Parental mediation of digital technology is influential within the dynamic and bidirectional process of digital socialization in which contemporary parents and children engage. Utilizing a person-centered approach and multidimensional measure of digital parental mediation strategies, four latent profiles of digital parental mediation styles were identified: one “high” and one “average” digital meditation style, and two “low” involvement styles, demarcated by parents’ emphasis on mediation by modeling. These profiles were generalizable across mothers and fathers, and differentially associated with relevant covariates (including parent income and race, child age, parent and child screen time, and parents’ technology-related confidence and worry). This study demonstrates the heterogeneity of digital parental mediation styles among a representative sample of parents in the United States.
Today’s children and adolescents were born into the digital era but their parents were born and raised prior to the advent of the smartphone and social media. In the space of a generation, the developmental contexts in which children learn, communicate, and play have expanded to include the internet, a context without spatial or temporal restrictions and with affordances distinct from those in face-to-face settings (Navarro & Tudge, 2022). Today’s parents find themselves in an unprecedented situation as they attempt to guide children and youth through quickly evolving virtual contexts that they themselves are also learning to manage. Two-thirds of parents feel that parenting is harder today than it was 20 years ago, with 26% citing technology as the source of this additional difficulty (Auxier et al., 2020). Some parents do not feel that they have the knowledge or skills to be able to effectively mediate their children’s engagement with digital technology, and that their children or adolescents typically have higher digital literacy and skills than they do (Krcmar & Cingel, 2016).
Although research on digital-specific parenting has increased rapidly in the last decade, most studies assume homogeneity across parents, treating parents as having similar patterns of knowledge or skills about how to mediate the influence of digital technology (e.g., Connell et al., 2015; Glatz et al., 2018; Hefner et al., 2019; Sonck et al., 2013; Vaala & Bleakely, 2015) and lacking a person-centered approach that incorporates multiple dimensions of digital-specific parenting (e.g., discussions, rules, modeling). Given that there is likely great diversity in how parents approach parenting related to technology, the identification of substantively and statistically significant subgroups is a useful mechanism to assist researchers and practitioners in developing and implementing targeted education, interventions, and support. The aim of this study is to explore different ways parents manage their children’s use of technology, see if these approaches vary between mothers and fathers, and examine how factors like parent characteristics, family makeup, and technology habits influence the use of these different approaches.
Theoretical Framework
This study seeks to understand parents’ digital mediation styles through neo-ecological theory (Navarro & Tudge, 2022), integrating parenting style typologies (Baumrind, 1971; Darling & Steinberg, 1993) and parental mediation theory (Clark, 2011; Jennings, 2017; Nathanson, 1999).
Neo-ecological Theory
Neo-ecological theory is recent adaptation (2022) of Bronfenbrenner’s bioecological theory for the digital age. Bioecological theory posits that microsystems are physical, face-to-face contexts (e.g., home, school, work) in which individuals engage in everyday interactions and activities. Neo-ecological theory expands the notion of the microsystem to include both the physical (as Bronfenbrenner delineated) and virtual microsystems to account for the pervasive influence of digital technologies and online spaces in daily life. This theory provides a framework through which to understand the influence of digital contexts—like social media, texting, Zoom, and virtual learning platforms—on human development. Neo-ecological theory offers a lens through with to explore how digital parenting may differs from traditional parenting styles and highlights how digital contexts influence (and are influenced by) other ecological forces. Building on Bronfenbrenner’s focus on proximal processes—the increasingly complex everyday interactions and activities that drive development—neo-ecological theory explains how parents and children shape each other’s development through interactions and activities in digital contexts. This ongoing process, referred to here as digital socialization, can be understood as a form of proximal process, where repeated interactions between parents and children help develop children’s digital skills over time (Bronfenbrenner & Morris, 2006; Navarro & Tudge, 2022).
Neo-ecological theory expands Bronfenbrenner’s bioecological model by offering a more dynamic view of macrotime—the socio-temporal context that shapes development. While bioecological theory primarily viewed macrotime through long-term societal shifts, neo-ecological theory recognizes that the rapid advancement of digital technology has significantly accelerated cultural change. In particular, the introduction of smartphones and social media has compressed generational cohorts, with shifts occurring within shorter timeframes, driven by the emergence of new digital platforms and devices (Bohnert & Gracia, 2020). This updated understanding of macrotime helps explain the challenges faced by today’s parents, who must navigate a landscape of digital norms and technologies that were absent during their own upbringing (Auxier et al., 2020). Neo-ecological theory thus offers a framework for understanding how macrotime interacts with digital environments to shape both parenting practices and child development in ways that the original bioecological model could not fully anticipate.
Parenting Styles
Parenting styles represent different patterns of attitudes and beliefs parents display across domains of child rearing (Darling & Steinberg, 1993) and related studies are ubiquitous throughout the child development and family studies literature, with early discussions of the issues by Baumrind (1978, 1991) and Maccoby and Martin (1983). Parenting styles are typically modeled orthogonally on perpendicular axes of warmth/responsiveness and control/demandingness, resulting in four parenting styles: (a) authoritative (high warmth, high control), (b) authoritarian (low warmth, high control), (c) indulgent (high warmth, low control), and (d) neglectful (low warmth, low control) (Baumrind, 1991; Maccoby & Martin, 1983). In research focusing on the experiences of White, middle-class Americans, the authoritative style has consistently been linked to better academic outcomes (Spera, 2005). Research with families from an array of sociocultural backgrounds suggests that although authoritative parenting can be beneficial across a wide variety of contexts, the efficacy of parenting styles varies by race/ethnicity, culture, and socioeconomic status (Pinquart & Kauser, 2018; Spera, 2005). This raises questions about the efficacy of different parenting styles across time: Might optimal parenting styles also vary by temporal context? A recent international study found that the children of parents with an indulgent style had the highest scores on self-esteem and the internalization of social values, higher than those of authoritative parents (Garcia et al., 2019). These findings suggest parenting styles and their relation to child development may not be immutable across macrotime, paralleling heterogeneity across sociocultural contexts.
Parental Mediation
Parents have specific values (i.e., attitudes about media) and utilize specific skills and practices when trying to control, monitor, and support their children’s use of media, termed parental mediation (Nathanson, 1999; Modecki et al., 2022). Parental mediation may also be viewed as a form of digital socialization, whereby parents attempt to guide the development of their child’s values and beliefs about the internet (Smith et al., 2015).
Building upon the dimensions of digital mediation outlined by Jiow et al. (2017), Sonck et al. (2013) and Vaala and Bleakely (2015), we previously (Navarro et al., 2022) identified four dimensions of digital parental mediation attitudes from this same dataset: (a) discursive mediation (i.e., parent–child discussions about online activities and interactions), (b) restrictive mediation and monitoring (i.e., limits on and tracking of child’s online activities and interactions), (c) participatory mediation (i.e., use of digital and social technologies to connect and communicate with their child), and (d) mediation by modeling (i.e., setting an example of expectations related to activities and interactions using digital technology) (See Table 1). These dimensions reflect attitudes about distinct strategies and practices contemporary parents engage in to mitigate the risks and amplify the benefits of digital technology and social media. In our previous study from this same dataset, we found that parents’ preference for these strategies varied by demographics, use of technology, attitudes about technology, and their general parenting attitudes. Taken together, these findings underscore that parenting related to technology, like parenting in general, is not monolithic; there is great heterogeneity in the skills and practices used by parents.
Table 1
Definitions and examples of mediation strategies
Mediation Type
Acronym
Definition
Example
Discursive Mediation
DM
Parents engage in open discussions with their children about online activities, content, and appropriate behaviors.
A parent talking with their child about the risks of sharing personal information online.
Participatory Mediation
PM
Parents actively engage with their children in online activities, guiding them through the digital world.
Co-playing a video game with a child and discussing in-game decisions and strategies.
Restrictive Mediation and Monitoring
RMM
Parents set rules and limitations on their children’s internet usage, including time limits and content restrictions.
Setting a time limit for social media use and blocking certain websites.
Mediation by Modeling
MM
Parents model responsible and appropriate digital behavior, hoping children will imitate their actions.
A parent limiting their own screen time during family meals to encourage similar behavior in their child.
