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Antecedent conditions to parental monitoring of child internet use Chow, Clifton 2005

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ANTECEDENT CONDITIONS TO PARENTAL MONITORING OF CHILD INTERNET USE by CLIFTON CHOW B.A., The University of British Columbia, 2003 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE FACULTY OF GRADUATE STUDIES (Family Studies) THE UNIVERSITY OF BRISITH COLUMBIA AUGUST 2005 © Clifton Chow, 2005 ABSTRACT Monitoring and the Internet ii Parental occupational conditions were explored in relation to its ability to predict child Internet monitoring. Utilizing Melvin Kohn's (1977) model of social class and socialization, this study extends Kohn's model as well as provide theoretical groundwork to the area of Internet monitoring. The question of the applicability of Kohn's model to the study of the regulation of Internet access was explored using subsamples from both the Canadian General Social Survey (N = 1142) as well as the American Panel Study of Income Dynamics (N = 1292). Findings showed that Kohn's occupational conditions were not directly predictive of child Internet monitoring behaviors in the parent. Post-hoc analysis examining other variables in the data sets revealed that perhaps an indirect relationship might exist linking occupational conditions and Internet monitoring through a mediating variable of intellectual flexibility. This study concludes with a proposal for a revised model of Kohn's theory that may be better suited to studying Internet monitoring. Monitoring and the Internet iii TABLE OF CONTENTS ABSTRACT ii TABLE OF CONTENTS HLIST OF TABLES v LIST OF FIGURES vi CHAPTER I Introduction 1 CHAPTER II Literature Review 4 Parental Monitoring 4 Kohn's Model of Class and Conformity 12 Occupational Conditions 13 Closeness of Supervision 16 Substantive Complexity 16 Routinization 17 Critiques of Kohn 8 Kohn and Monitoring 19 Statement of Research Purpose 20 CHAPTER III Method 22 SampleMeasures 25 Dependent Variable: Internet Monitoring 25 Independent Variable: Occupational Conditions 29 Closeness of Supervision 29 Substantive Complexity 32 Routinization 35 Monitoring and the Internet iv Controls 36 Encouragement of General Computer Use 37 Education 38 IncomeFamily Structure 38 CHAPTER IV Results 40 Test of Hypotheses 4Parameters of the Model 52 Post-Hoc Analysis 53 CHAPTER V Discussion 66 Applicability of Kohn's Model 6A Variation on Kohn's Model 8 Implications of the Revised Model 72 Limitations 73 Future Research 6 CHAPTER VI Conclusion 78 REFERENCES 79 APPENDIX A Variables Used in the Construction of Measures 83 APPENDIX B Measures Used in the GSS 84 APPENDIX C Measures Used in the PSID 90 Monitoring and the Internet v LIST OF TABLES Table 1. GSS Factor Loadings for Items Used in the Internet Monitoring Index 26 Table 2. GSS Descriptives and Correlation Coefficients 44 Table 3. PSED Descriptives and Correlation Coefficients 45 Table 4. GSS Descriptives and Correlation Coefficients for the Individual Items of Internet Monitoring 46 Table 5. GSS Descriptives and Correlation Coefficients for the Male Subsample 47 Table 6. GSS Descriptives and Correlation Coefficients for the Female Subsample 48 Table 7. PSID Descriptives and Correlation Coefficients for HEAD Occupational Conditions 49 Table 8. PSID Descriptives and Correlation Coefficients for the Male Subsample 50 Table 9. PSID Descriptives and Correlation Coefficients for the Female Subsample 51 Table 10. GSS Post-Hoc Items and Principle Component Factor Loadings 56 Table 11. GSS Post-Hoc Descriptives and Correlation Coefficients 57 Table 12. PSID Post-Hoc Items and Principle Component Factor Loadings 61 Table 13. PSID Post-Hoc Descriptives and Correlation Coefficients 62 Table 14. GSS Post-Hoc Regression Coefficients with the Dependent Variable of Internet Monitoring .63 Table 15. PSID Post-Hoc Logistic Regression Coefficients with the Dependent Variable of Internet Monitoring 64 Table 16. PSID Descriptives and Parental Rules Correlation Coefficients 65 Monitoring and the Internet vi LIST OF FIGURES Figure 1. Occupational Conditions and Monitoring 20 Figure 2. Kohn's Model of Social Class and Socialization 55 Figure 3.1. Revised Conceptual Model 71 Figure 3.2. Revised Research ModelMonitoring and the Internet 1 CHAPTER I Introduction A consensus among social scientist and researchers studying the impact of computers on families is that very few empirical studies have been done. "One of the gaps in our knowledge of family media usage has been the lack of a comprehensive picture of how families relate to and interact with media" (Gentile & Walsh, 2002, p. 158). This area is more important than would be indicated by the scant attention it has received from researchers. As computers and the Internet become increasing incorporated into a family's daily life, the impact may be significant. Possible consequences might range from changes in sleep patterns to decreased quality time interacting with family members. The internet and computers might compete for a share of the already strained "family time" that a household has in today's fast paced world of dual-incomes and after-school activities. One of the lesser-studied areas of Internet usage is parental monitoring. More specifically what are the parent or family characteristics that predict, promote or dampen the behavior of parental monitoring of child internet use. While there are inherent dangers of unsupervised computer access, the debate of whether one should monitor their children's computer usage is not the subject of the current project. What is of concern is who are the parents that are more likely to monitor? Irrespective of value judgments, what makes ones type of parent monitor their child and another sit back and allow their child total access to the information superhighway? When examining the relationships of what could explain the behavior of monitoring, the best theory that seems to be able to account for this behavior is Melvin Kohn's social class and socialization theory. In his model of social class and socialization, he argued that a parent's occupation instilled values that were transferred into the arena of parenting. For instance, jobs that required obedience such as assembly lines or jobs that required a protocol often do not have Monitoring and the Internet 2 room for innovation. These jobs required an employee to follow rules. There are usually time restrictions imposed on these kinds of work. Employees are required to be on time and "clock in". Work is done only on site, as nothing is brought back home. These jobs would create parents who valued obedience from their children because they have been socialized to see obedience as being a necessary characteristic in obtaining success in the world. The act of monitoring a child's Internet use was found by Orleans and Laney (2000) to be dependent on the original intentions of the parents in buying the child a computer. When that intention was one of specifically accomplishing schoolwork or set tasks, parents were likely to restrict access to those tasks only. This restriction is mirrored in Kohn's theory as being obedience. If on the other hand, parents had purchased a computer for the child to explore and learn, they were less likely to monitor, giving the child complete access to the technology. There was a sense of independence that the child gained through this act. This independence is the opposite end of the obedience discussed by Kohn. On the other end of the spectrum are the parents who work in jobs that require thought. These employees do not have to punch in a time card, they are expected to complete the task but without explicit supervision. Workers are allowed, and they usually do, bring their work home. The tasks are varied and require quick thinking and imagination. In those jobs that required thought and innovation, parents would also transfer those values to their children. Other researchers have also had similar findings (Curtner-Smith, Bennett, & O'Rear, 1995; Klute et al., 2001; Seccombe, 1986; Spade, 1991). In the realm of computer access, children with these parents would be allowed to explore the computer and the Internet without hindrance. There would be an understanding that self-regulation on the child's part would be needed This study will not be a critical test of Kohn's hypothesis of socialization, but rather an empirical extension of Kohn's theory. It is an attempt to frame the study of parental monitoring within a theoretical framework in which to explain the phenomenon. As the research that does Monitoring and the Internet 3 look at monitoring as a dependent variable is atheoretical, Kohn's theory is a good place to begin our understanding. Monitoring can be seen as an implementation of obedience and independence, two important concepts in Kohn's model. It also goes on to explain why some parents would value one over the other. While monitoring is usually used to see what kind of child outcomes arise from the degree of monitoring, the question of why parents monitor has never really been answered. The use of Kohn's theory will give us an intellectual way of interpreting the findings as well as discussing how they fit into the larger literature. Monitoring and the Internet 4 CHAPTER II Literature Review Parental Monitoring Monitoring, as conceptualized in the literature, is a dimension in the larger concept of "parenting." The parenting concept is composed of three dimensions: motivation, monitoring, and behavioral management (Dishion & McMahon, 1998). All three dimensions are mediated by the parent-child relationship quality. When a family has positive parent-child relations, parents' motivations to monitor their child and use healthy behavior management techniques are enhanced (Dishion & McMachon, 1998). Monitoring also has dimensions of its own, mainly attention, tracking, and stmcturing contexts. Stracturing can be done through actual physical modifications such as shutting down or locking the computer or verbal mediators such as rules the parents set up. In the few studies that tried to examine the factors that impact monitoring, Chilcoat, Breslau and Anthony (1996) found that those households that displayed the highest levels of monitoring were two-parent households. Single parents showed the lowest levels of monitoring. The authors attributed the difference to the lack of social support available to most single-parent households. While a lack of resources could potentially cause the difference in monitoring levels, Fisher, Leve, O'Leary and Leve (2003) found that family expectation of roles could also influence monitoring. These researchers examined family structure in relation to monitoring children's activities. They found that stepfathers differed from biological parents in that they were less likely to monitor. Stepmothers were found to have ho significant differences from both biological and stepfather families. Fisher et al. (2003) believed that these differences were due to the different family expectations of stepmother compared to stepfather. For example, stepmothers are usually expected to fill in the parenting roll to a far greater extent than stepfathers. As mentioned before, monitoring is considered a dimension of parenting. These Monitoring and the Internet 5 researchers argued that the conditions for a stepparent entering a family are usually different for stepmothers versus stepfathers. Divorce is less likely the reason for a father retaining custody. Usually this is due to the death of a wife, in which case the stepmother would more likely be expected to replace the biological mother (Fisher et al., 2003). There is strong opposition to the way that monitoring is conceptualized in the literature. Many researchers believe that monitoring studies lack the inclusion of agency on the child's part (Kerr & Stattin, 2000; Stattin & Kerr, 2000; S. Marshall, personal communication, April 26, 2004). These researchers believe that monitoring is actually measuring "parental knowledge" which is obtained through the child's own self-disclosure or through the parent's own surveillance of their child. These researchers argue that monitoring is conceptually flawed due to the need for children to initially disclose information. If this is truly the case, then monitoring should be reinterpreted as parental knowledge. Some of the limitations cited in the literature are the lack of measures for parental motivation of monitoring (Dishion & McMachon, 1998). These include the role of beliefs and values, which may impact the extent to which parents consider monitoring important. Monitoring of children's Internet access is argued by some as being increasingly relevant nowadays. Cooper, Putnum, Plancho and Boies (1999 as cited in, Freeman-Longo, 2000) reports that in America, access to the internet and pornography is on the rise. The literature on this issue is small, shedding very little light on actual numbers of children who access these websites and at what frequency. Parents who wish to get more information on how to supervise and monitor their children's internet access are sometimes faced with why they need to monitor in the first place. Freeman-Longo (2000), in his study of children and sexual addiction on the Internet, found that when the term "parental control" is typed into an internet search engine, not only do the results include ideas on parental control software such as Net-Nanny and Cyber-Patrol, but also web links to adult-pornographic sites. Monitoring and the Internet 6 With the increasing importance placed on technology for social mobility, such as needing to have computer skills for many jobs, there is a corresponding "information poverty" where certain lower classes miss out on skills because they lack resources such as a computer in the home (Facer & Furlong, 2001). In many circumstances, children facing this information poverty do not feel that they are missing out on anything (Facer & Furlong, 2001). Computer knowledge does not play a significant part in their daily lives. Facer and Furlong also found that with the lack of computer ownership, there is a lack of desire to pursue opportunities in computer related areas. These researchers feel that the non-ownership of computer technology can lead to and reinforce the inequalities already found in society among socioeconomic, gender and race lines. Looking at the use of electronic media, children from lower income families tended to spend more time watching television than using the computers compared to their higher income counterparts. Referring to American statistics, on average children 2 to 18 years in age spend about 5 hours and 48 minutes a day with electronic media. The majority of this time is spent in front of the television (3 hours and 25 minutes). The rest of the time is spend on the computer or listening to music (Gentile & Walsh, 2002). On average, American children living in homes with internet access spend about 36 minutes a day surfing the web. An interesting finding of the Facer and Furlong (2001) study is that those children without computer access in the home were less likely to access computers at a friend's house. This suggests, "a lack of access in the home is unlikely to be compensated for in other settings" (p. 458). Even those children who lack access at home but took advantage of a friend's computer used it primarily for games. When children have access to computers in the home, they are exposed to a more diverse set of software applications besides video games (Facer & Furlong, 2001). In this study, their analysis of interview data found that low income was a significant factor to whether or not children were accessing computers in general. Monitoring and the Internet 7 Salience of internet dangers might be one of the reasons that cause parents to monitor children's internet. These dangers are reported frequently in the media and literature, citing the unregulated nature of the web. Many adult sites require one to verify that they are at least 18 years of age before accessing the materials. Unfortunately, to verify one's age all is needed is to click a link that says "I am 18 years or over." Due to the nature of the Internet and its anonymity, there is really no way to truly verify that those who are viewing the site are adults (Freeman-Longo, 2000). While this study seemed to present both positives and negatives of the Internet experience, the researcher was clearly in support of parents monitoring children's access to computers, in particular the internet. Freeman-Longo advocates parental monitoring by placing the computer in the living room where parents can easily monitor their child's activities. Software is also recommended. Parents are told to try them out to see if they can crack them, an indication that their children might be able to do the same. While Freeman-Longo (2000) focused mostly on the apparent dangers of the Internet for children, the study did not examine what types of parents were more or less likely to monitor their children in the first place. While this research recommends placing computers in a common area, it nonetheless raises the research question "How are the kinds of parents who monitor different from those who don't monitor their children's Internet access?" Looking at it from a purely inquisitive perspective irrespective of a value judgment, "Who are the parents that are likely to install software and keep tabs on their children's surfing habits?" Related to internet dangers, misinformation may also factor into a parent's decision to monitor. In looking at the appropriateness and benefits of using the internet in the search for health related information, Ahmann (2000) suggest that the accuracy of information on the information superhighway could be suspect. She recommends using a site-checker or a published list of verified sites in order to prevent rmss-information. Ahmann included a section Monitoring and the Internet 8 entitled "special considerations" in which she recommend that children be monitored when using the Internet to look up health-related issues as well as in general. Although there are potential problems in unfettered Internet access, there are benefits as well. When computer access is unrestricted, this gives the child a sense of independence that he or she is ready to use his or her own self-judgment. Many of the monitoring software filter out chat rooms and other uncontrolled areas of the net. These programs are cautious due to the speed at which a conversation can turn sexual on the internet. By filtering these chat rooms, children are losing out on benefits of meeting new people on the world wide web. Some of the positives are related to social aspects such