The Association Between Child Abuse and Internet Addiction: A Three-Level Meta-Analysis

Abstract

Child abuse is an important factor for Internet addiction. Despite numerous researches had observed there was a positive correlation between child abuse and Internet addiction, the strength of this association differed considerably in the previous studies. This study aims to obtain reliable estimates for effect sizes and investigate the potential moderator of the association between child abuse and Internet addiction. Thirty-one studies reported the association between child abuse and Internet addiction (273 effect sizes and 55,585 participants) through a systematic literature search. Based on Preferred Reporting Items for Systematic Review and Meta-Analysis approach, a three-level model was employed to conduct a three-level meta-analysis. The current meta-analysis found that child abuse was significantly positively correlated with Internet addiction. Besides, the study found that the type of child abuse and publication year had significant moderating effects on the association between child abuse and Internet addiction. This study suggested child abuse was a risk factor for Internet addiction. Moreover, child abuse is an essential factor should be considered when strengthening interventions for individuals’ Internet addiction.

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https://doi.org/10.1177/15248380231209436
TRAUMA, VIOLENCE, & ABUSE
1 –15
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Review Manuscript
Introduction
The Internet has become an indispensable tool for people to
communicate, work, and study in the digital era. It is reported
that over 40% of the world population has access to the
Internet, and the number of active Internet users has been
over than 4 billion (Lozano-Blasco, Robres et al., 2022).
Great progress to the society as well as some problems along
with this phenomenon has been brought (Lozano-Blasco,
Latorre-Martínez et al., 2022; Lozano-Blasco, Robres et al.,
2022; Zhao et al., 2023). Internet addiction is a kind of addic-
tion caused by excessive use of the Internet (Chi et al., 2019;
Shen et al., 2021). The overuse of Internet is problematic,
excessive, and pathological, and it has become an important
new public problem (Love et al., 2022).
More and more researches indicated that child abuse is a
significant cause of Internet addiction (Hu et al., 2022; Lo
et al., 2021). According to the life history theory, childhood
experiences can affect individuals’ life strategies (Szepsenwol
et al., 2019). Individuals who have experienced child abuse
were likely to have volatile and pessimistic expectations for
the future, so they may prefer short-term gains and immediate
gratification (Wei et al., 2020). Besides, based on Bowlby’s
attachment theory (Byrne et al., 2005), individuals with expe-
rience child abuse are more likely to develop insecure
attachment, which damaging their social skills, increasing the
risk of Internet addiction (Nakhoul et al., 2020). These theo-
ries all suggest that child abuse has a potential effect on
Internet addiction.
It is worth noting that there were some differences in the
sample characteristics, publication characteristics, and
assessment and study design characteristics of previous stud-
ies examining the association between child abuse and
Internet addiction (Hou et al., 2021; Zheng, 2021). Hence,
this might have led to the relationship between the child
abuse and Internet addiction being influenced by these dif-
ferences. Meanwhile, there was a lack of systematic review
to examine the relationship between the child abuse and
Internet addiction, which hindered our understanding of the
relationship between the two. Therefore, the present study
1209436TVAXXX10.1177/15248380231209436TRAUMA, VIOLENCE, & ABUSEZhang et al.
review-article2023
1Department of Psychology, Institute of Education, China West Normal
University, Nanchong, China
2College of Preschool and Primary Education, China West Normal
University, Nanchong, China
Corresponding Author:
Qi Zhang, College of Preschool and Primary Education, China West
Normal University, No.1 Shida Road, Shunqing District, Nanchong
637002, China.
Email: zhangqikashi@163.com
The Association Between Child Abuse
and Internet Addiction: A Three-Level
Meta-Analysis
Qiongzhi Zhang1, Qi Zhang2, Guangming Ran1,
and Yishuang Liu1
Abstract
Child abuse is an important factor for Internet addiction. Despite numerous researches had observed there was a positive
correlation between child abuse and Internet addiction, the strength of this association differed considerably in the previous
studies. This study aims to obtain reliable estimates for effect sizes and investigate the potential moderator of the association
between child abuse and Internet addiction. Thirty-one studies reported the association between child abuse and Internet
addiction (273 effect sizes and 55,585 participants) through a systematic literature search. Based on Preferred Reporting Items
for Systematic Review and Meta-Analysis approach, a three-level model was employed to conduct a three-level meta-analysis.
The current meta-analysis found that child abuse was significantly positively correlated with Internet addiction. Besides, the
study found that the type of child abuse and publication year had significant moderating effects on the association between
child abuse and Internet addiction. This study suggested child abuse was a risk factor for Internet addiction. Moreover, child
abuse is an essential factor should be considered when strengthening interventions for individuals’ Internet addiction.
Keywords
child abuse, Internet addiction, overall relation, moderator variables, three-level meta-analysis

2 TRAUMA, VIOLENCE, & ABUSE 00(0)
employed a three-level meta-analysis to quantitatively sum-
marize the existing empirical literature, so as to help people
understand the true impact of child abuse on Internet addic-
tion and to strengthen the intervention of individuals’ Internet
addiction.
The Association Between Child Abuse and
Internet Addiction
Internet addiction is generally defined as a kind of problem-
atic Internet use behavior, referring to individual’s excessive
use of the Internet, resulting in neurological damage, psy-
chological distress, and reducing socialization in daily life
(Servidio et al., 2021). Although it’s controversial whether
Internet addiction constitutes a behavioral addiction because
it was not explicitly acknowledged in the DSM-5, mounting
scholars believe that it is an important addiction disorder
(Černja et al., 2019; Su et al., 2020). With the increasing pen-
etration rate of the Internet, the harm of Internet addiction is
becoming more and more serious (Yang et al., 2023; Zhou
et al., 2022).
