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Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

Bias

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

Regression Toward the Mean

Regression Toward the Mean

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...

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Modeling Early Gambling Behavior Using Indicators from Online Lottery Gambling Tracking Data: Longitudinal Analysis.

Gaëlle Challet-Bouju^1,2, Jean-Benoit Hardouin^2,3, Elsa Thiabaud¹

¹Addictology and Psychiatry Department, Centre Hospitalier Universitaire de Nantes, Nantes, France.

Journal of Medical Internet Research

|April 8, 2020

Summary

This study identified five gambling profiles for online lottery players, with two groups showing higher risk and potentially benefiting from early intervention. Understanding these trajectories aids in developing targeted responsible gambling strategies.

Keywords:

early detection gambling gambling tracking data growth mixture modeling internet latent class analysis trajectory

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Area of Science:

Behavioral Psychology
Data Science
Public Health

Background:

Online gambling presents risks for excessive play but offers opportunities for real-time monitoring and intervention.
Early identification of gambling trajectories is crucial for developing effective support strategies.

Purpose of the Study:

To model the early gambling trajectories of individuals participating in online lottery games.
To identify distinct profiles of online lottery gamblers based on their behavior and problem indicators.

Main Methods:

Analysis of anonymized gambling records from 1152 French national lottery clients over six months.
Utilized growth mixture modeling and latent class analysis on gambling activity and problem indicators.
Validated profiles using covariates like age, gender, net losses, and responsible gambling tool classifications.

Main Results:

Identified five distinct online lottery gambling profiles.
Three profiles (56.8%, 14.8%, 13.9%) showed low-to-medium activity and low problem indicators.
Two profiles (9.7%, 4.8%) exhibited higher activity, broader game involvement, and increased risk markers, with one group using self-exclusion measures.

Conclusions:

Recreational gambling profiles were identified, alongside profiles indicating potential risk for future gambling problems.
Individuals in higher-risk profiles (Classes 4 and 5) may benefit from early preventive measures and interventions.
Modeling gambling trajectories provides insights for targeted responsible gambling initiatives.