Digital-Specific Parenting Styles
Using these three existing frameworks, we view parental mediation of digital and social media as a digital-specific aspect of parenting that is shaped by parents’ general parenting style. While parents generally aim to use socialization strategies that reflect their core attitudes and beliefs (Livingstone et al., 2015), the rise of digital technology has complicated this process as it challenges traditional parent-child power dynamics and introduces concerns that previous generations did not face (Modecki et al., 2022; Nikken & Opree, 2018). Neoecological theory offers lens through which to conceptualize how parents’ behaviors differ may between physical and virtual microsystems. Virtual microsystems, unlike physical ones, are not limited by time or space and allow 24/7 interaction, which can be both beneficial (e.g., emotional support) and problematic (e.g., sleep disruption). Virtual interactions also have more permanence, as content can be accessed indefinitely (Navarro & Tudge, 2022).
Consequently, research into styles of digital parental mediation (i.e., attitudes and values about parenting related to digital and social media) may offer additional insights, beyond parenting styles in general, into the process of digital socialization and family- and child-level outcomes. This assertion is supported by research into other domain-specific parenting that has found that general parenting styles were unrelated to child outcomes, whereas domain-specific approaches to socialization were significantly related (Vereecken et al., 2009). Considering the ubiquity of virtual contexts and rapid macrotemporal change contemporary parents are experiencing, the focus of the current study is on styles of digital parental mediation.
Neoecological theory posits that the proximal processes of digital socialization, during which children and parents negotiate values about the use of the internet and digital media across childhood and adolescence (Nelissen et al., 2019; Smith et al., 2015), are synergistically and iteratively influenced by person characteristics (of parent, child, and other relevant persons) and context over time (Navarro & Tudge, 2022). As a result, parents’ styles of digital parental mediation are likely dynamic across time and vary depending on their constellation of person- and context-level influences. To explore this heterogeneity in styles of digital mediation, the current study takes a person-centered approach to modeling parent attitudes about how to mediate the influence of interactions and activities in virtual microsystems.
Person-Centered Approaches To Parenting Styles
Scholars have traditionally explored parenting styles through variable-centered approaches, such as regression, which assume homogeneity across a sample. However, person-centered approaches (e.g., cluster analysis and mixture modeling) allow for the identification of heterogeneous sub-groups of parents with similar attitudes or practices. Bergman and Magnusson (1997) argued that these approaches better capture the complexity of family systems, as they account for the multidimensionality of socialization. Person-centered methods have been used to identify sub-groups of parents aligned with Baumrind’s typologies, relating to various outcomes such as adolescent achievement (Aunola et al., 2000), delinquency (Hoeve et al., 2008), and adjustment (Lee et al., 2006). Recent studies have applied mixture modeling to identify parent subgroups (e.g., Borden et al., 2013; Deng et al., 2020; Padilla-Walker et al., 2021).
Despite many person-centered studies of general parenting, few have focused on digital-specific parenting. Two studies at the University of Minnesota used this approach to examine parents’ technology use (Walker et al., 2011) and monitoring strategies (Rudi & Dworkin, 2018). Walker and colleagues explored how frequently parents use technology, the types of devices they use, and their attitudes about technology. Rudi and Dworkin (2018) examined parental monitoring, separating face-to-face from technology-mediated strategies, and found three clusters: moderate–moderate, high–high, and high–low for in-person and digital monitoring, respectively. This study showed that technology-mediated monitoring supplements but does not replace face-to-face monitoring. In addition, Wu and colleagues (2020) used latent profile analysis to identify three subgroups of parents based on their technology use, attitudes, support, rules, and self-efficacy: quiescent users (low engagement), compliant users (moderate engagement), and active users (high engagement). These profiles reflect varying levels of digital technology engagement and digital-specific parenting.
These studies highlight the heterogeneity in how parents use technology and monitor their children’s digital activities, but there is a lack of person-centered research examining digital-specific parenting through the lens of parental mediation theory. This study addresses that gap by identifying parent subgroups based on attitudes toward four dimensions of digital parental mediation—discursive mediation, restrictive mediation, participatory mediation, and modeling. As this study examines attitudes about digital parenting, as opposed to parenting behavior, these profiles reflect styles of digital parental mediation rather than digital-specific parenting practices (Darling & Steinberg, 1993).
The Current Study
To explore whether there were sub-groups of parents in our sample who shared similar digital-specific parenting styles, we modeled participants’ attitudes across four dimensions of digital mediation using the Digital Parental Mediation Attitudes Scale (DPMAS; Navarro et al., 2022 ) and latent profile analysis (LPA). The following research questions guided our study:
Research Question 1
What distinct profiles of digital parenting styles emerge among parents based on their attitudes toward digital mediation strategies?
Hypothesis 1
We expect to identify digital-specific parenting profiles that reflect traditional styles, such as authoritative, authoritarian, indulgent, and neglectful.
In an authoritative profile, parents are likely to combine active and participatory mediation—which promote warmth, support, and critical thinking (Clark, 2011)—with moderate restrictive mediation, setting fair limits on screen time and content. This balanced approach reflects the responsiveness and demandingness associated with positive youth outcomes (Elsaesser et al., 2017; Maccoby & Martin, 1983). In contrast, an authoritarian profile may involve a stronger focus on restrictive mediation, with less emphasis on active or participatory strategies. This control-heavy approach often leads to increased parent-child conflict and reduced openness (Clark, 2011; Katz et al., 2019). An indulgent profile may be characterized by lower levels of active mediation and minimal rule enforcement, though parents may still apply some restrictive mediation, leading to mixed outcomes for children’s digital behaviors (Krcmar & Cignel, 2016). Finally, a neglectful profile may show low engagement in all forms of mediation, with parents offering little guidance or rules. This lack of involvement may leave children unsupported in navigating digital spaces, which can negatively impact their development (Maccoby & Martin, 1983). As mediation by modeling is a more recent addition to the parental mediation literature, we were uncertain how this construct might fit within our hypotheses related to general parenting styles.
Research Question 2
How do digital parenting profiles differ between mothers and fathers in our sample?
Hypothesis 2
We hypothesize that mothers and fathers will have different digital mediation profiles, both in terms of profile structure (i.e., attitudes making up the profile) and distribution across the profiles (i.e., differing proportions across profiles). Specifically, we expect fathers to show more positive attitudes about participatory mediation, while mothers may place more emphasis upon restrictive mediation and monitoring.
Prior studies have consistently demonstrated gender-based differences in how mothers and fathers engage in digital mediation, suggesting different approaches to supporting and regulating their children’s technology use. Connell et al. (2015) found that fathers were more likely to engage in video game co-play than mothers, indicating that fathers may be more inclined to adopt participatory mediation strategies to learn about new games and applications. Padilla-Walker et al. (2021) demonstrated that mothers who valued autonomy granting allowed their adolescents more freedom over media use and relaxed restrictions earlier compared to mothers who were less autonomy-focused. Additionally, mothers who had strong relationships with their children and engaged in frequent discussions were more likely to practice digital mediation. Navarro et al. (2022) found that mothers scored higher on general parenting measures than fathers, aligning with existing literature that suggests mothers are more involved in caregiving and overall child engagement (Connelly, 2015; Warren, 2017). In line with this, we chose to investigate profile similarity in a stepwise fashion, as delineated by Morin and colleagues (2016), before moving on to models examining associations between profile membership and other study variables.
Research Question 3
How do parental person characteristics and contextual influences relate to membership in different digital-specific parenting profiles?
Hypothesis
a (Parent age): We expect that younger parents may be more likely to be members of profiles emphasizing more engaged digital parenting attitudes, whereas older parents may be more likely to be members of profiles characterized by restrictive attitudes.
Research suggests that younger parents use information communication technology (ICT) more frequently than do older parents (Rudi et al., 2015) and that younger parents reported using more supportive internet-related parenting practices (Valcke et al., 2010).
Hypothesis
b (Parent education and income): We expect that parents with higher education and income levels will be more likely to fall into profiles that emphasize active and participatory attitudes about digital parenting.
Studies suggest that education and income, often used as indicators of social class, are strongly linked to higher ownership and use of digital technology, greater digital literacy, and more confidence in digital parenting (Livingstone et al., 2015; Modecki et al., 2022; Nikken & Opree, 2018; Rudi et al., 2015; Valcke et al., 2010; Vogels, 2021). These factors also contribute to more positive attitudes toward technology, reflecting a digital divide between parents who have the resources and time to access and use digital technology and those who may not. Neoecological theory supports this view, as socioeconomic status is considered a key influence on development, shaping how parents engage with their children’s digital environments (Navarro & Tudge, 2022).
