpretation.
- Reproducible examples and code are provided for practical application.
More Related Videos
Related Concept Videos
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
26
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
26
02:30Naturalistic Observations
15.4K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
15.4K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
02:01Group Design
8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K