Ki Sook Kim; Kyung Hee Kim

doi:10.4040/jkan.2010.40.3.378

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Original Article
A Prediction Model for Internet Game Addiction in Adolescents: Using a Decision Tree Analysis: Ki Sook Kim, Kyung Hee Kim; Journal of Korean Academy of Nursing 2010;40(3):378-388.
DOI: https://doi.org/10.4040/jkan.2010.40.3.378
Published online: June 30, 2010

¹Fellow, College of Nursing, Ajou University, Suwon, Korea.

²Professor, Department of Nursing, Chung-Ang University, Seoul, Korea.

Address reprint requests to: Kim, Kyung Hee. Department of Nursing, Chung-Ang University, 221 Heukseok-dong, Dongjak-gu, Seoul 156-756, Korea. Tel: 82-2-820-5670, Fax: 82-2-824-7961, kyung@cau.ac.kr

• Received: December 22, 2009 • Accepted: June 9, 2010

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Abstract
NOTES
REFERENCES

Abstract

Purpose
This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors.
Methods
The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors.
Results
From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet cafe@, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents).
Conclusion
The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.
Keywords: Addiction; Adolescent; Attitude; Decision tree; Internet

This article is based on a part of the first author's doctoral thesis from Chung-Ang University.

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Figure 1

Predictive Model for Internet Game Addiction in Adolescence.

RA=Rearing attitude; PI=Payment to Internet cafe; CT=Communication type; ERS=Expectation regarding adolescent' study; PAI=Parent's ability to use Internet.

Table 1

Internet Game Addiction According to General and Internet Game Characteristics (N=1,318)

IGA=Internet game addiction; GU=General user; PRU=Potential risk user; AU=Addicted user.

Table 2

Internet Game Addiction According to Parents related Factors (N=1,318)

IGA=Internet game addiction; GU=General user; PRU=Potential risk user; AU=Addicted user; PAI=Parent's ability to use Internet; ERS=Expectation regarding adolescent' study.

Table 3

Accuracy of the Predictive Model for Internet Game Addiction in Adolescence

GU=General user; PRU=Potential risk user; AU=Addicted user.

Figure & Data

REFERENCES

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A Prediction Model for Internet Game Addiction in Adolescents: Using a Decision Tree Analysis

J Korean Acad Nurs. 2010;40(3):378-388. Published online June 30, 2010

DOI: https://doi.org/10.4040/jkan.2010.40.3.378

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A Prediction Model for Internet Game Addiction in Adolescents: Using a Decision Tree Analysis

Figure 1 Predictive Model for Internet Game Addiction in Adolescence. RA=Rearing attitude; PI=Payment to Internet cafe; CT=Communication type; ERS=Expectation regarding adolescent' study; PAI=Parent's ability to use Internet.

Figure 1

A Prediction Model for Internet Game Addiction in Adolescents: Using a Decision Tree Analysis

Table 1

Internet Game Addiction According to General and Internet Game Characteristics (N=1,318)

IGA=Internet game addiction; GU=General user; PRU=Potential risk user; AU=Addicted user.

Table 2

Internet Game Addiction According to Parents related Factors (N=1,318)

IGA=Internet game addiction; GU=General user; PRU=Potential risk user; AU=Addicted user; PAI=Parent's ability to use Internet; ERS=Expectation regarding adolescent' study.

Table 3

Accuracy of the Predictive Model for Internet Game Addiction in Adolescence

GU=General user; PRU=Potential risk user; AU=Addicted user.

Table 1 Internet Game Addiction According to General and Internet Game Characteristics (N=1,318)

IGA=Internet game addiction; GU=General user; PRU=Potential risk user; AU=Addicted user.

Table 2 Internet Game Addiction According to Parents related Factors (N=1,318)

IGA=Internet game addiction; GU=General user; PRU=Potential risk user; AU=Addicted user; PAI=Parent's ability to use Internet; ERS=Expectation regarding adolescent' study.

Table 3 Accuracy of the Predictive Model for Internet Game Addiction in Adolescence

GU=General user; PRU=Potential risk user; AU=Addicted user.