An increasing amount studies suggested that there were
numerous negative effects, such as poor academic perfor-
mance, troubles in interpersonal relationship, decline physi-
cal and mental health (Hao et al., 2022; Zhou et al., 2022).
Moreover, studies have shown that Internet addiction has a
significant impact on the three stages of suicidal behavior
(Chi et al., 2020; Huang et al., 2020; Peng et al., 2021a,
2021b).
It is necessary to study the risk factors of Internet addic-
tion due to the diverse and serious harm. It is worth noting
that child abuse may be a risk factor for Internet addiction
(Qin et al., 2022; Sheng et al., 2022). Child abuse, an impor-
tant issue in the worldwide, refers to any form of psychologi-
cal/or physical abuse, sexual abuse, neglect or negligent
handling, or commercial or other exploitation, leading to
actual or potential harm to the health survival, development
or dignity of the child in the situation of a relationship of
responsibility, trust, or power (Alazri & Hanna, 2020 Gubbels
et al., 2021; Zhang, Ma et al. 2022). There are four categories
of child abuse: psychological abuse (also known as emo-
tional abuse), sexual abuse, physical abuse, and neglect
(Cooley & Jackson, 2022; Wu, Cao et al., 2022; Wu, Liu
et al., 2022). The definitions of different types of child abuse
were depicted in Table 1.
A growing number of studies have explored the relation-
ship between child abuse and Internet addiction (Peng et al.,
2021a, 2021b; Yang et al., 2022). To be more specific, indi-
viduals who have experienced child abuse are more likely to
develop Internet addiction. According to the compensatory
satisfaction theory, Internet addiction is the result of the lack
of realistic needs compensated in the Internet (Liu et al.,
2016, 2019). Individuals who have been abused in childhood
cannot get the attention of their parents or other caregivers,
and their psychological needs failed be met in reality; hence,
the Internet with high attraction and high feedback is easy to
get them addicted (Lin et al., 2021).
Many studies have confirmed that child abuse is signifi-
cantly positively correlated with Internet addiction (Qin
et al., 2022; Sheng et al., 2022). However, some studies
failed to find such association (Hou et al., 2021). Therefore,
there is a need a meta-analysis and systematic review to clar-
ify this uncertainty. Integrating the diverse literature on the
relationship between child abuse and Internet addiction is
necessary. So far, there is no meta-analysis about the associa-
tion between child abuse and Internet addiction. However,
our research will address this issue.
Impact of Moderator Variables
Some studies have shown that the relationship between child
abuse and Internet addiction differs significantly between
individuals of different genders (Dong et al., 2021; Yates
et al., 2012). More specifically, after experiencing child
abuse, males have a higher risk of Internet addiction than
females. This could be explained by the normative male
alexithymia hypothesis, boys may be limited in their emo-
tional expression due to some socialization processes in
childhood, so they may suppress their emotions after abuse
and turn their attention to the Internet, which leads to Internet
addiction (Chung & Chen, 2020). Therefore, the strength of
the relationship between child abuse and Internet addiction
may influenced by gender.
The association between child abuse and Internet addic-
tion might also be moderated by participants’ age. According
to viewpoints of the perspective of lifelong psychological
development, the impact of negative experiences on individ-
uals may change over time (Ran, Zhang et al. 2022). Besides,
from the perspective of developmental pathology, older indi-
viduals are more mature (Ran, Li et al., 2022; Ran, Zhang
et al. 2022; Zhang et al., 2020). These indicated that the
Table 1. Definitions of Different Types of Child Abuse.
Variables Definitions
(a) Psychological
abuse
Caregivers repeatedly convey to children
that they are flawed, unwanted,
unloved, and worthless (Wu, Cao etal.,
2022; Wu, Liu etal., 2022).
(b) Sexual abuse An attempted or completed sexual act or
sexual exploitation of a child (Scoglio
etal., 2021).
(c) Physical abuse Interactions that are within the
reasonable control of parental
authority, responsibility, or trust that
cause actual or potential harm to the
body (Sarkar etal., 2020).
(d) Neglect Parents fail to provide shelter, clothing,
nutrition, safety, health, protection,
education or care for their children
(Gülırmak & Orak, 2021).

Zhang et al. 3
relationship between child abuse and Internet addiction may
differ among individuals of different ages. Since there is no
meta-analysis examining the effect of age on the relationship
between child abuse and Internet addiction, the current study
seeks to make it clear.
As mentioned above, there were four categories of child
abuse: psychological abuse, sexual abuse, physical abuse and
neglect (Karsberg et al., 2019; Wu, Cao et al., 2022; Wu, Liu
et al., 2022). Existing researches indicated that the type of
child abuse may be a significant moderating factor for the
relationship between child abuse and Internet addition (Dong
et al., 2021; Hu et al., 2022; Yang et al., 2022). Different types
of child abuse affect individuals differently (Chen, 2022).
Psychological abuse is more likely to cause individual psy-
chological distress, whereas physical abuse may cause more
physical symptoms in individuals, sexual abuse may put vic-
tims at increased risk of sexual victimization, and neglect
may lead to more mental illness in individuals (Collins et al.,
2023; Fix et al., 2021; Kim et al., 2023; Stickley et al., 2020;
Tjoelker et al., 2022). Psychological abuse may be associated
with higher levels of Internet addiction than neglect (Dong
et al., 2010; Yang et al., 2017). Additionally, compared with
individuals who experienced physical abuse, those who expe-
rienced psychological abuse are more likely to become
addicted to the Internet (Dong et al., 2021).
The Current Study
So far, there are still inconsistent results in the empirical
researches investigating the association between child abuse
and Internet addiction (Hou et al., 2021; Hu et al., 2022).
