Underpinnings of Internet Parenting Styles: The Development and Validation of the Internet Parenting Scale Using Repeated Cross-Sectional Studies

Abstract

The overwhelming majority of parents tend to mediate their children’s Internet use via different Internet parenting styles. Recent research suggests that Internet parenting is closely related to the Internet use behavior, development, and well-being of young people. However, despite this, little prior research has investigated the different Internet parenting styles exercised by parents in the developing world. Similarly, the recent literature has also pointed out the urgent need to develop new empirical measures of Internet parenting. This open research gap is addressed by developing a 10-item scale measuring 4 types of Internet parenting style, namely parental encouragement, parental permission, parental worry, and parental monitoring, using a 3-stage investigation involving repeated cross-sectional surveys. The prior Internet parenting literature exclusively focused on developed countries in the West and Far East, while developing countries have rarely been studied. The present study has addressed this gap by recruiting adolescent and young-adult Internet users from India. The study results suggest that the Internet parenting scale has a stable factorial structure, and sufficient instrument validity and reliability over time. Furthermore, it is also valid for adolescents attending public schools and young-adult Internet users. This study offers different theoretical and practical implications for researchers engaged in interdisciplinary research on the Internet and youth.

Keywords

adolescent instrument development Internet use behavior Internet parenting styles psychometrics repeated cross-sectional study validity and reliability youth

Introduction

The emergence of the Internet and related technologies has brought new opportunities for children to play, have fun, and learn (Ebata & Dennis, 2011; Jackson, Ervin, Gardner, & Schmitt, 2001). The Internet enables children to develop their social and cognitive abilities (Greenfield & Yan, 2006; Valkenburg & Peter, 2007) and gain important skills considered crucial in modern society (Kuhlemeier & Hemker, 2007). However, despite the fact that children are spending an increasing amount of time on Internet use, they are still not fully aware of the cyber realities, particularly the threats related to their safety and well-being (Livingstone, 2008). Due to this, the majority of children are dependent on their parents to obtain the necessary knowledge and guidance concerning their Internet use (Valcke, Schellens, Van Keer, & Gerats, 2007). Furthermore, the inappropriate use of the Internet by children also involves a great deal of thoughtfulness on the part of parents, which later translates into their anxiety and uneasiness (Ihmeideh & Shawareb, 2014; Punamäki, Wallenius, Hölttö, Nygård, & Rimpelä, 2009; Shepherd, Arnold, & Gibbs, 2006). Scholars have identified different contributing factors to the rising anxiety of parents, including easy access to inappropriate content (i.e., sexually explicit material and material promoting violence and hate; Ihmeideh & Shawareb, 2014; Livingstone, 2008; Mascheroni & Ólafsson, 2014); adverse health effects including physiological impairment such as poor eyesight and attention deficit disorder (Shepherd et al., 2006); communication with strangers such as bullies and pedophiles (Mascheroni & Ólafsson, 2014; Shepherd et al., 2006); and threat from viruses, spyware, hackers, and spamming (Shepherd et al., 2006). Due to all these reasons, the overwhelming majority of parents tend to mediate their children’s Internet use; for example, 67% of parents in one study accepted supervising their children’s Internet use, at least to some extent (Shepherd et al., 2006).

Parenting is a complex activity that involves specific acts and behaviors that may work in isolation or together, with an ultimate goal of influencing the child’s outcome (Darling & Steinberg, 1993). Parents tend to exercise different forms of Internet parenting at home because they believe that they play a more important role in mediating the Internet use behavior of their children compared with schools (Valcke et al., 2007). Internet parenting is part of the modern riskaverse culture, popular nowadays among parents (Ghebari, 2015). Internet parenting aims to transform young children into knowledgeable, productive, sociable, and trustworthy entities (Davis, Gibbs, Arnold, & Nansen, 2008). Scholars have argued that parents tend to exercise different Internet parenting styles through communication, coaccessing, encouraging children to explore, sharing their own Internet use experiences, answering possible doubts or problems encountered, being actively involved in the discussion, and closer monitoring of their Internet use (Chou, Chou, & Chen, 2016; Duerager & Livingstone, 2012; Eastin, Greenberg, & Hofschire, 2006; Valcke, Bonte, De Wever, & Rots, 2010; Wong, 2010).

To date, in the traditional child-rearing literature, several prior studies have attempted to understand different types of parenting styles and their impact on children’s development (Baumrind, 1966; Cohn, Cowan, Cowan, & Pearson, 1992; Greco & Morris, 2002; Maccoby & Martin, 1983). However, in comparison, only a little prior research has examined the nature of Internet parenting and its impact on children’s development, behavior, and attitudes. Similarly, scholars have also argued that little prior research has examined the different Internet parenting styles (Chou et al., 2016; Stern, Cotton, & Drentea, 2011). Due to this, there are very few valid and reliable measurement instruments examining different Internet parenting styles available. Regarding this issue, Valcke et al. (2010) observed that although some promising research has been carried out on Internet parenting styles, its empirical basis is limited, and it did not focus on the different dimensions of parenting styles. In addition to this, it was also observed that most of the prior available research has been mainly carried out in Western and Far Eastern countries, while developing countries (e.g., India) have mostly been ignored (Ihmeideh & Shawareb, 2014). It is important to obtain a deeper understanding of the different Internet parenting styles because it will enable scholars to better understand the nature of Internet parenting and its effect on children’s development and well-being. Furthermore, Internet parenting styles have been found to be significantly associated with the Internet use behavior of children, particularly their engagement in risky online behavior and compulsive Internet use (e.g., Leung & Lee, 2012; Liau, Khoo, & Ang, 2005; Lwin, Stanaland, & Miyazaki, 2008). Therefore, deeper understanding of different Internet parenting styles can actually give us new insights into the challenges experienced by children in the online world.

