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Parents' Socializing Behavior and Children's Participation in Math, Science,and Computer Out-of-School ActivitiesSandra D. Simpkins a; Pamela E. Davis-Kean a;Jacquelynne S. Eccles b

a Institute for Research on Women and Gender University of Michigan. b Institute for Research onWomen and Gender and Department of Psychology University of Michigan.

To cite this Article Simpkins, Sandra D. , Davis-Kean, Pamela E. andEccles, Jacquelynne S.(2005) 'Parents' SocializingBehavior and Children's Participation in Math, Science, and Computer Out-of-School Activities', Applied DevelopmentalScience, 9: 1, 14 — 30To link to this Article: DOI: 10.1207/s1532480xads0901_3URL: http://dx.doi.org/10.1207/s1532480xads0901_3

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Parents’ Socializing Behavior and Children’s Participation inMath, Science, and Computer Out-of-School Activities

Sandra D. Simpkins and Pamela E. Davis-KeanInstitute for Research on Women and Gender

University of Michigan

Jacquelynne S. EcclesInstitute for Research on Women and Gender and Department of Psychology

University of Michigan

This study examined associations between multiple parental behaviors and children’sparticipation in out-of-school math, science, and computer activities for children in2nd (n = 125), 3rd (n = 123), and 5th grade (n = 200). Mothers and fathers reportedhow often they encouraged their children’s activities, participated in activities withtheir child, provided activity-related materials, and participated in activities them-selves, as well as how often their children participated in activities. The youth de-scribed how often they participated in math, science, and computer activities. Resultsindicate that parents’ behavior is a strong, positive predictor of children’s participa-tion. Although there were significant mean level gender differences, relations betweenparents’ behavior and children’s participation were similar for boys and girls.

Children have many opportunities to develop talentsand cognitive skills through informal, everyday activi-ties. Many cross-cultural and sociocultural psychologystudies have discussed differences in children’s skills,knowledge, and abilities resulting from cultural andcontextual differences in their daily informal activities(Cole, 1996; Gauvain, 1999; Guberman, 1999; Rogoff,1990). Recently, research has shown that children’s in-formal activities during middle childhood, such asmath, sport, and planning activities, have implicationsfor their beliefs in these domains (e.g., importance,self-concept of ability), cognitive abilities, selection ofhigh school courses, and participation in later formaland informal activities (e.g., Eccles, Wigfield, &Schiefele, 1998; Gauvain, 1999; Simpkins, Fredricks,Davis-Kean, & Eccles, 2003). Although these studiesprovide compelling evidence that children’s participa-tion in out-of-school activities is associated with

short-term and long-term outcomes, few studies haveexamined the contextual factors that promote and sus-tain children’s participation. This study addresses thisissue by examining the associations between one set ofthese contextual factors; parents’ behavior; and ele-mentary school children’s participation in math, sci-ence, and computer activities.

Math, science, and computer out-of-school activi-ties are particularly interesting for two reasons. First,many of these activities are complex (e.g., maneuver-ing through computer programs) and parents’ encour-agement and assistance may be critical to children’smotivation, enjoyment, and eventual persistence inthese challenging activities. Second, these activitiesare traditionally stereotyped as more appropriate forboys than girls (Bamossy & Jansen, 1994; Huston,1985; Huston-Stein & Bailey, 1973; Jacobs, 1991;Jacobs & Eccles, 1992; Shashaani, 1994b; Wilder,Mackie, & Cooper, 1985). By studying these activities,we can address two issues: (a) test relations betweenparents’ socializing behavior and children’s participa-tion in out-of-school math, science, and computer ac-tivities and (b) test gender differences in these indica-tors and relations between parents’ behavior andchildren’s participation.

Relations Between Parental SocializingBehavior and Children’s Participation

Many theories emphasize the importance of consid-ering multiple parental behaviors in relation to chil-dren’s development. For example, work by Epstein

Applied Developmental Science2005, Vol. 9, No. 1, 14–30

Copyright © 2005 byLawrence Erlbaum Associates, Inc.

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This research was supported by Grant HD17553 from the Na-tional Institute for Child Health and Human Development toJacquelynne Eccles, Allan Wigfield, Phyllis Blumenfeld, and RenaHarold, Grant 0089972 from the National Science Foundation toJacquelynne Eccles and Pamela Davis-Kean, and grants from theMacArthur Network on Successful Pathways through Middle Child-hood to Eccles. We would like to thank the principals, teachers, stu-dents, and parents of the cooperating school districts for their partici-pation in this project. We would also like to thank the followingpeople for their work on the project: Amy Arbreton, PhyllisBlumenfeld, Carol Freedman-Doan, Rena Harold, Janis Jacobs,Toby Jayaratne, Mina Vida, Allan Wigfield, and Kwang Suk Yoon.