as the formation of romantic relationships, developing attachments not based on physical appearance^ as well as improving one's interpersonal skills (Freeman-Longo, 2000). According to Watt and White (1999), while it is very important for parents to become involved and monitor their children for materials that are inappropriate, this task seems to be an impossible one. These researchers believe that with the constant state of flux in computer technology, parents are overwhelmed while in many cases children are surpassing their parents in knowledge of how to manipulate attempts made by parents to censor or block certain websites. Similar to the declines in family time due to internet use (Watt & White, 1999), television has also been linked to decreases in family time as well as decreases in school performance (Gentile & Walsh, 2002). Parents seem to be more likely to monitor and limit the types of programs that a child watches on the television than they are likely to monitor and limit they type of internet access the child has (Gentile & Walsh, 2002). The monitoring behavior of parents regarding these two forms of media are greatly disproportionate to the dangers that both present. Most parents monitor television, which is fairly regulated with standards and practices, than monitor the internet, which is unregulated. While parental supervision of television is often Monitoring and the Internet 9 based on the restriction of time, parental supervision is more difficult with the Internet because it not only requires restriction of time, but also restriction of what materials are accessed. Concerning internet monitoring, Gentile and Walsh (2002) found that the majority of their sample always monitored their children when on the computer. Unfortunately this study did not go into what internet monitoring entails. Simply enforcing when a child uses the computer is not truly monitoring the content that is accessed. Another limitation was that their sample was not random. Their sampling method tried to place a greater emphasis on lower income families. Their reasoning for this is that these families are usually harder to sample. While this might shed some light on these seldom-sampled families, their results cannot be generalized to the population. This all ties back to their finding that the majority of their sample "always" monitors their children's internet use. The question of "who monitors" is once again ambiguous. Turow (2001) conducted a review of the internet and family boundaries. Focusing on four areas, one of which was filters and monitors, he found that there are no studies looking at the use and implications of internet filters in the home. This is evident despite heated debate about censorship and its use in libraries. Since research on "who monitors" is almost non existent, some possible hints might be found in studies that indirectly have relevant findings. Orleans and Laney (2000) found that the use of software filters by parents was usually related to their financial situation as well as their reasons for giving their child computer access. If the reason children were provided with Internet was simply to accomplish specific tasks such as schoolwork, parents were much more likely to use software filters than those parents who provided the Internet so their child could explore and learn. In the same study, the researchers hypothesized a curvilinear relationship between the amount of parental involvement and the children's computer proficiency. Their original conceptualization was that parents who simply gave their child a computer without providing Monitoring and the Internet 10 supervision and guidance would have children who would not know what to do with the technology, thus squandering potential. High parental involvement and supervision would also limit potential as children would only be allowed to use the computer and internet for applications that parents deem appropriate (Orleans & Laney, 2000). Children would come to regard the computer as an appliance to be used only for specific purposes, without the creative exploration that children with lesser supervision develop. The researcher's hypothesis turned out to be unsupported. Except for the youngest users, parental involvement was generally not required to jumpstart interest in the computer and the Internet. Information technology usually goes through a gatekeeper in a family and is then redistributed to other members (Kiesler et aL, 2000). This person tends to be the one who shows the most interest for the technology and is thus most competent. Regarding computers, in a majority of families, this gatekeeper is usually a child rather than a parent. What this does is shift the traditional flow of information from "parent to child" into "child to parent." With this comes a change in the child's independence. For instance, when parents have computer problems, they will usually turn to the child. Children and teenagers in these homes saw the computer as more than an appliance, but something to master (Kiesler et al., 2000). Similar to the role of television in the past, the internet is today's information technology children use to blur the boundaries between adults and children. Like television, the internet allows children to access information about the world, adult behavior, and social possibilities that parents of days past might have been able to delay (Kiesler et al., 2000). This blurring of boundaries creates advisor roles as well as facilitates the development of independence in children. "Personal computers may have now taken over television's prominent position as the newest, most feared piece of technology in the home" (Coffey & Horst, 1997 as cited in Orleans & Laney, 2000, p. 57). Monitoring and the Internet 11 What Kiesler et al. (2000) found was that in order for the child to develop this expertise, they require unrestricted access to the computer. Without the supervision of parents, children were able to explore and master the skills of the computer as well as exercise self-regulation in their use. Those parents who were proud of their child's knowledge were ones who did not mind them spending hours in front of the computer screen (Kiesler et al., 2000). These parents did not seem to limit the amount of time the child could use the computer. Contrasting this with other forms of media restrictions, these parents were not more lenient than those who did limit computer time. In regard to television, they were similar to those who monitored child computer access in that they would limit the time children spent in front of the television. Perhaps parents perceive a value in their children having computer expertise, as opposed to the perception that nothing of value is gained through hours of studying and using the television. There is probably truth in these perceptions as computers are an interactive technology that requires some skill of the user, while television is a passive technology that requires no skill of the user. Kiesler et al's (2000) study showed that when children have complete access to the technology, they were more likely to be knowledgeable about computers and the internet than children who were monitored. This knowledge gives children independence and authority in their families. Reviewing the literature on monitoring, there are many studies that use this concept as an independent variable. These studies try to see what are the outcomes of children who lack supervision. Almost nothing is done looking at monitoring as a dependent variable. The importance of this concept, evident with the debate over how it is measured and its role in the realm of computers, is clear yet there is no research examining the antecedent conditions to bring about this behavior. Perhaps a reason for the lack of empirical studies is that the technology is constantly changing. Usually, children in a household are more knowledgeable than parents on the topic of computers. There is some evidence that what causes parents to monitor or not is their initial intentions for allowing their child to access the internet. In cases where exploration Monitoring and the Internet 12 was the primary motivation, these parents usually resign from the task and allow their children full reign of the computer. Kohn's Model of Class and Conformity Kohn (1977) hypothesized that certain life conditions influence one's values, which are then passed onto children though parental behaviors. He specifically looked at social class and how it was related to parental values and behaviors. Kohn saw social class as being the most important aspect of social structure, which he believed affected a persons "conditions of life" (Gecas, 1978). He argued that these different conditions cause members in a society to experience the world differently, leading to the development of different views of social reality and what is deemed desirable (Kohn, 1963 as cited in Gecas, 1978). Kohn defines the concept of "values" as being what an individual deems desirable. These values are therefore linked to one's conditions in life, specifically their occupational sphere. Kohn proposes that class relationships are the combination of education and occupational experience, where education enables one to have the capability for self-direction and occupational experience enables one to have a venue for exercising self-direction (Gecas, 1978). Kohn (1977) states that social class directly influences the education and occupational status that an individual obtains. Being born in a wealthy social level will most likely give advantages to the kinds of schools available to the individual as well as the financial security to actually afford the education. Class also influences the amount of prestige and opportunities, which increases with more education. Socioeconomic status (SES) is found by Kohn, Schooler, and other colleagues to be the most powerful social structural predictor of parental values, "even more so than race, ethnicity, religion, and demographic characteristics" (Spade, 1991, p. 344). Comparing across diverse countries and societies, this relationship appears to hold. SES has been found to be related to parental values in the United States, Poland, Japan, and Italy (Kohn, Naoi, Schoenbach, Schooler & Slomczynski, 1990; Schooler & Schoenbach, 1994). Monitoring and the Internet 13 Other researchers have found that education plays a bigger role in the value development of self-direction (Wright & Wright, 1976). While Kohn is in agreement that education plays a distinct role, he focuses more on occupational conditions rather than education being the primary factor in the development of values. The studies that found education to be a stronger predictor only examined occupational status and not occupational conditions, which Kohn stated was the factor that had the most predictive power. In Kohn's model, a person's values affected the kinds of parental behavior employed. He examined specifically behaviors of punishment and found that those who valued self-direction in children would take into account the motives behind the child's behavior. Since these parents wanted to instill a sense of self-reliance in their children, they needed to find out what caused their child to misbehave before administering a punishment. On the other hand, those parents who valued conformity were found to only consider the consequences of the child's action while disregarding the motives behind the behavior (Gecas, 1978; Gecas & Nye, 1974). Luster, Rhoades and Haas (1989) studied the relationship between values and behavior in Kohn's model. He found that parents who valued self-direction would place greater emphasis on supportive parenting, while parents who valued conformity emphasized the "obligation to impose constraints" (Luster et al., 1989, p. 145). Luster et al. also attempted to expand Kohn's theory by including parents' beliefs about appropriate child rearing. Those parents who valued self-direction would believe few restrictions should be placed on children's exploration of their environment. Those who value conformity would in turn believe that effective parenting includes placing restrictions and controls over children's freedom to explore (Tudge, Hogan, Snezhkova, Kulakova & Etz, 2000). Occupational Conditions Kohn's (1977) model encompassed many concepts, one being occupational conditions, which were especially important and has been examined by other researcher (Klute et al., 2001; Monitoring and the Internet 14 Kohn & Schooler, 1983; Seccombe, 1986; Spade, 1991). This concept as described by Kohn (1977) has three dimensions: substantive complexity, closeness of supervision, and routinization. This concept is an integral part in the development of conformity values that were common in the lower classes. The level of education and occupation would be related to the degree of occupational and intellectual flexibility inherent in the job. Those in lower levels of a corporation hierarchy would have the lowest chances for intellectual flexibility. These jobs would usually require one to work with data or materials instead of individuals (Gecas, 1978; Kohn, 1977). Kohn (1977) argued that as one examines higher occupational statuses, they would see that the amount of supervision decreases as well as the complexity of the tasks involved increases. One is no longer working with data, but working with people where improvisation and innovation are valued skills. The decreased supervision is usually associated with these positions enabling one to take their work home with them. Examining family social capital and child behavior problems, Parcel and Menaghan (1993) hypothesized that workers who had more autonomy and self-supervision on the job would translate that value into the home by having less direct parental control over their children, instead opting for the promotion of their children to internalize parental norms, which would lead to lower levels of behavior problems. They propose that occupational conditions would influence child behaviors because those conditions alter a family's use and access to social capital. This hypothesis was supported by their research in regard to fathers and only partially for mothers. Having a substantively complex job for mothers lowered child problem behaviors only when they are separated or divorced (Parcel & Menaghan, 1993). Perhaps with the absence of fathers, mothers have unified control over how to implement parenting styles and behaviors. The dimensions of occupational conditions have been replicated in a number of studies through the years (Klute, Crouter, Sayer, & McHale, 2001; Schooler & Schoenback, 1994; Monitoring and the Internet 15 Seccombe, 1986; Spade, 1991). In almost every case, the three core dimensions are present. Seccombe (1986) utilized Kohn's theory of socialization to examine the intergenerational transmission of values, specifically the housekeeping role. While Kohn's model was examining the influences on parental behavior, Seccombe was interested in the intragenerational transmission of the role behavior between spouses. The dimensions that were operationalized for occupational flexibility stayed very closely to Kohn's (1977) original dimensions. Seccombe's (1986) dimensions were work autonomy, work component, and work complexity. The work component is concerned with whether the tasks are involving people, data, or ideas. They hypothesized that those subjects in occupations with more flexibility would internalize the ridgedness of the housekeeping role as "women's work." While the researchers did not find an intragenerational transmission of the housekeeping roles of spouses, they did confirm Kohn's (1977) original hypothesis that occupational flexibility permeated into other areas of life, in this case, the flexibility of the housekeeping role. Furthermore, the flexibility of roles was successfully transferred to children (Seccombe, 1986), Klute et al. (2001) on the other hand had differing findings in their study of the effects of occupational self-direction on marriage roles. They found that when they included a measure of values, it was found that this variable mediated the relationship between occupational self-direction and marital roles. They commented that had they excluded this measure, they would have also found similar findings as Seccombe (1986). Klute et al. (2001) concluded that occupational self-direction has an effect on the internalization of values influencing the type of marital arrangements people prefer and adopt. Other researchers who also used the concept of occupational self-direction have mirrored Kohn's original dimensions (Crouter, Bumpas, Head, & McHale, 2001; Naoi & Schooler, 1990; Schooler & Schoenback, 1994). A measure of work conditions was developed by Lennon (1994) that was based on Kohn's dimension of occupational self-direction. Lennon was originally looking at work conditions and its impact on the well-being of women. In this measure, Kohn's Monitoring and the Internet 16 dimensions were divided into subscales of autonomy, control over others, organization, control, complexity, routinization, and closeness of supervision gauged on a four-point Likert scale (Klute et al., 2001). This measure went on to be used in other studies that wanted to assess occupational self-direction (Crouter et al., 2001; Klute et al., 2001). Closeness of Supervision A second job condition that Kohn saw as critical in facilitating occupational self-direction was closeness of supervision. This condition involves how closely the manager dictates what and how a task is done. Also it involves the freedom to disagree and offer suggestions on how something could be done differently (Kohn, 1977; Kohn et al., 1990; Kohn & Schooler, 1982). Closeness of supervision would also allow for an employee to bring their work home with them, enabling them to have more control over when and how fast to do a task. The lack of supervision on a job is argued by Kohn to instill a sense of freedom and reliance on one's own internal standards. When there is constant monitoring in an occupation, parents internalize the value that conformity to these external standards and authority is necessary to succeed in society, which is then passed on to their children (Spade, 1991). Naoi and Schooler (1990) utilized this condition in their study of the psychological effects of job conditions on Japanese wives. They found that Japanese women who performed self-directed work had more intellectual flexibility enabling them to have less traditional attitudes, though women were less likely to have self-directed occupations than their husbands. Naoi and Schooler argue that even in a culture where self-direction in women is devalued, the effects of occupational conditions