Predictors of Protective Factors for Internet Game Addiction in Middle School Students using Data Mining Decision Tree Analysis

Young-Ran Kweon; Se-Young Kim

doi:10.12934/jkpmhn.2014.23.1.12

Journal List > J Korean Acad Psychiatr Ment Health Nurs > v.23(1) > 1058003

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Kweon and Kim: Predictors of Protective Factors for Internet Game Addiction in Middle School Students using Data Mining Decision Tree Analysis

Original Article

Journal of Korean Academy of Psychiatric and Mental Health Nursing 2014; 23(1): 12-20.

Published online: 31 March 2014

DOI: https://doi.org/10.12934/jkpmhn.2014.23.1.12

Predictors of Protective Factors for Internet Game Addiction in Middle School Students using Data Mining Decision Tree Analysis

Young-Ran Kweon¹, Se-Young Kim²

¹Department of Nursing, Chosun University, Gwangju, Korea.

²Department of Nursing, Sehan University, Yeongam, Korea.

Corresponding author: Kim, Se-Young. Department of Nursing, Sehan University, 1113 Noksaek-ro, Samho-eup, Yeongam 526-702, Korea. Tel: +82-61-469-1308, Fax: +82-61-469-1317, seyeong77@sehan.ac.kr

Received 19 October 2013 Revised 18 March 2014 Accepted 20 March 2014

(open-access):

This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose

This study was done to identify protective factors which predict internet game addition in middle school students using data mining decision tree analysis.

Methods

The participants were 557 (Male=233, Female=324) middle school students from G city. Data were collected using structured questionnaires from March, 25 to May, 4, 2013, and analyzed using the descriptive analysis, t-test, ANOVA, decision tree, using SPSS 20.0 program.

Results

The result of this research showed the prediction model for protective factors related to internet game addiction. Causative factors included gender, family support and father's attitude as the family protective factor, and planning ability as the personal protective factor. Level of accuracy of the decision tree was 70.6%.

Conclusion

The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to gender and planning ability in internet game addiction.

Keywords: Adolescent, Internet, Addictive behavior, Data mining, Decision trees

Figures and Tables

Figure 1

The construction of decision tree.

Table 1

Degree of the Internet Game Addiction and Protective Factors (N=557)

Table 2

Difference of Internet Game Addiction by General Characteristics (N=557)

Table 3

Misclassification Table (Prediction Rate) according to Decision Tree Analysis (N=557)

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