Related Experiment Video
Updated: Aug 18, 2025

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder
Published on: June 23, 2023
A Comparison of Mathematical and Statistical Modeling with Longitudinal Data: An Application to Ecological Momentary
Sijing Shao1, Judith E Canner2, Rebecca A Everett3
1Department of Psychology, University of Notre Dame, Notre Dame, IN, USA.
Abstract:
Ecological momentary assessment (EMA) has been broadly used to collect real-time longitudinal data in behavioral research. Several analytic methods have been applied to EMA data to understand the changes of motivation, behavior, and emotions on a daily or within-day basis. One challenge when utilizing those methods on intensive datasets in the behavioral field is to understand when and why the methods are appropriate to investigate particular research questions. In this manuscript, we compared two widely used methods (generalized estimating equations and generalized linear mixed models) in behavioral research with three other less frequently used methods (Markov models, generalized linear mixed-effects Markov models, and differential equations) in behavioral research but widely used in other fields. The purpose of this manuscript is to illustrate the application of five distinct analytic methods to one dataset of intensive longitudinal data on drinking behavior, highlighting the utility of each method.
More Related Videos
08:45Modeling Alcohol Consumption in Rodents Using Two-Bottle Choice Home Cage Drinking and Microstructural Analysis
Published on: November 8, 2024
07:31Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice
Published on: January 7, 2019
Related Concept Videos
Longitudinal Research
Mechanistic Models: Compartment Models in Individual and Population Analysis
Noncompartmental Analysis: Statistical Moment Theory
Longitudinal Studies
Regression Toward the Mean
Statistical Methods for Analyzing Epidemiological Data