Meanwhile, synthetized analysis of the relationship between
the two variables is not available. Hence, to help people bet-
ter understand the relationship between child abuse and
Internet addiction, the first aim of present meta-analysis was
to quantitatively summary of empirical researches on the
relationship between the child abuse and Internet addiction
and estimate the overall relationship between the two vari-
ables (Research Question 1). Our second purpose is to test
the possible moderator variables which may influence the
relationship between child abuse and Internet addiction
(Research Question 2). Based on existing theoretical frame-
work and previous research results, it can be assumed that
the design characteristics of samples, assessment and
research design characteristics affect the strength of the asso-
ciation between child abuse and Internet addiction (e.g.,
Dong et al., 2021; Yang et al., 2022).
Method
The current meta-analysis was conducted according to
Preferred Reporting Items for Systematic Review and Meta-
Analysis checklist (Li, Ran et al., 2019; Moher et al., 2015;
Ran, Li et al., 2022; Ran, Zhang et al. 2022). Besides, for the
purpose of increasing transparencies and preventing unpre-
meditated duplication of effort, we preregistered the proto-
col of the meta-analysis at the International Prospective
Register for Systematic Reviews; registration number:
CRD42023391611; URL: https://www.crd.york.ac.uk/pros-
pero/) (Lakens et al., 2016).
Data Sources and Study Selection
Relevant studies were retrieved in the following databases in
January 2023, including Web of Science, the PsycINFO, Web
Online Library, ScienceDirect, Google Scholar, and China
National Knowledge Infrastructure. The present literature
search was performed independently by two authors (Q.Z.
and G.R.). Additionally, the authors were in consultation with
two peer researchers to develop the database search strategy.
We made inquiry of all terms about our topic in the dic-
tionaries. Then, keywords of high frequency in the multiple
databases were extracted and the keywords of low frequency
were removed. Subsequently, the two authors (Q.Z. and G.R.)
evaluated the remaining keywords independently in accor-
dance with their relevance to our topic. All the discrepancies
were solved by the discussion between the raters. Finally, the
selected keywords were evaluated by a peer expert. The final
search string was comprised of two elements: (a) child abuse
and (b) Internet addiction. As depicted in Table 2, for the ele-
ment of child abuse, the search elements of child abuse were
as follows: “child abuse” OR “child maltreatment” OR “child
mistreatment” OR “childhood abuse” OR “childhood mal-
treatment” OR “childhood mistreatment” OR “children
abuse” OR “children maltreatment” OR “children mistreat-
ment.” For the element of Internet addiction, these keywords
were used: “internet addiction” OR “problematic internet
use” OR “pathological internet use” OR “internet overuse”
OR “internet use disorder” OR “internet dependence.”
Relevant studies contained at least one key word in the title,
abstract, and key words of the databases to obtain the primary
studies. Furthermore, in order to search as much literature as
possible, some supplementary searches (e.g., manual search
of related journals, forward searching, and backward search-
ing) were conducted.
Table 2. Key Words of Two Search Elements.
Search Elements
(a) Child abuse:“child abuse” OR “child maltreatment” OR
“child mistreatment” OR “childhood abuse” OR “childhood
maltreatment” OR “childhood mistreatment” OR “children
abuse” OR “children maltreatment” OR “children
mistreatment”
(b) Internet addiction:“internet addiction” OR “problematic
internet use” OR “pathological internet use” OR “internet
overuse” OR “internet use disorder” OR “internet
dependence”

4 TRAUMA, VIOLENCE, & ABUSE 00(0)
Table 3. Characteristics of the 31 Studies Included in the Meta-Analysis.
First Author (year) K N Gender Age Culture CA Scale IA Scale Variable of CA
Informant
of CA
Publication
Status
Arslan (2017) 1 401 0.42 A W Other IAT Psy S Published
Cao et al. (2021) 1 532 0.42 M E CTQ AIPUS Ot S Published
Dalbudak et al. (2014) 6 271 0.41 A W CTQ CIAS Psy/Phy/S/N/Ot S Published
Dong etal. (2010) 72 1,193 0.55 M E CPANS CIAS Psy/N/Ot S Published
Dong etal. (2021) 7 1,749 0.50 A E CTQ IAT Psy/Phy/S/N/Ot S Published
Du (2010) 2 874 0.45 M E CPANS IAT Psy/N S Unpublished
EŞKİSU (2021) 1 286 0.33 A W CTQ CIAS Ot S Published
Estévez et al. (2019) 1 182 0.00 A W CTQ Other S S Published
Guo (2010) 38 1,193 0.55 M E CPANS CIAS Psy/N S Unpublished
Hsieh etal. (2016) 2 6,233 0.50 M E Other CIAS N S Published
Hou etal. (2021) 6 2,572 0.42 A E CTQ AIPUS Psy/Phy/S/N/Ot S Published
Hu etal. (2020) 1 539 0.28 A E CTQ CIAS Ot S Published
Hu etal. (2022) 5 3,323 0.50 M E CTQ IAT Psy/Phy/S/N S Published
Liu (2016) 42 371 0.53 M E CPANS AIPUS Psy S Unpublished
Meng (2021) 42 1,117 0.48 M E CPMS AIPUS Psy S Unpublished
Peng etal. (2021a, 2021b) 2 16,130 0.52 M E Other IAT Phy/N S Published
Qin etal. (2022) 1 918 0.43 A E Other CIAS Ot S Published
Schimmenti et al. (2017) 3 358 0.43 A W Other IAT Psy/Phy/S S Published
Sheng etal. (2022) 6 844 0.51 M E CTQ IAT Psy/Phy/S/N/Ot S Published
Song (2013) 15 1,106 0.60 A E CTQ Other Psy/Phy/S/N S Unpublished
Wei (2014) 1 829 0.39 A E CTQ Other Ot S Unpublished
Wei etal. (2020) 1 1,162 0.44 A E CTQ Other Psy S Published
Xie et al. (2022) 1 854 0.33 A E CTQ CIAS Ot S Published
Yang etal. (2014) 1 3,798 0.52 M E Other CIAS Ot S Published
Yang etal. (2017) 2 388 0.30 A E CPANS IAT Psy/N S Published