Hypothesis
c (Parent racial identity): We hypothesize that parent race/ethnicity will be associated with differences in digital parenting profiles.
Neoecological theory prompted us to explore whether race/ethnicity, as a macrosystemic influence tied to the oppression and marginalization of people of color in the U.S., was related to digital parental mediation styles. Few U.S. studies have examined race/ethnicity and digital parenting beyond its use as a control variable, leading to mixed and limited findings on its role in digital mediation and little research into within-group heterogeneity (e.g., Chesley & Fox, 2012; Lauricella et al., 2016; Modecki et al., 2022; Ochoa & Reich, 2020). This is an important area of inquiry, particularly given a Pew survey showing significant racial and ethnic differences in children’s platform use—50% Black and 40% of Hispanic parents reported that their child aged 11 or younger watches YouTube daily, compared to 29% of White parents (Auxier et al., 2020).
Hypothesis
d (Parental technology skills and attitudes): We expect that parents who report higher use of technology and higher technology-related confidence would have a higher probability of membership in profiles demarcated by more positive attitudes towards all four digital-specific parenting domains.
Previous research suggests that parents who use technology more frequently and who are more confident in their digital skills are more comfortable utilizing digital mediation strategies, including active mediation, restrictive mediation, and co-use (Connell et al., 2015; Glatz et al., 2018; Rudi & Dworkin, 2018; Shin et al., 2017; Valke et al., 2010; Wu et al., 2020).
Hypothesis
e (Family size and structure): We hypothesize that parents of larger families will be more likely to be members of profiles with greater emphasis on restrictive mediation.
In a previous study (Navarro et al., 2022), we found that larger family size was linked to greater use of restrictive mediation, consistent with earlier findings (Sonck et al., 2013). While Valcke et al. (2010) did not find family size to affect controlling behaviors, they observed that parents of smaller families were more likely to engage in warm communication, echoing general research suggesting that larger families may experience fewer positive parenting behaviors (Jenkins et al., 2003). From a neoecological perspective, family size is a key microsystemic factor that influences digital mediation and the parent-child dynamic (Navarro & Tudge, 2022).
Research Question 4
How does child age influence parents’ use of digital-specific parenting style?
Hypothesis 4
We expect that parents of younger children will be more likely to belong to profiles characterized by higher reliance on restrictive mediation, with this tendency decreasing as children age and gain greater digital autonomy. Conversely, we expect parents of adolescents to belong more frequently to profiles that emphasize more positive attitudes toward active and participatory mediation.
Research suggests that parental use of restrictive mediation peaks in late childhood and early adolescence, when youth begin using digital devices independently, and declines as adolescents develop greater digital skills and autonomy. However, patterns for active mediation and co-use are less consistent. Some studies show that parents reduce discursive techniques as children age (Lauricella et al., 2016; Padilla-Walker et al., 2021; Warren, 2017), while others report positive or insignificant associations (Navarro et al., 2022 ; Nikken & Jansz, 2014; Glatz et al., 2018; Sonck et al., 2013). Co-use findings are similarly mixed, with studies showing it is more common among parents of adolescents (Navarro et al., 2022; Rudi et al., 2015) or having no significant relationship with child age (Vaala & Bleakley, 2015).
Methods
Sample and Procedure
A sample of 555 parents in the United States was recruited through CloudResearch, an online research platform, in January of 2020 to participate in an online survey. To be eligible, participants had to have at least one child between the ages of 5 and 18. Participants in the study were financially recompensed for their participation in the study in accordance with CloudResearch’s policies. Institutional review board approval was granted for this survey, and all participants provided informed consent prior to completing the survey. The survey was administered through Qualtrics and took approximately 10 to 15 min to complete. Data were cleaned to ensure that all participants met eligibility criteria and to remove observations of poor response quality. Participants who did not have children (n = 3), completed less than 80% of the survey (n = 10), and completed the survey more quickly than expected (i.e., participants who completed the survey in less than 588 s, the 95th percentile of reading speed for the 12,000-character survey (Buchanan & Scofield, 2018) (n = 82), were removed from further analysis, leaving 460 parents in the sample. On average, participants were 40.7 years of age and had 2.4 children with a mean age of 12.7 years. Participants in the study were mostly married (69.6%), with the 61.1% identifying as cisgender female, 38.9% identifying as male (38.5% cisgender male, 0.4% transgender male). Compared to the U.S. census data, the sample was somewhat representative of the racial/ethnic composition of the United States as a whole, with 75.2% White (76.3% U.S.), 11.3% Black or African American (13.4% U.S.), 7.8% Hispanic or Latino (18.5% US), 2.0% Asian (5.9% US), and 3.7% endorsing other race/ethnicities . The sample was also diverse in terms of education and income (see Table 2). The sample for the current study was also used in a previous publication delineating the development and validation of the Digital Parental Mediation Attitudes Scale; see Navarro et al. (2022) for more detailed information about the sample, measures, and procedures.
Table 2
Sample Educational and Income Attainment
Highest Educational Attainment
N (%)
Household Income
N (%)
Less than HS degree
11 (2.4)
Less than $10,000
24 (4.3)
HS graduate (or GED)
81 (17.5)
$10,000-29,999
73 (13.2)
Some college but no degree
93 (20.1)
$30,000-49,999
91 (16.4)
Associate degree
60 (13.0)
$50,000-69,999
91 (16.4)
Bachelor’s degree
82 (17.7)
$70,000-89,999
62 (11.2)
Master’s degree
105 (22.7)
$90,000-149,999
114 (20.5)
Doctoral degree
18 (3.9)
$150,00 or more
100 (18.0)
Professional degree (e.g., JD, MD)
13 (2.8)
Measures
Descriptive statistics and correlations between all study variables for both mothers and fathers are presented in Table 3.
