Effects of Internet on Child Development

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to learn was reported in 65 cases, to play was reported in
57 cases, to browse in 35
cases, and to communicate in 27
cases. Thus, the five indices of child home Internet use in
cluded: 1) the continuous variable years of home Internet
access and the dichotomous (report
ed-unreported) variables of child home In
ternet use to 2) learn, 3) play, 4)
browse, and 5) communicate.
Family Socioeconomic Characteristics
The parent questionnaire assessed five family characteris
tics commonly used to determine socioeconomic status
(Bradley & Corwyn, 2002; Sirin, 2005). Two items queried father’s and mother’s employment status. Approximately 70% of mothers and 96% of fathers were employed, full-time or part-time. Two questionnaire items requested father’s and mother’s level of education, coded as: elementa ry = 1, junior high school = 2, high school incomplete =

3, high school complete = 4, technical school/college (complete or incomplete) = 5 and university (complete or incomplete) = 6. The mean educational level 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 = 1.02) 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 following 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 approximately $60,000 CD (M = 4.07, SD = 1.48).

Table 2 presents a summary of measured constructs which
includes: four tests of children’s cognitive development,
five indices of children’s home Internet use, and five fa
mily socioeconomic characteris
tics. Which are the better
predictors of cognitive development during childhood, -- el
ements of the microsystem or elements of the techno-
subsystem? Two series of stepwise regression 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 Internet use (elements of the techno-subsystem) were the independent variables.
Tab le 2
. Description of Constructs and Measures
Ecological System
System Elements Specific Measures
Bioecology Cognitive Development Expressive Language
Metacognitive Planning
Visual Perception
Auditory Memory
Techno-Subsystem Home Internet
Use Years of Internet Access
Online Learning
Online Playing
Online Browsing
Online Communication
Microsystem Family Characteristics Father Employment
Mother Employment
Father Education
Mother Education
Annual Family Income
Results of analyses revealed that fa
mily socioeconomic characteristics (eleme
nts of the microsystem) explained a
modest (but significant) amount
of the variation in children’s cognitive deve
lopment scores. As presented in Table 3,
adjusted R
values indicated that father’s level of education accounted for approximately 7% of the variation in children’s level of expressive language (as measured by the WISC-IV vocabulary subtest), 5% of the variation in children’s visual perception and auditory memory (as measured by the CAS nonverbal matrices subtest and CAS 181

word series subtest, respectively). Whether or not moth
ers were employed, part-tim
e or full-time, accounted for
approximately 6% of the differences in children’s capacity to execute metacognitive functions such as planning (as measured by the CAS matching numbers subtest). While the other measures of familial socioeconomic status (e.g., mother’s education and family income) explained some of the variance in children’s cognitive development, such measures did not improve upon the predictive utility of fa

ther’s education or maternal employment;...
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