Yang, Hu etal. (2021) 1 539 0.28 A E CTQ CIAS Ot S Published
Yang etal. (2022) 6 866 0.51 M E CTQ IAT Psy/Phy/S/N/Ot S Published
Yates etal. (2012) 3 1,470 0.37 A - Other IAT Ot S Published
Yue etal. (2020) 1 903 0.48 M E CTQ Other Ot S Published
Zhang et al. (2012) 1 3,798 0.51 M E Other Other Phy S Published
Zheng (2021) 1 786 0.37 A E Other Other N S Published
Note. K = number of effect sizes; N = number of participants; Gender = percentage of males; For age: A = adults; Ad = adolescents; For culture: E = Eastern;
W = Western; CB = child abuse; IA = internet addiction; For CA scale: CTQ = Childhood Trauma Questionnaire/Childhood Trauma Questionnaire-
Short Form/Chinese version of Childhood Trauma Questionnaire-28 items Short Form, CPANS = Child Psychological Abuse and Neglect Scale,
CPMS = Childhood Psychological Maltreatment Scale; For IA scale: CIAS = Chinese Internet Addiction Scale, IAT = Internet Addiction Test,
AIPUS = Adolescent Pathological Internet Use Scale; For variable of child abuse: Psy = Psychological abuse, Phy = Physical, S = Sexual abuse, N = Neglect,
Ot = Others; For informant of child abuse: S = Self-report.
Primary studies were eligible for the current meta-anal-
ysis if they: (a) investigate the association between child
abuse and Internet addiction, (b) measured child abuse
and Internet addiction with objective instruments, (c)
reported at least one correlation coefficient between child
abuse (four forms of child abuse) and Internet addiction,
(d) showed statistics that could be transformed into cor-
relation coefficients, (e) were cross-sectional or longitudi-
nal studies, and (f) were written in Chinese or English
(Ran et al., 2021). In addition, exclusion criteria were as
follows: (a) participants experienced abuse in adulthood,
(b) examined mobile phone addiction, (c) examined
Internet addiction, (d) studies were not empirical research
articles, such as case reports or review articles, and (e)
included a sample less than 30. A total of 31 eligible stud-
ies were included in the current meta-analysis (see Table
3). The flow chart of systematic search for studies was
described in Figure 1.
Coding the Studies
According to the guidelines suggested by Lipsey and Wilson
(2001), a detailed coding scheme was created for various
descriptors and characteristics of each study. Study descrip-
tors contained the basic information of primary studies,
including article title, the name of the first author, effect

Zhang et al. 5
size, and sample size. Meanwhile, study characteristics
which might moderate the relationship between child abuse
and Internet addiction were divided into three forms of mod-
erator variables: sample characteristics, publication charac-
teristics, and assessment and research design characteristics
(Ran, Li et al., 2022; Ran, Zhang et al. 2022). On account of
the coding work was done by two authors independently, it
was necessary to calculate the interrater reliability. For the
interrater agreement, intraclass correlation coefficient (ICC)
was used to calculate the continuous variables and categori-
cal variables were calculated using Cohen’s Kappa (k) (Ran
et al., 2021; Ran, Li et al., 2022; Ran, Zhang et al. 2022).
For the sample characteristics, one interrater reliability
coefficient was calculated for gender (ICC = 0.987), age
(k = 1.000), and culture (k = 1.000). For the publication char-
acteristics, an interrater reliability coefficient was acquired
for publication year (ICC = 1.000) and publication status
(k = 1.000). For the assessment and research design charac-
teristics, one interrater reliability coefficient was obtained
for study design (k = 1.000), type of child abuse (k = 1.000),
measurement for child abuse (k = .995), and measurement
for aggressive behavior (k = .990). In summary, the results
showed a good interrater reliability, indicating a good agree-
ment in the characteristics of studies between the two
authors. The coding manual was available upon request
(Milner et al., 2022).
Figure 1. The Preferred Reporting Items for Systematic Review and Meta-Analysis flow chart used to identify studies for detailed
analysis of child abuse and Internet addiction.

6 TRAUMA, VIOLENCE, & ABUSE 00(0)
Sample Characteristics
Three kinds of sample characteristics were included in the
current meta-analysis. First, we coded gender on the basis of
the percentage of men included in the samples. Second, par-
ticipants’ age was coded as two categories: minors (0–
18 years) and adults (over 18 years). It was noted that the
participants in a sample of college students were coded as
adults, whereas the primary school participants, junior and
senior high school participants were classified as minors.
Third, culture was coded as either Eastern or Western, based
on the culture in participants’ country.
Publication Characteristics
Publication characteristics consisted of publication year and
publication status. We coded publication year as a continuous
variable. In addition, publication status was coded as a two-
category variable (published and unpublished). The published
status referred to publish on one academic journal, whereas
the unpublished status included master-doctoral dissertation
and conference abstracts. Besides, some attempts also were
made to contact authors of some articles for gray literatures.
Assessment and Research Design Characteristics
Research design was coded as two categories (cross-sectional
and longitudinal study). Besides, the current study coded the
variable of child abuse was created as a five-category variable
(Psychological abuse vs. Sexual abuse vs. Physical abuse vs.