are present. Substantive Complexity The substantive complexity dimension of occupational flexibility is gauged through whether one's work involved data, people, or things. Jobs that rank high on this dimension are those that involve making many decisions while taking into account conflicting contingencies (Kohn et al., 1990). Kohn (1977) saw self-direction as being higher in jobs working with ideas. Monitoring and the Internet 17 "Since work with things typically is least self directed and work with data typically is most self-directed, we should expect men who work mainly with things to be the most likely to value conformity for children, and men who work mainly with data to be the most likely to value self-direction" (Kohn, 1977, p. 145). Those occupations with higher substantive complexity work with ideas, which are obtained though observations, investigations, or mental creation (Seccombe, 1986). These jobs seem to characterize artistic endeavors. After "ideas" came "people" followed by "things" in the order of requiring initiative or thought. By examining subject's daily contact with each of the three categories, Kohn (1977) confirmed these dimensions. In order to be accounted for as working with "people" it had to have relevance for the job, such as talking to the boss, advising, supervising, or teaching others (Kohn, 1977). Managerial positions would be such an example. While Kohn's (1977) study only examined men, it was found that the degree of working with the three categories was correlated with their valuation of conformity or self-direction for their children. Routinization The final dimension in Kohn's occupational self-direction is routinization. This concerns the amount of repetitiveness and predictability of one's job (Kohn, 1977; Kohn et al., 1990). This measure involves a subjective analysis. In Kohn's (1977) original study, this dimension was assessed by asking subjects whether their job involved doing the same thing in the same way repeatedly or whether there was a variety of tasks that needed to be performed. Kohn found that those whose work was varied valued self-direction while those with routinized jobs valued conformity. When one is faced with a job that does not really require an individual to display independent thought, these people tend to internalize the value of conformity because only by following external rules set up by the employer is one doing a "good job". In occupations that are heavily routinized, innovation is not welcomed therefore employees would come to see the Monitoring and the Internet 18 importance of doing what one is told. Compared with occupations where there is a variety of tasks, these jobs welcome new ways of thinking and doing things where employees would come to see the importance of self-direction and self-reliance, values that are passed onto children by giving them more freedom and independence to exercise their own self-direction. Critiques of Kohn Some criticism of Kohn's model is that is does not explain the transmission of valuing conformity as well for women as it does for men (Gecas, 1978). Spade (1991) offered his insights into why this might be the case. While the findings by Kohn showed that the direction of the relationship was similar for both men and women, the strength of the relationship was weaker for women (Kohn, 1986 as cited in Spade, 1991), Spade proposes that the weaker relationship is due to the exclusion of household work from the assessment of occupational flexibility. In his study looking at the difference between men and women on occupational self-direction and parental values, he found that although women occupied a lower job status, they valued self-direction in children to a greater extent than men. Spade argued by not accounting for household work, which is usually performed by women, the assessment of occupational self-direction for women is greatly inaccurate. Since there is a great deal of self-direction in household work, by accounting for this "second job", women's level of occupational self-direction should increase, therefore accounting for the discrepancy found in the Spade's female sample. Family structure seems to be a variable that some say is missing from Kohn's model. Since Kohn's (1977) study excluded families in poverty and elite families, Gecas argues that there are other "conditions of life" that could affect patterns of child rearing (p. 383). An interesting note is that the literature looking at monitoring as a dependent variable tends to focus on family structure as the variable explaining differences in the amount of monitoring a child's activities. Monitoring and the Internet 19 Curtner-Smith et al. (1995) point out that Kohn's model gives the notion that conformity will lead to negative child outcomes whereas self-direction will lead to positive outcomes. These researchers point out that this is not necessarily the case. Conforming to appropriate authorities such as law enforcement will enhance a child's social functionality rather than diminish it. They suggest that Kohn's model should develop a different assessment strategy as to incorporate a combination of self-directing and conformity values (Curtner-Smith et al., 1995). This criticism is only aimed at the potential value judgments placed on either conformity and/or self-directing values. Gecas also criticizes Kohn for not providing evidence for the direct link between values and parental behaviors. Instead, Kohn (1977) provides indirect evidence for the link by arguing that "social class should be related to parental behavior became of its relationship to parental values via their link to occupational condition of life" (Gecas, 1978, p. 382). Kohn showed this link through examining parent's child discipline behavior. Kohn argues that since middle-class parents value self-direction in their children, they would likely take into account the child's internal motive for misbehaving. This is contrasted with lower-class parents who are more likely to punish based on external consequences of the child's actions (Gecas, 1978). Their findings were also supported by Gecas and Nye (1976). It should be noted that Luster et al. (1989) conducted a study showing a direct link between values and parenting behavior. His findings supported Kohn's original hypothesis. Despite these flaws, Gecas does point out that Kohn's model is impressive and it appears to be "the strongest explanations for the variables considered" (p. 383). Kohn and Monitoring Monitoring of child internet use by parents fits in nicely in Kohn's model. Monitoring for this technology can be conceptualized in terms of parents enforcing conformity or granting independence. As Kohn stated, parents with low occupational self-direction would value Monitoring and the Internet 20 conformity in their children, while those with high occupational self-direction would value independence in their children. Parcel and Menaghan (1993) also propose the same. They state that those working in jobs with autonomy and self-supervision would not directly enforce parental control but instead promote their child to use self-judgment and internalize parental norms. In terms of computer access, parents with occupational flexibility would not monitor their children, opting for them to use self-supervision. Parents who monitor computer access, for example by checking their child's web history or installing filtering software, are using more overt parental control. Parcel and Menaghan, as well as Kohn, would probably say these parents work in occupationally ridged jobs and value conformity. Statement of Research Purpose This present study will attempt to examine the conditions that elicit different types and frequencies of monitoring strategies of child Internet access. Through the use of Kohn's (1977) theory of social class and socialization, this study will examine how occupational conditions influence the parental strategies of child Internet monitoring. This study hopes to provide insight into the seldom-studied outcome variable of parental monitoring, as well as provide an extension of Kohn's theory to the area of computer-use socialization. The outcomes of this study will either give evidence for or against Kohn's application in the area of parental monitoring, specifically monitoring of child internet. Figure 1 below illustrates the conceptual model that will guide this study. Figure 1. Occupational Conditions and Monitoring Occupational Conditions - Substantive Complexity - Closeness of Supervision - Routinization Hla, Hlb, Hlc Parental Monitoring Monitoring and the Internet 21 Following from this model, the hypotheses are: Hypothesis la: Closeness of Supervision at work is positively associated with parental monitoring. If an individual's job dictates that a supervisor keep close monitoring of them, or if their occupation requires them to conduct their work on-site, then these people would be more likely to monitor their children's internet access due to their internalization of conformity from their jobs. Hypothesis lb: Occupational Substantive complexity is negatively associated with parental monitoring. Individuals whose occupation involves working with machinery or materials would lack self-direction in the jobs compared to those occupations that require work with other people or ideas. Being required to deal with people and /or ideas on the job internalizes independence, as one's job requires spontaneous thinking and improvisation. Those with low substantive complexity would monitor their children's Internet access to a greater degree than those parents whose occupations are higher in substantive complexity. Hypothesis lc: Occupational Routinization is positively associated with parental monitoring. Jobs that are highly routinized requires strict adherence to procedures. These parents would monitor computer access to a greater degree due to their internalized beliefs of conformity to an external authority. In the case of child Internet access, the external authority would be the parent. Monitoring and the Internet 22 CHAPTER m Method Sample This study utilized data sources from two different countries: the 14th cycle of the General Social Survey (GSS) from Canada, and the Panel Study of Income Dynamics (PSID) from the United States. The GSS survey was conducted by Statistics Canada from January to December of 2000 with the goal of measuring the access and use of technology by Canadians. A specific focus was placed on the use of computers and the Internet. Statistics Canada contacted 31,063 households and had a response rate of 80.8% resulting in 25,090 cases. Canadians 15 years and older were the target population. Surveys were collected through telephone interviews by random digit dialing. A random person aged 15 and older was chosen from the household to be the respondent in the survey. The majority of respondents were 35 to 44 years of age. The gender breakdown was 53.1% female, and 46.9% males. This study is a secondary analysis using data from those respondents that have children still residing in the home and who have Internet access. Also, respondents must have used a computer in their jobs in the last 12 months. To select these cases, the sample was filtered. Respondents were asked, "the number of children aged from 0 to 14 living in the respondent's household". Those that responded with no children were excluded from the sample (n=18122). The number of respondents in the GSS sample with at least one child aged 0 to 14 living in the household was 6,968. The next step in obtaining our sample was to filter out cases that did not have Internet access in the home. Respondents were asked, "Is your household connected to the Internet". Those participants that identified having an Internet connection in the household were included in the study (n=3311). In addition, respondents were also asked, "Do any of your children use the Internet at home". Since the focus is parental Internet monitoring, those households with an Internet connection and with children who used the Internet at home were Monitoring and the Internet 23 included (n=1761). Finally, since the study is interested in job conditions impacting monitoring behavior, only those respondents who answered the variables related to job conditions were included in our sub-sample. The GSS restricts the variable measuring this dimension to those respondents who had used a computer at their jobs in the past 12 months, as such those parents who did not use a computer at their job in the past 12 months or were unemployed were excluded from our study (n=619). Our sub-sample had 1142 respondents. Since our sample was filtered for those who had children, no participants were below the age of 20. The majority of respondents fell within the age ranges of 35 to 44 (64%). Females outnumbered males in our sample with 52.9% and 47.1% respectively. All provinces were represented with the majority of respondents residing in Ontario (31.3%). The PSID is a longitudinal study of a representative sample of US individuals that started in 1968 with a new wave every year until 1997 when the study switched to biennial waves. Conducted by the Survey Research Center at the University of Michigan, the main focus of the PSID is on economic and demographic behavior, but it also contains items of sociological and psychological measures. The 2001 wave contained data from 8733 cases. Data collection consisted of computer assisted telephone interviews. The PSID has changed methods of administration over the years. Previously an in-person paper and pencil survey, it switched to telephone interviews after 1972. In 1999,97.5% of all interviews were conducted over the phone with computer assistance. This study will only utilize the respondents who also participated in the Child Development Supplement (CDS). This component of the PSID was introduced relatively recently in 1997 and re-administered in 2002. The CDS focuses primarily on various aspects of the child's life from school to home conditions. Responses are gathered from both the child and the primary caregiver. For the 1997 wave, 2,705 families were selected from the larger PSID Monitoring and the Internet 24 sample. Of those, 88% chose to participate. Of this sub^sample, 91 % chose to return for the second wave in 2002, resulting in a sub-sample of 2,907 cases. The responses from the 2002 component of the CDS and the 2001 primary PSID will form the data used in this study. Inclusion in the CDS required that respondents have at least one child under 18 living in the household. Since the PSID measured both the working conditions for the primary and secondary wage earners (referred to as HEAD and WIFE in the PSID), it was important that the proper responses were used in the construction of our measures. The responses on Internet monitoring were linked with the primary caregiver (PCG) of the child, thus each case had to be assessed for whether they were the HEAD or the WIFE in order to determine which set of occupational responses would be used. Both HEAD and PCG occupational conditions were used to see whether one was more predictive than the other. Also, as occupational conditions were only measured for the HEAD and WIFE, those PCG's who fell outside these two categories were excluded from our sub-sample. As such, after determination of PCG status was completed, the gender distribution of the PSID became heavily skewed towards females as they consisted the majority of the PCG population. Finally, only households with a computer connected to the Internet were included. The resulting sample consisted of 1292 cases with 94.1% females and 5.9% males. The reason that two different data sets were used was because each one tapped into a different dimension of Internet monitoring. The GSS measured monitoring by the types of methods employed by the parent, while the PSID framed the question in terms of the frequency of monitoring. These two dimensions are theoretically highly correlated and both may yield interesting findings concerning the details of how and how often parental Internet monitoring of children is occurring. Evidenced in Kohn's (1977) original study, Gecas (1978) states that Kohn's socialization theory did not work as well with the female population in showing a Monitoring and the Internet 25 relationship between occupational conditions and the transmission of valuing conformity onto children. As such, this study examined the data to determine whether Kohn's theory, when applying it to the area of child Internet monitoring, is equally applicable to both genders or whether it is more predictive for one specific gender. Measures Scales made up with five or more variables were component factor analyzed to see if the factor structure held up. For scales constructed of less than five variables, an alpha scale analysis was done as this analysis yields similar results when analyzing with fewer items. Scales constructed from more that five variables might have some items that are inter-correlated or cluster together, this is why factor analysis is preferred over an alpha scale analysis, which does not look at the clustering among the variables. Only the monitoring scale of the GSS was subjected to a component factor analysis, while the other scales had alpha reliability analyses done, as they are composed of fewer variables. Dependent Variable: Index of Parental Internet Monitoring GSS. For the dependent variable, the primary interest is the specific type or types of Internet monitoring employed by parents. Four different items that were rated either "yes" or "no" assessed different methods that may be used in the monitoring of children's Internet access. These four items were: 1) Disable or lock the computer; 2) Use software; 3) Use an internet provider who filters out unacceptable sites; and 4) Supervise their time on the Internet. Additionally, two more variables were included in the measurement of our dependent variable. Respondents were asked, "Do you help your children use the Internet?" This question gauged whether the parent was actively engaging with the child's Internet experience or whether they left them alone to explore the Internet by themselves. The degree of the importance placed on Internet monitoring was also of importance as it indicated a value orientation. Thusly, respondents were asked, "How important is it to you that your children's Internet use is Monitoring and the Internet 26 monitored?" Subjects chose from three responses: 1) Very; 2) Somewhat; and 3) Not at all important. Principle component analysis revealed that there are two dimensions present. It appeared that monitoring strategies that involved the use of software loaded separately than strategies that required the actual presence of the parent, such as direct supervision. See Table 1 for factor weights. These two dimensions explain 47.4% of the variance. Table 1 GSS Factor Loadings for Items Used in the Internet Monitoring Index Components Items 1 2 Disable or lock the computer .461 .403 Use software .334 .616 Use Internet provider filter .417 .591 Supervise time .632 -.492 Do you help your child with the Internet? .494 -.297 How important is children's Internet monitored? .632 -.286 Total variance explained = 47.7% The construction of a scale variable with these six items yielded a distribution that was fairly normal overall (M = 0, SD = 3.08), but with some significant skewness (.432, SE = .074). Since five out of the six items had binary response categories of "yes" and "no", a better choice for the construction of a measure seemed to be the creation of an index. The sixth item, "How important is children's Internet monitored", was the only item that had three response categories of "not important at all", "somewhat important", and "very important". Since the other items used in the construction of this variable had clear "yes" or "no" response categories, it made sense to combine the "somewhat important" responses with the "very important" responses to create a similarly clear separation of "not important" and "important" in this final item of monitoring. Monitoring and the Internet 27 As all items that were used to create the dependent variable had dummy responses, an index was created from these raw scores. The index created from these items was much closer to a normal distribution, fulfilling one of the assumptions required for the use of regression analyses. The range of this index was from 0 to 6. A distribution analysis showed that the responses had a fairly normal distribution (M= 2.63, SD~\ .03), with a slight negative skew (-.223, SE = .074). Reliability analysis showed that the index had a low alpha (Cronbach's alpha = .4023). Running alphas on the first and second dimensions, as found through principle component analysis, did not yield substantially different alphas (Cronbach's alpha = .4116 and .3493 respectively). Please refer to Table 2 for means and standard deviations of GSS variables. PSID. The primary interest is in the frequency of Internet monitoring. Three items were used in the construction of our index. 