Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Survival Tree01:19

Survival Tree

463
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
463
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.7K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.7K
Decision Making: P-value Method01:09

Decision Making: P-value Method

7.1K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
7.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Diabetes and Risk of Radial Artery Occlusion After Distal Radial Access: Insights From the KODRA Registry.

Heart, lung & circulation·2026
Same author

Functional and Genomic Features of a Lytic Salmonella Phage vB_StyS_KFSST1 for Development as New Feed Additive.

Food science of animal resources·2026
Same author

Contrast-Enhanced Ultrasonography of Cholecystohepatic Communication Secondary to Gallbladder Rupture in a Dog: A Case Report.

Veterinary sciences·2026
Same author

Large Language Model-Based Simplification of Digital Therapeutics Explanations for Insomnia and Nicotine Dependence: Two Randomized Online Experiments.

JMIR human factors·2026
Same author

Proposed Requirements for Clinical Trial Management Systems in Digital Therapeutics Trials.

Studies in health technology and informatics·2026
Same author

Neural and behavioral evidence of free shipping on consumer decision making.

PloS one·2026

Related Experiment Video

Updated: Mar 16, 2026

Transcranial Direct Current Stimulation for Online Gamers
06:01

Transcranial Direct Current Stimulation for Online Gamers

Published on: November 9, 2019

8.7K

Predictors and patterns of problematic Internet game use using a decision tree model.

Mi Jung Rho1,2, Jo-Eun Jeong3, Ji-Won Chun3

  • 11 Department of Medical Informatics, College of Medicine, The Catholic University of Korea , Seoul, Republic of Korea.

Journal of Behavioral Addictions
|August 9, 2016
PubMed
Summary

Problematic Internet gaming is a growing concern. Key predictors include gaming costs and time spent gaming, revealing distinct gamer patterns like cost-consuming, socializing, and solitary types.

Keywords:
chi-square automatic interaction detectordecision tree analysispatternpredictorsproblematic Internet game use

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Related Experiment Videos

Last Updated: Mar 16, 2026

Transcranial Direct Current Stimulation for Online Gamers
06:01

Transcranial Direct Current Stimulation for Online Gamers

Published on: November 9, 2019

8.7K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Area of Science:

  • Psychology
  • Sociology
  • Public Health

Background:

  • Problematic Internet game use presents significant social and economic challenges.
  • Understanding the factors contributing to excessive gaming is crucial for intervention.

Purpose of the Study:

  • To identify predictors of problematic Internet game use.
  • To explore distinct patterns of problematic Internet gaming behavior.

Main Methods:

  • Online surveys collected data from 5,003 respondents in 2014.
  • 511 problematic Internet game users were identified using DSM criteria.
  • Propensity score matching created a control group; 1,022 participants analyzed with CHAID.

Main Results:

  • Six key predictors identified: gaming costs (50%), weekday gaming time (23%), offline community attendance (13%), weekend/holiday gaming time (7%), marital status (4%), and self-perceived addiction (3%).
  • Three gamer patterns emerged: cost-consuming, socializing, and solitary.

Conclusions:

  • Findings offer insights into screening for problematic Internet game use in adults.
  • Predictors and patterns can inform targeted prevention and intervention strategies.