Table 3
Correlations and Descriptive Statistics for All Study Variables
Variables
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
1
DPMS - AM
-
0.636**
0.791**
0.733**
0.043
0.085
0.055
0.176**
0.005
0.145*
0.149*
0.093
-0.044
-0.126*
0.140*
0.175**
0.085
0.073
0.083
0.119*
0.157*
0.114
0.030
2
DPMS - RMM
0.493**
-
0.528**
0.506**
0.011
0.119*
0.144*
0.089
-0.073
-0.043
-0.047
0.149*
-0.006
-0.150*
0.122*
-0.026
0.148*
0.027
0.063
-0.007
0.129*
0.122*
0.114
3
DPMS - PM
0.849**
0.499**
-
0.736**
0.078
0.111
0.040
0.159*
0.029
0.190**
0.228**
0.041
-0.061
-0.072
0.191**
0.206**
0.077
0.088
0.087
0.147*
0.094
0.121*
0.059
4
DPMS - MM
0.835**
0.513**
0.816**
-
0.046
0.152*
0.144*
0.081
-0.042
0.136*
0.187**
0.088
-0.096
-0.043
0.075
0.074
0.134*
0.054
0.086
0.082
0.162*
0.062
0.003
5
Education - College +
0.256**
0.141*
0.321**
0.253**
-
0.449**
0.150*
-0.028
-0.009
0.111
0.002
-0.141*
-0.135*
-0.010
0.109
0.136*
0.172**
0.193**
0.156*
0.114
0.016
0.069
0.084
6
Income
0.351**
0.184**
0.423**
0.414**
0.726**
-
0.428**
-0.133*
-0.117*
0.218**
-0.005
-0.087
-0.160*
0.019
-0.066
0.014
0.244**
0.149*
0.217**
0.124*
-0.004
0.034
0.115
7
Married
0.38**
0.134*
0.449**
0.346**
0.483**
0.468**
-
-0.137*
-0.047
0.104
-0.002
0.077
-0.097
-0.063
-0.072
-0.114
0.039
0.006
0.029
-0.030
-0.026
0.022
0.077
8
Black
-0.030
-0.004
-0.045
-0.107
-0.304**
-0.377**
-0.042
-
-0.146*
0.004
0.008
0.044
-0.015
0.000
0.166**
0.187**
0.113
0.105
0.167**
0.046
0.086
-0.010
-0.080
9
Hispanic/Other
0.004
0.003
-0.028
0.002
0.059
0.02
-0.023
-0.119
-
-0.039
-0.026
0.154*
0.045
0.045
0.026
0.016
-0.010
-0.044
-0.084
0.030
0.053
0.070
0.116*
10
Parent Age
-0.078
-0.125
-0.073
-0.130*
-0.138*
-0.160*
0.144*
0.143
-0.106
-
0.329**
-0.094
0.025
-0.006
-0.040
0.230**
0.101
-0.003
0.050
0.134*
-0.169**
-0.065
-0.001
11
Adolescent in HH
0.214**
-0.043
0.217**
0.182*
0.051
0.187**
0.121
-0.145
0.017
0.055
-
0.207**
-0.071
-0.089
0.063
0.142*
-0.086
0.032
0.026
0.178**
-0.041
-0.053
-0.092
12
# Children
0.063
0.101
0.085
0.068
0.098
0.153*
0.140*
0.212**
0.011
0.039
0.254**
-
-0.210**
-0.339**
0.037
-0.040
0.000
0.045
0.022
0.068
0.082
-0.063
0.074
13
All male children in HH
-0.100
-0.062
-0.117*
-0.098
-0.052
-0.096
-0.134*
-0.098
0.091
-0.080
-0.128
-0.266**
-
-0.339**
-0.034
-0.041
-0.069
-0.142*
0.003
-0.205**
-0.076
-0.035
-0.052
14
All female children in HH
0.072
-0.03
-0.003
0.029
-0.233**
-0.219**
-0.007
0.063
-0.075
0.185**
-0.041
-0.220**
-0.262**
-
-0.062
0.008
0.002
-0.042
-0.079
-0.029
0.016
0.019
-0.049
15
Parent Screen Time
0.325**
0.216**
0.417**
0.324**
0.128
0.250**
0.056
0.017
-0.079
-0.217**
0.105
0.176*
-0.042
-0.122
-
0.313**
0.118*
0.111
0.080
0.105
0.312**
0.013
0.070
16
Child Screen Time
0.248**
0.071
0.350**
0.258**
0.110
0.246**
0.085
-0.167*
-0.137*
-0.096
0.178*
0.155*
0.010
-0.048
0.471**
-
0.090
0.082
0.155*
0.167**
0.060
0.086
0.053
17
# Parent Devices
0.25**
0.081
0.326**
0.289**
0.358**
0.491**
0.186**
-0.310**
-0.011
-0.090
0.205**
0.197**
0.037
-0.198**
0.273**
0.313**
-
0.568**
0.537**
0.376**
0.221**
0.064
0.145*
18
# Parent Platforms
0.224**
0.084
0.263**
0.171*
0.217**
0.269**
0.092
-0.258**
-0.027
-0.137*
0.069
0.055
-0.013
-0.047
0.242**
0.167*
0.625**
-
0.418**
0.569**
0.315**
0.107
0.103
19
# Child Devices
0.276**
0.142*
0.309**
0.296**
0.143*
0.308**
0.08
-0.198**
-0.043
-0.139*
0.195**
0.146*
0.012
-0.193**
0.345**
0.319**
0.686**
0.516**
-
0.567**
0.173**
-0.022
-0.007
20
# Child Platforms
0.236**
0.071
0.283**
0.223**
0.005
0.178*
0.095
-0.161*
0.084
-0.064
0.232**
0.199**
-0.096
-0.122
0.237**
0.204**
0.486**
0.608**
0.666**
-
0.213**
-0.020
-0.036
21
Tech-related Confidence
0.272**
0.064
0.312**
0.286**
0.313**
0.283**
0.253**
-0.115
-0.018
-0.123
0.033
-0.047
-0.06
-0.117
0.356**
0.188**
0.203**
0.167*
0.229**
0.181*
-
-0.074
-0.077
22
Tech-related Worry
0.146*
0.079
0.056
0.101
0.153*
0.128*
-0.054
-0.086
0.067
-0.069
0.092
0.128*
0.165*
-0.064
0.007
0.03
0.264**
0.175*
0.156*
0.103
-0.018
-
0.583**
23
Tech-related Conflict
-0.065
0.049
-0.062
-0.024
0.078
0.149*
-0.101
-0.146*
0.085
-0.082
0.020
0.178*
-0.055
-0.160*
0.009
-0.008
0.201**
0.310**
0.195**
0.194**
-0.031
0.353**
-
M (Fathers)
0.12
0.05
0.28
0.14
0.76
5.52
0.86
0.10
0.11
40.61
0.70
2.31
0.33
0.13
8.10
6.88
5.01
5.39
4.06
4.73
8.74
4.13
2.57
SD (Fathers)
0.69
0.85
0.75
0.71
0.43
1.67
0.35
0.30
0.32
7.31
0.46
1.12
0.47
0.33
2.95
3.01
2.02
1.56
1.89
1.63
1.41
4.62
0.87
M (Mothers)
-0.07
-0.03
-0.17
-0.08
0.32
3.88
0.62
0.12
0.14
40.76
0.65
2.47
0.33
0.19
6.85
5.07
3.86
4.66
3.35
4.14
8.21
3.46
2.66
SD (Mothers)
0.62
0.78
0.81
0.77
0.47
1.62
0.48
0.32
0.35
7.14
0.48
1.37
0.47
0.39
3.05
2.77
1.55
1.61
1.33
1.63
1.75
4.02
1.00
Note. All correlations above the diagonal are for mothers and below the diagonal are for fathers
* p < .05; ** p < .01
Profile Indicators
Digital Parental Mediation Attitudes Scale
Participants completed the 44-item Digital Parental Mediation Attitudes Scale (DPMAS), a measure of parents’ attitudes about parenting practices related to digital and social media (Navarro et al.,2022 ). Parents rated the perceived importance of each item (“How important do you think it is to do the following with your child(ren) or adolescent(s)?”) on a 5-point Likert scale (1-not important, 5-very important). The DPMAS has a bifactor structure, with one general factor and four digital-specific factors (Navarro et al., 2022 ). A bifactor measurement model has two components: (a) a general factor that represents shared variance across all the items in the scale, and (b) subscales that represent additional shared variance among clusters of items (Reise, 2012). As the goal of the current study was to elucidate digital-specific styles of parenting, the general factor was omitted from the profiles as Navarro et al. (2022) argued that the general factor represents variance related to parenting in general. The DPMAS has four subscales representing digital-specific mediation practices: (a) active mediation (8 items, \(\:\omega\:\) = 0.90), (b) restrictive mediation and monitoring (15 items, \(\:\omega\:\) = 0.95), (c) participatory mediation (13 items, \(\:\omega\:\) = 0.94), and (d) mediation by modeling (9 items, \(\:\omega\:\) = 0.93). A confirmatory factor analysis (CFA) was performed and supported the bifactor structure of the DPMAS. This bifactor model was an excellent fit to the data (\(\:{\varvec{\chi\:}}^{2}\)(848) = 1494.62, p < .001, RMSEA = 0.042 [Upper-bound 90% CI = 0.045], SRMR = 0.040, CFI = 0.934) and tests of measurement invariance showed that the measure worked equally well for mothers and fathers at the scalar level (see Navarro et al. (2022) for a more detailed explanation of measure development and validation).
Demographic Variables
Parent Characteristics
Gender reflects self-identified gender identity (0 = male, 1 = female), regardless of sex assigned at birth. Educational attainment was modeled dichotomously (0 = some college or less, 1 = college graduate or more), and income was modeled continuously (1 = less than $10,000 per year to 7 = more than $150,000 per year). Participants’ marital status was also modeled dichotomously (0 = not currently married, 1 = currently married). Race/ethnicity was recoded into three categories of White (largest and reference group), Black, and Other race/ethnicities represented by two dummy codes. Parent age was modeled continuously (range from 20 to 69, M = 40.67, SD = 7.20).