This pressing need is addressed in the present study by developing a valid and reliable measurement instrument examining the four different Internet parenting styles, namely parental encouragement, parental worry, parental monitoring, and parental permission. This proposed instrument can be used by scholars to better understand the impact of parenting on the Internet use behavior and well-being of children and young people. For example, scholars can use this instrument in their investigations to uncover new knowledge pertaining to Internet parenting styles. Consequently, this instrument significantly contributes to and also advances the field of research on Internet parenting. The current study also addresses the lack of cross-cultural empirical studies on Internet parenting by administering repeated cross-sectional surveys over a period of 2 years with Internet users in the developing world (i.e., Indian adolescents and young-adult Internet users were recruited). India has emerged as the world’s second largest Internet user base, with over 462 million Internet users. Furthermore, India has witnessed a nearly 500% increase in the Internet penetration rate in the past 5 years (Tech Asia, 2016). Due to this sudden rise in the Internet use of Indian households, parents of many digital natives (i.e., those born in the late 1990s; Prensky, 2001) might not be fully equipped to mediate the Internet use of their children. Scholars argue that Internet parenting of digital natives is difficult and challenging (Wong, 2010). Consequently, there is an urgent need to understand the Internet parenting styles experienced by digital natives in India. The following are the main research questions (RQ) of this study: RQ1: What are the different Internet parenting styles exercised by parents to mediate their children’s Internet use? RQ2: Does the developed instrument have a stable factorial structure, validity, and reliability over time? RQ3: Is the developed instrument reliable and valid for adolescent Internet users attending public schools? RQ4: Is the developed instrument reliable and valid for young-adult Internet users (attending university)?

Background Literature

Traditional Parenting Styles

Young children and particularly adolescents spend much of their time at home, due to which parents play an important role in their overall development (Chou et al., 2016). Most scholars agree that positive parental involvement is associated with positive development and performance in the later stages of a child’s life (Weiss, Caspe, & Lopez, 2006). Similarly, parenting and parental support have long been associated with their well-being. Williams and Lyon (1976) argued in the traditional child-rearing literature that lower parental warmth and higher conflicts result in suicide ideation among adolescents. Furthermore, adolescents tend to develop different coping skills to escape the difficult family environment (Campbell, Milling, Laughlin, & Bush, 1993). Similarly, poor family support and conflicts have been strongly associated with Internet addiction among adolescents in Korea (Park, Kim, & Cho, 2008). Scholars have also found that insufficient parenting and related support results in psychological instability and a higher tendency of engagement in Internet overuse and risky online behavior (Leung & Lee, 2012; Liau et al., 2005; Lwin et al., 2008).

The prior literature presents a rich history of several decades of rigorous research on traditional parenting styles. To begin with, Baumrind (1966, 1971) introduced three parenting styles, namely authoritative (i.e., setting clear rules through guidance, involvement, discussion, and encouragement); authoritarian (involving strict rules, punitive disciplinary measures, verbal hostility, and low warmth); and permissive (i.e., enabling children to self-regulate their Internet use instead of constructing rules and giving more freedom to children). Later, Maccoby and Martin (1983) replaced permissive parenting with neglectful (i.e., indifferent parents who rarely provide any feedback or emotional support due to low interaction with their children) and indulgent (e.g., those parents who seldom discipline their children) parenting as the third and fourth types of parenting styles. Furthermore, they reclassified all four styles into two groups, namely responsiveness (i.e., adapting and responding to the child’s needs and signals, communicating with children and providing them with required support) and demandingness (or parental restrictiveness, e.g., exercising consistent discipline and expecting moral maturity). Based on their reclassification, authoritative parenting involves high demandingness and high responsiveness; neglectful parenting involves low demandingness and low responsiveness; indulgent parenting refers to low demandingness and high responsiveness; and authoritarian parenting involves high demandingness but low responsiveness. This two-dimensional typology of responsiveness and demandingness was also later accepted by Baumrind (1991) in her work. Scholars have also referred to parental responsiveness as parental warmth and demandingness as parental permissiveness (Becker, 1964) or parental control (Schaefer, 1965). In comparison with these works, Hoffman (1970) also identified three different parenting styles, namely withdrawal of love, assertion of power, and induction. The withdrawal of love refers to different ways of creating anxiety among children after misbehavior, for example, withholding attention, approval, and affection from the child. The assertion of power involves use of superior power to transform a child’s attitudes and behavior. In comparison, induction refers to modifying a child’s behavior by stressing why a specific behavior was wrong and what are its possible negative consequences. All of these fundamental traditional parenting styles form the basis of the Internet parenting styles presented in Section Internet Parenting Styles.

Internet Parenting Styles

With the rising affordances of Internet use among children, parents started with applying various mediation strategies to intervene in their children’s Internet use (e.g., website block software; Eastin et al., 2006). Scholars believe that Internet parenting styles exercised by parents are effective, while school-based interventions are ineffective in mediating children’s Internet use (Valcke et al., 2007). The prior extended literature has used different terms to denote parenting styles, namely parent–child interaction, parental disciplinary techniques, socialization, and parental mediation strategies (Chou et al., 2016). For the convenience of our readers, we have used Internet parenting styles as a term to denote parental mediation of their children’s Internet use.

