Reducing Harm From Media: A Meta-Analysis of Parental Mediation

Abstract

This research investigated the effectiveness of parental mediation on reducing harm from media. Specifically, it examined the magnitude of the effects of three main parental mediation strategies on reducing the amount of media use and the incidence of media-related risks. A meta-analysis was conducted using 52 empirical studies on parental mediation, which represented a total sample of 74,159 participants and yielded 122 independent correlations. The results indicated that restrictive mediation was more effective than active mediation in decreasing the amount of time children spent on media, whereas the effects of active mediation and co-using were greater than those of restrictive mediation on reducing the incidence of media-related risks. According to the moderator analyses, age, risk type, medium, and culture significantly moderated the effects of active and restrictive mediation on reducing the amount of media use and the incidence of media-related risks.

Keywords

parental mediation meta-analysis media use media-related risks children adolescents

Since the advent of electronic media, particularly the Internet, media have surrounded children and adolescents and, thus, have become an essential part of their lives. Media provide children and adolescents with substantial opportunities to obtain a large amount of information and communicate with other individuals, thereby promoting their social and emotional development (Kowalski, Limber, & Agatston, 2008; Moreno & Kolb, 2012). According to socialization theory (Maccoby, 2007), parents—the primary agents of children’s socialization—play crucial roles in the process through which children acquire and develop social attitudes and behaviors. They are also responsible for supervising their children’s media use to balance the opportunities and risks that media pose (Livingstone & Helsper, 2008; Nathanson, Eveland, Park, & Paul, 2002). In communication research, scholars use the term parental mediation to describe the interactions between parents and children in regard to media use (Lee & Chae, 2012). Many scholars have defined parental mediation as strategies that parents adopt to mitigate against the negative effects of media on children and have examined the effectiveness of these strategies (e.g., Atkin, Greenberg, & Baldwin, 1991; Nathanson, 1999; Valkenburg, Krcmar, Peeters, & Marseille, 1999; Warren, 2001). However, few scholars have explored the effects of parental mediation on enhancing media benefits (e.g., Kirkorian, Wartella, & Anderson, 2008; McMillan & Morrison, 2006).

The current media-rich environment may leave young individuals vulnerable to a larger number of media-related risks than in the past. Children and adolescents have been exposed to inappropriate content, undesirable contacts, and cyberbullying, which threaten their physical and psychological health (Livingstone, 2003; Livingstone & Helsper, 2008; Wolak, Mitchell, & Finkelhor, 2007). Numerous studies have explored how parents regulate their children’s media use to protect them from media-related risks and have examined whether parents’ strategies successfully reduce these risks and decrease children’s media use (Atkin et al., 1991; Livingstone & Helsper, 2008; Lee & Chae, 2012; Lwin, Stanaland, & Miyazaki, 2008; Shin & Kang, 2016; Valkenburg et al., 1999; Warren, 2001). However, this research has yielded mixed results. For example, Nathanson (1999), Lee and Chae (2012), and Lwin et al. (2008) reported that parental restrictive mediation reduces children’s exposure to media-related risks, whereas Shin and her colleagues (Shin & Ismail, 2014; Shin & Kang, 2016) indicated that parental restrictive mediation was positively associated with online risks. Thus, the entire range of empirical evidence should be systematically analyzed to understand the effectiveness of various parental mediation strategies on reducing harm from media and to identify the potential reasons for the mixed findings.

This study aims to systematically analyze the effects of various parental mediation strategies on reducing children’s media use and media-related risks as well as to examine the magnitude of the effects of various parental mediation strategies across 54 empirical studies. Furthermore, four potential moderators—age, risk type, medium, and culture—are selected and analyzed to explain the variance between the directionality or magnitude of the effects of parental mediation strategies across studies.

Parental Mediation of Children’s Media Use

Since the proliferation of television and the Internet, children have been exposed to various media risks, including inappropriate content and undesirable contacts (Livingstone et al., 2017). Parents have engaged in various mediation strategies to support their children in becoming competent media users to maximize the opportunities and minimize the risks posed by media. Scholars have investigated how parents influence their children’s adoption of new media technologies (e.g., McMillan & Morrison, 2006) and enhance their children’s learning outcomes after they view educational programs (e.g., Kirkorian et al., 2008). However, the majority of parental mediation research has focused on how parents help their children avoid potential harm from media use. Many studies have explored how parental mediation affects the amount of time children spend on a certain medium (Desmond, Hirsch, Singer, & Singer, 1987; Kalmus, Blinka, & Ólafsson, 2015; Lee & Chae, 2012; Lin & Atkin, 1989; Vandewater, Park, Huang, & Wartella, 2005) or how different mediation strategies affect children’s exposure to various media-related risks (Livingstone & Helsper, 2008; Lee & Chae, 2012; Lwin et al., 2008; Shin & Kang, 2016; Valkenburg et al., 1999). The current meta-analysis follows this harm reduction approach to examine the effectiveness of parental mediation strategies.