1) "How often do you set limits on computer games that the child can play"; 2) "How often do you set limits on what the child can do on the Internet"; 3) "How often do you set limits on the child's use of email". A 4-point Likert scale with the options of "never", "hardly", "sometimes", or "often" assessed these questions. Preliminary attempts at the construction of a scale from the PSID data did not yield a normal distribution. The spread of the responses were more inline with a bimodal distribution. It is important that our dependent variable not violate the assumptions of regression, specifically the requirement of a normal distribution. The presence of a bimodal distribution would not be desirable for the use of regression analysis. The distributions of the individual raw responses showed that a bimodal distribution was already present in these initial items. Since the items themselves were skewed, an attempt at the construction of an index was made. As such, the three items were recoded into dummy variables. The original four response categories were "never", "hardly ever", "sometimes", and "often". These categories were recoded into responses of "seldom" and "often" in regard to whether or not they limited their child's access to computer games, Internet, or emaiL The Monitoring and the Internet 28 reason for splitting the responses in this manner was due to consideration of the original response categories. Since initially there was an attempt at delineating between the degree of frequency of Internet monitoring, I argue that despite being transformed into a dummy variable, the recoding should still attempt to separate those that monitor "often" from those that "seldom" monitor. This captured the original intention of the question better than if the items had been coded as "never monitor" and "monitor". The three items were summed to create the Internet monitoring index with a range from 0 to 3. Reliability analysis showed that this index had moderate alpha (Cronbach's alpha - .69). The distribution for this index remained skewed (-.386, SE = .068) similar to the preliminary attempts at constructing a scale variable. With this negative skewness, transformations as suggested by Tabachnick and Fidell (2001) were attempted to correct the distribution. Square root as well as log transformations did not create any acceptable changes or compensations to the skewed distributions in terms of normality. While statistical transformations are suggested as attempts to correct non-normal distributions (Tabachnick & Fidell, 2001), this researcher tends to strive for readily interpretable results. A transformation of the data would create difficulty in the interpretation of findings, which I would rather like to avoid. Since none of the statistical transformations were satisfactory in achieving a normal distribution, for the final version of the measure, the dependent variable in the PSID was recoded into a binary measure and analyzed through logistic rather than OLS regression. The index was split into two categories of "seldom" and "often" in regard to restricting their child's Internet access. Those that scored a 0 in the initial index would have responded "seldom" to all three items; as such these responses will remain coded as "seldom". The respondents who scored a 1 to 3 on the index would have needed to answer "often" to at least one of the three Internet related activities. As such, the scores of 1 to 3 were recoded as "often" in the new binary dependent Monitoring and the Internet 29 variable. Of the respondents, 20.1 % did not monitor their child's Internet use while 79.9% of respondents monitored. Refer to Table 3 for means and standard deviations of PSID variables. The GSS and PSID measures only examined the monitoring of child Internet access and should not be interpreted as a general parental monitoring measure. In addition to the index, for the GSS data, this study also individually analyzed the monitoring types to assess whether certain types are more strongly influenced by occupational conditions. Independent Variable: Occupational Conditions For the independent variable, there were three dimensions taken into account. According to Kohn (1977), there are specific dimensions to occupational conditions, mainly closeness of supervision, substantive complexity, and routinization. These dimensions were measured through a variety of variables found in the GSS and the PSID. Closeness of Supervision GSS. In order to measure this dimension, questions relating to the degree of freedom in their work environments were analyzed. Specifically, the intent for this measure was to assess whether the respondent was able to bring their work home with them. When transferring work to the home environment is possible, the level of supervision is greatly reduced due to the freedom to dictate when a task is handled and at what speed. Also of importance to this study was whether the respondent was able to use their work time for personal reasons. To assess these areas, a series of questions were asked of the respondents: 1) "In the last month, how often did you use your home computer for work-related reasons"; 2) "In the last month, how often did you use your work computer for personal reasons"; and 3) "Excluding overtime, do you usually work any of your scheduled hours at home". Question one and two were assessed using a four-point Likert scale. The responses were: "everyday", "several times a month", "a few times a month", and "not in the last month". Question three was assessed by a "yes" or "no" response. Monitoring and the Internet 30 The three items used to construct this Closeness of Supervision measure had a moderate alpha (Cronbach's alpha = .5271). Analyses of the distributions for the raw scores of the responses indicated that there was a negative skew to all three items. The majority of respondents of the GSS reported that they did not use their home computers for work related reasons and they also did not use their work computers for personal reasons in the last month. The majority of respondents also did not work any of their hours at home. The Closeness of Supervision scale was constructed by suniming the z-scores of the items. The scale had a negative skew (-.665, SE ~ .076), with a negative kurtosis (-.556, SE -.151). Statistical transformations to achieve greater normality in the distributions were attempted. As recommended by Tabachnick and Fidell (2001), a square root statistical transformation was applied to the scale to correct the negative skew, but the results of the transformation were unsatisfactory. The resulting distribution was distorted to a greater degree than the original skewed distribution. Since none of the statistical transformations resulted in a better distribution this scale remained in its initial form as this configuration had the most easily interpretable scores. Refer to Table 2 for means and standard deviations. PSJD. Closeness of supervision was assessed by two variables looking at how the respondents were paid at their job as well as whether they were self-employed. To assess how they were paid, respondents were asked, "On your main job, are you salaried, paid by the hour, or what?" Choice of options were 1) salaried; 2) salaried plus commission; 3) paid by the hour; 4) hourly plus tips; 5) hourly plus commission; or 6) other. Since having an employer keep track of an employee's hours is indicative of more supervision than a job that is paid by salary instead of hours worked, this variable is fit to assess closeness of supervision. A second variable looking at self-employment was assessed by asking "are you self-employed, are you employed by someone else, or what?" Responses were on a 3-point Likert scale: 1) Someone else only; 2) Both someone else and self; or 3) Self-employed Monitoring and the Internet 31 only. Being self-employed and thus one's own boss would mean that these individuals would have low or no supervision from another employer. Analysis of the two items used for this measure showed that they had heavy bimodal distributions. For the item "Are you salaried, paid by the hour, or what?" the responses tended to fall on only two of the possible five response categories: "salaried" and "paid by the hour". The remaining three options, which were more specialized such as "salaried plus commission", had very few responses. For the second item, "Are you self-employed, employed by someone else or what?" there were three response categories, but the distributions also tended to be bimodal. The majority of responses were for "self-employed only" and "employed by someone else". Since both items were basically bimodal towards only two responses, these items were recoded into binary responses. For the item inquiring about salary, those responses for "salaried plus commission were folded into the "salaried" category and responses of "hourly plus tips" and "hourly plus commission" were incorporated into the "paid by the hour" category. For the item asking about self-employment, the response "employed by both someone else and self was recoded along with "self-employed only" into a new category of "self-employed". The resulting index that was created from these items had a range from 0 to 2, although distribution analysis indicates that it had a significant negative skew (-.761, SE = .097). Only 2.2% of the responses scored a 0 on the index. This score indicates respondents who were both self-employed and salaried. Since the distribution of the index was heavily bimodal, the Closeness of Supervision was dummied into "low supervision" and "high supervision". Scores of 0 and 1 were recoded into "low supervision" and scores of 2 were recoded into "high supervision". The majority of respondents were highly supervised with 60.3% while 39.6% were classified as having low supervision. See Table 3 for descriptive statistics. As respondents who scored a 2 oh the initial index were both paid by the hour and employed by someone else, these people would be more highly supervised than respondents who were salaried. Being salaried allows for when Monitoring and the Internet 32 and how fast job tasks should be completed, characteristics of less supervision on the job (Kohn, 1977). Substantive Complexity GSS. This measure was created by combining the North American Industrial Classification system with the Standard occupational classification system. This yielded a more accurate picture of the respondent's substantive complexity, whether they worked primarily with ideas, people, or things. The Standard Occupational Classification System (SOCS) has 10 categories: 1) Management; 2) Business, finance and administrative; 3) Natural and applied sciences; 4) Health; 5) Social science and education; 6) Artistic/culture/recreation/sport; 7) Sales and service; 8) Trades, transport and equipment; 9) Occupations unique to primary industry; and 10) Occupations unique to processing and manufacturing. Substantive complexity is higher when operating in an environment where one is required to deal with ideas and people rather than dealing with things (Kohn, 1977). These categories in the standard occupational classifications work well to describe whether the job is mainly dealing with ideas and people or materials and things. Primary industry and processing/manufacturing would be ranked lower in substantive complexity compared to management and education, where there are significant exposure in dealing with ideas and other individuals. The North American Industrial Classification System (NAICS) contains 16 categories ranging from agriculture to health care professions, which were ranked according to their likelihood of working primarily with people, ideas, or things. Agriculture was ranked lower than professionals. Adding the responses on this variable with the responses on SOCS gave us our index. For example, respondents who answered "managers" on SOCS and "professional" on NAICS would have a higher index score than an individual who responded as "managers" on Monitoring and the Internet 33 SOCS and "agricultural" on NAICS. This index allowed us to assign substantive complexity scores more accurately than if only one classification system had been used. The two variables used in the construction of the substantive complexity index had a moderate alpha (Cronbach's alpha = .5884). These items in the GSS were recoded in regard to whether the occupations dealt primarily with things, people, or ideas. Originally respondents were asked to indicate their type of occupation on the Standard Occupational Classification system (SOCS) composed of ten response categories as well as the North American Industrial Classification system (NAICS) composed of sixteen response categories. These ten and sixteen classifications were given to three graduate students to be coded in regard to Kohn's (1977) substantive complexity categories of whether the occupation works primarily with things, people, or ideas/data. An analysis of inter-rater reliability was conducted through Cohen's Kappa for both items. The final reliability of the SOCS was high (Cohen's Kappa = 1.0) and the NAICS was also satisfactory (Cohen's Kappa = .79). Since Kohn (1977) argued that self-direction is highest in those jobs working with ideas and lowest in jobs working with things, occupations dealing with things were coded 1, people were coded 2 and ideas/data were coded 3. These two categorized items from the GSS were then used in the construction of an index that ranged from 2 to 6. A higher score on the index indicates that the respondent's occupation dealt increasingly with ideas and data. Distribution analysis showed that the index was fairly normally distributed, although there was a slight negative skew (-371, SE = .074) and negative kurtosis (-535, SE = .148). PSID. Similar to the GSS measure, the substantive complexity in the PSID data was also assessed by combining the primary occupation codes with the primary industry codes of the respondents. These codes were from the 1970 Census of Population and were obtained by asking the respondents, "what is your main occupation", "what sort of work do you do", "what Monitoring and the Internet 34 are your most important activities or duties", and "what kind of business or industry is that in". The main occupation codes resulted in 12 categories ranging from "professional workers" to "private household workers". The main industry codes also resulted in 12 categories ranging from "agriculture, forestry, & fisheries" to "public administration". Kohn argued that jobs working with people had higher substantive complexity than jobs working with things such as machinery (Kohn, 1977). Interactions with people were required to be specific to the job, such as advising, supervising, or teaching (Kohn, 1977), characteristics of managerial positions. It follows that managerial positions would have more work related interactions with people than positions unique to the primary industry. The two variables used in the construction of the substantive complexity index for the PSID had a low alpha (Cronbach's alpha = .3049). Similar to the GSS, the items in the PSID needed to be recoded in regard to whether the occupations were working primarily with things, people, or ideas. Originally respondents were asked to indicate their type of occupation with the Primary Occupation codes composed of twelve response categories as well as the Primary Industry codes also composed of twelve response categories. These initial classifications were given to three graduate students to be coded in regard to Kohn's (1977) substantive complexity categories of whether the occupation works primarily with things, people, or ideas/data. Cohen's Kappa was used to assess inter-rater reliability. The final reliability of the POC and the PIC were acceptable (Cohen's Kappa = 1.0 and .74 respectively). Once again, like the GSS, occupations dealing with things were coded 1, people were coded 2 and ideas/data were coded 3. These two categorized items from the PSID were then summed to create an index that ranged from 2 to 6. A higher score on the index indicated that the respondent's occupation dealt increasingly with ideas and data. Distribution analysis showed that the index is fairly normally distributed, although there is a slight negative skew (-328, SE = .094) and negative kurtosis (-428, SE = .187). Monitoring and the Internet 35 Routinization GSS. In order to measure