The parenting of Internet use in the home has a long history, with its origin lying in the traditional parenting styles for managing the use of computers, television, and gaming sessions at home (Mascheroni & Ólafsson, 2014; Shepherd et al., 2006). For example, scholars have identified three parental mediation strategies in the context of media use (e.g., TV, gaming, and computer use) based on the different traditional parenting strategies. These include active or conversation mediation (e.g., holding conversations with the child regarding the content they are currently assessing), restrictive or negative mediation (e.g., constructing specific rules regarding the amount of time spent and type of content assessed), and coviewing of the media (Nathanson,1999; Valkenburg, Krcmar, Peeters, & Marseille, 1999). Similar findings were observed by the scholars in the context of Internet parenting styles. Downes (2002), Koolstra and Lucassen (2004), and Nikken and Jansz (2003) found that parents tend to practice restrictive parenting (e.g., time- and content-based restrictions) and coviewing strategies to mediate the use of the Internet at home. Afterward, Eastin et al. (2006) identified three types of Internet parenting style, namely factual, evaluative, and restrictive parenting. They found that authoritative parents are more likely to use evaluative and restrictive parenting compared with those who have adopted neglectful and authoritarian parenting styles. Subsequently, Livingstone and Helsper (2008) identified four types of parental mediation of children’s Internet use, namely social mediation (i.e., combining active mediation and co-use), interaction restrictions (i.e., imposing restrictions on children’s interactions online), technical restrictions (e.g., parental controls that limit online activities and time spent online), and the active monitoring of children’s online practices. A few years later, the EU Kids Online survey identified five Internet parenting mediations, namely active mediation of Internet safety, restrictive mediation, monitoring, and technical mediation (Livingstone, Haddon, Görzig, & Ólafsson, 2010). In contrast, Valcke et al. (2010) identified mixed Internet parenting styles that actually suggest a median level of parental demandingness and parental responsiveness to mediate their Internet use. However, in comparison, other scholars have used a four-dimensional parenting model of Internet parenting styles (i.e., authoritative, authoritarian, permissive, and neglectful; see Chan & Koo, 2011; Horzum & Bektas, 2014; Ihmeideh & Shawareb, 2014). Similarly, Lau and Yuen (2013) used a four-dimensional Internet and communication (ICT) parenting scale representing the encouragement and worry (high and low warmth), and monitoring and permission (high and low control) dimensions. Lau and Yuen (2013) investigated whether the ICT parenting styles correlate with different forms of risky online behavior. Despite all of these investigations, the literature still lacks valid and reliable psychometric instruments for examining different styles of Internet parenting exercised by parents to mediate their children’s Internet use. It is important to address this pressing need because valid and reliable measures of Internet parenting styles will enable scholars to use them for their investigations. Therefore, such an instrument will certainly lead to advancements in the research on Internet parenting. The present study has addressed this pressing need by developing a 10-item instrument representing four types of Internet parenting style.

Challenges in Internet Parenting

Practicing different styles of Internet parenting at home is considered complex and not easy to do; for example, 66% of parents in one study agreed that it is challenging and difficult (Wang, Bianchi, & Raley, 2005). Based on our systematic review of the prior literature, three major challenges in successfully practicing different Internet parenting styles with children were identified. These complexities pertaining to the parenting of Internet use at home actually evidence the urgent need for new empirical studies for a better understanding of Internet parenting styles and related attitudes and behaviors. Furthermore, to obtain this new knowledge, there is a need to develop valid and reliable measurement instruments examining different Internet parenting styles.

Conflict between children and parents

Internet parenting could potentially lead to disputes and controversies where parents are often accused by their children of breaching their privacy (Ringheim, 2014). Parents tend to believe that excessive Internet use has undesirable effects on children and are relatively much more concerned; in contrast, their children do not have such perceptions and are less concerned about the potential dangers of Internet use (Koolstra & Lucassen, 2004; Shepherd et al., 2006). According to Mascheroni and Ólafsson (2014), parents should understand that children are active recipients of Internet parenting at home because they tend to negotiate, resist, and even ignore Internet parenting exercises carried out by their parents. Furthermore, parents are required to understand the child’s perception of the online risks and enable them to develop suitable coping strategies (Staksrud & Livingstone, 2009). This debate between children and their parents concerning the risks involved in the use of the Internet results in a situation of conflict, distrust, and anxiety between them (Shepherd et al., 2006; Wong, 2010). Consequently, there is a growing need to understand the impact of different forms of Internet parenting on parent–child relationships, for which empirical measures on Internet parenting are first required.

Technological gap between children and parents

Children possess more technological know-how compared with their parents, due to which they often tend to resist Internet parenting (Shepherd et al., 2006). Similarly, Wong (2010) observed that if parents are ill-equipped in terms of the proper Internet parenting styles, then their children would usually be attracted to the entertainment and gaming gratifications of the Internet. Wong (2010) argued that the digital impoverishment of parents is a big hurdle for their children to make use of the full potential of the Internet. Furthermore, the narrowing of the digital gap between parents and children is key to experiencing satisfaction while exercising Internet parenting at home (Wong, 2010). Due to this, parents require technological know-how, parenting and communication skills, and a conducive environment to mediate their children’s Internet use (Wong, 2010). Furthermore, not only do they need technical know-how, but parents must be able to afford and own the required technologies for successful parenting of Internet use (Tynes, 2007).

Lack of knowledge

Parents often remain inconclusive, undecided, and restless, even after serious deliberations on how much, to what extent, and when children should be allowed to use the Internet (Shepherd et al., 2006). Similarly, parents often tend to make unsuccessful attempts to resolve the dilemma between transforming their children into competent adults through the use of Internet-based technologies while at the same time controlling their Internet use by either ignoring or restricting different opportunities for Internet use (Stakrud & Livingstone, 2009). Wong (2010) argued that parents often deliberate between the opportunity cost and excessive Internet use at home. For example, parents often view every minute spent on Internet use as a lost opportunity to carry out hobbies, engage in academic work, communicate with family, and play outdoors (Wong, 2010). Consequently, parental attitudes toward their children’s use of the Internet are often viewed as ambiguous, nuanced, and even distrustfully anxious (Shepherd et al., 2006). The situation is no different in the context of digital natives who have never experienced a world without the Internet (Valcke et al., 2010). Internet parenting is totally new for many parents of digital natives, especially in the developing world (e.g., India) because the earlier generations of parents may not have encountered the Internet before (Wong, 2010). Consequently, there is a pressing need to understand the Internet parenting of digital natives, especially in developing countries like India that have recently witnessed a drastic change in the percentage of Internet penetration (Tech Asia, 2016). The present study has bridged this gap by developing and validating a 10-item instrument representing four Internet parenting styles tested in the context of adolescents as well as young-adult Indian Internet users.