The theoretical framework of parental mediation is rooted in media effects and information processing theories, as well as interpersonal communication theories, and it explains how interpersonal communication between parents and children reduces the negative effects of media on children (Clark, 2011; Shin & Ismail, 2014). Based on different types of interpersonal interactions between parents and children, three broad strategies of parental mediation have been identified in the contexts of television viewing and Internet usage, that is, active mediation, restrictive mediation, and co-using. Therefore, this meta-analysis explores the extent to which these three different parental mediation strategies influence the amount of media use and the incidence of media-related risks.

Active mediation, also referred to as instructive mediation, refers to parents’ positive engagement with their children to discuss and explain media content and to talk to or guide them regarding appropriate media use (Nathanson, 2001b; Valkenburg et al., 1999; Valkenburg, Piotrowski, Hermanns, & Leeuw, 2013). Parent–child discussions on television content have been shown to help children cultivate a discriminating and critical stance toward media (Nathanson, 1999; Singer & Singer, 1986), which enhances the content that the children learn from media (Desmond, Singer, Singer, Calam, & Colimore, 1985; Messaris & Sarett, 1981), changes their attitudes toward media pornography and violence (Corder-Bolz, & O’Bryant, 1978; Rasmussen, Ortiz, & White, 2015), and lowers the levels of television-induced aggression (Nathanson, 1999). In more recent years, an increasing number of studies have been conducted to examine how parental mediation influences children’s Internet use. These studies have indicated that active mediation is effective in decreasing the length of Internet use (Sasson & Mesch, 2014) and the likelihood of being engaged in online risks, such as contact risks (Shin & Ismail, 2014), privacy disclosure (Lwin et al., 2008), and cyberbullying (Chang et al., 2015). According to self-determination theory (Deci & Ryan, 1985), when parental mediation is active, children typically feel that their parents respect their perspectives and support their autonomy, which increases their sense of self-determination and fosters moral internalization. As a result, children tend to obey parental guidance and engage less in antisocial behavior (Grusec & Davidov, 2007; Valkenburg, Piotrowski, Hermanns, & de Leeuw, 2013). Based on these considerations, active mediation is negatively associated with both the amount of media use and the incidence of media-related risks. Restrictive mediation, also referred to as rule-making, refers to parental efforts to set rules to limit the time that their children are allowed to engage with media as well as restrict the content that their children are exposed to (Atkin et al., 1991; Lwin et al., 2008; Nathanson, 2001b; Valkenburg et al., 2013). For example, to regulate their children’s Internet use, many parents have used parental filters or monitoring software to check and restrict the online activities and content that their children are allowed to engage with (Lee & Chae, 2012; Mesch, 2009). Parental restrictions have been reported to decrease the time that children spend with media (Kalmus et al., 2015; Lee & Chae, 2012; Lin & Atkin, 1989; Marcum, Higgins, & Ricketts, 2010; Wolak, Finkelhor, & Mitchell, 2008). Many studies have also suggested that restrictive mediation is effective in buffering against negative media influences on children (Lee & Chae, 2012; Livingstone & Helsper, 2008; Marcum et al., 2010; J. N. Navarro & Jasinski, 2012; Wolak et al., 2008). For example, Livingstone and Helsper (2008) reported that restricting online interactions could lower the likelihood of children experiencing content and contact risks online. Marcum et al. (2010) and Wolak et al. (2008) suggested that parental restrictions were negatively associated with children’s exposure to sexual solicitations. J. N. Navarro and Jasinski (2012) determined that children of parents who used filtering software were less likely to be victims of cyberbullying. However, several studies have demonstrated that overly restrictive parental controls may boomerang. Research by Nathanson (2002) indicated that restrictive mediation was related to more positive attitudes toward television violence and sex as well as the viewing of such content. Shin and Ismail (2014) indicated that restrictive mediation was positively associated with risky social media behavior among children. Therefore, this study examines the extent to which restrictive mediation is associated with the length of children’s media use and their exposure to media-related risks.

Co-using, also referred to as co-viewing or co-surfing, refers to parents engaging in media together with their children, without actively engaging in discussions (Nathanson, 2001b; Valkenburg et al., 2013). Valkenburg et al. (1999) and Warren (2003) indicated that parents engaged in less co-using than active and restrictive mediation. Research on the effectiveness of co-using has produced mixed results (Nathanson, 2001b). Studies have demonstrated that co-using may enhance children’s learning from media (Dorr, Kovaric, & Doubleday, 1989) and reduce children’s exposure to online content risks (Kirwil, 2009). However, co-using has also been positively associated with the amount of television viewing (Vandewater et al., 2005) and levels of television-induced aggressive inclinations (Nathanson, 1999). Thus, this meta-analysis explores the degree to which co-using is associated with the time that children spend with media and the likelihood of children being engaged in media-related risks.