Household Composition and Child Characteristics
Parents reported on the number of children in their family; this variable was modeled as a continuous variable (M = 2.41, SD = 1.29). As the participants in the study had varying numbers of children, we felt that a mean-based approach to child age may mask parent’s differentiation of mediation strategies by developmental stage. We anticipated that parents of adolescents would be more likely to belong to profiles reflecting positive attitudes about digital mediation strategies, regardless of domain, as their child(ren) are likely heavier users of technology (Mullan & Chatzitheochari, 2019) and they have had more time and experience in which to develop their digital parenting style. We did not expect to find significant associations between child gender and profile membership, as existing research does not indicate significant differences in parental mediation of digital technology by child gender (Nikken & Opree, 2018; Mullan & Chatzitheochari, 2019; Valke et al., 2010; Warren, 2017). As a result, we modeled child age dichotomously to reflect developmental differences between parent’s attitudes towards mediation of digital technology for children and adolescents (0 = all children in household are 13 years and younger, 1 = at least one child in the household is 14 years and older).
Similarly, we modeled child gender categorically to account for the various combinations of family structures within our sample: (a) families that had only female child(ren) (1 = female only, 0 = all others), (b) families that had only male child(ren) (1 = male only, 0 = all others), and (c) families that reported having both male and female children (reference group).
Technology Ownership and Use
Study participants reported which digital devices they and their child(ren) used (e.g., desktop, laptop, smartphone, mobile phone, tablet, smartwatch, gaming console, kindle, other). The total number of these devices was used to create sum scores of (a) number of parent devices (M = 4.26, SD = 1.85) and (b) number of child devices for the first child they reported on (M = 3.60, SD = 1.62). Participants also reported which software applications they and their child (i.e., the first child they reported on) used for different purposes (e.g., photo and video sharing, texting and messaging, gaming, reading and education, music and podcasts, entertainment (e.g., Netflix), news, and other). These applications were summed to yield (a) number of parent platforms (M = 4.92, SD = 1.63) and (b) number of child platforms (M = 4.34, SD = 1.66). Participants also reported on their average screen time per day in number of hours (M = 7.32, SD = 3.09) and their child(ren)’s average screen time per day in number of hours (M = 5.77, SD = 2.99).
Technology-related Attitudes and Interactions
Participants rated their confidence in using digital technology (“How confident do you feel in your ability to use digital technology (e.g., smartphones, tablets, gaming systems, computers, etc.)?”) on a 13-point scale (M = 8.41, SD = 1.65). Participants also rated their worry about their child(ren)’s use of digital technology (“Overall, how worried are you about your child(ren)’s or adolescent(s)’s use of digital devices?”) on a five-point Likert scale (1-not at all worried, 5-extremely worried) (M = 3.69, SD = 4.27). Finally, participants reported the frequency with which they experienced conflict with their child related to digital technology (“Overall, how often do you experience conflict with your child(ren) or adolescent(s) over their use of digital devices?”) on a five-point Likert scale (1-never, 5-always) (M = 2.64, SD = 0.97).
Analytic Strategy
The first goal of this study was to identify subgroups of parenting styles related to digital and social media (RQ1). Latent profile analyses (LPA), a form of mixture modeling using continuous indicators where variances are assumed to be equal across classes, were estimated in stepwise fashion; we increased the numbers of profiles until the models had issues converging (Spurk et al., 2020). Factor scores from the four digital-specific subscales of the DPMAS (Navarro et al., 2022) were used as profile indicators. Factor scores reflect participants’ optimally weighted scores on each subscale, and although factor scores do not account for error as well as latent variables, they are superior to unit-weighted scoring approaches (e.g., sum scoring) as they reflect the factor loadings of the measurement model (McNeish & Wolf, 2020).
LPA with one to five profiles were estimated for both mothers and fathers using MPlus 8.6 (Muthén & Muthén, 2017) with the robust maximum likelihood (MLR) estimator. Missing data were handled using full information maximum likelihood (FIML) and to avoid local maxima or local solutions, we used 10,000 random sets of start values, 500 iterations, and retained 250 solutions for final stage optimizations (Gillet et al., 2018; Spurk et al., 2020). Both statistical and substantive criteria were used to identify the optimal number of profiles of digital mediation style. Statistical criteria included: the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the consistent AIC (CAIC), the sample-size adjusted BIC (ABIC), the Parametric Bootstrapped Likelihood Ratio Test (BLRT), and the Adjusted Lo, Mendell and Rubin’s Likelihood Ratio Test (aLMR) (Morin & Wang, 2016). Lower AIC, BIC, CAIC, and ABIC values suggest a better fit to the data and were plotted graphically to identify the elbow of the plot (i.e., the number of profiles after which the plotted fit indices flatten out) (Morin & Wang, 2016). Likelihood ratio tests compare a model with k profiles to a model with k -1 profiles to determine if the k profile has significantly better fit (i.e., p\(\:\le\:\) 0.05). We also examined entropy to assess the classification accuracy of the model; higher entropy values are better, with 0.6 and 0.8 for the cutoffs for moderate and high classification accuracy, respectively (Morin & Wang, 2016; Spurk et al., 2020). In addition, we considered the substantive and theoretical meaning, interpretability, and size of the profiles in deciding how many profiles were optimal.
Following the identification of the optimal profile enumeration for both mothers and fathers (separately), we combined these two models into a multi-group LPA model (Morin et al., 2016)(RQ2). We employed a multi-group analysis to ensure that fathers and mothers were not treated as a homogenous group, allowing us to rigorously examine whether the latent profiles were similar in terms of structure (the number and nature of profiles) and configuration (the proportion of participants in each profile). Tests for profile similarity between mothers and fathers were conducted in line with the stepwise approached outlined by Morin et al. (2016). This series of tests parallels measurement invariance testing but seeks to establish that profiles are not significantly different across samples. The first step is to establish configural similarity (i.e., the same number of profiles is optimal for both mothers and fathers). The second step evaluates if the profiles have structural similarity (i.e., means are constrained). The third step assesses the extent to which there is dispersional similarity between mothers and fathers (i.e., means and variances constrained). The fourth step (distributional similarity) ascertains whether the size of the profiles remains the same across the samples (i.e., means, variances, and class probabilities are constrained). We then extended to tests of predictive similarity to assess the extent to which the associations between the profiles and covariates were statistically invariant across mothers and fathers. To evaluate if each subsequent and more constrained model is supported, Morin et al. (2016) recommended that at least two fit indices out of the BIC, CAIC, and ABIC should have lower values than the less constrained model.
Following tests of profile similarity, multiple-group multinomial logistic regressions were estimated to examine whether demographics (including parent characteristics and household composition), technology ownership and use, and technology-related attitudes and interactions were significantly related to profile membership (Spurk et al., 2020)(RQ3 & RQ4). We used Vermunt’s three-step procedure (Asparouhov & Muthén, 2014) to estimate these models, as this procedure avoids altering the size or structure of the profiles when covariates are entered into the mixture model (Morin & Wang, 2016). We first estimated a model with demographic covariates and included statistically significant covariates in subsequent models estimating the associations between profile membership and (a) technology ownership and usage, and (b) technology-related confidence, worry, and conflict.
Results
Research Questions 1 & 2: Model Fitting & Parent Gender Profile Similarity
The fit indices for models with 1- to 5-profile solutions are presented in Table 4. For both mothers and fathers, we accepted the 4-profile solution because: (a) the plot of fit indices flattened around four profiles (see Fig. 1), (b) the a-LMR likelihood ratio tests indicated that the 4-profile solution was a better fit than the 3-profile solution and that the 5-profile solution was not significantly better, and (c) because the four profiles were substantively meaningful.
Fig. 1
Plot of Fit Indices for Mothers and Fathers. Note AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; CAIC: consistent AIC; ssBIC: sample-size adjusted BIC.
Following enumeration of the same number of profiles, we used multiple-group LPA to test for profile similarity across mothers and fathers. The results from these tests are presented in the bottom section of Table 4. We found support for configural, structural, and distributional similarity between mothers and fathers, as these models had subsequently lower BIC, CAIC, and aBIC values. However, we did not find support for distributional similarity across mothers and fathers, suggesting that different proportions of mothers and fathers belonged to the four profiles. As a result, we used the dispersion similarity model to test for predictive similarity, which examines if the relations between predictors (i.e., exogenous variables) and profiles are the same across mothers and fathers (Morin et al., 2016). The test of predictive similarity resulted in lower BIC, CAIC, and ABIC values compared to an unconstrained model, where the associations with covariates were allowed to vary across mothers and fathers. Taken together, these results suggest that the profiles and their associations with study covariates were not significantly different between mothers and fathers.