Method

Data Collection and Study Participants

This research involved a 2-year-long investigation comprising three waves of data collection spread over several repeated cross-sectional studies (see Figure 1). The different stages of data collection comprise several cross-sectional studies, namely Study A (N = 539) in January 2013; Study B2 (N = 2,369) exactly 1 year after the first study (i.e., January, 2014); and the remaining four studies, Study C (N = 997), Study D (N = 2,129), Study E (N = 274), and Study F (N = 311) 2 years after the first study (i.e., carried out in January 2015). The study methodology was influenced by repeated cross-sectional studies with adolescent (aged 12 to 19 years) and young-adult, also including late adolescent (aged 17 to 23 years) Indian Internet users. Such repeated cross-sectional studies are popularly referred to as pseudolongitudinal studies and address the inherent challenges of longitudinal studies. For example, in repeated cross-sectional studies, participants can be selected from outside the original pool; it is cost effective to perform resampling (e.g., in case of the high attrition of participants); and inclusion of new participants enables scholars to maintain a steady proportion of sample reliability unlike in longitudinal investigations (Levin, 2006; Yee & Niemeier, 1996). The target population of adolescent Internet users in Studies A, B, and C were recruited from private educated junior and senior high schools from Northern India, while Study D comprises adolescent Internet users from Southern India. In all of these four studies, the participating institutions cater to students from the lower to upper middle-income strata, to which the largest Internet user base in India belongs (Dhir, 2015). The participating institutions had English as the medium of instruction and communication. Study E consisted of public school attending adolescents, and Study F was composed of young-adults and late adolescent Internet users who came from two private large-sized universities (see Table 1 for details of the demographic distribution). All six substudies comprise a similar research process and data collection methodology, particularly the participation recruitment and evaluation. First, a list of junior and senior high schools was randomly chosen from an online directory. The chosen schools were contacted via phone or email, and the research objectives, related process, and anticipated benefits were explained. At this stage, the interested schools invited the lead author to have face-to-face discussions, and the lead author sought approval for conducting the research. After receiving the due approval from the participating schools, the study objectives, RQ, related process, and anticipated benefits were advertised among the target population. All participating schools allocated one or more time slots for interested students to participate in our study. The lead author along with one or more school teachers administered and managed the survey-answering sessions inside the classrooms. The pen-and-paper surveys were administered in English. The collected data samples were self-selected, and every student received equal chances of study participation, which was kept voluntary, anonymous, and confidential. The descriptive statistics of the demographic profile of the participants of each of the studies are presented in Table 1.

Figure 1.

Different stages of the instrument development.

Table 1.

Descriptive Statistics of the Participants’ Demographic Information.

Demographic variables	Category	Percentage (frequency)
Demographic variables	Category	Study A (N = 539)	Study B (N = 2,369)	Study C (N = 997)	Study D (N = 2,129)	Study E (N = 274)	Study F (N = 331)
Age	12–13	10.9 (62)	23.0 (541)	13.1 (131)	15.3 (327)	1.5 (4)	–
	14	24.1 (130)	28.3 (663)	34.5 (344)	36.9 (786)	.7 (2)	–
	15	35.8 (193)	29.5 (692)	36.1 (360)	19.7 (420)	4.7 (13)	–
	16	19.5 (105)	12.9 (304)	12.7 (127)	21.3 (454)	35.8 (98)	–
	17–19	8.7 (47)	6.4 (151)	2.7 (27)	5.2 (110)	56.5 (155)	57.2 (178)
	20	–	–	–	–	–	20.6 (64)
	21–23	–	–	–	–	–	22.2 (69)
	M (SD)	14.91 (1.17)	14.50 (1.26)	14.67 (3.44)	14.62 (1.19)	16.60 (.93)	19.51 (1.65)
	Range (age)	12–18	12–19	12–18	12–18	12–19	17–23
Gender	Male	47.9 (258)	59.6 (1,412)	59.4 (592)	61.3 (676)	49.3 (135)	62.7 (195)
Gender	Female	51.0 (275)	40.1 (950)	40.0 (399)	38.3 (423)	50.0 (137)	37.3 (116)

Internet Parenting Instrument

The different phases of the proposed instrument development and validation are presented in Figure 1. In the beginning, an initial item pool of 17 items representing three different Internet parenting styles, namely parental worry, parental encouragement, and parental permission, was used. The 17-item pool represented the ICT parenting styles and was developed by the researchers of the research project Educational Inequality and ICT Use in Schools: Bridging the Digital Divide at The University of Hong Kong (Lau & Yuen, 2013). The item stems of the original 17-item pool examined the parenting styles in the context of both Internet and computers where the terms Internet and computers were used interchangeably. However, in comparison, we modified the item stems by using only Internet to access the different Internet parenting styles.

A pilot study with 25 adolescent Internet users from the participating Indian schools was carried out in December 2012 to examine the face and content validity of the item pool. The pilot study was important because in the present study, the main focus has been on Internet parenting and not ICT parenting. The former focuses exclusively on Internet use-related parenting, while the latter focuses on different forms of ICT including computers, the Internet, handheld devices, and consoles. The pilot study focused on locating problematic and unclear statements from the 17-item pool and concluded with various suggestions concerning the wording of the item-stems. The 17-item pool was subsequently updated based on the pilot study. Later, participants of Study A (N = 539) evaluated the updated item pool of 17 items using a 5-point response scale anchored with strongly disagree and strongly agree. The exploratory factor analysis (EFA) was performed using this collected data and resulted in a 10-item scale representing three different Internet parenting styles, namely parental worry, parental encouragement, and parental permission. In the next step, a 10-item pool was again complemented by adding 4 new items using the updated literature on Internet parenting styles in September 2013 (e.g., Lau & Yuen, 2013). Parental monitoring was the fourth dimension included in the item pool that resulted in the development of a 14-item scale presenting four dimensions of Internet parenting. Afterward, a second pilot study was carried out with 20 adolescent Internet users from similar schools. This was important to ascertain the face validity of the new pool of items. Similar to the first pilot study, various suggestions concerning the wording of the item-stems were incorporated; the 14-item pool was updated accordingly and was later evaluated by the participants of Study B (N = 2,369) in January 2014. Afterward, confirmatory factor analysis (CFA) of the hypothesized four-dimensional structure was performed using Study B. During the analysis, four items were deleted because they did not satisfy the conditions of convergent and discriminant validity and possessed low factor loadings. Later, the CFA of the 10-item Internet parenting scale representing a four-factor structure was performed, and it resulted in a good model fit and satisfactory instrument validity and reliability for the four-dimensional structure.