According to the previously discussed literature review, the following three research questions are proposed:

RQ1: To what degree does active mediation affect (a) the amount of media use and (b) the incidence of media-related risks?

RQ2: To what degree does restrictive mediation affect (a) the amount of media use and (b) the incidence of media-related risks?

RQ3: To what degree does co-using affect (a) the amount of media use and (b) the incidence of media-related risks?

Method

Literature Search

To retrieve relevant studies, the following key words were used: parental mediation, active mediation, instructive mediation, evaluative mediation, parental guidance, parental instructions, restrictive mediation, regulated mediation, regulate mediation, parental restrictions, parental limits, parental control, co-using, co-surfing, co-viewing, co-playing, parental presence, shared television viewing, and shared media. We searched the following databases: PsycINFO, MEDLINE, Communication & Mass Media Complete, EBSCO, Educational Resources Information Center (ERIC), Web of Science Direct, PubMed, and Nursing & Allied Health Source. We searched all studies published before January 2017. Furthermore, the reference lists of the collected publications were reviewed for additional studies. Non-peer-reviewed papers, such as book chapters and conference papers, were also reviewed to reduce the potential influence of publication bias (Sohn, 1996).

Filter Process

Studies were included in this meta-analysis based on the following inclusion and exclusion criteria: (a) The article should focus on the effects of parental mediation strategies on the media consumption behaviors of children. Research on the antecedents of parental mediation was excluded. (b) The quantitative information for meta-analytical purposes that could be computed or converted into effect size estimates must be available in the selected studies. Essential data include sample sizes, test statistics, means, correlations, and odds ratios. To include more studies in this meta-analysis, emails were sent to some authors to obtain relevant data.

The original search process identified 296 studies. We reviewed all abstracts and found that 74 studies examined the effects of parental mediation on the media-related behaviors of children. Furthermore, we obtained the quantitative information to calculate the effect sizes from 52 of these 74 studies. Therefore, after the filtering process, the final sample for this meta-analysis included 52 studies published from 1987 to 2016, which represented a total sample of 74,159 participants and yielded 122 independent correlations (Table 1).

Table 1.

Summarizing and Categorizing Predictors Examined in Previous Studies.

