Effects of Internet on Child Development

one hundred eighty to learn was report in 65 cases, to play was reported in 57 cases, to browse in 35 cases, and to communicate in 27 cases. Thus, the vanadium indices of churl rest kin mesh hold in cluded 1) the regular variable long time of home net access and the dichotomous (report ed-unreported) variables of squirt home In ternet use to 2) learn, 3) play, 4) browse, and 5) communicate. Family Socioeconomic Characteristics The levy questionnaire assessed five family characteris tics unremarkably used to determine socioeconomic stipulation (Bradley &038 Corwyn, 2002 Sirin, 2005). 2 items queried fathers and lets employment status. Approximately 70% of mothers and 96% of fathers were employed, full-time or part-time. Two questionnaire items requested fathers and mothers aim of training, coded as elementa ry = 1, junior high develop = 2, high school uncomplete = 3, high school complete = 4, technical school/college (complete or incomplete) = 5 and university (comple te or incomplete) = 6. The mean educational pick out of mothers was 4. 79 (SD = 0. 95) suggesting that many mothers had post-secondary education the mean educational level of fa thers was 4. 45 (SD = 1. 2) suggesting that some fathers had post-secondary education. The final socioeconomic item on the questionnaire asked parents to indicate annual family income by selecting one of the pastime options < $20 000 = 1, $20 000 to $40 000 = 2, $40 000 to $60 000 = 3, $60 000 to $80 000 = 4, $80 000 to $100 000 = 5, > $100 000 = 6. Annual income for participating families was almost $60,000 CD (M = 4. 07, SD = 1. 48). Table 2 presents a summary of measured constructs which includes four tests of childrens cognitive development, five indices of childrens home cyberspace use, and five fa ily socioeconomic characteris tics. Which are the better predictors of cognitive development during childhood, &8212 el ements of the microsystem or elements of the techno- subsystem? Two series of s tepwise retroflection analysis we re conducted with the four c ognitive development scores as the dependant variables. In the first regression analyses , family socioeconomic characteristics (elements of the microsystem) were the independent variables. In the second analyses, indices of home mesh use (elements of the techno-subsystem) were the independent variables. Tab le 2 Description of Constructs and Measures Ecological dodge System Elements Specific Measures Bioecology Cognitive Development communicatory Language Metacognitive homework Visual Perception Auditory Memory Techno-Subsystem substructure meshwork manipulation historic period of Internet Access Online nurture Online Playing Online Browsing Online conversation Microsystem Family Characteristics Father Employment beget Employment Father Education dumbfound Education Annual Family Income Results Results of analyses revealed that fa mily socioeconomic characteristics (eleme nts of the microsystem) explained a odest (but significant) amount of the reading in childrens cognitive deve lopment scores. As presented in Table 3, adjusted R 2 values indicated that fathers level of education accounted for almost 7% of the variation in childrens level of expressive lyric (as measured by the WISC-IV vocabulary subtest), 5% of the variation in childrens optical perception and auditory memory (as measured by the CAS sign- actors line(a) matrices subtest and CAS 181 word series subtest, respectively). Whether or not moth ers were employed, part-tim e or full-time, accounted for pproximately 6% of the discordences in childrens capacity to execute metacognitive functions such as readiness (as measured by the CAS matching numbers subtest). While the other measures of familial socioeconomic status (e. g. , mothers education and family income) explained some of the random variable in childrens cognitive development, such measures did not improve upon the prognostic utility of fa thers education or maternal employment variation is prerequisite to prediction. Almost all fathers were employed and almost all mothers had consummate high school. For participating middle- crystallize families, fathers education a d mothers employment were more sensitive to childrens cognitive development scores than were family income, fathers employment, and mothers education. Tab le 3 . Stepwise Regression Analysis Family Characteristics Predicting barbarian Cognitive Development Cognitive make up Predictor Beta cant t value R 2 (adj) F value Expressive Language Father Education . 292 2. 70** . 074 (1, 78) = 7. 29** Metacognitive prep Mother Employed . 270 2. 46* . 061 (1, 77) = 6. 05* Visual Perception Father Education . 244 2. 22* . 047 (1, 78) = 4. 93* Auditory Memory Father Education . 258 2. 6* . 054 (1, 78) = 5. 55* *p < . 05 **p < . 01 Results of analyses further revealed th at indices of home Internet use (elements of the techno-subsystem), in general, explained more of the varia tion in childrens cognitive de velopment than did family socioeconomic characteristics (elements of the microsystem). Summarized in Table 4, specific types on online behavior (i. e. , instruction, communicating, and playing) and years of home In ternet access combined to predicted child cognitive developmental outcomes. Indicated by adjusted R 2 , childrens online communication, ears of home Internet access, and online learning (as reported by parents) accounted for ap proximately 29% of the varia tion in childrens level of expressive language as measured by the WISC-IV vocabulary subtest. Online learning and communicating (reported- unreported) combined to explain 13. 5% of the variation in childrens metacognitive planning. Online learning and playing (reported-unreported) combined to explain 10. 9% of the variation in childrens auditory memory. years of home Internet access explained approximately 3% of the diffe rences in childrens visual perception scores. With the xception of visual perception, indices of home Internet use (elements of the techno-subsystem) were better predictors of childrens cognitive development than were family socioeconomic characteristics (elements of the microsystem). Tab le 4 . Stepwise Regression Analysis Home Internet Use Predicting Child Cognitive Development Cognitive Score Predictor/s Beta Weight t value R 2 (adj) F value Expressive Language Online Communication . 