Neglect behavior vs. Other). In addition, in the empirical lit-
erature, child abuse was assessed by the Childhood Trauma
Questionnaire (CTQ), Child Psychological Abuse and Neglect
Scale (CPANS), and Childhood Psychological Maltreatment
Scale (CPMS) (Deng et al., 2007; Pan et al., 2010; Zhao et al.,
2005). Moreover, there were some measurements of Internet
addition: Chinese Internet Addiction Scale (CIAS), Internet
Addiction Test (IAT), and Adolescent Pathological Internet
Use Scale (AIPUS) (Chen et al., 2003; Lei & Yang, 2007;
Young, 2007). Hence, we created four measurement catego-
ries of child abuse (CTQ vs. CPANS vs. CPMS vs. Other) and
Internet addiction (CIAS vs. IAT vs. AIPUS vs. Other).
Supplementary Information
The present meta-analysis included correlation coefficients
for each sample when studies contained more than one sam-
ple. In addition, the correlation coefficients in a female and
male sample were coded separately. Furthermore, correlation
coefficients for each dimension of child abuse and Internet
addiction were coded. Finally, coding errors were inevitable
because the data of the meta-analysis containing data from
multiple sources. To solve this issue, the coding work of each
preliminary study was done independently by two authors
(Q.Z. and G.R.).
Quality Assessment
In order to minimize the potential bias of primary studies
included in the meta-analysis, the meta-analysis literature
quality rating scale suggested by Zhang et al. (2019) was
employed in this study. The quality was assessed indepen-
dently by the two authors (Q.Z. and G.R.), and all differences
were agreed upon via discussion. As assessed by the two
authors, the quality of the articles included in this meta-anal-
ysis was good and hardly had risk for the current study.
Data Analysis
Correlation coefficients (rs) were selected as the effect size
index in our meta-analysis given that they were widely used
and easily interpreted (Ran et al., 2021). All correlation coef-
ficients (rs) were first transformed to Fisher’s z to obtain the
variance stabilized correlation coefficients (Card, 2012;
Hedges & Olkin, 1985), and then converted back to r after
performing meta-analysis for interpretability (Ran, Li et al.,
2022; Ran, Zhang et al. 2022). Traditional univariate meta-
analysis assumed that all effect sizes should be independent
of each other, which suggested that each primary study can
include an effect size (Assink & Wibbelink, 2016; Lipsey &
Wilson, 2001). When multiple effect sizes were extracted
from the same study, the results of a univariate meta-analysis
were exaggerated (Assink et al., 2015). Nevertheless, the
multiple-level meta-analysis could include multiple effect
sizes that extracted from the same study because the inde-
pendence was considered (Konstantopoulos, 2011). As pre-
viously mentioned, multiple effect sizes were extracted in
the present meta-analysis. Therefore, the three-level random
effects model was used for to examine the overall relation-
ship between child abuse and Internet addiction and conduct
moderator analyses in this study.
Three forms of sources for variance were contained in
the model of three-level meta-analysis: the sampling
variance in effect sizes (Level 1), the variance between
effect sizes that extracted from the same sample (Level
2), and the variance between studies (Level 3) (Gao
et al., 2019). Compared with the univariate meta-analy-
sis, the three-level meta-analysis was capable for realiz-
ing the maximum statistical power (Assink et al., 2015).
There were three steps in the data analysis of the meta-
analysis. First, the observation of magnitude and direc-
tion of the association between child abuse and Internet
addiction was achieved by estimating the overall mean
effect size. Second, the log-likelihood-ratio test was
applied to determine heterogeneity at levels 2 or 3
(Assink & Wibbelink, 2016). Finally, if there was sig-
nificant heterogeneity in effect sizes within or between
studies, we continued to conduct moderated analyses for
sample, publication, and assessment and research design
characteristics. R version 3.5.3 was employed to carry
out statistical analyses of the present meta-analysis,

Zhang et al. 7
according to the model guidelines of multiple-level ran-
dom-effect that was formulated by the tutorial with the
metafor package (Assink & Wibbelink, 2016).
Publication bias referred to the included articles failed to
represent the research filed some studies were unpublished or
available (Gao et al., 2017). In order to minimize the impact
of publication bias on our meta-analysis, the current study
included as many eligible articles as possible. To assess this
issue, symmetry of a funnel plot was inspected qualitatively
(Duval & Tweedie, 2000). For the funnel plot, a symmetrical
funnel shape might suggest that the publication bias could be
ignored (Li, Dai et al., 2019; Li, Ran et al., 2019). Besides, we
conducted Egger’s test to quantitatively detect the symmetry
of the funnel plot (Egger et al., 1997). For the Egger’s test, if
the linear regression was not significant (p > .05), publication
bias could be assumed to be absent (Duval & Tweedie, 2000).
Furthermore, the statistic Rosenthal’s Fail-safe N was
employed to test the number of missing studies (Rosenthal,
1979). For the statistic Rosenthal’s Fail-safe N, the number of
missing studies was larger than Rosenthal’s criterion (5k +
10, k is the number of effect sizes included), indicating that
we could ignore publication bias.
Results
Study Characteristics
As shown in Table 3, the present meta-analysis retrieved 273
effect sizes from 31 studies, with 55,585 participants. The
size of sample ranged from 182 to 16,130, and the mean
average age of participants was 14.33 years. The maximal
number of effect size in a sample was 72, and the minimal
one was 1. In these included studies, the publication year
ranged from 2010 to 2022. There were continuous and cate-
gorical moderator variables. For the continuous variables,
the numbers of effect sizes for gender were 273 and for pub-
lication year was 273. For the categorical moderator vari-
ables, the numbers of effect size were as follows: age
(minors: 221; adults: 52), culture (Eastern: 258; Western:
12), publication status (published: 133; unpublished: 140),
study design (cross-sectional: 273; longitudinal: 0), type of
child abuse (psychological abuse: 146; sexual abuse: 11;
physical abuse: 13; neglect: 68; other: 35), measurement of
child abuse (CTQ: 60; CPANS: 156; CPMS: 42; other: 15),
and measurement of Internet addiction (CIAS: 124; IAT: 35;
AIPUS: 91; other: 23).