No.	Studies included	Sample size	Parental mediation	Outcomes	Age group	Risk type	Medium	Culture
1	An and Lee (2010)	348	Active	MR		Content	TV	East
			Restrictive
			Co-viewing
2	Atkin, Greenberg, and Baldwin (1991)	823	Active	TM	Adolescent		TV	West
2	Atkin, Greenberg, and Baldwin (1991)		Restrictive
3	Benrazavi, Teimouri, and Griffiths (2015)	296	Active	MR	Adult	Addiction	Game	East
3	Benrazavi, Teimouri, and Griffiths (2015)		Restrictive
4	Bleakley, Jordan, and Hennessy (2013) Study 1	1,550	Restrictive	TM	Child		TV	West
4	Bleakley, Jordan, and Hennessy (2013) Study 1		Co-viewing
5	Bleakley, Jordan, and Hennessy (2013) Study 2	629	Restrictive	TM	Adolescent		TV	West
5	Bleakley, Jordan, and Hennessy (2013) Study 2		Co-viewing
6	Buijzen and Valkenburg (2005)	427	Restrictive	TM	Child		TV	West
7	Chang et al. (2015)	1,808	Active	MR	Adolescent	Addiction	Internet	East
			Restrictive			Contact
						Conduct
8	Chang et al. (2016)	3,334	Active	TM	Adolescent		Internet	East
8	Chang et al. (2016)		Restrictive	TM			Game
9	H. Chen, Beaudoin, and Hong (2016)	528	Active	MR	Adolescent	Contact	SM	West
9	H. Chen, Beaudoin, and Hong (2016)		Restrictive
10	Chng, Li, Liau, and Khoo (2015)	3,079	Active	MR	Adolescent	Addiction	Internet	East
10	Chng, Li, Liau, and Khoo (2015)		Restrictive
11	Chng, Liau, Khoo, and Li (2014)	1,084	Active	MR	Adolescent	Conduct	Internet	East
11	Chng, Liau, Khoo, and Li (2014)		Restrictive
12	Choo, Sim, Liau, Gentile, and Khoo (2015)	2,974	Restrictive	TM	Child		Game	East
13	De Morentin, Cortés, Medrano, and Apodaca (2014)	1,238	Active	TM	Adolescent		Internet	West
13	De Morentin, Cortés, Medrano, and Apodaca (2014)		Restrictive
14	Desmond, Hirsch, Singer, and Singer (1987)	66	Active	TM	Child		TV	West
15	The Harrison, Liechty, and STRONG (2012) Kids Program	402	Active	TM	Child		TV	West
			Restrictive
			Co-using
16	Kalmus, Blinka, and Ólafsson (2015)	18,709	Active	TM	Adolescent		Internet	West
16	Kalmus, Blinka, and Ólafsson (2015)		Restrictive
17	Kirwil (2009)	1,949	Co-using	MR	General	Content	Internet	West
18	Lee and Chae (2012)	566	Restrictive	TM	Adolescent	Contact	Internet	East
18	Lee and Chae (2012)			MR		Content
19	Len-Ríos, Hughes, McKee, and Young (2016)	354	Restrictive	TM	Adolescent		SM	West
20	Leung and Lee (2012)	718	Restrictive	MR	Adolescent	Content	Internet	East
20	Leung and Lee (2012)					Contact
21	Livingstone and Helsper (2008)	789	Restrictive	TM	Adolescent	Contact	Game	West
21	Livingstone and Helsper (2008)			MR			Internet
22	Liau, Khoo, and Hwaang (2005)	1,124	Restrictive	MR	Adolescent	Contact	Internet	East
23	Lwin, Stanaland, and Miyazaki (2008)	319	Active	MR	Adolescent	Contact	Internet	East
23	Lwin, Stanaland, and Miyazaki (2008)		Restrictive
24	Marcum, Higgins, and Ricketts (2010)	744	Restrictive	MR	Adult	Contact	Internet	West
25	Mesch (2009)	935	Restrictive	MR	Adolescent	Contact	Internet	East
26	Mishna, Khoury-Kassabri, Gadalla, and Daciuk (2012)	2,186	Restrictive	MR	Adolescent	Contact	Internet	West
26	Mishna, Khoury-Kassabri, Gadalla, and Daciuk (2012)					Conduct
27	Mittal (2011)	212	Restrictive	TM	Child		TV	East
28	J. N. Navarro and Jasinski (2012)	935	Restrictive	MR	Adolescent	Contact	Internet	East
29	R. Navarro, Serna, Martínez, and Ruiz-Oliva (2013)	1,068	Active	MR	Child	Contact	Internet	West
29	R. Navarro, Serna, Martínez, and Ruiz-Oliva (2013)		Restrictive
30	Nathanson (1999)	394	Active	MR	Child	Content	TV	West
			Restrictive
			Co-using
31	Nathanson (2002)	167	Active	MR	Adolescent	Content	TV	West
			Restrictive
			Co-viewing
32	Nikken and de Graaf (2013)	1,294	Active	MR	Adolescent	Content	Internet	West
32	Nikken and de Graaf (2013)		Restrictive
33	Nikkelen, Vossen, Piotrowski, and Valkenburg (2016)	1,017	Active	MR	Adolescent	Content	TV	West
33	Nikkelen, Vossen, Piotrowski, and Valkenburg (2016)		Restrictive
34	Ngula, Mberia, and Miller (2016)	427	Active	MR	Adolescent		TV	East
			Restrictive
			Co-using
35	Notten and Nikken (2016)	8,554	Active	MR	Adolescent		Internet	West
			Restrictive
			Co-using
36	Panek (2014)	454	Restrictive	TM	Adult		SM	West
37	Rasmussen, Ortiz, and White (2015)	313	Active	MR	Adult	Content	TV	West
38	Rasmussen, Rhodes, Ortiz, and White (2016)	326	Restrictive	MR	Adult	Content	Internet	West
38	Rasmussen, Rhodes, Ortiz, and White (2016)		Active
39	Roberto, Eden, Savage, Ramos-Salazar, and Deiss (2014)	1,606	Restrictive	MR	Adult	Conduct	Internet	West
40	Sasson and Mesch (2014)	495	Restrictive	TM	Adolescent	Contact	Internet	East
40	Sasson and Mesch (2014)		Active	MR
41	Schooler, Kim, and Sorsoli (2006)	847	Active	TM	Adolescent		TV	West
41	Schooler, Kim, and Sorsoli (2006)		Restrictive
42	Shin and Ismail (2014)	469	Active	MR	Adolescent	Contact	SM	East
42	Shin and Ismail (2014)		Restrictive
43	Shin and Huh (2011)	1,102	Co-using	TM	Adolescent		Game	West
44	Shin, Huh, and Faber (2012)	381	Active	MR	Adolescent	Contact	Internet	East
44	Shin, Huh, and Faber (2012)		Restrictive
45	Shin and Kang (2016)	746	Active	TM	Adolescent	Contact	Internet	East
			Restrictive	MR			Game
							SM
46	Shin and Lwin (2016)	746	Active	MR	Adolescent	Contact	Internet	East
47	Vaala and Bleakley (2015)	629	Restrictive	TM	Adolescent		Internet	West
47	Vaala and Bleakley (2015)		Co-using
48	Vandewater, Park, Huang, and Wartella (2005)	1,065	Restrictive	TM	Child		TV	West
48	Vandewater, Park, Huang, and Wartella (2005)		Co-using
49	Heirman and Walrave (2012)	1,318	Restrictive	MR	Adolescent		Internet	West
49	Heirman and Walrave (2012)		Co-using
50	Warren and Bluma (2002)	180	Restrictive	TM	Child		Internet	West
50	Warren and Bluma (2002)		Co-using
51	Warren (2003)	129	Active	TM	Child		TV	West
			Restrictive
			Co-using
52	Zhao and Phillips (2013)	171	Active	TM	Child		TV	West
			Restrictive
			Co-using

Note. SM = social media; TM = amount of time on media use; MR = media-related risks.