Table 4
Profile Enumeration for Mothers and Fathers and Tests of Profile Similarity
Model
LL
Parameters
AIC
BIC
CAIC
ABIC
Entropy
a-LMR
BLRT
Smallest Class %
Mothers (N = 277)
1 Profile
-1239.215
8
2494.43
2523.42
2531.42
2498.06
-
-
-
-
2 Profiles
-1034.006
13
2094.01
2141.12
2154.12
2099.90
0.839
< .001
< .001
33%
3 Profiles
-929.439
18
1894.88
1960.11
1978.11
1903.03
0.866
0.105
< .001
16%
4 Profiles
-882.647
23
1811.29
1894.65
1917.65
1821.72
0.876
0.052
< .001
9%
5 Profiles
-855.778
28
1767.56
1869.03
1897.03
1780.24
0.908
0.181
< .001
1%
Father (N = 160)
1 Profile
-723.506
8
1463.01
1487.61
1495.61
1462.29
2 Profiles
-586.623
13
1199.25
1239.22
1252.22
1198.07
0.899
0.008
< .001
29%
3 Profiles
-522.223
18
1080.45
1135.80
1153.80
1078.82
0.885
0.101
< .001
11%
4 Profiles
-487.808
23
1021.62
1092.34
1115.34
1019.54
0.880
0.020
< .001
10%
5 Profiles
-468.786
28
993.57
1079.68
1107.68
991.04
0.896
0.017
< .001
1%
Profile Similarity Across Mothers and Fathers (N = 437, k = 4)
Configural
-1657.505
47
3409.01
3600.77
3647.77
3451.61
0.880
Structural
-1700.893
31
3463.79
3590.26
3621.26
3491.89
0.863
Dispersional
-1710.818
27
3475.64
3585.79
3612.79
3500.11
0.862
Distributional
-1721.465
24
3490.93
3588.85
3612.85
3512.68
0.858
4 Profiles: Predictors
-1579.963
81
3321.93
3650.53
3731.53
3393.48
0.880
Predictive
-1610.893
54
3329.79
3548.85
3602.85
3377.49
0.867
Note. LL: Log likelihood; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; CAIC: consistent AIC; ABIC: sample-size adjusted BIC; aLMR: Adjusted Lo, Mendell and Rubin’s likelihood ratio test; BLRT: Parametric Bootstrapped Likelihood Ratio Test
As a result of this dispersional and predictive similarity across mothers and fathers, subsequent models were completed using the entire sample (mothers and fathers together) using the dispersional similarity 4-profile model (where means and variances are constrained, and the probability of class membership are allowed to vary). The dispersional similarity model yielded four meaningfully distinct profiles; see Table 5, which summarizes indicator and covariate statistics by profile, and Fig. 2, which graphically summarizes the average factor score by indicator for each profile. The plurality of parents (41%; grey line in Fig. 2) fell into an Average Digital Mediators profile characterized by scores near the sample mean on all four indicators. The second largest profile (High Digital Mediators; 34% of sample; yellow line in Fig. 2) was characterized by relatively high scores on all indicators, although parents in this profile placed somewhat less of an emphasis on restrictive mediation and monitoring than the other digital-specific strategies. An additional 17.5% of sample parents fell into the Moderately Digitally Uninvolved profile (blue line in Fig. 2), which was characterized by below average scores on all four indicators, suggesting that parents in this profile do not place great importance on digital-specific parenting strategies compared to the other parents in the sample. The smallest profile (Digitally Disengaged; 7.5%; orange line in Fig. 2) was also characterized by lower (i.e., far below average) scores on all indicators, but markedly lower scores on active mediation, participatory mediation, and modelling than the Moderately Digitally Uninvolved profile.
Table 5
Descriptive Statistics by Latent Profile Membership
Research Question 3: Profile Membership & Parent/Household Characteristics
Next, we used Vermunt’s three-step procedure (Asparouhov & Muthén, 2014) to estimate multinomial logistic regression models to analyze whether profile membership was significantly related to demographic covariates, technology ownership and use, and technology-related attitudes and interactions (Table 6). In the initial model, summarized in the upper panel of Table 6, which included parent characteristics and household composition, only parent gender, income, identifying as Black, and the presence of an adolescent (14 + years) were significantly related to profile membership. Mothers in our sample were 0.37 times (Odds Ratio, 95% Confidence Interval: 0.15–0.94) as likely to be in the High Digital Mediators profile than in the Moderately Digitally Uninvolved profile, reflecting different proportions of profile membership between mothers and fathers as indicated by the test of distributional similarity. Parents who reported a higher income (i.e., one unit higher on a 7-point scale) were 0.59 times (OR, CI: 0.37–0.95) as likely to be in the Digitally Disengaged profile and 1.33 times (OR, CI: 1.00-1.76) more likely to be in the High Digital Mediators profile than in the Moderately Digitally Uninvolved profile. In addition, parents who reported higher incomes were 1.89 (OR, CI: 1.23–2.89) and 2.24 (OR, CI: 1.44–3.48) times more likely to be in the Average Digital Mediators and High Digital Mediators profiles, respectively, than in the Digitally Disengaged profile. Taken together, this suggests that parents with higher incomes tend to fall into the more involved digital parental mediation styles in our sample. In terms of racial identity, parents who identified as Black were 3.94 times (OR, CI: 1.02–15.14) more likely to be in the High Digital Mediators profile than in the Moderately Digitally Uninvolved profile. In addition, parents of adolescents were 2.36 times (OR, CI: 1.12–4.96) and 5.45 times (OR, CI: 1.70-17.47) to be in the Average Digital Mediators profile than in the Moderately Digitally Uninvolved and Digitally Disengaged profiles. Parents of adolescents were also more likely (OR: 4.38, CI: 1.33–14.44) to be in the High Digital Mediators profile than in the Digitally Disengaged profile in our sample.
Model 3: Technology-related Attitudes and Interactions
Confidence
1.00
0.66-1.50
1.04
0.85-1.26
1.26*
1.02-1.57
1.04
0.72-1.52
1.27
0.86-1.87
1.22*
1.01-1.40
Worry
1.03
0.90-1.19
1.04
0.94-1.14
1.11*
1.01-1.22
1.00
0.88-1.14
1.07
0.94-1.22
1.07
0.99-1.15
Conflict
0.75
0.43-1.31
1.00
0.70-1.44
0.73
0.49-1.10
1.33
0.83-2.16
0.97
0.57-1.65
0.73
0.51-1.05
Note. Model 2 included significant covariates from Model 1 (i.e., gender, income, identifying as Black, presence of an adolescent in the household). Model 3 additionally included significant covariates from Model 2 (i.e., parent and child screen time). * p < .05; ** p < .01
The second multinomial regression model estimated associations between technology ownership and usage and profile membership, while controlling for significant demographic covariates (i.e., gender, income, race, and the presence of an adolescent in the household). Of the technology use variables, only parent screen time and child screen time were significantly related to profile membership. Parents who reported higher screen time use (i.e., one additional hour) were 1.16 (OR, CI: 1.03–1.33) and 1.25 times (OR, CI: 1.09–1.43) more likely to be in the Average Digital Mediators and High Digital Mediators profiles, respectively, than in the Moderately Digitally Uninvolved profile. In addition, parents in our sample who reported more screen time use of their child (i.e., a one-hour increase) were 1.14 (OR, CI: 1.00-1.31) and 1.11 times (OR, CI: 1.00-1.23) more likely to be in the High Digital Mediators profile than in the Moderately Digitally Uninvolved and Average Digital Mediators profiles, respectively.
The third multinomial regression model examined relations between technology-related confidence, worry, and conflict and profile membership in our sample, alongside significant covariates from both previous models. Only parents in the High Digital Mediators profile were significantly differentiated by these variables compared to the other profiles retained in our sample. Parents who reported higher technology-related confidence (i.e., a one unit increase on the 13-point scale) (OR: 1.26, CI: 1.02–1.57) and worry (i.e., a one unit increase on a five point scale) (OR: 1.11, CI: 1.01–1.22) were more likely to be in the High Digital Mediators profile than in the Moderately Digitally Uninvolved profile, and were 1.22 (OR, CI: 1.01–1.40) more likely to be in the High Digital Mediators profile than in the Average Digital Mediators profile.