routinization, this variable needed to see whether the respondent's work conditions accommodated change. Participants were asked whether their jobs had any new computer software being introduced in the past 12 months and whether they were required to learn new skills to keep up with the change. If participants had responded that there was new software along with having to learn new skills, this indicated that their jobs were not as routinized as those participants who said there were no new introductions. The questions used to create the index were: 1) "Has new computer software been introduced into your job in the past 12 months"; 2) "Did you have to learn new skills in order to keep up with this change"; 3) "Did [hardware] upgrades require you to learn new skills"; and 4) "Over the last five years, has your work become more interesting, less interesting or stayed the same as a result of the introduction of computers or automated technology". The first 3 questions were coded either "yes" or "no", with the last question coded with a 3-point Likert scale with responses of "more interesting", "stayed the same", or "less interesting". In the construction of the routinization scale, the "yes" responses were scored 1 while the "no" responses were scored 2. The fourth question had "more interesting" scored as 1, "stayed the same" scored as 2, and "less interesting" scored as 3. Summing the Z-scores of these four items created the routinization scale. Scale reliability analysis of the four routinization variables showed that the alpha was low (Cronbach's alpha = .4592). The scale constructed from the z-scores of these items revealed a distribution that was severely positively skewed (.874, SE - .109). Analysis of the raw item distributions showed that all four were positively skewed. A construction of an index would not seem to help with achieving a normal distribution, therefore the decision was made to retain the scale and perform a transformation. Monitoring and the Internet 36 According to Tabachnick and Fidell (2001), an inverse transformation is the recommended procedure for severe positive skewness. While transformations by log.were also attempted, it appeared that an inverse transformation was the best equation for achieving a more normal distribution. The resulting skewness through an inverse transformation was significantly decreased (.126, SE = .109). While statistical transformations will help with normality, it creates more difficulty in the interpretation of the results. PSID. A dummy variable was created to capture routinization. The sample was categorized as having occupations that were low routinized or high routinized depending on whether there had been a change in job positions within a four-year period. This concept was assessed by six variables in the PSID looking at whether there had been a change in position within their jobs between the year 1997 and 2001. Respondents were asked about their recent promotions and whether there was change in duties or not. There were two question assessing change within the three assessment years of 1997,1999, and 2001. Response categories were 1) promotion with higher pay; 2) major change in duties but with same pay; or 3) other. Since respondents who did not have a change in position did not answer this question, the non-response on these two items were recoded to represent no change in position indicative of routinization on the job. The initial distribution of this item was heavily skewed towards no change. As such, this measure was dummied to create two response categories of "change in position" or "no change in position" during the 1997 to 2001 period. The distribution of the responses in this measure indicates that the majority of respondent in the PSID did not have a change in position during this period. As such, these individuals were categorized as highly routinized for this dimension of occupational conditions. Controls These control variables account for several sets of factors that have been noted in the research on monitoring and Kohn's theory of socialization. These factors are encouragement of Monitoring and the Internet 37 general computer use, education, income and family structure. The desire for a parent to have their child use the computer as a tool for exploration or simply to restrict their use to schoolwork has been linked in the literature to Internet monitoring. Family structure was noted as possibly having an effect on the amount of monitoring that parents employ. Education was noted as having an influence on the type of occupational status. This study has controlled for these variables in order to minimize the variance attributed to these factors, thus gaining a more accurate measure of our dependent and independent variables. Encouragement of General Computer Use Research has shown that the motivations for providing computer access to the child plays a part in monitoring computer use (Orleans & Laney, 2000) thus these were controlled for by measures that looked at the encouragement that parents provided to their children in the kinds of use for the computers. GSS. From the GSS, respondents were asked: 1) "Did (or will) your household purchase a computer specifically for your children"; 2) "Do you encourage your children to use the Internet for entertainment". Parents who value conformity would not encourage entertainment purposes. Those who value independence would encourage entertainment, as well as provide a computer specifically for their child (Orleans & Laney, 2000). Summing the Z-scores of these two questions created our scale. A high number on this scale indicated a higher encouragement of general computer use. PSID. This control variable was assessed by three questions. 1) "How often do you encourage your child to play certain computer games"; 2) "How often do you encourage your child to use Internet websites for certain things; and 3) "How often do you encourage your child to use e-mail". These question were assessed by a 5-point Likert scale with responses of "none of the time", "a little of the time", "some of the time", "most of the time", or "all of the time". Monitoring and the Internet 38 These three variables were Z-scored then summed to create a scale of "encouragement of computer use" where a high score indicated a high degree of encouragement and a low score indicated a low degree of encouragement. Education Amount of education has been linked to the occupational status of the individual (Wright & Wright, 1976). Since the concern is with the effects of occupational conditions and not occupational status or education, this study statistically controlled for education. GSS. Education level was measured by asking the respondent to choose from a set of options for their highest level of education completed. Ten possible responses ranged from "elementary/no schooling" to "doctorate graduate". PSID. Education was measured with a variable that asked respondents to indicate the number of years of formal education they received. Scores of 1-16 indicated their actual number of years completed, while a score of 17 indicated that they completed as least some post-graduate work. Income Since one of the questions in the "encouragement of general computer use" variable is influenced by the family's financial situation, the income of the family will be included as a control variable. GSS. Income was measured with a variable asking the respondent to report their yearly household income. Twelve possible responses ranged from "no income" to "$100,000 or more". PSID. Income was measured by asking the respondent to report on their actual household income for the 2000 income tax year. Family structure Family structure has been noted as one of the possible factors affecting the monitoring of children (Chilcoat et al., 1996; Fisher et al., 2003). Of the little research on monitoring as a Monitoring and the Internet 39 dependent variable, this factor has been used to explain differences in monitoring levels. We controlled this variable to isolate family structure from the effects of occupational conditions. GSS. Taken from the GSS composition matrix, our respondents were classified into one of the following four categories: 1) Intact family; 2) Stepfamily with common children; 3) Stepfamily with no common children; or 4) Lone-parent family. PSID. Family structure was measured from the variable assessing the marital status of the respondent. Five response categories were given; 1) married; 2) never married; 3) widowed; 4) divorced, annulled; or 5) separated. Variables from the GSS and the PSID used in the construction of the measures in this study are summarized in Appendix A. Monitoring and the Internet 40 CHAPTER IV Results Test of Hypotheses With the measures finalized, a bivariate correlation matrix was created for both data sets (See Tables 2 and 3). This matrix allowed us to see whether multicollinearity existed between our independent variables of "closeness of supervision", "substantive complexity", and "routinization". Additionally, these matrixes detennined whether or not any of the items were correlated with our dependent variable of Internet monitoring. This last purpose informed the study whether or not to proceed with multiple regression analyses. Since bivariate correlations give a larger estimate of the relationship between variables than regression, which is much more conservative, an indication of no relationship in the correlation matrix indicates that there will also be no relationship in a regression analysis between those particular variables. GSS, The correlations between the independent variables in the GSS were not significantly correlated except for the relationship between substantive complexity and closeness of supervision (See Table 2). It does not appear that there is a problem of multicollinearity between the independent variables as the correlations were weak even for the closeness of supervision-substantive complexity relationship (r = -.182,/? < .01). Examining the relationship between the independent variables with the dependent variable of Internet monitoring, the correlation matrix showed that there was no significant relation between any of the independent variables with Internet monitoring. Examination of scatterplots dispelled the possibility of a curvilinear relationship between occupational conditions and Internet monitoring. The hypotheses of this study predicted that closeness of supervision would be positively related to Internet monitoring. The opposite relationship was weakly shown in the correlation matrix but the relation was not significant. Similarly, while the direction of the relation between substantive complexity and Internet monitoring was not related in the Monitoring and the Internet 41 hypothesized direction, the strength of the relation was weak and non-significant. Routinization was predicted to be positively related to Internet monitoring, which was shown in the correlation matrix but was an extremely weak, non-significant positive relationship. Refer to Table 2 for correlations. All the strengths of the relationship between the independent variable of occupational conditions and our dependent variable of Internet monitoring were extremely weak and non-significant; the strengths of the correlations were all below 0.03. The weak relationships between our variables of interest in the correlation matrix indicate that there would not be any significant relationship uncovered through regression analysis. As no statistically significant relationships were found between occupational conditions and the Internet monitoring index, an attempt was made to see if any of the individual items used in the construction of the Internet monitoring index was correlated with occupational conditions. Results showed that "helping the child with the Internet" was negatively correlated with the respondent's closeness of supervision (r = -.099,/? < .001), as well as positively correlated with substantive complexity (r = .082, p < .001). The directions of these two relationships are inconsistent with the hypotheses of this study. Refer to Table 4 for the correlations of the individual monitoring items. Citing the criticism of Gecas (1978) who found that Kohn's model failed to predict the transmission of values in the female population, the hypotheses of this study were examined with the sex of the respondent in mind. Comparing findings from a bivariate correlation matrix of the male and female subsamples of the GSS showed no difference in the ability of occupational conditions to predict the parental behavior of Internet monitoring. None of the three dimensions of occupational conditions in either the female or male subsamples were statistically significantly correlated with Internet monitoring. Refer to Tables 5 and 6 for the results of the bivariate correlation matrix as well as descriptive statistics of the male and female subsamples for the Monitoring and the Internet 42 measures. There was no significant difference in the dependent variable between males and females. PSID. Consistent with the GSS, the correlations between the independent variables in the PSID were not statistically significantly correlated except for the relationship between substantive complexity and closeness of supervision (See Table 3). Multicollinearity does not seem to be a problem between the independent variables as the correlations were weak even for the closeness of supervision-substantive complexity relationship (r = -.145,/? < .01). Examining the correlation matrix for the relationship between the three independent variables with the dependent variable of Internet monitoring showed that there was no statistically significant relation between any of the occupational dimensions with Internet monitoring. Closeness of supervision was predicted to be positively related to Internet monitoring but the correlation indicates that it is an extremely weak, statistically non-significant relationship. While the direction of the relation between substantive complexity and Internet monitoring was not as hypothesized, the strength of the relation was weak and statistically non significant. Routinization was predicted to be positively related to Internet monitoring. A reverse relationship was weakly shown in the correlation matrix but the relation was not statistically significant. Unfortunately, all the strengths of the relationships between the independents and our dependent variables were extremely weak and statistically non-significant. The weak relationships between our variables of interest in the correlation matrix indicate that there would not be any statistically significant relationship uncovered through regression analysis. It should be noted that the significant relationship between substantive complexity and closeness of supervision was found in both data sets (see Tables 2 and 3). Despite having these dimension operationalized slightly differently across the GSS and the PSID, this negative relationship between these two variables was fairly consistent in both significance and strength. Monitoring and the Internet 43 Concerns that the primary caregiver (PCG) might not be the one making the decisions for monitoring Internet use in the household, that the head of the household might hold more power in dictating monitoring norms, the occupational conditions of the "head" of the household was used to see if it would predict the degree of Internet monitoring better than the occupational conditions of the PCG. The result of the correlation matrix as well as the descriptive statistics of the PSID HEAD occupational conditions can be found in Table 7. The results of the correlation between Internet monitoring and the occupational conditions of these particular respondents were also not statistically significant. None of the three occupational dimensions of the head of the household were correlated with Internet monitoring. No difference appeared between Internet monitoring and occupational conditions for either the primary caregiver of the child or the head of the household. Examining the sex of the respondent in response to the criticism that Kohn's model failed to predict the transmission of values in the female population (Gecas, 1978), the hypotheses of this study were examined with the male and female respondents of the PSID separately. Comparing findings from a bivariate correlation matrix of the male and female subsamples (Tables 8 and 9, respectively) of the PSID showed no difference in the ability of occupational conditions to predict the parental behavior of Internet monitoring. Consistent with findings from the GSS, none of the three dimensions of occupational conditions in either the female or male subsamples were statistically significantly correlated with Internet monitoring. Additionally, there was no significant difference in the dependent variable for males and females. Table 2 GSS Descriptives and Correlation Coefficients Variable 1 2 3 4 5 6 7 8 9 1. Internet monitoring 2. Closeness of supervision -.027 _ 3. Substantive complexity .027 -.182** — 4. Routinization .003 .019 -.038 — 5. Encouragement of .013 -.016 .029 -.002 computer use 6. Education -.003 -.174** .285** -.012 -.026 -7. Family structure -.030 .015 .068** .042 -.028 .028 — 8. Number of children .206** -.059 -.025 .065 .025 .019 -.168** _ 9. Income -.067* -.113** .027 -.019 .025 .163** -.349** .008 M 2.634 -.004 4.433 -.348 .003 7.266 2.45 1.79 9.95 SD 1.026 2.223 1.176 .697 1.445 2.338 1.011 .708 1.947 alpha .402 .527 .588 .459 - - - - -*p<.05 (2-tailed) **p<.01 (2 tailed) Table 3 PSID Descriptives and Correlation Coefficients Variable 1 2 3 4 5 6 7 8 9 1. Internet monitoring -2. Closeness of supervision .024 — 3. Substantive complexity .046 -.145** — 4. Routinization -.048 .009 .038 — 5. Encouragement of .304** .039 -.003 -.084* computer use 6. Education .035 -.338** .325** -.069 -.030 -7. Family structure -.027 -.004 -.013 -.058 .015 -.071* — 8. Number of children .149** .102* -.083* -.011 .056* -.033 -.086** 9. Income -.016 -.149** .094* .041 -.061* .287** -.185** -.024 -M .799 .604 4.551 .814 .463 13.487 1.623 2.005 77198.12 SD .401 .489 1.010 .389 2.276 2.315 1.195 .965 92097.71 alpha .691 .122 .305 - .565 - - - -*p<.05 (2-tailed) **p<.01 (2 tailed) Table 4 GSS Descriptives and Correlation Coefficients for the Individual Items of Internet Monitoring Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Disable or lock computer -2. Use software .157* -3. Use Internet provider filter .184* .212* -4. Supervise time .064* -.037 .005 -5. Help child with Internet .088* .011 .018 .228* -6. How important is child .065* .058 .075* .324* .119* -Internet monitoring? 