Coding Procedure

All articles were coded by the first author of this study. In addition, a random sample of 15 studies (i.e., 27.27% of the studies) was coded by the second author to establish interrater agreement. Specifically, the study characteristics, predictors, moderators, and statistical indexes in each study were coded. The interrater agreements, Krippendorff’s (2005) alphas, were .88 (parental mediation type), .83 (outcome), .91 (sample size), .86 (age group), .83 (risk type), .86 (medium), and 1.00 (culture), which are acceptable.

Calculation of Effect Sizes

To compute the effect sizes, we obtained the statistics of comparable formats as reported in each study. The data formats mainly included correlations, odds ratios, and means with standard deviations. We entered the data in the comprehensive meta-analysis (CMA) software program and subsequently converted the data into one uniform form of the Pearson product–moment correlation (Pearson’s r). According to Cohen’s guidelines regarding the effect size for social sciences (Cohen, 1988), a tiny effect size is indicated by a Pearson’s r of 0 to .10, a small effect size is indicated by a Pearson’s r of .10 to .30, a medium effect size is indicated by a Pearson’s r of .30 to .50, and a large effect size is indicated by a Pearson’s r of .5 or greater. The total effect sizes of parental mediation and outcomes were averaged Pearson’s r across studies, and each observed correlation was weighted according to the study’s sample size and standard error (Hedges & Olkin, 1985).

In terms of the homogeneity test, the Q statistic was used in this meta-analysis. According to the guideline presented by Hedges and Vevea (1998), when the Q statistic is significant, the random effect model should be employed (Hochberg, 1988). In this meta-analysis, the random effect model was used, as most Q statistics were significant.

Results

Weighted Correlations

The weighted correlations between the three parental mediation strategies and outcomes are presented in Table 2. Restrictive mediation (r = -.12, p < .001) was more effective than active mediation (r = -.05) in decreasing the time that children spent with media. According to Cohen’s (1988) guidelines, the effect size of restrictive mediation was small, whereas the effect size of active mediation was tiny. Both weighted effect sizes were significantly different from zero. However, the results showed that co-using did not significantly affect the amount of media use, as its 95% confidence intervals (CIs) crossed zero.

Table 2.

Meta-Analysis of Parental Mediation.

Outcomes	Parental mediation	k	n	Effect size	95% CI		Q
Outcomes	Parental mediation	k	n	Effect size	Low	High	Q
Media use time	Active mediation	14	31,786	−.05	−.07	−.02	33.57***
	Restrictive mediation	29	43,729	−.12	−.18	−.06	1,003.20***
	Co-using	9	5,857	.03	−.12	.18	263.95***
Media-related risks	Active mediation	25	22,763	−.08	−.11	−.03	256.72***
	Restrictive mediation	38	43,758	−.06	−.16	−.02	252.75***
	Co-using	7	13,040	−.08	−.16	−.01	64.488***

Note. Effect size calculations were based on the random effects model. k = number of studies; n = total sample size for all studies combined; effect size is Pearson’s r; 95% CI = lower and upper limits of 95% confidence interval for effect size.

p < .05. **p < .01. ***p < .001.

With regard to the incidence of media-related risks, the effects of two mediation strategies, active mediation (r = -.08, p < .001) and co-using (r = -.08, p < .001), were tiny but larger than those of restrictive mediation (r = -.06, p < .001). All three effect sizes were significantly different from zero, as their 95% CIs did not cross zero.

Moderator Analyses

Based on the recommendations of many scholars (Huedo-Medina, Sánchez-Meca, Marín-Martínez, & Botella, 2006, Rosenthal, 1991), a significant Q statistic indicates significant heterogeneity of effect sizes across different studies, which is required for a moderator analysis. In this study, all effect sizes were significantly different. Thus, moderator analyses were conducted for all effect sizes (Nielsen & Einarsen, 2012).