Discussion
The first goal of this study was to identify sub-groups of parents who share similar parenting attitudes towards digital technology across four mediation domains (active, restrictive/monitoring, participatory, and modeling), which we call styles of digital parental mediation, using latent profile analysis. We found four profiles which are differentiated in large part by degree of importance parents placed upon the different dimensions of digital mediation. We found one “high” group (i.e., High Digital Mediators), which was characterized by above average scores on all four indicators, but relatively lower scores on restriction. The largest profile, Average Digital Mediators, was characterized by near-average scores on all four indicators, thus representing an average or “medium” profile.
We found two “low” profiles, Moderately Digitally Uninvolved and Digitally Disengaged, both characterized by below-average scores on all indicators. Parents in the Digitally Disengaged profile had the lowest scores overall, with markedly lower scores on mediation by modeling relative to parents in the Moderately Digitally Uninvolved. Parents in the Moderately Digitally Uninvolved and Digitally Disengaged profiles both placed the highest emphasis upon restrictive mediation and monitoring relative to active and participatory mediation strategies, suggesting that parents in these profiles see restrictive mediation and monitoring practices as being more viable strategies for mediating their child(ren)’s use of digital technology. Interestingly, restrictive mediation and monitoring had the least spread (1.55 points) of mean factor scores across all four profiles. Less variability related to restrictive mediation and monitoring could be reflective of the fact that rule setting and enforcement practices are some of the most used mediational strategies (Lauricella et al., 2016). Given the moral panic surrounding digital and social media in the US, it is not surprising that parents in our sample reached for restrictive practices to mitigate risks, both real and imagined.
Although the parental mediation literature has considered modeling to some extent (e.g., Hefner et al., 2019; Vaala & Bleakely, 2015), the current study is the first to incorporate modeling as a domain of parental mediation from a person-centered perspective. Further, the literature does not delineate modeling as being supportive or directive, but rather as a bidirectional influence related to social learning. As a result, we were uncertain how modeling might fit within our digital-specific typologies. We found that mediation by modeling had the largest spread of all indicators (2.37 points) and was key in differentiating our two “low” profiles, beyond differences in magnitude. Parents in the Moderately Digitally Uninvolved profile saw their own use of technology as being relatively similar in importance to other forms of mediation, whereas parents in the Digitally Disengaged profile saw their own technology use as a negligible source of influence.
Digital and General Parenting Styles
Paralleling general parenting typologies organized along axes of warmth/responsiveness and control/demandingness (Baumrind, 1978, 1991; Maccoby & Martin, 1983), we expected that we might find digital mediation styles characterized by (a) higher emphasis on all mediation practices (i.e. a digital analogue to the authoritative style), (b) higher emphasis on active/participatory strategies and less emphasis on restrictive/monitoring (i.e. a digital analogue to the indulgent style), (c) higher emphasis on restriction and monitoring and less emphasis on active/participatory strategies (i.e. a digital analogue to the authoritarian style), and (d) little emphasis placed on any of the four mediation strategies (i.e. a digital analogue to the neglectful style), as other scholars (of offline parenting) using variable-centered approaches have found (e.g., Konok et al., 2020; Valcke et al., 2010).
The results of our person-centered approach to digital-specific parenting typologies mirrored general parenting styles to a limited degree, as we found support for authoritative and uninvolved styles, but not for authoritarian nor indulgent. Parents in the High Digital Mediators profile scored above average on all four profile indicators, suggesting a digital mediation style characterized by both high digital warmth/responsiveness and high digital control/demandingness, in line with the authoritative style we expected to find (Baumrind, 1978, 1991; Maccoby & Martin, 1983). These parents also placed the least importance on restrictive mediation and monitoring of all four digital-specific indicators and slightly more emphasis on participatory mediation, suggesting that this profile may better reflect Baumrind’s (1991) democratic parenting style, an indulgent sub-type characterized by moderate demandingness and high responsiveness.
Garcia et al. (2019) proposed that indulgent styles, like the High Digital Mediators profile we identified, may be more adaptive in the digital era. Considering the reduced temporal and spatial limitations of virtual microsystems (Navarro & Tudge, 2022) and research suggesting that excessive use of controlling practices related to virtual microsystems can lead to parent–adolescent conflict and disconnection (Clark, 2011; Katz et al., 2019; Krcmar & Cignel, 2016), Garcia and colleagues’ findings and our High Digital Mediators profile may reflect parents’ attempts to navigate the novel challenges of the digital era.
Parents in both the Moderately Digitally Uninvolved and Digitally Disengaged profiles, together comprising 25% of the sample, placed below average emphasis on all mediation strategies, similar to an uninvolved or neglectful general parenting style (Baumrind, 1978, 1991; Maccoby & Martin, 1983).
Similarities and Differences between Mothers and Fathers
The second goal of this study was to explore whether mothers and fathers in our sample shared similar profiles of digital mediation style, as gender is a key person characteristic influencing development (Navarro & Tudge, 2022). We found that the profiles were relatively similar for both mothers and fathers in our sample. However, mothers and fathers had different relative makeups (i.e., the proportions of mothers and fathers) in each profile. For example, the High Digital Mediators profile was more prevalent among fathers (45.0% of fathers) than mothers (26.0% of mothers), whereas the Digitally Disengaged and Average Digital Mediators profiles were more prevalent among mothers (22.0% and 44.4% respectively) than fathers (10.6% and 38.8% respectively). This suggests that fathers in our sample were more likely (relative to mothers) to be members of profiles characterized by average and above average scores on all four indicators.
Our demographic regression model also suggests significant differences in profile membership by parent gender; fathers in our sample were significantly less likely to be in the Digitally Disengaged profile than in the High Digital Mediators profile. While our findings do not match hypothesis of different profiles across mothers and fathers, our findings do partially support our expectations in that fathers were more likely to belong to the High Digital Mediators profile which was characterized by higher active, participatory, and modeling mediation relative to restrictive mediation and monitoring. These results support variable-centered findings of more co-use (Connell et al., 2015) and less restrictive mediation (Valcke et al., 2010) among fathers. It could be the fathers in our study had an increased capacity, both emotional and temporal, to embrace more engaged digital mediation styles because they engage in less child caregiving overall (Connelly, 2015; Navarro et al., 2022 ; Warren, 2017).
Profile Membership by Parent and Household Characteristics
The third goal of the current study was to test whether parent characteristics, household composition, and parent technology use and attitudes were differentially related to profile membership. Here again we saw some similarity in profile membership across these dimensions, with several interesting differences emerging.
Lower-income parents were significantly more likely to be members in the Digitally Disengaged profile than in any of the other profiles, consistent with previous research suggesting that lower income parents tend to engage in less digital mediation (Livingstone et al., 2015; Warren & Aloia, 2019). Parent income was the only significant covariate to differentiate membership between the Digitally Disengaged and Moderately Digitally Uninvolved profiles, which is also reflected by average income (i.e., ~$32,600 and ~$48,400, respectively). As highlighted above, these two profiles were demarcated by magnitude and a stark difference related to mediation by modeling, such that parents in the Digitally Disengaged profile placed far lower importance on modeling than did parents in the Moderately Digitally Uninvolved profile.
Our results replicate findings from Europe (Livingstone et al., 2015) and the US (Warren & Aloia, 2019), and raise questions about why socioeconomic status appears to be a pervasive macrosystemic influence despite the ubiquity of digital technology across socioeconomic classes (Perrin, 2021). For example, cellphone and smartphone ownership rates for adults aged 30–49 (Mstudy = 40.7 years) in the United States are near saturation (100% and 95%, respectively; Perrin, 2021) and European research suggested little difference in device ownership by income (Livingstone et al., 2015). Further, low-income parents in the US have the highest screen time (Lauricella et al., 2016) and, in the current study, we did not find that parental technology use was a significant predictor of profile membership between the two “low” profiles. Instead, we speculate that socioeconomic status influences digital parental mediation because the additional time pressures and stressors faced by lower-income parents may reduce opportunities for (and thus emphasis on) mediational practices, and in particular, mediation by modeling (Livingstone et al., 2015; Warren & Aloia, 2019). Research also suggests that lower-income parents may feel less competence and greater insecurity related to digital technology and consequently find it more difficult to engage in active mediation and co-use (Nikken & Opree, 2018), as supported by the positive correlation between income and technology-related confidence in the current study. It could be that some of the lower-income parents in our study felt that their use own of technology was not an adequate or appropriate example for their child(ren) and/or adolescent(s). Clearly, socioeconomic status is a pervasive macrosystemic influence on parents’ attitudes about digital mediation; further research is needed to delineate the mechanisms behind this phenomenon and to elucidate specific strategies to support low-income parents.