Later in January 2015, exactly 2 years after Study A, the 10-item Internet parenting scale was evaluated again with the adolescent Internet users of Study C (N = 997) who were recruited from the same schools that had participated in Studies A and B. In addition to this, the 10-item scale was also evaluated with adolescents from the Southern part of India in Study D (N = 2,129), public school attending adolescents in Study E (N = 274), and university attending young-adult and late adolescent Internet users in Study F (N = 311). The CFA using data from Studies C, D, E, and F also confirmed that the 10-item scale on Internet parenting styles possessed a stable factorial structure and sufficient instrument validity and reliability.

Data Processing

The item pool representing different Internet parenting styles was normally distributed because their skewness and kurtosis values were in the acceptable range of ± 1 across all six data sets (Byrne, 2010; George & Mallery, 2003; Hair, Anderson, Tatham, & Black, 1998). The z score for the item pool was computed in each of the six data sets to locate any potential outliers in the data. Tabachnick and Fidell (2007) suggested the threshold value of 3.29 for the item z score, and use cases that exceeded this threshold were deleted. The resulting data sets were used for further analysis (see Table 1).

Results

A three-step process was followed to develop and validate a psychometrically valid and reliable scale for the different Internet parenting styles. In the first step, because there was no a priori factorial structure for the proposed item pool that was originally developed by researchers in Hong Kong, EFA was the natural choice; it was performed using Study A to find the optimal factor structure for the proposed instrument. Second, after obtaining the a priori structure for the item pool, the literature on Internet parenting styles was reviewed, and the item pool was complemented. At this stage, a four-factor structure was hypothesized, and CFA using Study B was conducted to confirm the dimensionality. Third, the process of CFA was repeated using the data from Studies C, D, E, and F to confirm the presence of the factorial stability and other forms of instrument validity and reliability over time.

EFA

Study A was suitable for performing EFA because it returned a good value of 0.72 for the Kaiser–Meyer–Olkin test that evaluates sampling adequacy (Kaiser, 1970) and a statistically significant value for Bartlett’s test for sphericity (χ²= 1251.88, df = 45, p < .01; Bartlett, 1954). Afterward, EFA was performed using the maximum likelihood algorithm with varimax rotation and 0.40 as the threshold limit for successful item loading. The process of deleting items was repeated until a stable set of items and factors was obtained. It resulted in the construction of 10 items representing three factors, namely parental permission, parental encouragement, and parental worry with an eigenvalue > 1.0 as per the Kaiser criterion (Bantz, 1982; see Table 2)

Table 2.

Exploratory Factor Analysis Using Study A.

Internet parenting styles	Study A (N = 539)
Internet parenting styles	Permission	Encouragement	Worry
Parental permission (λ = 2.98, σ²= 29.76)
PP1: My parents allow me to meet friends online	.65
PP2: My parents allow me to download songs or movies	.79
PP3: My parents allow me to play online games	.75
PP4: I always follow the online time limit rules set by my parents	–
PP5: I never visit the websites that my parents forbid me to visit	–
PP6: My parents have to urge me to shut down the Internet when the time is up	–
Parental encouragement (λ = 1.25, σ²= 12.49): My parents…
PE1: encourage me to use the Internet more	.44	–
PE2: think that being good at the Internet is useful for my future		–
PE3: always talk to me about the benefits of using the Internet		.53
PE4: share their Internet use experience with me		.74
PE5: always help me with Internet use		.46
PE6: always communicate with me about the benefits of the Internet		–
Parental worry (λ = 1.76, σ²= 17.57): My parents…
PW1: worry about my health problems when I am using the Internet (e.g., eyes, bones)			–
PW2: believe that the more I am online, the less communication they have with me			.44
PW3: worry about my thinking ability if I depend too much on the Internet			.99
PW4: worry about the online risks I face (e.g., negative information, pornography, violence)			.49
PW5: always ask me what I do on the Internet			–

Note. λ = Eigenvalue, σ²= percentage variance explained, total variance explained = 59.82%, internal reliability (α) = .72

CFA

The prior measurement literature has recommended the threshold values for good model fit as follows: χ²/df < 3, comparative fit index (CFI) ≥ .92, Tucker–Lewis index (TLI) ≥ .92, and root mean square error of approximation (RMSEA) < .08 (Byrne, 2010; Hu & Bentler, 1999). CFA was performed on the 14-item pool using the Study B data (see Table 3). Four items were deleted due to poor factor loading and because they did not satisfy the conditions of convergent and discriminant validity (see Section Validity and Reliability). Later, the 10 items representing a four-dimensional structure were examined using CFA, and it resulted in a good model fit (see Table 4). Therefore, study B clearly suggested that the 10-item scale examining Internet parenting styles possesses a four-dimensional structure representing four types of Internet parenting style.

Table 3.

CFA of the Internet Parenting Scale Using Different Study Samples.