With regard to the moderator selection, first, parental mediation research has indicated that children are more likely than adolescents to obey parental authority (Lwin et al., 2008). In general, as children’s age increases, the significance of parents’ influence on their children’s socialization decreases. Thus, age may moderate the effects of parental mediation. Second, parental mediation studies have documented that the effects of parental mediation may vary depending on the type of risk. For example, Leung and Lee (2012) determined that parental mediation was effective in reducing children’s consumption of pornographic and violent content but not in reducing privacy disclosure or cyberbullying victimization. Mishna, Khoury-Kassabri, Gadalla, and Daciuk (2012) indicated that active mediation is more effective in preventing conduct risks than contact risks. Third, the “generational divide” in media knowledge has become a factor that complicates the effectiveness of parental mediation (Valcke, Bonte, De Wever, & Rots, 2010). Parental mediation is more effective when parents have a high level of media knowledge and skills (Symons, Ponnet, Walrave, & Heirman, 2017). Parents typically have less knowledge of and experience with social media and online games compared with television and the Internet (Nikken, Jansz, & Schouwstra, 2007). Thus, children typically follow parental suggestions or instructions only in the context of television and the Internet. Finally, in Asia, parental power is strongly valued. Most Asian children show reverence to their parents and obey their instructions (Y. Chen, 2016; Sugirtharajah, 2013). In contrast, children in Western cultures tend to pursue autonomy and are more likely to challenge parental authority (Renzaho, Dhingra, & Georgeou, 2017). Thus, parental mediation may vary in effectiveness depending on the culture. Based on these considerations, age, risk type, medium, and culture were selected as potential moderators in this study.

According to the moderator analyses, age significantly moderated the effect of restrictive mediation on the time that children spent with media (QB = 17.95, p < .001; Table 3). The effect of restrictive mediation on the length of media use declined as children’s age increased. Moreover, restrictive mediation strategies backfired when used with emerging adults. Risk type was also a significant moderator that explained the differences among the effect sizes of restrictive mediation (QB = 21.40, p < .001) when assessed for media-related risks (Table 4). Specifically, restrictive mediation was effective in mitigating contact risks and content risks. Moreover, its effectiveness was stronger for contact risks than for content risks. However, restrictive mediation also increased the likelihood of children being addicted to media.

Table 3.

Moderator Analysis: Age.

Outcomes	Parental mediation	Age group	k	Effect size	95% CI		Q
Outcomes	Parental mediation	Age group	k	Effect size	Low	High	Q
Media use time	Restrictive mediation	Adult	1	.10	.01	.19	17.95***
		Adolescent	18	−.12	−.17	−.07
		Child	10	−.14	−.29	−.03

Note. Effect size calculations were based on the random effects model. k = number of studies; effect size is Pearson’s r; CI = confidence interval.

p < .05. **p < .01. ***p < .001.

Table 4.

Moderator Analysis: Risk Type.

Outcomes	Parental mediation	Risk type	k	Effect size	95% CI		Q
Outcomes	Parental mediation	Risk type	k	Effect size	Low	High	Q
Media-related risks	Restrictive mediation	Content	7	−.09	−.16	−.02	21.40***
		Contact	24	−.11	−.22	−.01
		Conduct	4	−.04	−.10	.02
		Addiction	3	.09	.03	.14

Note. Effect size calculations were based on the random effects model. k = number of studies; effect size is Pearson’s r; CI = confidence interval.

p < .05. **p < .01. ***p < .001.

Furthermore, medium was a significant moderator of restrictive mediation and the amount of media use (QB = 7.70, p < .05), and it was a significant moderator of active mediation on the incidence of media-related risks (QB = 11.61, p < .001; Table 5). Restrictive mediation was only effective in reducing the risks and the time spent on television and the Internet; it was not effective for video games and social media. Finally, culture significantly moderated the effects of active mediation on the amount of media use (QB = 4.42, p < .05) and the incidence of media-related risks (QB = 3.96, p < .05; Table 6); the effectiveness of active mediation was greater in Eastern than in Western countries.

Table 5.

Moderator Analysis: Medium.

Outcomes	Parental mediation	Medium	k	Effect size	95% CI		Q
Outcomes	Parental mediation	Medium	k	Effect size	Low	High	Q
Media use time	Restrictive mediation	Game	5	−.10	−.19	.02	7.70*
		Social media	3	.04	−.08	.16
		Internet	11	−.19	−.31	−.07
		Television	10	−.07	−.12	−.02
Media-related risks	Restrictive Mediation	Game	1	.14	.03	.25	11.61***
		Internet	32	−.10	−.18	−.02
		Television	5	−.06	−.18	−.01

Note. Effect size calculations were based on the random effects model. k = number of studies; effect size is Pearson’s r; CI = confidence interval.

p < .05. **p < .01. ***p < .001.

Table 6.

Moderator Analysis: Culture.

Outcomes	Parental mediation	Culture	k	Effect size	95% CI		Q
Outcomes	Parental mediation	Culture	k	Effect size	Low	High	Q
Media use time	Active mediation	East	6	−.06	−.10	−.03	4.42*
Media use time	Active mediation	West	8	−.02	−.03	−.01
Media-related risks	Active mediation	East	17	−.10	−.16	−.05	3.96*
Media-related risks	Active mediation	West	8	−.03	−.07	−.01

Note. Effect size calculations were based on the random effects model. k = number of studies; effect size is Pearson’s r; CI = confidence interval.

p < .05. **p < .01. ***p < .001.