7. Closeness of supervision -.023 -.041 -.053 .034 -.099* .120* -8. Substantive complexity -.056 .021 -.014 .044 .082* -.037 -.182* -9. Routinization -.088 .021 .009 .030 .022 .001 .019 -.038 -10. Encourage computer use .009 .009 .000 .004 .043 -.066* -.016 .029 -.002 -11. Education -.083* .011 -.025 .013 .089* -.045 -.174* .285* -.012 -.026 -12. Family structure .011 -.010 .027 -.020 -.052 -.044 .015 .068* -.42 -.028 .028 -13. Number of children .055 .040 -.004 .145* .212* -.099* -.059 -.025 .065 .025 .019 -.168* -14. Income -.048 -.006 .015 -.066* -.014 -.097* -.113* .027 -.019 .025 .163* -.349* .008 M 1.092 1.083 1.077 1.728 1.735 2.589 2.634 -.004 4.433 -.348 .003 7.266 2.45 SD .289 .278 .267 .445 .442 .645 1.026 2.223 1.176 .697 1.445 2.338 1.011 alpha - - - - - - .402 .527 .588 .459 - - -*p<.05(2-tailed) **p<.01 (2 tailed) Table 5 GSS Descriptives and Correlation Coefficients for the Male Subsample Variable 1 2 3 4 5 6 1. Internet monitoring 2. Closeness of supervision -.017 — 3. Substantive complexity .034 -.267** — 4. Routinization .065 -.016 -.088 — 5. Encouragement of -.001 -.004 .063 -.010 computer use 6. Education -.027 -.151** .305** -.014 -.065 -7. Family structure .024 .026 .027** .027 -.070 .030 8. Number of children .236** -.098* .014 -.123* .016 .013 9. Income -.096* -.095 -.007 .016 .030 .130** .129** M 2.600 -.449 4.107 -.365 .095 7.325 2.250 1.830 10.330 SD 1.032 2.290 1.321 .704 1.441 2.417 .753 .724 1.755 alpha .403 .514 .635 .452 ..... *p<.05 (2-tailed) **p<.01 (2 tailed) Table 6 GSS Descriptives and Correlation Coefficients for the Female Subsample Variable 1 2 3456 789 1. Internet monitoring 2. Closeness of supervision -.052 -3. Substantive complexity .006 -.179** — 4. Routinization -.073 .056 .026 5. Encouragement of .030 .005 .014 .015 computer use 6. Education .021 -.202** .288** -.008 .008 -7. Family structure -.075 -.059 .045 .047 .012 .036 8. Number of children .183** .005 -.047 -.005 .026 .023 -.187** 9. Income -.035* -.067 .147** -.063 .004 .191** -.426** M 2.664 .424 4.553 -.328 -.079 7.214 2.62 1.75 9.62 SD 1.020 2.071 .985 .689 1.447 2.266 1.168 .691 2.045 alpha .407 .507 .588 .459 ..... **p<.01 (2 tailed) Table 7 PSID Descriptives and Correlation Coefficients for HEAD Occupational Conditions Variable 1 2 3 4 5 6 7 8 9 1. Internet monitoring 2. Closeness of supervision .023 -3. Substantive complexity .023 -.309** — 4. Routinization -.020 .041 -.005 — 5. Encouragement of .304** .073* -.009 -.051 computer use 6. Education .028 -.447** .350** -.082** -.008 -7. Family structure -.027 .109** .209** -.002 .015 -.054 8. Number of children .149** .008* -.051 .005 .056* -.068* -.086** 9. Income -.016 -.272** .103** -.044 -.061* .291** -.185** M .799 .482 3.964 .794 .463 13.457 1.623 2.005 77198.12 SD .401 .500 1.098 .404 2.276 2.364 1.195 .965 92097.71 alpha .691 .211 .453 - .565 -*p<.05 (2-tailed) **p<.0l (2 tailed) Table 8 PSID Descriptives and Correlation Coefficients for the Male Subsample Variable 1 2 3 4 5 6 7 8 9 1. Internet monitoring -2. Closeness of supervision .189 — 3. Substantive complexity -.020 -.286* — 4. Routinization -.087 -.195 .251* 5. Encouragement of .276* .243 -.222 -.293* computer use 6. Education -.056 -.502** .358** -.090 -.078 -7. Family structure -.189 .019 .066 -.046 .017 -.009 — 8. Number of children .200 .196 .002 -.124 .149 -.229 -.118 _ 9. Income .023 -.367** .150 .084 -.061 .350** -.187 -.090 -M .737 .565 4.015 .843 -.202 13.736 1.790 1.816 91482.66 SD .443 .500 1.249 .367 1.870 2.753 1.310 .948 82881.96 alpha .656 .269 .477 - .491 - - - -*p<.05 (2-tailed) **p<.01 (2 tailed) § I a oo g a. 5-a =T a 3 rt © Table 9 PSID Descriptives and Correlation Coefficients for the Female Subsample Variable 1 2 3 4 5 6 7 8 9 1. Internet monitoring -2. Closeness of supervision .004 — 3. Substantive complexity .054 -.132** — 4. Routinization -.044 .029 .023 5. Encouragement of .304** .017 .007 -.068 computer use 6. Education .044 -.316** .333** -.068 -.025 -7. Family structure -.013 -.007 -.033 -.058 .017 -.078** — 8. Number of children .144** .093* -.084* -.001 .048 -.017 -.083** 9. Income -.016 -.125** .104* .036 -.058* .283** -.187** -.018 -M .803 .609 4.610 .811 .504 13.471 1.613 2.016 76305.33 SD .398 .488 .964 .392 2.293 2.285 1.187 .965 92601.02 alpha .692 .098 .303 - .566 - - - -*p<.05 (2-tailed) **p<.01 (2 tailed) Monitoring and the Internet 52 Parameters of the Model While none of the hypotheses in this study were supported, there is some evidence mthat the findings of this study were not the result of poor operationalization. While some of the measures had low alphas, by examining the correlation matrix of both the GSS and the PSID (Tables 2 and 3), it can be seen that other paths hypothesized by Kohn (1977) in his original model are supported. See Figure 2 for a diagram of Kohn's model of socialization and social class. While not all of the paths predicated by Kohn can be shown with the variables in the GSS and the PSID, one of the more important links, education to occupational conditions can be gleaned from the results of the two correlation matrixes. Kohn hypothesized that education would be positively related to self-direction in one's occupational conditions. Breaking occupational conditions down into Kohn's three dimensions of "closeness of supervision", "substantive complexity", and routinization", education is hypothesized in Kohn's model to be negatively related to both closeness of supervision and routinization, and positively related to substantive complexity (Kohn, 1977). The results of the bivariate correlation analysis indicate that the relation of education for all three dimensions of occupational conditions was operating in the hypothesized directions. Education was significantly related to both closeness of supervision and substantive complexity. While the relation with routinization was in the direction predicted by Kohn's model, it was not a statistically significant finding (Refer to Tables 2 and 3). The highest level of education obtained was significantly negatively correlated with closeness of supervision in both the GSS and the PSID (r = -.174, p < .01 and r = -.338, p < .01 respectively). Similarly, substantive complexity's relationship with education was also supported across both data sets with a positive significant relationship (r = .2S5,p < .Oland r = .325, p < .01 respectively). These findings strongly suggest that, in the GSS and PSID, Kohn's model was properly operationalized. As the hypothesized path from education to occupational Monitoring and the Internet 53 conditions was supported by both the GSS and the PSID, it give strong evidence that in the case of parental Internet monitoring, occupational conditions may not be applicable in properly predicting this parental behavior. Post-Hoc Analysis As none of the main hypotheses of the study were supported, a post-hoc analysis was conducted to determine if other concepts might explain parental Internet monitoring. Additional items in the GSS and the PSID that were conceptually related to Internet monitoring were correlated with the dependent variable to ascertain the strength of the relationships. Items that were found to be significantly correlated with Internet monitoring were included in an exploratory principle component analysis to determine what possible concepts may arise. GSS. Initial factor analysis of statistically significantly correlated items in the data set revealed an overwhelming number of components. These items were further filtered based on their factor loadings. Excluding items with factor coefficients less than 0.4 reduced the factor analysis to a manageable 12 items. Factor analysis of these 12 items indicated the presence of four components explaining 60.8% of the total variance. Details of this analysis can be found in Table 10. While statistically there were four different dimensions, conceptually there appeared to be only three dimensions: frequency of computer use, preferred language of accessing the Internet, and age of family members. Frequency of computer use had six items that were summed to create an additive index as they all shared the same response categories of "not in the last month", "a few times a month", "several times a week", or "everyday". The index ranged from 8 to 24 with a high score indicating a high frequency of computer use. Cronbach's alpha was moderate at .643. Descriptive statistics on all the post-hoc measures can be found in Table 11. Preferred language of accessing the Internet had three items that were originally nominally coded. These items were recoded into dummy variables of "English" or "other Monitoring and the Internet 54 language". These three items were then summed to create a dummy variable of this concept with 74.4% preferring English and 25.6% preferring another language. Cronbach's alpha for this index was high at .881. Age of family members had three items: age of respondent, age of respondent's spouse, and age of the youngest child. Refer to Table 11 for descriptive statistics and correlations of these post-hoc variables. OLS regression was used to determine if these items explained a significant proportion of the variance in the dependent variable of Internet monitoring. The model was statistically significant (p < .001). The results of the regression showed that preferred language of accessing the Internet (p < .01), age of the respondent (p < .001), and age of respondent's youngest child (p < .001) were statistically significantly related to Internet monitoring. Both the age variables were negatively related to Internet monitoring according to the regression results, which can be found in Table 14. The amount of variance explained by the model was 10.3%. An attempt at a more parsimonious model by only including the variables found to be statistically significant in the previous regression analysis did not yield a greater proportion of variance explained. Results of this regression can be found in model 2 of Table 14. Figure 2. Kohn's Model of Social Class and Socialization Occupational Status ± Social Class Education Occupational Conditions Intellectual Flexibility A Values —• Parental Behavior Source: Gecas, 1978 2 § 3. 3 § Q. % Monitoring and the Internet 56 Table 10 GSS Post-Hoc Principle Component Factor Loadings for Items Correlated with Internet Monitoring Components 12 3 4 Items Age group of the respondent -.003 -.201 .777 -.007 Age group of respondents spouse/partner -.009 -.232 .803 -.007 Age of respondent's youngest child -.006 -.210 .706 .005 Last month, used home computer for personal reasons .768 .005 .008 -.323 Last month, how often use Internet at home? .794 .007 .010 -.319 Last month, how often send email? .418 -.222 .003 .562 Last month, how often email family/relatives? .338 -.161 -.001 .609 Last month, how often email inside community? .512 -.158 -.005 .376 Use Internet for personal interest/entertainment .674 .007 .002 -.225 Language used for accessing Internet sites .010 .738 .256 .228 Preferred language when accessing Internet sites -.006 .764 .217 .010 Respondent's household language .003 .771 .156 .114 Total variance explained = 60.8% Monitoring and the Internet 57 Table 11 GSS Post-Hoc Descriptives and Correlation Coefficients Variable 1 2 3 4 5 6 1. Internet monitoring -2. Frequency of computer use .070* 3. Preferred language .091** .001 -4. Age group of respondent -.215** -.003 -.015 -5. Age group of respondent's -.165** -.031 .031 .544** spouse/partner 6. Age of respondent's youngest child -.279** .012 -.011 .368** .436** M 2.634 16.673 .744 3.11 3.11 8.69 SD 1.026 3.328 .436 .616 .614 3.699 alpha .402 .643 .881 - -*p<.05 (2-tailed) **p<.01 (2 tailed) Monitoring and the Internet 58 PSID. The items in the PSID that were statistically significantly correlated with the dependent variable of Internet monitoring were placed into a principal component factor analysis. As with the GSS, those items that had factor loadings greater than 0.4 were selected and a second factor analysis was conducted. Thirteen items were included in this second factor analysis resulting in four components explaining 64.9% of the variance. These four components could be conceptually interpreted as parental rules, knowledge of child's friends, age of family members, and family size (refer to Table 12 for items and factor loadings). The measure of parental rules consisted of seven items. Since the items related to parental rules all had the same response categories of "yes" and "no", an additive index was created with a range from 0 to 7. Respondents with higher scores indicate a higher number of parental rules. Cronbach's alpha for this index was 0.790. Knowledge of child's friends consisted of two items: "Do you know the child's friends?" and "Do you know their friend's parents?" Both items shared the same five response categories of "none of them", "only a few", "about half, "most of them", or "all of them". An additive index was created with a range from 2 to 10. A higher score indicated greater knowledge of their child's friends and parents. Cronbach's alpha for this index was high at 0.831. Two items concerning family age were included: "age of the individual" and "age of the youngest child". For family size the items were: "number in family unit" and "number of children in family unit". Descriptive statistics for these items can be found in Table 13. Since the dependent variable of Internet monitoring in the PSID was a dummy variable, logistic regression was used in place of OLS regression. Results of the logistic regression indicated that three of the six items included in the analysis were statistically significantly related with Internet monitoring: parental rules (p < .001), age of the individual (p < .05), and number in family unit (p < .05). The Cox and Snell R2 statistic indicated that 10.1% of the variance in the Internet monitoring measure was accounted for by these six variables. The overall correct Monitoring and the Internet 59 prediction of Internet monitoring with this model was 81%. Refer to model 1 of Table 15 for the results of this regression analysis. Respondents who reported a greater number of parental rules were 1.4 times more likely to monitor their child's Internet use. The age of the respondent was negatively related to Internet monitoring. Every additional year in age decreased monitoring by a factor of 0.97. With every increase in the number in the family unit, Internet monitoring increased by 1.3 times. A second, more parsimonious model was attempted (see Table 15). Only the three items that were statistically significantly related to Internet monitoring in the previous model were included. The "parental rules", "age of respondent", and "number in family unit" variables of this second model indicated that only 9.8% of the variance in the dependent variable was explained according to the Cox and Snell statistic. Overall correct prediction dropped slightly from 81% in the first model to 80.9% in the second model. For the second model, respondents who reported a greater number of parental rules were 1.4 times more likely to monitor their child's Internet use. Every additional year in age decreased monitoring by a factor of 0.98. With every increase in the number in the family unit, Internet monitoring increased by 1.4 times. Parental Rules While there were similar groupings in terms of items measuring family age in both the GSS and the PSID, a particular concept of interest found in the PSID was parental rules. An attempt was made to see if an index of parental rules would be significantly related to occupational conditions. While this study found that Kohn's hypothesis did not seem to be applicable to the area of Internet monitoring, it would be interesting to see if occupational conditions could predict parental rules to a greater degree than what was found with Internet monitoring. Monitoring and the Internet 60 A correlation matrix was the first step in determining whether a relationship between occupational conditions and parental rules was present in the data. The results of the matrix with this dependent variable showed that there was a statistically significant relationship between parental rules and closeness of supervision (r = .081, p < .05). The direction of the relationship was positive indicating that those who were closely monitored in their jobs also had a greater number of parental rules. The remaining two occupational condition relationships were not statistically significant, although the relationship with substantive complexity is consistent with Kohn's (1977) model. Refer to Table 16 for the complete results of this correlation matrix. Monitoring and the Internet 61 Table 12 PSID Post-Hoc Principle Component Factor Loadings for Items Correlated with Internet Monitoring Components Items 1 2 3 4 Age of the respondent -.508 .191 .008 .671 Age of respondent's youngest child -.668 .366 -.009 .428 Number in family unit .142 -.841 .259 .333 Number of children in family unit .306 -.861 .219 .172 Rules on the amount of TV .607 .009 -.009 .253 Limits on the kinds of TV .703 .101 -.002 -.005 Rules on bedtime .651 .154 -.113 -.003 Limits on sweets .717 .010 -.006 -.005 Rules on who child interacts with .490 .179 -.008 .389 Rules on after-school activities .590 .189 -.010 .338 Rules about homework .650 .202 -.197 .007 Know child's friends .127 .316 .863 -.001 Know friends' parents .207 .299 .850 -.004 Total variance explained = 64.9% Table 13 PSID Post-Hoc Descriptives and Correlation Coefficients Variable 1 2 3 4 5 6 7 1. Internet monitoring -2. Parental rules .292** -3. Parental knowledge .083** .108** -4. Age group of respondent -.157** -.305** .003 -5. Age of respondent's youngest child 6. Number in family unit -.183** .104** -.430** -.006 -.084** -.025 .573** -.003 -.235** 7. Number of children in family unit 149** .112** -.029 -.200** -.438** .808** -M SD alpha .799 .401 .691 4.646 2.145 .792 7.202 2.144 .828 38.761 7.366 8.248 4.586 4.033 1.157 2.016 .965 *p<.05 (2-tailed) **p<.01 (2 tailed) Monitoring and the Internet 63 Table 14 GSS Post-Hoc Regression Coefficients with the Dependent Variable of Internet Monitoring Model Variables 1 2 Preferred language .095** .085** Age group of the respondent _149*** -.135*** Age of respondent's youngest child -.221*** -.225*** Age group of the respondent's spouse/partner .018 -Frequency of computer use .062 -R2 .103 .099 * p < .05 **p<.01 ***p<.001 Monitoring and the Internet 64 Table 15 PSID Post-Hoc Logistic Regression Coefficients with the Dependent Variable of Internet Monitoring Model 1 2 Variables B Exp(B) B Exp(B) Parental rules .325*** 1.384 .318*** 1.375 Age of the respondent -.026* .975 -.023* .978 Number in family unit .254* 1.289 .326*** 1.386 Number of children in family unit .172 1.188 - -Age of respondent's youngest child .026 1.027 - -Parental knowledge .067 1.069 - -Cox&SnellR2 .103 .098 * p < .05 ** p < .01 *** p < .001 Table 16 PSID Descriptives and Parental Rules Correlation Coefficients Variable 1 2 3. 