Internet parenting styles	Study B (N = 2,369)		Study C (N = 997)		Study D (N = 2,129)		Study E (N = 274)		Study F (N = 331)
Internet parenting styles	β	R ²	β	R ²	β	R ²	β	R ²	β	R ²
Parental encouragement (My parents…)
share their experience of using the Internet with me	.74	.55	.80	.64	.74	.54	.68	.46	.70	.48
always help me with using the Internet	.72	.52	.59	.35	.63	.40	.88	.78	.78	.61
Parental worry (My parents worry that if I use the Internet too often, it will…)
cause health issues	.61	.38	.74	.54	.66	.44	.75	.56	.62	.38
decrease the time of communication with them	.63	.39	.77	.60	.69	.48	.78	.61	.75	.55
affect my thinking ability if I depend too much on it	.64	.41	.69	.48	.67	.44	.73	.53	.68	.46
have a negative impact on me because of the online risks (e.g., online violence, pornography)	.62	.39	.64	.41	.65	.42	.67	.45	.66	.44
Parental monitoring (My parents always ask me…)
what I do on the Internet	.86	.74	.84	.71	.84	.70	.74	.55	.81	.65
who I chat with on the Internet	.60	.36	.76	.58	.59	.34	.86	.75	.73	.54
Parental permission (My parents allow me to…)
download songs or movies	.55	.30	.39	.15	.74	.54	1.01	1.01	.49	.24
play online games	.94	.88	1.29	1.67	.72	.52	.49	.24	1.26	1.58

Note. Standardized regression coefficients = β, squared multiple correlation = R². CFA = confirmatory factor analysis.

Table 4.

Model Fit Indices for the Internet Parenting Scale Using Different Study Samples.

Model fit indices	Study B (N = 2,369)	Study C (N = 997)	Study D (N = 2,129)	Study E (N = 274)	Study F (N = 331)
X ² /df	5.15	3.59	3.53	1.86	2.11
CFI	.97	.97	.98	.97	.96
TLI	.96	.96	.97	.95	.94
RMSEA	.04	.05	.03	.06	.06

Note. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation.

Stability of Factorial Structure

To investigate the stability of the factorial structure over time, the four-dimensional structure was again tested using Study C (see Table 3). The 10-item scale with a four-dimensional structure suggested a good model fit (see Table 4). This clearly suggests that the instrument examining four different Internet parenting styles possessed a stable factorial structure. Later on, the factorial stability of the Internet parenting scale was also examined in the context of adolescent Internet users from Southern India using Study D, adolescents attending public schools via Study E, and young-adult Internet users attending universities in Study F. Similar to Studies B and C, the four-dimensional structure representing four different Internet parenting scales indicated a good model fit (see Table 4). This clearly suggests that the instrument also possesses a four-dimensional structure in the cases of adolescents from Southern India, those attending public schools, and young-adult Internet users.

Validity and Reliability

The examination of instrument validity and reliability is considered of utmost important for the newly developed instrument because different statistical indicators are required to ascertain genuineness and applicability (Hinkin, 1998). Consequently, the different forms of instrument validities and reliabilities were examined in the context of the 10-item Internet parenting scale. These are discussed and presented as follows.

Content validity

This refers to examining the appropriateness of the content, that is, whether the chosen items of the instrument actually present the different aspects of the proposed concept, for example, different styles of Internet parenting in our case. The present study ensured the content validity of the developed instrument through the following steps. First, the initial pool of 17 items representing three different ICT parenting styles that was evaluated in Study A was originally developed by a group of researchers from The University of Hong Kong (Lau & Yuen, 2013) who were well-versed in the prior literature on Internet parenting styles, and the items had already been cross-examined by international readers and researchers. Second, two pilot studies were carried out with a target group of adolescent Internet users, one before Study A and the second before Study B, to ascertain the content validity of the item pool.

Factorial validity

This investigates the degree to which the developed instrument actually produces a stable and recoverable factor structure, that is, an instrument with highly predictable dimensionality. The present study used four repeated cross-sectional studies, namely Studies C, D, E, and F, which found that the four-dimensional structure of the Internet parenting scale is a stable and recoverable factorial structure.

Discriminant validity

According to the structure equation modeling literature, discriminant validity examines if the theoretically dissimilar study constructs are in fact unrelated to each other (Anderson & Gerbing, 1988). Prior literature has suggested different tests for confirming the presence of sufficient discriminant validity for the developed instrument, namely, correlation between any two pairs of the study constructs should not exceed .60 (Campbell & Fiske, 1959), the average shared variance (ASV) should not exceed the corresponding average variance extracted (AVE; Barclay, Higgins, & Thompson, 1995), and AVE should also be greater than the corresponding maximum shared variance (MSV) for the study constructs. It was found that all four Internet parenting styles satisfied the aforementioned conditions of discriminant validity across Studies B, C, D, and E. However, in the case of Study F, AVE < MSV for parental worry, but it satisfied the other two conditions for ensuring discriminant validity. Therefore, the present study established sufficient discriminant validity for the 10-item Internet parenting scale using five different studies (see Table 5).

Table 5.

Examination of the Validity and Reliability of the Internet Parenting Scale Using Different Study Samples.

Internet parenting styles	Study B (N = 2,369)				Study C (N = 997)				Study D (N = 2,129)				Study E (N = 274)				Study F (N = 331)
Internet parenting styles	CR	AVE	MSV	ASV	CR	AVE	MSV	ASV	CR	AVE	MSV	ASV	CR	AVE	MSV	ASV	CR	AVE	MSV	ASV
Encouragement	.70	.53	.07	.03	.66	.50	.02	.02	.69	.43	.10	.05	.76	.62	.06	.03	.71	.55	.04	.03
Worry	.72	.39	.27	.10	.80	.51	.30	.11	.76	.44	.30	.11	.82	.54	.16	.07	.77	.46	.50	.18
Monitoring	.70	.55	.27	.09	.78	.65	.30	.10	.68	.52	.30	.11	.79	.65	.16	.08	.75	.60	.50	.18
Permission	.73	.59	.07	.03	.94	.91	.02	.01	.69	.53	.10	.04	.75	.63	.06	.04	.95	.92	.04	.02

Note. CR = composite reliability; AVE = average variance extracted; MSV = maximum shared variance; ASV = average shared variance.