344 4. 00*** Years of Internet Access . 263 3. 12 ** Online Learning . 256 2. 99** . 287 (3, 101) = 14. 97*** Metacognitive Planning Online Learning . 287 3. 03** Online Communication . 201 2. 12* . 35 (2, 101) = 9. 06*** Visual Perception Years of Internet A ccess . 192 1. 99* . 028 (1, 104) = 3. 98* Auditory Memory Online Learning . 242 2. 60* Online Playing . 228 2. 46* . 109 (3, 101) = 14. 97*** *p < . 05 **p < . 01 ***p < . 001 Discussion A variety of mechanisms linking family socioeconomic status to child cognitive development energize been proposed in cluding parenting (Petrill, Pike, Price, &038 Plomin, 2004 Mistry, Biesanz, Chien, Howes, &038 Benner, 2008) and 182 resources (Bradley &038 Corwyn, 2002). For the topical samp le of middle class children, paternal education and maternal employment were associated with measures of hild cognitive development. More educated fathers tended to have offspring who scored high on three of the four cognitive measures (expressive language, visual perception, and auditory memory). Mothers who were employed tended to have children who scored high on the measure of metacognitive planning. ameliorate fathers and employed mothers may genetically transmit to their offspring some neurological processing advantage (bioecology). Simultaneously, educated fathers may provide enhanced language models and stimulating environments that facilitate the cognitive development of their children (microsystemic influence). Employed mother may provide models of organization and place increased demands on chi ldren to self- regulate thereby enhancing the metacognitive planning abilities of their offspring (microsystemic influence). Family socioeconomic status (as measur ed and for the current sample) accounted for 5% to 7% of differences in child cognitive development scores. In contrast, indices of home Internet use (as measured and for the current sample) accounted for 3% to 29% of differences in child cognitive development scores. Me ta-analysis confirms that the impact of socioeconomic status on academic achie vement is eroding over time (Sirin, 2005). Increasingly ffective structures of social equali zation (e. g. , ordinary education, quality daycare, preschool intervention, and prenatal programs) and the expanding middle class create the need for more precise verbal description of home environments. Current results suggest th at indices of home Internet use (i. e. , elements of the ecological techno- subsystem) provide more useable entropy regarding cognitive development than do family socioeconomic characteristics (elements of the microsystem). Only both of five family socioeconom ic characteristics added to the regres sion equation, suggesting that some measures (i. e. , family income father employment, and mother education) did not differ in relation to childrens cognitive development. In contrast, four of the five indices of home Internet use during childhood added to the regression equation, suggesting that these measures differe d in relation to childrens cognitive development. In the context of the current investigation, soci oeconomic status is a crude construct re lative to home Internet use. Internet use includes both organized (e. g. , search) and disorganized (e. g. , browse) interactions with both human (e. g. , chat) and bloodless (e. g. , database) elements in online environments (Johnson &038 Kulpa, 2007).Internet use is a complex set of behaviors that vary widely across individuals and th at is influenced by cognitive and personality characteristics (Joinson, 2003). For the current sample of children, patterns of home Internet use explained more of the variation in cognitive development than did family socioeconomic characteristics. In the context of middle class families, elements in the techno-subsystem (e. g. , Internet access) may not necessarily facilitate child cognitive development effective use of those elements, highly dependent upon parent behavior, may promote development.For example, Cho and Cheon (2005) surveyed families and found that parents perceived control, obtained through shared nett activities and family cohesion, reduced childrens exposure to negative Internet content. Lee and Chae (2007) reported a positive relations hip between parental intermediation techniques (website recommendation and Internet co-use) and childrens educa tional attainment. In the current investigation, the cognitive experiences provided to children by employed moth ers may include Internet skills instruction (e. g. , sending email) and models of information management (e. g. acc essing websites for informa tion). Such experiences, over time, may provide children with enhanced opportunities to direct their own cognitive development via more and more sophisticated uses of the Internet. According to Livingston and Bober (2005), a new divide is opening up between those for whom the internet is an increasingly rich, diverse, engaging and stimulating resource and those for whom it remains a narrow, unengaging, if occasionally recyclable , resource of rather less significance (p. 2). Bruner (2005) recen tly reiterated that our minds ap propriate ways of representing th populace from using and relating to the codes or rules of available technology (p. x). Cognitive abilities prerequisite to use of Internet applications constitute an implicit component of contemporary notions of intelligence (Maynard, Subrahmanyam, &038 Greenfield, 2005). The ecological techno-s ubsystem furthers our understanding of environmental influences on child development by emphasizing the impact of digital technologies on cognitive growth during childhood. The techno- subsystem provides precise description of microsystemic mechanisms of developmental influence which lead to intervention strategies.According to Livingston and Bober ( 2005), many parents lack the skills to run and support their childrens Internet use and Intern et-literate parents have Internet-litera te children. Subsequent research may evaluate the effectiveness of techno-subs ystem interventions for elementary school children at-risk, for example, the provision of home Internet access and pa rent Internet literacy training. As stated elsewhere, current anxiety surrounding childrens Internet use should be for those whose cognitive processes are not influenced by the pagan tool (Johnson, 2006, p. 570).