4 5 6 7 8 1. Parental Rules 2. Closeness of supervision .081* — 3. Substantive complexity -.026 -.145** _ 4. Routinization -.018 .009 .038 — 5. Encouragement of computer use 6. Education .254** -.016 .039 -.338** -.003 .325** -.084* -.069 -.030 7. Family structure .011 -.004 -.013 -.058 .015 -.071* — 8. Number of children .112** .102* -.083* -.011 .056* -.033 -.086** 9. Income -.165** -.149** .094* .041 -.061* .287** -.185** -.024 10. Internet monitoring .292** .024 .046 -.048 .304** .035 -.027 .149** M 4.646 .604 4.551 .814 .463 13.487 1.623 2.005 77198.12 .799 SD 2.145 .489 1.010 .389 2.276 2.315 1.195 .965 92097.71 .401 alpha .792 .122 .305 - .565 - - - - .691 *p<.05 (2-tailed) **p<.01 (2 tailed) Monitoring and the Internet 66 CHAPTER V Discussion The purpose of this study was to see if Kohn's (1977) model predicting parental behaviors from occupational conditions was applicable to the specific parental behavior of child Internet monitoring. Specifically, this study looked at three aspects of occupational conditions as outlined by Kohn: closeness of supervision, substantive complexity, and routinization. Kohn's model hypothesized that these three dimensions would be able to predict parental behavior. Using Kohn's model, these three dimensions were assessed to see if they were successful in predicting child Internet monitoring. Additionally, other variables of interest in the literature related to Kohn's model were included such as education (Wright & Wright, 1976) and family size (Gecas, 1978). The outcome of these findings would suggest evidence to the applicability of Kohn's model to this relatively new area of parental behavior. Applicability of Kohn's Model The hypotheses from this study derived from Kohn's model were not supported by either of the data sets. This provides evidence that the model and its respective hypotheses are not applicable to this area of parental behavior. The possibility of a curvilinear relationship was also examined but was not supported by the data. Perhaps there is something inherently different about the Internet that causes Kohn's hypotheses to break down. In any case, consistent findings in this regard from both the GSS and the PSID provide support that the relationship between occupational conditions and parental behavior in Kohn's model is not effective at predicting child Internet monitoring. An interesting finding that appeared in the Canadian GSS data set was the relationship between Internet monitoring and the age of the child. It was found that as a child got older, the amount of parental Internet monitoring decreased. Apparently parents feel that younger children are more vulnerable to the more mature materials on the Internet or that it may be more difficult Monitoring and the Internet 67 to monitor adolescents. This finding may indicate some possible scenarios. One reason may be that parents are granting their older child more independence and responsibility for their Internet browsing, or a second reason may be that as the child gets older, their Internet knowledge surpasses the parents, creating a situation where the parents resign from attempting to regulate their child's Internet access. Examining the relationship between the sex of the respondent and their Internet monitoring did not show any significant differences. There was no difference found in the degree of monitoring for either males or females respondents. This would suggest that this form of behavior is not influenced by the gender of the parent. While the applicability towards the female sex was a criticism directed towards Kohn's theory (Gecas, 1978; Spade, 1991), this concern was not evident in either the PSID or the GSS. This is not to say that sex of the parent would not make a difference to the degree of other forms of parental behaviors, but suggests only that the sex of the individual is not moderating the relationship between occupational conditions and the specific behavior of Internet monitoring. A reason for why the hypotheses were not supported could be that the research model is not appropriate to the area of Internet monitoring. While studies have found Kohn's model to be predictive of parental behaviors and the intergenerational transmission of values (Klute et al., 2001; Kohn & Schooler, 1983; Luster et al., 1989; Seccombe, 1986; Spade, 1991), occupational conditions' ability to predict the parental behavior of Internet monitoring might not be as direct as initially hypothesized. Since the data did not support the hypotheses of this study, it suggests that Kohn's theory, in its present state, would either not be applicable to Internet monitoring, or require some modification and addition to the original relationships hypothesized. While the hypotheses for the relationships between occupational conditions and Internet monitoring were not found, the link between education and occupational conditions were found to be statistically significant and operating in a direction consistent with Kohn. While a direct Monitoring and the Internet 68 relationship between occupational conditions and parental behavior was not found, the model has support to being properly operationalized. Specifically, the conditions of closeness of supervision and substantive complexity were both significantly related to education. As one's level of education increased, their supervision on the job decreased and their occupational substantive complexity increased. This indicates that as one gained more education, they were more likely to have a job that dealt primarily with ideas rather than things. This was consistent with Kohn's model. While the supported relationships between education and occupational conditions do not serve to predict Internet monitoring, it does signal that Kohn's model has some validity, at least in those particular aspects. The parental behavior of Internet monitoring may not be completely outside Kohn's model. The post-hoc analysis of the data served to highlight concepts that may be operating as mediators between the relationship of occupational conditions and Internet monitoring. This next section will propose a modification of the original hypotheses in an attempt to predict Internet monitoring from other aspects in Kohn's model of social class and socialization. A Variation on Kohn's Model While the findings from this study suggest that there is no direct relationship between occupational conditions and Internet monitoring as hypothesized by Kohn's model, it does provide some findings regarding variables that may be mediating the relationship between occupational conditions and child Internet monitoring. By examining existing variables in the GSS and PSID data sets, statistically significantly correlated items with the dependent variable of child Internet monitoring were selected. From here, these items were factor analyzed to arrive at a handful of concepts that were then incorporated into two models: a revised conceptual model and a revised research model. The post-hoc analysis had revealed that a modification to Kohn's theory with the addition of other concepts was necessary and seemed to function in bridging the Monitoring and the Internet 69 gap between the research hypotheses and it's application onto the area of Internet monitoring. A possibility exists that Kohn's model may have no bearing on Internet monitoring, but the post-hoc analysis revealed that mediating variables are needed to link the relationships between occupational conditions and the parental behavior of Internet monitoring. The post-hoc analysis discovered that there were three distinct and interpretable concepts that were significantly related: parental rules, preferred language, and family size. The results of the post-hoc seem to be similar to Kohn's model than initially thought. Theoretically, two of these concepts, parental rules and family size, can serve as proxies for concepts already existing within Kohn's larger model (see Figure 2). While the results of the language concept showed that a preference for English was positively correlated with Internet monitoring, the concept itself does not seem to be congruent with Kohn's model. Such a relationship could possibly be spurious without further research to back it up. Of the two concepts that can be conceptualized as proxies for concepts already existing in Kohn's model, family size serves to proxy SES. This is instead of being a mediating variable between occupational conditions and child Internet monitoring. While statistically significant correlations with education and income with family size were not present in the data, conceptually family size seems to be related to SES. Studies by Rosen (1961, as cited in Gecas, 1978) support this conceptual link. Larger families tend to be related to a lower socioeconomic status. Those with higher SES tend to have fewer children and as a result, their family size is smaller (Rosen, 1961, as cited in Gecas, 1978; Wagner & Schubert, 1985). Correlation matrixes support the relationship between family size and occupational conditions. With the revised model, closeness of supervision was the only occupational condition that was significantly correlated with the other concepts in the model. Taking closeness of supervision as the concept of occupational conditions, its relationship with family size supports the conceptualization of Monitoring and the Internet 70 family size as a proxy for SES. The relationship with occupational condition is operating in the hypothesized direction as predicted by Kohn's model. While the direct relationship in the transmission from occupational conditions to parental behavior was not supported in this study, there exists a second path in Kohn's model that was not initially tested. This path is from intellectual flexibility to parental behaviors. Intellectual flexibility is the ability to take on other perspectives and views. The literature has linked this concept to a valuation of autonomy over conformity and obedience (Duncan & Magnuson, 2001). Operating with this definition, the measure of "parental rules" appears to be a suitable proxy for intellectual flexibility. A greater degree of parental rules would signal a lower level of intellectual flexibility. This relationship was present in the PSID data. While the direct relationship was not supported, this indirect relationship through intellectual flexibility was supported through post-hoc analysis. These results led to a reformulation of the conceptual model and following from this, a revised research model both of which are depicted in Figures 3.1 and 3.2 respectively. It should be noted that some of the relationships that lack a correlation from the GSS in the research model are due to a lack of the specific variables in the data set, not from a lack of statistical significance. Being informed by the post-hoc analysis, the revised conceptual model contains four concepts: socioeconomic status (SES), occupational conditions, intellectual flexibility, and parental behavior. These concepts are consistent with the concepts contained in Kohn's (1977) original model of social class and socialization (See Figure 2). Derived from this conceptual model is the research model (Figure 3.2). Education, income, and family size are the variables used to proxy SES. Closeness of supervision and substantive complexity are the variables measuring occupational conditions. Intellectual flexibility is represented by parental rules, and finally, Internet monitoring is the parental behavior. Monitoring and the Internet 71 Figure 3. J. Revised Conceptual Model SES Occupational Conditions Intellectual Flexibility Parental Behavior w w w Figure 3.2. Revised Research Model Education Income Closeness of Supervision A i r i Family Size Substantive Complexity Parental Internet Rules w Monitoring Monitoring and the Internet 72 The relationships found in the data sets between these variables are all consistent with Kohn's model. Education and income are both negatively related to closeness of supervision and positively related to substantive complexity. Family size is positively related to closeness of supervision and negatively to substantive complexity. These findings indicate that as SES increases, a person's job is less likely to be closely supervised and more likely to be substantively complex. Consistent with Kohn, closeness of supervision and substantive complexity are negatively related so that as a job becomes more substantively complex, the amount of supervision decreases. The relationship between the amount of self-direction in occupational conditions and intellectual flexibility was positive in Kohn's original model. Examining the revised research model (Figure 3.2), the findings are in support of Kohn's original hypothesized relationship. Since the proxy for intellectual flexibility is parental behaviors, it would be hypothesized that a job that is more closely supervised would be positively related with parental rules and negatively related with substantively complexity. These hypotheses were supported with the data. Similarly, the positive relationship between parental rules and Internet monitoring would also be consistent with what Kohn's model would have hypothesized. Implications of the Revised Model What all this suggests is that intellectual flexibility may play a larger role than originally thought. Instead of having all paths within Kohn's original model be in operation, it appears that it is dependent on the variable in question. Particularly for child Internet monitoring, it appears to lack the direct relationship between occupational conditions and monitoring as hypothesized by Kohn. What is in effect is the indirect relationship through intellectual flexibility. It is difficult to hypothesize what mechanism causes the direct link between occupational conditions and parental behavior to break down for this specific behavior. It might have to do with the relative uncertainty of the Internet, both in parental skills and knowledge about it, as Monitoring and the Internet 73 well as the reversed power imbalance with children being more knowledgeable, but this parental behavior is creating difficulty in the application of the model. What seems to be consistent from Kohn's original model to the specific behavior of Internet monitoring is the relationship from occupational conditions to Internet monitoring through the concept of intellectual flexibility. Kohn's theory has been tested through the years as it has held up to empirical testing (Klute et al., 2001; Kohn & Schooler, 1983; Luster et al., 1989; Seccombe, 1986). The results of this study on child Internet monitoring suggests that a particular link in the model might not be as applicable to all types of parental monitoring as initially thought. Much stronger conclusions could have been made had the indirect relationship been arrived at through a deductive process rather than an inductive one. As such, the second possible implication is that Kohn's model has no relevance for this particular form of parental behavior at all, which would require the revision of what Kohn's model deems as "parental behavior" since it failed to predict the parental behavior of child Internet monitoring. Limitations One of the limitations from this study was the low alpha on some of the measures. While the relationships found through these measures with low alphas had significant relationships that influence the findings of this study, we should be cautious of these relationships. This is why the possibility of Kohn's model being irrelevant has not been excluded. The post-hoc argument that Kohn's model may still be in operation through the indirect link is contingent on the relationship between closeness of supervision and parental rules, the prior of which has a low alpha score. There is evidence that the low alpha may not be as damaging to the findings of the model. Other relationships predicted by the same theory, such as the relation between education and occupational conditions, have been supported and found to be operating in the direction consistent with Kohn. Additionally, the post-hoc relationships between SES variables, Monitoring and the Internet 74 occupational conditions, and the additional variables of intellectual flexibility and family size were all significantly related and consistent with the paths found in Kohn's theory. Although the occupational measures of closeness of supervision and substantive complexity were both found to be significantly related to education, the relationship of these occupational measures with Internet monitoring must be interpreted with caution. Due to being selected from secondary data that was not collected specifically for examining Kohn's occupational concepts, the validity of the measures may be in question. The presence of significant relationships between the occupational measures and education in the direction hypothesized by Kohn (1977), combined with the fact that both closeness of supervision and substantive complexity were operationalized differently across the two data sets, yet yielding consistent findings, give some support as to the validity of the measures for the independent variables. The validity of the dependent variable of Internet monitoring is also a concern. The GSS dependent variable had a low alpha score, but when examining the items used in its construction, the questions asked of the respondent are what this researcher would have asked had this been a primary study. Similarly, in the PSID the items were assessing whether the parent placed limits on the child's Internet access. The assessments of what types of monitoring strategies used and whether or not parents used monitoring seem to have face validity as measures of Internet monitoring. This researcher believes these items are indeed tapping the concept of Monitoring. A possible selection bias and a cohort bias may exist within this study. With the GSS data, as a subset of the larger GSS sample was used, the findings are restricted to those individuals who have children under 14 years of age. Other restrictions to the generalizability of our findings are confined to those who had used a computer in their workplace and had a computer at home connected to the Internet. These conditions were necessary because of the nature of the questions in the GSS. The variables that fit with our conceptual model were only Monitoring and the Internet 75 asked of respondents that fell within this subset. Since our main concern in this study is the monitoring of children's Internet use, requiring participants to have a home computer is not as restrictive as initially