Convergent validity

This investigates the degree to which theoretically similar constructs are in fact related to each other. The prior structure equation modeling literature suggested different conditions for confirming the presence of sufficient convergent validity. This includes the requirement that standardized estimates for the instrument items must be significant (Anderson & Gerbing, 1988), and AVE values should be greater than .50 but should not exceed the corresponding composite reliability (CR) value (Hair, Black, Babin, Anderson, & Tatham, 2006). It was found that the 10-item Internet parenting scale fulfilled all of these criteria for confirming the presence of sufficient convergent validity in the case of Studies C, D, and E. However, in the case of Studies B and F, AVE < .50 for the parental worry dimension, but it satisfied the other two conditions for ensuring convergent validity. Therefore, the developed instrument possesses sufficient convergent validity across all five different studies (see Table 5).

Internal reliability

This examines the internal reliability of the developed scale as a whole. Scholars have suggested that a Cronbach’s alpha (α) value ≥ .70 is required for confirming the presence of sufficient internal reliability of any developed scale (Cronbach & Meehl, 1955; DeVellis, 2003; Nunnally, 1978; Nunnally & Bernstein, 1994). The examination of α value across all five repeated cross-sectional studies suggests that this instrument possesses sufficient internal reliability (α = .70, .72, .70, .73, .70).

CR

Recently, scholars have argued that CR should be used instead of α because the latter is known for underestimating or overestimating the true reliability of the instrument (Peterson & Kim, 2013). The threshold limit for CR value is ≥.70 for confirming sufficient reliability of the different dimensions of Internet parenting. The results suggest that all four Internet parenting styles possess sufficient reliability across all five studies with some exceptions (see Table 5). It was observed that the encouragement dimension has CR < .70 for Studies C and D, but for all other study samples, CR > .70. It should be noted that encouragement was measured using two items only, due to which CR was unstable and low. Furthermore, the sample size for Study D was the lowest compared with all other study samples. The monitoring and permission dimensions had CR < .70 for Study D, but on rounding off, the CR value was .70. Furthermore, CR for the encouragement, monitoring, and permission dimensions was fairly stable across all five studies. This indicates that all four Internet parenting styles possess sufficient instrument reliability across all studies (see Table 5).

Discussion

Parenting plays an important role in the all-round development and well-being of young children. Similarly, recent promising research has suggested that Internet parenting is also closely related to children’s development and significantly impacts children’ Internet use behavior. However, despite this, only limited prior research has examined the different aspects of Internet parenting and its impact on their children’s well-being. Due to this, valid and reliable psychometric instruments measuring different Internet parenting styles is missing from the prior literature. In addition, the prior research also lacks cross-cultural studies that investigate Internet parenting and related issues in the context of developing countries (such as India) that have observed rapid penetration of the Internet in their households. The present study addresses some of these open research gaps by developing a 10-item valid and reliable instrument that measures four different Internet parenting styles, namely parental encouragement, parental permission, parental worry, and parental monitoring. The developed instrument was validated using several repeated cross-sectional studies with adolescent and young-adult Internet users, which is rare among the new media and computer-mediated communication literature.

The first research question (RQ1) examines the different Internet parenting styles exercised by parents for mediating their children’s Internet use. To address this RQ, a 10-item scale addressing four different styles of Internet parenting was developed. To develop the item pool, the prior child-rearing literature as well as empirical studies on Internet parenting styles was reviewed. Relatively recent Internet parenting literature has recommended the need to consider both demandingness and responsiveness as part of the Internet parenting styles (Valcke et al., 2010). Parental demandingness is similar to parental control (Schaefer, 1965), while parental responsiveness is equivalent to parental warmth (Becker, 1964). Similarly, Lau and Yuen (2013) emphasized the importance of parental control (high and low) and parental warmth (high and low) as two dimensions of Internet parenting. Therefore, we derived the four dimensions of our Internet parenting scale from Lau and Yuen’s (2013) work in which they used parental encouragement (high warmth) and parental worry (low warmth) as part of parental warmth, and parental permission (low control) and parental monitoring (high control) as part of parental control. This classification is also consistent with the dimensions of the traditional parenting styles; for example, parental encouragement is similar to the responsiveness (Maccoby & Martin, 1983; Valcke et al., 2010) and authoritative styles of traditional (Baumrind, 1966, 1971) as well as Internet parenting (Chan & Koo, 2011; Horzum & Bektas, 2014; Ihmeideh & Shawareb, 2014). Parental monitoring is part of active or conversation mediation (Nathanson,1999; Valkenburg et al., 1999) as well as active monitoring of children’s online practices (Livingstone & Helsper, 2008). Similarly, parental permission is similar to parental demandingness (Maccoby & Martin, 1983; Valcke et al., 2010) and the authoritarian style of traditional (Baumrind, 1966, 1971) as well as Internet parenting (Chan & Koo, 2011; Horzum & Bektas, 2014; Ihmeideh & Shawareb, 2014). The EFA and CFA concluded with the development of a four-factor scale representing four different Internet parenting styles.

The second research question (RQ2) examined if the developed instrument has a stable factorial structure, validity, and reliability over time. The four-dimensional structure (i.e., obtained from Study B) was evaluated through the CFA again using Study C (i.e., 2 years after Study A). Furthermore, the same structure was also evaluated in the context of adolescent Internet users from Southern India (i.e., Study D). The CFA suggested that the four-factor structure has a good model across both the studies; hence, the 10-item instrument has a stable factorial structure. Similarly, different forms of instrument validity and reliability were also examined in the context of the 10-item instrument using the Study C and D samples. The results suggested that the developed instrument is valid and reliable over time.