Requests for reprints should be sent to Sandra D. Simpkins, De-partment of Family and Human Development, Box 872502, ArizonaState University, Tempe, AZ 85287–2502. E-mail: sandra.simpkins@asu.edu

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(1995) suggests that parents are involved in children’seducation through a variety of behaviors in the home(e.g., helping with homework) and at the school (e.g.,parent–teacher conferences). The premise of includingmultiple parental behaviors has also resonated in mea-sures of family influences through the home environ-ment. The most well-known measure of the home envi-ronment, the Home Observation for Measurement ofthe Environment inventory (HOME; Bradley, Corwyn,Burchinal, Pipes McAdoo, & Garcia Coll, 2001;Elardo & Bradley, 1981), characterizes the generalhome environment of the child. Although HOMEitems change across development, they often includesuch items as materials in the home, parental respon-siveness, parental use of language, and parental in-volvement in children’s education or daily lives. Thelarge volume of research emphasizing parental behav-iors in the home finds that these promotive behaviorsare powerful correlates of children’s cognitive devel-opment (e.g., Bradley et al., 1989; Epstein, 1995).

Theories specifically related to the areas of chil-dren’s motivation and activities have also emphasizedthe multifaceted nature of parental influences.Grolnick and Ryan (1989), for instance, considered theinterplay of three parental behaviors as critical in chil-dren’s internalization of behaviors and development ofvalues: parenting involvement and interest, support forautonomous behaviors, and provision of adequatestructure. The Eccles’ expectancy-value model ex-tended work by Grolnick one step further by describ-ing the links between parents’ beliefs, parents’ behav-iors geared toward specific activities, children’sbeliefs, and children’s activity choices (Eccles, 1993).In this model, parents are an immediate, significant in-fluence on children’s activity participation and beliefsthrough their behaviors relating to specific activities(e.g., encouraging reading, doing computer activitieswith their child). Although researchers have examinedrelations between multiple parental behaviors aimed atpromoting children’s overall development and chil-dren’s social and cognitive adjustment (e.g., Bradley etal., 1989, Epstein, 1995; Grolnick & Slowiaczek,1994), few researchers have investigated associationsbetween parents’ behavior geared toward specific ac-tivities (e.g., computers) and children’s participation inthose out-of-school activities (e.g., Fredricks & Eccles,2002; Fredricks, Simpkins, & Eccles, in press). In thisarticle, we aim to fill this gap by testing relations be-tween children’s activity participation and multiple pa-rental behaviors specific to those activities. We predictthat children’s participation in math, science, and com-puter activities will be predicted by parents’ (a) partici-pation in these activities on their own (i.e., without thechildren); (b) encouragement of math, science, andcomputer activities; (c) provision of materials relatedto these activities; and (d) participation with their chil-dren in these activities.

Associations between parents’ participation ormodeling of activities and children’s participation inactivities can be explained by social learning theoryand specifically observational learning. The main tenetof observational learning is that children learn behav-ior by observing others (Bandura, 1997). Children maybe particularly motivated to learn and model parents’behavior because most young children strive to be liketheir parents (Eccles, 1993). Although children’s ob-servational learning has been documented in many ar-eas, little work addresses the relations between par-ents’ participation and children’s participation inout-of-school math, science, and computer activities.For example, researchers have found that parents’ useof computers and participation in math activities is as-sociated with children’s intentions to use computers(Pulos & Fisher, 1993) and expectations for futuremath performance (Eccles, Adler, & Kaczala, 1982)but these studies did not examine children’s participa-tion in these activities. The associations between par-ents’ and children’s participation, however, have beenexamined in the domain of sports, in which researchershave consistently found positive relations betweenthese indicators (for reviews see Fredricks & Eccles,2002; Taylor, Baranowski, & Sallis, 1994;Vilhjalmsson & Thorlindsson, 1998). This investiga-tion extends these findings by studying these associa-tions in the domains of math, science, and computers.

Parental encouragement is another contextual fac-tor that may influence children’s activity participa-tion. Parents can encourage children’s activities byverbally reinforcing children’s behavior or by provid-ing children with materials geared toward particularactivities, such as computer software or science sets(Elardo & Bradley, 1981). Eccles (1993) suggestedparental encouragement is linked to activity participa-tion through three main pathways. First, parents’ en-couragement regarding children’s competence canenhance their ability self-concepts and values for aparticular activity, thus increasing the likelihood offuture participation (e.g., Frome & Eccles, 1998;Grolnick & Ryan, 1989). Second, encouragement cansignal the value parents place on an activity, which inturn can motivate children’s participation becausethey may internalize their parents’ values and want toplease their parents. Third, if encouragement pro-duces a positive affective state in children, classicalconditioning can create a positive affective signifi-cance for the associated activity. Preliminary worksupports the positive associations between encourage-ment and activity participation. For example, parents’general encouragement of children (i.e., encourage-ment not associated with a specific activity) was posi-tively related to children’s participation in communityactivities (Fletcher, Elder, & Mekos, 2000). Similarly,parents’ encouragement of specific activities was pos-itively associated with children’s selection of and

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continued participation in sports and math activities(Fredricks, 2000; Fredricks & Eccles, 2002). Basedon empirical findings and theoretical work, we pre-dict that parents’ encouragement of specific activitiesand provision of activity-related materials will bepositively associated with children’s participation.