thought. Similarly with the PSID data, only respondents who also participated in the Child Development Supplement were included, but as the research questions inherently require a child to be residing in the home, as well as require the home to have a computer with Internet access, this restriction did not present much of a problem. Since the PSID primarily examined the PCG's occupational conditions, this created a situation where only single working parents, who were both the head of the household and the PCG, and dual earner families, where the PCG was not the head of the house, were sampled. This did not seem to present problems for the study since there were consistent findings in the GSS, which did not limit the sample to just single parents and dual earner households. This leaves the only major restriction of the study being the exclusion of those individuals who don't use a computer in their jobs, as well as the exclusion of household with teens older than 14 years of age for the GSS and children over age 18 for the PSID. There is also a possible concern with the construction of our measures in the GSS. The original variables in the GSS on which our indexes are based had a limited range of response categories. Many were only dummy variables with "yes" or "no" responses. Thus, the construction of the scales had a small range, possibly affecting the variability of the measures. Although there were limitations to the study, consistent findings were present in both data sets. Irrespective of whether it supported or refuted the applicability of Kohn's theory, both the GSS and the PSID had similar results. Additionally, the relationship between the education variable in both data sets were related to the majority of the occupational measures. Many of the relationships in Kohn's model were supported, such as the link between SES and occupation. As such, the lack of support for the relationship between occupational conditions and Internet Monitoring and the Internet 76 monitoring led to the conclusion that perhaps occupational conditions were not a good candidate for predicting the degree of Internet monitoring used by parents. While having data sets from two different countries, each operationalized differently, limits the ability and opportunity to compare across the countries, having findings that share similarities in the data sets suggests that there are mechanisms that are in operation that transcend cultural boundaries. Having consistent findings in the data give evidence that Kohn's theory may be applicable across different countries, at the very least, applicable to the United States and Canada. Findings from this study shed light on the factors that impact the Internet monitoring behavior of parents. This study also has theoretical merit, as the findings demonstrate an interesting application of Kohn's socialization theory to the relatively recent area of computer technology. Taking into consideration the power of the Internet in conjunction with the lack of research examining the antecedent conditions of Internet monitoring, this study has provided some needed theoretical basis in an atheoretical area. Future Research Directions for future research with Kohn's model in this area should incorporate other aspects of the model, particularly the indirect path though intellectual flexibility from occupational conditions to child Internet monitoring. Since this path is consistently supported though a variety of parental behaviors, it seems to be the more robust relationship between the job variables and parental behaviors. Future studies should also take into account the concepts that were related to Internet monitoring though the post-hoc analysis. More research is needed to confirm the applicability of Kohn's model to the area of Internet monitoring, more specifically, studies are needed that can operationalize concepts to a degree that is more closely related to Kohn's original intentions. This would have to be done though original data collection rather than using secondary data. The results of such a study Monitoring and the Internet would provide stronger evidence to the question of Kohn's relevance to parental behaviors concerning the computer and the Internet. Another aspect that could use further study is Internet monitoring compared to other forms of parental behaviors. The outcomes of this study suggest that the act of Internet monitoring is perhaps different from other forms of parent behaviors. While there exists a relationship though the mediating variable of intellectual flexibility, a direct relationship was observed in previous studies using Kohn's model to predict parental behaviors. What mechanisms are in operation that causes monitoring of child's activities on the Internet to be inherently different than say, monitoring of child's television consumption? Future studies would do well to try and uncover the characteristics that make this specific area of monitoring different from other forms of media monitoring. Monitoring and the Internet 78 CHAPTER VI Conclusion The Internet is fast becoming a major element in the family household as well as the workplace. Having the computer skills to successfully maneuver the World Wide Web is increasingly becoming a vital job skill. Studies have found that children with unrestricted access to computers are more knowledgeable about the technology than children who have restricted access. While most studies look at the effects of Internet access, the question of who monitors that access has not been sufficiently studied in the literature. This study attempted to remedy that gap by testing the applicability of Melvin Kohn's theory examining occupational conditions and parental behavior. The hypotheses set out at the start of the study were not supported. A direct relationship between occupational conditions and parental Internet monitoring was not found. Post-hoc evidence showed that perhaps Kohn's model required a revision to include additional variables such as intellectual flexibility. Correlations from a post-hoc examination showed that occupational conditions of the parent mediated though parental rules were related to the degree of child Internet monitoring. Parents who worked in jobs that were highly supervised with low substantive complexity were more likely to have more parental rules and a greater degree of child Internet monitoring. It appears that a modified version of Kohn's theory may be more suited to the area of child Internet monitoring. The findings of this study suggest that occupational conditions may not directly predict parental Internet monitoring, but in conjunction with variables measuring intellectual flexibility, a relationship may arise. Further research testing the revised model developed though this study will either confirm or disconfirm the applicability of Kohn's stake in the area of Internet monitoring. While more work specifically looking at the question of Internet monitoring is still greatly needed, this study provides a step in uncovering a suitable theoretical framework in order to pursue this needed future research. Monitoring and the Internet 79 REFERENCES Ahmann, E. (2000). 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Measure Index Composed of: GSS PSID Dependent Internet Monitoring Lock computer + Software + Filters + Supervise + Help with internet + Importance of monitoring Limits on computer games + Limits on internet + Limits on email Closeness of Supervision Home computer for work + Work computer for personal + Work hours at home Job is salaried, paid by the hour, or what? + Are you self-employed, employed by someone else or what? ndependen Substantive Complexity Standard occupational class + North American industrial class Primary occupation codes + Primary industry codes Routinization New computer software + Learn new software skills + Learn new skills due to hardware change + Work became more interesting Change in job during 1997 to 2001 Encouragement of Computer Use Computer for child + Encourage for entertainment + Encourage computer games + Encourage internet + Encourage email Controls Education Highest level of education completed Number of years of formal education Controls Income Household Income Household Income Family Structure Family type Marital status Monitoring and the Internet 84 APPENDIX B Measures Used in the GSS Index of Parental Internet Monitoring Four items, each prefaced by, "Do you use the following method at home to monitor your children's Internet usage? Two response categories: 1 "yes," 2 "no." 1. Disable or lock the computer? 2. Use software? 3. Use an Internet provider who filters out unacceptable sites? 4. Supervise their time on the Internet? Do you help your children use the Internet? Two response categories: 1 "yes," 2 "no." How important is it to you that your children's Internet use is monitored? Three response categories: 1 "Very important," 2 "Somewhat important," 3 "Not important at all." Occupational Conditions Closeness of Supervision In the last month, how often did you use your HOME computer for work-related reasons? Four response categories: 1 "everyday," 2 "several times a week," 3 " a few times a month," 4 "not in the last month." Monitoring and the Internet 85 In the last month, how often did you use your WORK computer for personal reasons? Four response categories: 1 "everyday," 2 "several times a week," 3 " a few times a month," 4 "not in the last month." Excluding overtime, do you usually work any of your scheduled hours at home? Two response categories: 1 "yes," 2 "no." Substantive Complexity Standard Occupational Classification. Ten response categories: 1 "management," 2 "business," 3 "natural sciences," 4 "health occupations," 5 "social sciences," 6 "artistic/sport," 7 "sales and services," 8 "trades/equipment," 9 "primary industry," 10 "manufacturing." North American Industrial Classification System. Sixteen response categories: 1 "agriculture," 2 "forestry and gas," 3 "utilities," 4 "construction," 5 "manufacturing," 6 "trade," 7 "transport/warehouse," 8 "finance/leasing," 9 "professional services," 10 "management support," 11 "education services," 12 "health/social assistance," 13 "information," 14 "accommodation services," 15 "other services," 16 "public administration." Routinization Has new computer software been introduced into your job in the past 12 months? Two response categories: 1 "yes," 2 "no." Did you have to learn new skills in order to keep up with this change (in your job)? Two response categories: 1 "yes," 2 "no." Monitoring and the Internet 86 Did this upgrade require you to learn new skills? Two response categories: 1 "yes," 2 "no." Over the last five years, has your work become more interesting, less interesting or stayed the same as a result of the introduction of computers or automated technology? Three response categories: 1 "more interesting," 2 "less interesting," 3 "stayed the same." Controls Encouragement of General Computer Use Did (or will) your household purchase a computer specifically for your children? Two response categories: 1 "yes," 2 "no." Do you encourage your children to use the Internet for entertainment? Two response categories: 1 "yes," 2 "no." Education Ten response categories: 1 "doctorate graduate," 2 "bachelors degree," 3 "diploma/college," 4 "diploma/technical," 5 "some university," 6 "some college/nursing," 7 "some trade/technical," 8 "high school diploma," 9 "some secondary/high school," 10 "elementary/no schooling." Income Income of the respondent's household. Twelve response categories: 1 "no income," 2 "less than $5000," 3 "$5000 to $9999," 4 "10000 to $14999," 5 "$15000 to $19999," 6 "$20000 Monitoring and the Internet 87 to $29999," 7 "$30000 to $39999," 8 "$40000 to $49999," 9 ^$50000 to $59999," 10 "$60000 to $79999," 11 "$80000 to $99999," 12 "$100000 or more." Family Structure Five response categories: 1 "couple only," 2 "intact family," 3 "stepfamily with a common child," 4 "stepfamily with no common child," 5 "lone parent family." Post-Hoc Items Age of respondent Age group of the respondent. Seven response categories: 1 "15 to 24," 2 "25 to 34," 3 "35 to 44," 4 "45 to 54," 5 "55 to 64," 6 "65 to 74," 7 "75 years and over." Age of respondent's spouse/partner Age of respondent's spouse/partner. Seven response categories: 1 "15 to 24," 2 "25 to 34," 3 "35 to 44," 4 "45 to 54," 5 "55 to 64," 6 "65 to 74," 7 "75 years and over." Age of respondent's youngest child Age of respondent's youngest child. Responses 1 to 24 indicate the actual age of the child. Response of 25 indicate age of child as 25 and over. Computer use for personal reasons In the last month, how often did you use your HOME computer for personal reasons? Four response categories: 1 "everyday," 2 "several times a week," 3 " a few times a month," 4 "not in the last month." Monitoring and the Internet 88 Internet use at home In the last month, how often did you use the Internet at HOME? Four response categories: 1 "everyday," 2 "several times a week," 3 " a few times a month," 4 "not in the last month." Use of email In the last month, how often did you send email? Four response categories: 1 "everyday," 2 "several times a week," 3 " a few times a month," 4 "not in the last month." Emailing family and relatives In the last month, how often did you email family/relatives? Four response categories: 1 "everyday," 2 "several times a week," 3 " a few times a month," 4 "not in the last month." Emailing the community In the last month, how often did you email inside the community? Four response categories: 1 "everyday," 2 "several times a week," 3 " a few times a month," 4 "not in the last month." Internet for personal or entertainment reasons In the last month, how often did you use the Internet for personal interests or entertainment? Four response categories: 1 "everyday," 2 "several times a week," 3 " a few times a month," 4 "not in the last month." Monitoring and the Internet 89 Language for accessing Internet sites Language used when accessing Internet sites. Seven response categories: 1 "English only," 2 "French only," 3 "other language only," 4 "English and French equally," 5 "English and other equally," 6 "French and other equally," 7 "English, French and other equally." Preferred language for Internet access What is your preferred language to use when accessing Internet sites? Five response categories: 1 "English," 2 "French," 3 "Chinese," 4 "Spanish," 5 "Other." Household language What is your household language? Three response categories: 1 "English," 2 "French," 3 "Other language." Monitoring and the Internet 90 APPENDIX C Measures Used in the PSID Index of Parental Internet Monitoring Three items each beginning with the phrase, "How often do you set limits on..." Four response categories for each item: 1 "never," 2 "hardly ever," 3 "sometimes," 4 "often." 1. The computer games child can play? 2. What child can do on the Internet? 3. Child's use of email? Occupational Conditions Closeness of Supervision On your main job, are you salaried, paid by the hour, or what? Six response categories: 1 "salaried," 2 "salaried plus commission," 3 "paid by the hour," 4 "hourly plus tips," 5 "hourly plus commission," 6 "other." Are you self-employed, are you employed by someone else, or what? Three response categories: 1 "someone else only," 2 "both someone else and self," 3 "self-employed only." Substantive Complexity Primary Occupational Codes. Twelve response categories: 1 "laborers," 2 "farm laborers," 3 "farm managers," 4 "private household workers," 5 "transport operatives," 6 Monitoring and the Internet 91 "service workers," 7 "operatives," 8 "craftsman," 9 "clerical," 10 "sales workers," 11 "managers," 12 "professionals." Primary Industry Codes. Twelve response categories: 1 "agriculture, forestry, and fisheries," 2 "mining," 3 "construction," 4 "manufacturing," 5 "transportation, communication, and other utilities," 6 "wholesale and retail trade," 7 "finance, insurance, and real estate," 8 "business and repair services," 9 "personal services," 10 "entertainment and recreation services," 11 "professional and related services," 12 "public administration." Routinization Change in position or work situation during 1997 to 2001. Two response categories: 1 "yes," 2 "no." Controls Encouragement of General Computer Use Three items each beginning with the phrase, "How often do you..." Five response categories for each item: 1 "none of the time," 2 "a little of the time," 3 "some of the time," 4 "most of the time," 5 "all of the time." 1. Encourage child to play certain computer games? 2. Encourage child to use Internet websites for certain things? 3. Encourage child to use email? Education Monitoring and the Internet 92 Number of years of education. Responses of 1 to 16 indicate the actual number of years. Response of 17 indicates some post-graduate work. Income Household income for the tax year 2000 Family Structure Marital status of the primary wage earner. Five response categories: 1 "married," 2 "never married," 3 "widowed," 4 "divorced, annulled," 5 "separated." Post-Hoc Items Age of the respondent Age of the respondent. Responses indicate the actual age. Age of the respondent's youngest child What is the age of your youngest child? Responses indicate the actual age. Family size Number in the family unit. Responses indicate actual number. Number of children in the family Number of Persons Now in the FU Under 18 Years of Age. Responses indicate actual number. Monitoring and the Internet 93 Rules on the amount of TV Do you have rules about how much time CHILD can watch TV in a day? Two response categories: 1 "yes," 2 "no." Limits on the kinds of TV Do you have rules about what TV programs CHILD watches? Two response categories: 1 "yes," 2 "no." Rules on who child interacts with Do you have rules about which children CHILD can spend time with? Two response categories: 1 "yes," 2 "no." Rules on after-school activities Do you have rules about how CHILD spends time after (school/daycare)? Two response categories: 1 "yes," 2 "no." Rules about homework Do you have rules about when CHILD does (his/her) homework? Two response categories: 1 "yes," 2 "no." Know child's friends How many of CHILD'S close friends do you know by sight and by first and last name? Six response categories: 1 "none of them," 2 "only a few," 3 "about half," 4 "most of them," 5 "all of them," 6 "(he/she) does not have any friends." Monitoring and the Internet Know friend's parents How many of CHILD'S close friends' parents do you know by sight and by first and last name? Six response categories: 1 "none of them," 2 "only a few," 3 "about half," 4 "most of them," 5 "all of them," 6 "(he/she) does not have any friends." 

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