Publication Bias
As demonstrated in Figure 2, the funnel plot was symmetri-
cally distributed, indicating the publication bias could be
ignored. However, it was difficult to evaluate whether the
funnel plot was completely symmetrically distributed based
on subjective judgment. Hence, to further investigate publi-
cation bias, Eggers’ test and Rosenthal’s Fail-safe N were
employed. The results showed that the p-value in Egger’s
test was not significant (t = 1.550, p = .122); the number of
missing studies was 1,081,455, which was lager that
Rosenthal’s criterion (1,375), suggesting there was no publi-
cation bias in our meta-analysis (Li, Ran et al., 2021). Given
that shape of funnel plot, Eggers’ test, and Rosenthal’s Fail-
safe N of this study were relatively reliable. Therefore, it was
not necessary to apply the trim-and-fill technique to address
the issue of publication bias in the present study (Ran, Li
et al., 2022; Ran, Zhang et al. 2022).
Overall Relation Between Child Abuse and
Internet Addiction
A random-effect model was employed to calculate the over-
all relationship between child abuse and Internet addiction.
The results showed that there was a significant positive asso-
ciation between child abuse and Internet addiction (r = .229,
p < .001). Additionally, significant heterogeneity in both
variance within a study (Level 2) and variance between stud-
ies (Level 3) (p < .001) were observed in the log-likelihood
ratio test. Of the total effect size variance, that variance at the
sampling (Level 1), within-study (Level 2), and between-
study level (Level 3) was 6.45%, 36.67%, and 56.88%. This
distribution suggested that it was necessary to investigate the
moderating effects of characteristic variables on the overall
relation (Ran, Li et al., 2022; Ran, Zhang et al. 2022). Thus,
analyses of moderation effect were conducted to investigate
whether the magnitude of the relationship between child
abuse and Internet addiction was affected by these character-
istic variables.
Moderator Effects for the Association Between
Child Abuse and Internet Addiction
Table 4 depicted the results of moderator analysis of the rela-
tionship between child abuse and Internet addiction. We
observed the publication year was a significant moderator in
the association between child abuse and Internet addiction (F
Figure 2. Funnel plot of the association between child abuse and
internet addiction.

8
Table 4. Results of Categorical and Continuous Moderators for the Association Between Child Abuse and Internet Addiction.
#Moderator Variables #ES
Intercept/Mean z [95%
CI] 1 [95% CI] Mean r F (df1, df2)apb
Levels 2
Variance
Levels 3
Variance
(1) Sample characteristics
a. Gender 273 0.217 [0.175; 0.259]*** −0.250 [−0.578; 0.079] 0.214 F (1,271) = 2.243 .135 0.005*** 0.007***
b. Age
Minors 221 0.226 [0.171; 0.281]*** 0.222 F (1, 271) = 0.121 .728 0.005*** 0.007***
Adults 52 0.239 [0.189; 0.290]*** 0.013 [−0.061; 0.087] 0.235
c. Culture
Eastern 258 0.246 [0.206; 0.286]*** 0.241 F (1, 268) = 2.959 .087 0.005*** 0.007***
Western 12 0.155 [0.060; 0.251]** −0.091 [−0.194; 0.013] 0.154
(2) Publication characteristics
a. Publication year 273 0.218 [0.181; 0.255]*** 0.005 [0.001; 0.010]* 0.215 F (1, 271) = 5.465 .020 0.005*** 0.006***
b. Publication status
Published 133 0.239 [0.198; 0.279]*** 0.235 F (1, 271) = 0.449 .503 0.005*** 0.007***
Unpublished 140 0.206 [0.117; 0.294]*** −0.033 [−0.131; 0.064] 0.203
(3) Assessment and research design characteristics
a. Type of child abuse
Psychological abuse 146 0.257 [0.216; 0.298]*** 0.251 F (4, 268) = 5.776 <.001 0.004*** 0.006***
Sexual abuse 11 0.227 [0.170; 0.285]*** −0.030 [−0.083; 0.024] 0.223
Physical abuse 13 0.147 [0.094; 0.201]*** −0.110 [−0.160; −0.060]*** 0.146
Neglect 68 0.229 [0.188; 0.269]*** −0.028 [−0.052; −0.005]* 0.225
Other 35 0.252 [0.212; 0.292]*** −0.005 [−0.040; 0.030] 0.247
b. Measurement of child abuse
CTQ 60 0.212 [0.164; 0.261]*** 0.209 F (3, 269) = 0.738 .530 0.005*** 0.006***
CPANS 156 0.269 [0.171; 0.367]*** 0.057 [−0.053; 0.166] 0.263
CPMS 42 0.311 [0.151; 0.471]*** 0.099 [−0.069; 0.266] 0.301
Other 15 0.242 [0.175; 0.309]*** 0.030 [−0.053; 0.112] 0.237
c. Measurement of Internet addiction
CIAS 124 0.211 [0.157; 0.266]*** 0.208 F (3, 269) = 0.667 .573 0.005*** 0.008***
IAT 35 0.260 [0.199; 0.321]*** 0.048 [−0.022; 0.119] 0.254
AIPUS 91 0.223 [0.165; 0.281]*** 0.011 [−0.023; 0.045] 0.219
Other 23 0.233 [0.154; 0.313]*** 0.022 [−0.074; 0.119] 0.229
Note. #ES = number of effect sizes; mean z = mean effect size (Fisher's z); 95% CI = 95% confidence interval; 1 = estimated regression coefficient; r = mean effect size expressed as a Pearson’s correlation;
df = degrees of freedom; Levels 2 variance = variance between effect sizes extracted from the same study; Levels 3 variance = variance between studies; For child abuse scale: CTQ = Childhood Trauma
Questionnaire/Childhood Trauma Questionnaire-Short Form/Chinese version of Childhood Trauma Questionnaire-28 items Short Form, CPANS = Child Psychological Abuse and Neglect Scale,
CPMS = Childhood Psychological Maltreatment Scale; For internet addiction scale: CIAS = Chinese Internet Addiction Scale, IAT = Internet Addiction Test, AIPUS = Adolescent Pathological Internet Use
Scale.
aOmnibus test of all regression coefficients in the model.
bp-value of the omnibus test.