Discussion

This meta-analysis not only reviews the effects of parental mediation strategies but also examines the directionality and magnitude of the effects of parental mediation. Specifically, the results indicated that the effect of restrictive mediation on decreasing the amount of media use was greater than that of active mediation. One plausible explanation is that restrictive mediation typically involves parents implementing rules to regulate their children’s usage of television and computers and to restrict the amount of time spent with this technology (Livingstone & Helsper, 2008; Warren, 2003), which directly reduces the time that children spend with media. Although active mediation involves parents explaining and discussing media content and appropriate media consumption behaviors (Nathanson, 2001b; Shin, Huh, & Faber, 2012), these methods do not overtly impose restrictions on the amount of media use. Nathanson’s (2001a) study indicated that restrictive mediation signaled parents’ disapproval of their children’s media use behaviors; however, many children considered active mediation as an endorsement of media use. Therefore, the effect of restrictive mediation is stronger than that of active mediation on reducing the amount of media use.

The results also demonstrated that active mediation and co-using were more effective than restrictive mediation in protecting children from media-related risks. This finding is consistent with most previous studies (Buijzen, Rozendaal, Moorman, & Tanis, 2008; Lwin et al., 2008; Shin et al., 2012). Active parent–child discussions regarding media and their content help children develop critical thinking skills and become educated media consumers who are able to protect themselves from media-related risks (Kirwil, 2009; Lwin et al., 2008; Valkenburg et al., 1999). Kirwil (2009) indicated that the main role of co-using is to help parents understand how their children use media as well as help children better understand guidance from their parents. Thus, co-using may represent an effective strategy, particularly when accompanied by other mediation strategies (Kirwil, 2009). Moreover, co-using does not typically include parent–child conversations regarding media content. This silent parental presence is viewed as supportive and may suggest that media content should not be taken seriously (Eastin, Greenberg, & Hofschire, 2006). Therefore, the effects of active mediation and co-using are greater than the effect of restrictive mediation in mitigating the incidence of media-related risks.

Although restrictive mediation directly reduced the time children spent on media, it increased media addiction. One plausible reason is that restrictive mediation may not be able to help children develop a less favorable or negative attitude toward media use. According to cognitive dissonance theory (Festinger, 1957), children may use “the constraints from parents” as the justification for their behavior of not using media. For example, a child may think “I did not watch TV because my parents did not allow me to do so; it is not because I do not like it.” Thus, this group of children may hold favorable attitudes toward media despite harsh parental restrictions and, consequently, may be more likely to become addicted. Furthermore, in terms of media types, restrictive mediation was effective in reducing harm from television and the Internet but not harm from video games. This finding may be explained by the fact that parents have less knowledge and experience regarding online games compared with television and the Internet. Children may be less willing to accept parental instructions in terms of playing online games, particularly when parents impose harsh restrictions on them. It also should be noted that this finding was documented in only one previous study, which merits caution in interpreting the results.

In addition, according to the results of the moderator analyses, age was a significant moderator of restrictive mediation and the amount of media use. More specifically, the effect of restrictive mediation on the length of media use declined as age increased. Moreover, restrictive mediation strategies backfired when used with emerging adults. The importance of parents in the socialization of children typically declines as children grow up (Maccoby, 2007). When children enter adolescence, they begin to pursue autonomy and independence (Blos, 1979). Thus, children typically obey their parents, whereas adolescents and emerging adults tend to resist parental authority, particularly when their parents strictly restrict their media use (Lwin et al., 2008).

Second, the moderator analysis of risk type demonstrated that the type of risk explained the variance in the effects of parental mediation. Specifically, restrictive mediation was effective in mitigating contact risks and content risks. Moreover, its effectiveness was stronger for contact risks than for content risks. However, restrictive mediation also increased the likelihood of children being addicted to media. Studies have also indicated that the effects of parental mediation vary across different types of risks (Mishna, Khoury-Kassabri, Gadalla, & Daciuk, 2012; Shin & Ismail, 2014). Thus, the findings suggest that parents should employ different strategies of parental mediation to protect children against diverse media-related risks. More specifically, parents may use restrictive mediation to reduce the incidence of contact risks and content risks. However, to address children’s conduct risks and addiction, parents should implement active mediation and co-using, as the effectiveness of these strategies does not vary across the various risk types. Thus, these two mediation strategies are effective in protecting children from the four different types of risks. However, their effectiveness in reducing contact and content risks is lower than that of restrictive mediation.

Third, medium significantly moderated the relationships between restrictive mediation and two outcomes: the amount of media use and the incidence media-related risks. More specifically, restrictive mediation was effective in reducing the risks and the time spent on television and the Internet but not on video games and social media. One plausible explanation is that most previous parental mediation studies only operationalized restrictive mediation as a rule-making strategy (Lwin et al., 2008; Valkenburg et al., 1999). However, parents may use many different approaches to check and restrict their children’s social media use. For example, parents may add their children as a friend on social media to determine when and how their children use social media (L. Chen & Ho, 2016; Kanter, Afifi, & Robbins, 2012). Thus, it is necessary to explore parental mediation strategies of children’s social media use in the future. Furthermore, studies have indicated that parents have less knowledge of contemporary digital media productions (such as social media and video games for children) than other types of media. Thus, children typically do not accept parental guidance and instructions for these mediums.

