Table 2.

Impacts of adolescent personal factors on adolescent internet addiction by logistic regression analysis^a,b

Risk Factors	Coefficient (Standard Error)	Odds Ratio	95%Confidence Interval	p-value
Gender
Female (ref.)	1.0	1.0
Male	0.26(0.12)	1.29	1.02-1.64	0.0361
Adolescent monthly money spending levels (RMB / month)
<100 (ref.)	1.0	1.0
≥300	0.41(0.16)	1.51	1.11-2.05	0.0092
100~299	0.51(0.13)	1.66	1.29-2.14	<0.0001
Academic achievements
Very good (ref.)	1.0	1.0
Very & relatively bad	1.57(0.33)	4.79	2.51-9.13	<0.0001
General	0.87(0.31)	2.38	1.29-4.41	0.0057
Relative good	0.52(0.33)	1.68	0.88-3.20	0.1186
Total hours online for a whole week (hours /month)
<7 (ref.)	1.0	1.0
>28	1.45(0.17)	4.28	3.06-5.99	<0.0001
21 ~28	1.23(0.21)	3.41	2.26-5.15	<0.0001
14 ~21	0.96(0.19)	2.61	1.81-3.77	<0.0001
7~14	0.89(0.15)	2.44	1.81-3.29	<0.0001
Main Purpose of using internet
Academic learning (ref.)	1.0	1.0
Playing game	1.94(0.34)	6.98	3.59-13.58	<.0.0001
Real-time chatting	0.97(0.36)	2.64	1.30-5.38	0.0073
Browsing news or e-mails only	0.17(0.40)	1.19	0.55-2.60	0.6625

^a This logistic regression model was fit to model the possibility of adolescent having internet addiction, internet addiction was defined as total score ≥ 163.

^b Adolescent age, gender, grade, school types, adolescent academic achievement, adolescent monthly spending levels, internet-use time, and the main purposes and places of adolescent internet use were adjusted in the models.