*p < .05. **p < .01. ***p < .001.

Zhang et al. 9
[1, 271] = 5.465, p = .020), the association between child
abuse and Internet addiction is stronger in the more recently
published literature. Moreover, the significant moderator
effect of type of abuse was found (F [4, 268] = 5.776,
p < .001). More specifically, the relationship between child
abuse and Internet addiction was stronger when children
experienced psychological abuse (r = .251, p < .001) than
those that encountered physical abuse (r = 0.146, p < .001).
We failed to observe the significant moderator effects for
participants’ gender (F [1, 271] = 2.243, p = .135), age (F [1,
271] = 0.121, p = .728), culture (F [1, 268] = 2.959, p = .087),
publication status (F [1, 271] = 0.449, p = .503), measurement
of child abuse (F [3, 269] = 0.738, p = .530), and measure-
ment of Internet addiction (F [3, 269] = 0.667, p = .573).
Discussion
Internet addiction has become a major issue worldwide,
which has a considerable negative effect on the physical and
mental health of individuals (Shan et al., 2021; Zhou et al.,
2022). Moreover, child abuse has brought a lot of adverse
effects on individual physical and mental health and subse-
quent development (Rodriguez et al., 2021). Previous studies
have shown that child abuse is related to Internet addiction
(Peng et al., 2021a, 2021b; Qin et al., 2022; Sheng et al.,
2022). However, meta-analysis conducting to synthesize
their findings was not available. Thus, the three-level meta-
analysis was employed to quantitatively summarize the
results of preliminary empirical studies to synthesize empiri-
cal evidence to better understand the relationship between
the two variables.
The results of the present meta-analysis showed a signifi-
cant positive correlation between child abuse and Internet
addiction, indicating that individuals who experienced child
abuse were more likely to become addicted to the Internet.
Moreover, the strength of the relationship was affected by
type of child abuse and publication year, suggesting that
these moderator variables played vital roles in the associa-
tion between child abuse and Internet addiction. The results
of our study may help to clarify the true effect of child abuse
on Internet addiction and improve the interventions for
Internet addiction.
Overall Association Between Child Abuse and
Internet Addiction
The results of present meta-analysis verified a significant
positive correlation between child abuse and Internet addic-
tion, suggesting that the individuals who experienced abuse
in early childhood were at a higher risk of Internet addic-
tion. The theory of social compensation provided a new per-
spective for understanding the relationship between child
abuse and Internet addiction (Mýlek et al., 2020). Individuals
who experienced child abuse did not like to express them-
selves in front of others, and the anonymous and hidden
Internet environment provides them with an ideal platform
for self-disclosure, and it finally led to Internet addiction
(Yue et al., 2020). As we mentioned above, both theories
and empirical studies suggested a significant positive cor-
relation between child abuse and Internet addiction (Qin
et al., 2022; Sheng et al., 2022; Szepsenwol et al., 2019; Wei
et al., 2020). Our study also obtained reliable effect, which
improve understanding of the relationship between the two.
As an important risk factor, child abuse could be considered
while intervening Internet addiction.
Explaining Heterogeneity with Moderators
The moderating effect of type of child abuse for the associa-
tion between child abuse and Internet addiction was observed.
More specifically, individuals who suffered psychological
abuse were more likely to be addicted Internet compared to
those suffered physical abuse. This difference in the strength
of the association was indicated in two previous empirical
studies (Hu et al., 2022; Schimmenti et al., 2017). Different
forms of child abuse influence individuals differently due to
the different effects on specific brain regions and circuits,
psychological abuse is associated with persistent errors and
difficulties (Montoya-Arenas et al., 2022). Individuals who
experienced psychological abuse had more difficulties in
building good interpersonal relationships than those who
experienced physical abuse (Yoon, 2020; Yoon et al., 2021).
As a result, they were unable to obtain effective interpersonal
support in real life and were more inclined to get satisfaction
through Internet, increasing the risk of Internet addiction (Li
et al., 2020). Furthermore, according to Young’s schema the-
ory, psychological abuse increased negative emotion regula-
tion and coping strategies, individuals were more likely to
lead to individuals immersed in the Internet (Zhu et al., 2022).
Moreover, the results of this study demonstrated publica-
tion year was a significant moderate factor. The included
empirical studies were published between 2010 and 2022.
The association between child abuse and Internet addiction
was stronger in studies published later than those published
earlier. In recent years, as we noticed, with the rapid develop-
ment of digital technology, the Internet penetration has
increased sharply, indicating people were more dependent on
the Internet in their lives (Chi et al., 2020; Xu et al., 2021).
This gives victims of child abuse a new platform to escape
their trauma and reveal themselves. Additionally, according
to The Interaction of Person-Affect-Cognition-Execution
conceptualization of affective and cognitive responses, indi-
vidual’s dependence on the Internet may correlate with the
environment (Servidio et al., 2021). Individuals decreased in
face-to-face social interactions as the result of the pandemic
and increasingly rely on the Internet for social activities,
which has also led to an increase in the number of Internet
addiction (Siste et al., 2020; Tahir et al., 2021).