Finally, culture significantly moderated the effects of active mediation in reducing the length of media use and the incidence of media-related risks. Furthermore, the results indicated that the effectiveness of active mediation was greater in Eastern than in Western countries. One plausible reason is that an active mediation strategy may be regarded as a normative influence. Parents tell their children what they want them to do or not do, and the children develop their perceived subjective norms of media use, such as “I think my parents think I should not go to that website.” Previous research has indicated that compared with Western individuals, who are more individualistic, Eastern individuals, who live in collective cultures, are more subject to the influence of subjective norms on the formation of their intention to perform a given behavior (Trafimow, 2007). Thus, this difference may be one plausible explanation for why active mediation is more effective in Eastern than in Western countries. This reason, however, requires further examination in a cross-cultural context.

Practical Implications

This meta-analysis provides practical implications. For example, to protect children from media-related risks, parents are encouraged to employ active mediation and co-usage, as the effectiveness of these two strategies in reducing the incidence of media-related risks is greater than that of restrictive mediation. Moreover, although restrictive mediation is effective in reducing the time children spend on media and some media-related risks, it is at the expense of the opportunities that media provide (Duerager & Livingstone, 2012). Parents should also be informed that this restrictive strategy may boomerang and increase children’s addictive media behaviors. In addition, age is a factor that parents must consider when employing restrictive mediation; the moderator analysis suggests that to avoid undesirable effects, parents should use less restrictive mediation when their children are entering emerging adulthood.

Limitations and Future Research

Despite our efforts, several limitations remain. First, previous research has indicated that in addition to reducing the harms of media, parental mediation improves its benefits (e.g., Kirkorian et al., 2008). However, this meta-analysis only addressed the effects of parental mediation on harm reduction. Second, this meta-analysis focused on the effects of parental mediation strategies; however, it did not examine the antecedents of these mediation strategies. Nevertheless, a substantial number of studies have examined how family communication, beliefs regarding media negative effects, and media consumption affect parental mediation (e.g., An & Lee, 2010; Austin, Bolls, Fujioka, & Engelbertson, 1999; Buijzen & Valkenburg, 2005). Thus, future research should meta-analyze the antecedents of different parental mediation strategies. Finally, many studies, particularly early studies, have not provided the relevant quantitative data for meta-analysis; thus, these studies were not included in our analysis.

Furthermore, this meta-analysis suggests several possibilities for future research on parental mediation. First, future research must devote greater effort to comparing the effectiveness of parental mediation on different risk types and various media and cultures, as the results indicate that the effectiveness of some mediation strategies is dependent on these factors. Second, although the results indicated that the effectiveness of parental mediation varies across different age groups of children, limited studies have examined the effects of parental mediation on emerging adults. Many scholars have indicated that emerging adults are also vulnerable to negative media effects (Kandell, 1998; Lindsay & Krysik, 2012). Thus, future studies should examine how parental mediation strategies affect emerging adults’ media consumption. Based on these findings, theoretical advances in parental mediation may be made more fruitfully. Third, although the current findings indicated that the parental mediation strategies are effective in reducing harm from media, the effect sizes are not large. Thus, gaining a deeper understanding of reducing media harm is warranted. For example, according to behavioral change theories, future research should explore the effects of other factors, such as normative influences from peers and perceived self-efficacy, on children’s media use.

Conclusion

This meta-analysis provides an exhaustive statistical synthesis and summary of the empirical evidence regarding the effects of parental mediation on reducing harm from media, and it clarifies the directionality and magnitude of the effects of three broad parental mediation strategies. Furthermore, the findings indicate the heterogeneity of effect sizes across different studies and, thus, identify moderators—age, risk type, medium, and culture—that account for the differences identified across studies. Thus, this meta-analysis addresses research gaps in the current parental mediation research.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Liang Chen (PhD, Nanyang Technological University, 2017) is an associate professor in Lab for Big Data and Communication, School of Communication and Design at Sun Yat-sen University, China. His research focuses on new media, children and media, and health communication. His prior work has appeared in journals such as New Media and Society, Computers & Education, and International Journal of Communication.

Jingyuan Shi (PhD, Nanyang Technological University, 2017) is a research assistant professor in the Department of Communication Studies at Hong Kong Baptist University. Her research interests converge at the intersection of persuasion, health communication, and new media. Her work has appeared in journals such as Health Communication, Communication Research, and Journal of Medical Internet Research.

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(2013). Parental influence on children during educational television viewing in immigrant families. Infant and Child Development, 22, 401-421.