The third research question (RQ3) investigated if the developed instrument is also reliable and valid for adolescent Internet users attending public schools. The four-dimensional Internet parenting scale was evaluated with the CFA of Study E (i.e., adolescents attending public schools); the four-factor structure returned a good model fit. Furthermore, the four-dimensional instrument also showcases sufficient instrument validity and reliability.

The last research question (RQ4) examined if the developed instrument was reliable and valid for young-adult Internet users (attending university). Similar to RQ3, the four-dimensional Internet parenting scale was tested using the CFA of Study F (i.e., young-adult Internet users attending universities). The results suggested that the four-dimensional structure has a good model fit and possesses sufficient instrument validity and reliability.

Study Implications

This study concludes with several theoretical and practical implications for scholars as well as practitioners engaged in the field of human–computer interaction, new media, Internet behavior, and the well-being of youth. The present study makes three significant theoretical contributions. First, it thoroughly examined the prior literature on traditional as well as Internet parenting to understand the nature and impact of Internet parenting on the well-being of young people. Based on this review, the existing challenges to successfully exercising Internet parenting as well as the limitations of the prior Internet parenting literature were highlighted. This was important to inform scholars of the current open issues that need to be addressed in the future research. Therefore, this study can help in accelerating the Internet parenting research agenda. Second, the prior Internet parenting literature has argued for examining the impact of Internet parenting on Internet use behavior as well as the well-being of young people. However, it is important to first develop valid and reliable measurement instruments on Internet parenting. Consequently, the present study has developed a 10-item scale examining different Internet parenting styles using several independent cross-sectional studies with adolescents and young-adults. The developed scale presents a broader picture of the different Internet parenting styles followed by parents. The instrument development is aligned with the recent literature that has highlighted and argued in favor of developing new empirical measures of Internet parenting (see Chou et al., 2016; Ihmeideh & Shawareb, 2014; Valcke et al., 2010). Moreover, the items and four-dimensional structure of the 10-item Internet parenting scale are consistent with the ICT parenting measure used by the recent Internet parenting literature (i.e., Lau & Yuen, 2013). Thus, this study contributes to the development of Internet parenting research by contributing to the growing body of research literature on Internet parenting. Third, it contributes to the scarce literature that examines Internet parenting in the context of developing countries such as India. As mentioned before, India has witnessed rapid Internet penetration (500% increase), and its current Internet user base is over 462 million. Due to this rapid Internet penetration in Indian households, many parents are not sufficiently prepared and nor do they possess the required Internet parenting experience. This calls for new empirical studies to study the impact of Internet parenting on the well-being of young children in India, for which valid and reliable measurement instruments are needed. The present study provides scholars with a new empirical valid and reliable instrument that can be used to investigate Internet parenting and related issues in a much broader and holistic manner. These theoretical implications clearly suggest that this study greatly expands our existing understanding of Internet parenting styles.

The three main practical implications of this study are as follows: First, the study results will encourage other scholars to use our Internet parenting scale and validate it in other cultural and demographic settings. Similarly, scholars can use this instrument to understand different open research gaps on Internet parenting (e.g., its impact on compulsive Internet use, risky online behavior, and moral behavior). Therefore, the developed instrument will enable scholars to obtain a deeper understanding of various critical issues pertaining to Internet parenting styles. Second, the developed instrument is short and concise; thus, it is economical for future investigations. Scholars working with young children experience problems related to fatigue and lack of motivation to participate in different intensive investigations related to the development and well-being of young children. Therefore, the developed instrument is an ideal psychometric measure for such investigations. Third, the study setup and the process of instrument development can motivate other scholars to develop other instruments related to human–computer interaction, new media, and the Internet behavior of youth.

Study Limitations and Future Work

The present study has three important limitations that must be addressed in future studies. First, the developed instrument has been validated with young Internet users only from India. Due to this, the generalizability and applicability of the developed instrument to other cultural settings is currently not known. Second, the instrument development process was overly influenced by the quantitative methodology, and the results are based on self-reported cross-sectional data that are known for their various methodological shortcomings (Podsakoff et al., 2003). Third, the present study did not try to uncover the different possible styles related to Internet parenting in Indian households but rather used the ICT parenting measure (Lau & Yuen, 2013) as the basis of its 10-item Internet parenting style survey. A qualitative intervention with adolescents and young-adults could have provided richer information on the different Internet parenting styles. Furthermore, this could have also influenced different dimensions or the factorial structure of the developed scale. To address these limitations, we recommend various directions for future work. Scholars should validate the developed instrument in other cultural contexts by recruiting adolescent and young-adult Internet users from countries other than India. This will enable the scientific community to examine the cross-cultural validity of this instrument. Similarly, future studies should involve qualitative insights (e.g., interviews, open-ended essays, focus discussions) that can provide much richer information on the different Internet parenting styles exercised by parents at home. Other than this, longitudinal data on Internet parenting should be collected because it will provide a much more controlled environment that is crucial to the examination of the change in Internet parenting styles over a given period of time. Similarly, future studies should also focus on investigating the impact of Internet parenting styles on the tendency among adolescents to engage in online risky behavior, compulsive Internet use, ethical online behavior, and so on. Such investigations will enable scholars to examine the impact of different Internet parenting styles on youth development and well-being, an area that has otherwise been largely overlooked.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Amandeep Dhir holds a PhD in psychology from the University of Helsinki, Finland as well as DSc (Tech) from the Department of Computer Science, Aalto University, Finland. He specializes in new media and adolescent research. His work has appeared in computers in human behaviour, social science in computer review, telematics and informatics, new media & society, International Journal of Information Management, and online information review.

Ashraf Khalil is the director of Research at the Abu Dhabi University and an associate professor of computer science. His research interests are ubiquitous computing, social and mobile computing, persuasive computing, and human–computer interaction. The results of his research studies are published in the top conferences in the field such as CHI, CSCW, and INTERACT.

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