This study failed to find the significant moderate effect of
gender. Despite there were researches suggesting that males

10 TRAUMA, VIOLENCE, & ABUSE 00(0)
may be more prone to the Internet addiction than females,
some studies suggested that females and males get addicted
to the Internet in different ways (e.g., females were might
prefer social network, whereas males might be addicted to
gaming applications) (Li, Dai et al., 2019; Li, Ran et al.,
2019; Ran, Li et al., 2022; Ran, Zhang et al. 2022). However,
this difference does not translate to gender difference in
Internet addiction. Besides, participants’ age failed to moder-
ate the association between child abuse and Internet addic-
tion, indicating this association was stable across age. This
may as a result of there was no difference in Internet addic-
tion among participants of different ages (Yang et al., 2014).
Furthermore, publish status, culture, measurement of child
abuse, and measurement of Internet addiction did not moder-
ate the association between child abuse and Internet addic-
tion, suggesting that the relationship between child abuse
and Internet addiction was relatively stable in these aspects.
These results suggested that we should pay attention to
the influence of child abuse, especially psychological abuse,
when preventing and intervening in Internet addiction. It
should be noted that the negative consequences of child
abuse and Internet addiction are mounting, what we should
be doing is working together to reduce the prevalence of
them and intervening with the victims.
Limitations and Future Directions
What was clear was that the present study had some limitations
and future directions. One limitation of this study was that the
empirical studies included in this meta-analysis had failed to
investigate the age of individuals experienced child abuse.
Giving that the age of individuals experienced child abuse may
have an important impact on an individual’s physical and men-
tal development (Cho & Braaten, 2022); hence, we strongly
recommend that future studies focus on the age at which chil-
dren begin to be abused, this will not only help to better under-
stand the relationship between child abuse and Internet addiction
but also contribute to the impact of child abuse on individuals.
In addition, since our meta-analysis had failed to include
longitudinal studies of child abuse and Internet addiction,
the developmental trajectory of the relationship between
child abuse and Internet addiction is not examined.
Therefore, future researches could apply longitudinal tech-
niques to better understand the relationship between child
abuse and Internet addiction.
Moreover, as the moderating variables in our research
were selected according to the variables reported in each
article, we have failed to investigate these variables that
may have a moderating effect on the relationship between
the child abuse and Internet addiction but were not reported
or rarely reported in the empirical articles. Therefore, we
strongly recommend future researches to pay attention to
these variables that may mediate the relationship between
child abuse and Internet addiction (such as physical activ-
ity, self-worth, and coping style).
Finally, on account of scholars have not reached an
agreement on the classification of Internet addiction, the
studies which investigated child abuse and other types of
Internet addiction were not included in this study. Hence,
we suggest that future studies pay more attention to the
various types of Internet addiction and come up with spe-
cific categories.
Conclusion
In this study, a three-level meta-analysis was used to
quantitatively synthesize the overall relationship between
child abuse and Internet addiction. The results showed
that there was a significant positive correlation between
child abuse and Internet addiction, suggesting that child
abuse was a significant factor for Internet addiction.
Subsequently, our moderating effect analysis found that
the type of child abuse and publication year had a signifi-
cant moderating effect on the relationship between child
abuse and Internet addiction. Our study had important
implications for understanding the role of child abuse in
Internet addiction and strengthening interventions for
individuals’ Internet addiction.
Summary of Critical Findings
 A total of 31 studies with 55,585 participants were
included.
 Child abuse was significantly correlated with Internet
addiction (r = .229).
 The association between child abuse and Internet
addiction was stronger for the participants who expe-
rienced psychological abuse than those who had expe-
rienced physical abuse.
 In all studies of child abuse and Internet addiction, the
association between the two was weaker in the earlier
studies than in the later ones.
Summary of Implications for Practice,
Research, and Policy
 Child abuse may be an important target of interven-
tions aimed at preventing or reducing Internet
addiction.
 Interventions targeting child abuse and Internet addic-
tion are equally important for boys and girls of all
ages.
 Interventions targeting individuals who with experi-
ence of psychological abuse may be more effective.
 A multi-stage longitudinal design with adequate fol-
low-up evaluation could be conducted in future
studies.
 Future research on child abuse and Internet addiction
can be used in a variety of reporting forms, such as
parent reports, teacher reports, and peer reports.

Zhang et al. 11
Author Contributions
Qz.Z. and Q.Z. conceived of the study, collected and analyzed the
data; Qz.Z. drafted the article; G.R. and Q.Z. revised the article;
Y.L. helped to collect and analyze the data. All authors read and
approved the final manuscript.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The authors received no financial support for the research, author-
ship, and/or publication of this article.
ORCID iDs
Qiongzhi Zhang https://orcid.org/0000-0002-3422-6396
Qi Zhang https://orcid.org/0009-0000-8728-7347
Supplemental Material
Supplemental material for this article is available online.
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Author Biographies
Qiongzhi Zhang, BS, is a graduate student at the Department of
Psychology, Institute of Education, China West Normal University,
China. Her major interests focus on child abuse, mental health edu-
cation, and problem behaviors of individuals.
Qi Zhang, MSc, is a lecture at the College of Preschool and Primary
Education, China West Normal University, China. Her research is
focused on children’s emotion, parental psychological control, chil-
dren anxiety, and problem behaviors of children.
Guangming Ran, PhD, is an assistant professor at the Department
of Psychology, Institute of Education, China West Normal
University, China. He is interested in child development and mental
health education. More specifically, his work focused on develop-
ment of children’s psychology, social anxiety, and school education
of mental health.
Yishuang Liu, BE, is a graduate student majoring in preschool at the
School of Education, China West Normal University. Her main
research interests are social anxiety and children’s problem behaviors.