In terms of race/ethnicity, we found that parents in our US-based sample who self-identified as Black were almost four times more likely to be in the High Digital Mediators profile than in the Moderately Digitally Uninvolved profile, paralleling previous research that has suggested that Black parents engage in more active mediation (Lauricella et al., 2016). However, when parent and child screen times were included in the model, race/ethnicity was no longer significant, suggesting that differences in technology usage account for a significant proportion of the differences between Black and White parents in our sample. Previous research has found that Black parents in the United States use technology more intensely (Lauricella et al., 2016) and that the children of Black parents are the most likely, compared to White and Hispanic parents, to watch YouTube daily (Perrin, 2021). Our research suggests that Black parents may be more engaged in their children’s digital socialization than are White parents, possibly in reaction to their children’s frequent engagement in virtual microsystems, or it could be that Black children use technology more intensely because their parents are more intensive users themselves and embrace a more democratic digital parenting style.
Additionally, our results also show different patterns in digital mediation strategies and technology usage between the Black mothers and fathers in our sample. We found positive correlations between mothers who identify as Black and active/participatory mediation and parent/child screen time, but negative correlations between fathers who identify as Black and child screen time and the number of parent/child devices and platforms. Although the size of our sample was prohibitive in exploring these within-group differences further, it does suggest that there may be nuanced differences between Black mother and fathers. This is an important avenue of future study, as research within virtual microsystems (e.g., Black Twitter) has identified macrosystemic influences (e.g., culturally specific “technoculutral” practices and structural inequalities) that may influence the digital parenting styles of Black Americans (Brock, 2012, 2018).
The presence of an adolescent in the home was a significant predictor of profile membership, as we had anticipated. Parents of at least one youth aged 14 or older were more likely to be in the Average Digital Mediators and High Digital Mediators profiles of than in the Digitally Disengaged or Moderately Digitally Uninvolved profiles. This could be because adolescents use digital technology more intensely than do children (Mullan & Chatzitheochari, 2019) and, consequently, parents of adolescents have had a more time and experience in which to develop their digital parenting style. Our findings support variable-centered research (e.g., Lauricella et al., 2016; Navarro et al., 2022 ; Nikken & Jansz, 2014; Rudi et al., 2015) that has found higher engagement in active and participatory mediation among parents of older youth. In light of this evidence, it is important that scholars of digital parenting approach mediation from a neo-ecological perspective; the person characteristics of all family members are essential to gaining insight into the complex and dynamic process of digital socialization.
As we expected, parent screen time, child screen time, and parents’ technology-related confidence were also positively related to membership in the more engaged digital mediation profiles, supporting previous findings suggesting that parents who use technology more frequently and who have higher digital self-efficacy are more likely to engage in digital mediation strategies, including active mediation, restrictive mediation, and co-use (Connell et al., 2015; Glatz et al., 2018; Rudi & Dworkin, 2018; Shin et al., 2017; Valke et al., 2010; Wu et al., 2020). For both clinicians and researchers, this suggests that increasing access and improving digital literacy may be potential avenues for supporting parent’s digital parenting efforts, as may support the development of digital parenting efficacy and confidence.
Limitations and Future Directions
To our knowledge, this is the first study to use a person-centered approach to explore digital parental mediation styles in the United States. In addition, our study embraced a recent methodological innovation (i.e., tests of profile similarity; Morin et al., 2016) to ensure the validity of our digital parental mediation styles across the mothers and fathers in our sample. Alongside these strengths, our study also has several limitations, including its cross-sectional study design, which limits casual inference.
The current cross-sectional study used neo-ecological theory (Navarro & Tudge, 2022) as a lens through which to organize, analyze, and process our hypotheses and data, which Bronfenbrenner and Morris (2006) described as “discovery mode” (p. 795), rather than a fully-fledged neo-ecological research design. We only examined digital mediation from a parental perspective and, as a result, we were unable to examine bidirectional and synergistic proximal processes of digital socialization. Further, time constraints precluded a thorough assessment and analysis of potentially different parenting attitudes and practices towards individual children within the family, and measures rather referred to the child(ren) in the family more generally. This potentially obscured nuances within the family system. Future neo-ecological research should utilize longitudinal designs with multiple informants, including all primary caregivers and children in the home to gain the greatest insight into the complex and dynamic family system. Careful thought must also be given to choosing substantively and theoretically relevant proximal processes, person characteristics, and contextual influences. In addition, the current study did not utilize measures of general parenting typologies and so we were unable to analyze these associations directly; future research should explicitly examine these questions to elucidate the relation between digital and general parenting.
It is important to note that we collected our data at a unique macrotemporal moment. We recruited participants and collected data throughout January of 2020, only a few weeks prior to the COVID-19-related shutdowns in the United States. In the intervening years, the internet became a lifeline for many families; children attended school virtually and, when possible, many adults worked from home. Parents’ attitudes about digital technology and their mediation strategies likely shifted during and after the pandemic and post-pandemic styles of digital parental mediation may be characterized by different priorities. Future research should explore how pandemic-related changes in technology use (e.g., virtual schooling, remote work, telehealth) and social isolation impacted the bidirectional process of digital socialization between parents and children.
In addition, our sample was comprised of a unique macrotemporal cohort of parents who did not grow up with extensive technology themselves but must help guide their own child through a technologized world. The parents in our sample were approximately 40 years old, on average, making them approximately 24 years old when Facebook was launched and 27 years old when the first iPhone was released. In the coming years, cohorts for whom social media and smartphones were part of their childhood and adolescence (i.e., digital natives) will they themselves become parents; their styles of digital parental mediation will likely differ substantially from previous generations.
Research into parental mediation related to digital technology has been completed around the world. In addition to US-based research (e.g., Auxier et al., 2020; Glatz et al., 2018; Warren, 2017), researchers from the United Kingdom (Livingston & Helsper, 2008), Germany (Hefner et al., 2019), Europe (Livingstone et al., 2015), the Netherlands (Krcmar & Cingel, 2016; Sonck et al., 2013), China (Wu et al., 2020), Australia (Jeffery, 2020), Israel (Katz et al., 2019), Singapore (Jiow et al., 2017), and Belgium (Symons et al., 2017) have undertaken research about what strategies parents use to mitigate the risks and amplify the benefits of digital and social media. Nevertheless, most of these studies were completed within WEIRD (Western, Educated, Industrialized, Rich, and Democratic) societies and generalizability beyond these contexts is limited. Our study suffers from the same limitation; while our sample was representative of the US population, our findings are unique to the temporal and cultural context in which our data were collected.
Conclusion
Despite these limitations, our study underscores the heterogeneity in how parents approach parenting related to digital and social media, and how a diverse array of person characteristics and contextual factors relate to parents’ digital mediation styles. Future longitudinal research should examine how digital parenting relates to child, youth, and family level outcomes in the short and long term. Evidence about the efficacy of digital parenting strategies will be essential in supporting practitioners, educators, and clinicians as they guide parents in adopting strategies that foster their children’s healthy development in the digital age. Our findings can help parents build on their existing approaches, such as open communication and shared engagement with technology, while also introducing them to new strategies that promote digital literacy, critical thinking, and their own responsible media use. Practitioners can draw on this evidence to help parents navigate the complexities of digital parenting, from setting boundaries to participating in their children’s online activities, ensuring they are equipped to adapt as technology evolves. Clinicians can also use this research to offer tailored support for families, helping parents and children alike develop skills that enhance their digital experiences and strengthen family relationships. By identifying strategies that balance structure with autonomy, this evidence will help parents create environments that encourage both safety and exploration in the digital world, ultimately supporting their children’s growth and well-being.
Declarations
Ethics Approval
This study was approved by the University of North Carolina Greensboro IRB committee (approval no. 20–0099).
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