Parent–child coactivity (i.e., parent–child participa-tion in an activity together) is another avenue throughwhich parents’ behavior may be associated with chil-dren’s activity participation. Parent–child coactivitygives parents an opportunity to give verbal encourage-ment, directly teach their children specific skills, andhelp children move through the zone of proximal de-velopment (ZOPED; Cole, 1996). Vygotsky theorizedthat the ZOPED is the space between what children cancurrently complete on their own and what they can ac-complish with the guidance and assistance of moreknowledgable people. When parents’provide adequatestructure and help children move through the ZOPED,children gain knowledge and skills, which can buildtheir self-concepts and enable them to excel in futureendeavors (Grolnick & Ryan, 1989). Fredricks andcolleagues supported Vygotsky’s theory through thepositive links they found between coactivity andgrowth in children’s knowledge, competence, and ac-tivity participation in sports and arts (Fredricks et al.,2002). In the area of computer activities, parent–childcoactivity was associated with increases in children’sand parents’ computer knowledge (Duran, Duran,Perry-Romero, & Sanchez, 2001). Researchers haveyet to extend this work to include the relations betweencoactivity and children’s participation in math, sci-ence, and computer activities but previous researchwould suggest that these relations ought to be positive.

These studies provide solid support for the hypothe-sis that parents’ behavior is positively associated withchildren’s participation in math, science, and computeractivities. Yet, few researchers have used these parentalbehaviors to predict children’s participation in theseactivities (e.g., Fletcher et al., 2000; Fredricks &Eccles, 2002). Furthermore, previous research predict-ing children’s participation in activities has typicallyfocused on one or two parental behaviors, only oneparent, and only one activity domain (e.g., Duran et al.,2001; Fletcher et al., 2000). We know from researchpredicting other child outcomes that both mothers andfathers are influential on children’s development, par-ticularly father involvement in children’s social devel-opment (e.g., Parke, 2002). Additionally, work on fam-ily management (Furstenberg, Cook, Eccles, Elder, &Sameroff, 1999; Parke & Ladd, 1992), the home envi-ronment (e.g., the HOME inventory; Bradley et al.,2001; Elardo & Bradley, 1981), and other work onparenting (e.g., Grolnick & Slowiaczek, 1994) havehighlighted the importance of considering multiple pa-rental behaviors in relation to children’s development.In this study, we include maternal and paternal behav-

iors regarding various activities. In sum, we examinethe influence of several parental behaviors acrossmothers and fathers on children’s participation inmath, science, and computer activities. To this end,we model the multivariate associations between anoverall indicator of parents’ behaviors and children’sparticipation.

Gender Differences in Behavior andParticipation

A second goal of this article is to examine genderdifferences in children’s activity participation, parents’behaviors, and the relations between these indicators.Research on gender differences in children’s beliefsand expectations concerning math, science, and com-puter activities suggests boys are more likely to partici-pate in these activities than girls because boys havemore positive beliefs and higher expectations (Andre,Whigham, Hendrickson, & Chambers, 1999; Bamossy& Jansen, 1994; Eccles & Harold, 1991; Jacobs, 1991;Jacobs & Eccles, 1992; Shashaani, 1994b). In supportof this hypothesis, researchers have found that boys’activity participation is higher than girls in com-puter-related activities (Chen, 1986; Culley, 1988;Rocheleau, 1995; Shashaani, 1994a) and science-re-lated activities (Adamson, Foster, Roark, & Reed,1998; Johnson, 1987; Kahle & Lakes, 1983) but lowerin math activities (Eccles & Harold, 1991). We willbuild on this work by examining gender differences inchildren’s participation in these leisure activities dur-ing elementary school.

Although many of the gender differences in math,science, and computer activities are pronounced dur-ing middle childhood and adolescence, some of thesedifferences emerge before kindergarten (Wilder et al.,1985). If some of these differences are present beforethe school years, it is likely that children form these at-titudinal and behavioral differences at least in partfrom their parents and home environment. Eventhough researchers have found that parents’ socializa-tion does not vary across girls and boys in many areas,it does differ regarding children’s early play activities(Huston, 1983; Lytton, 2000; Lytton & Romney, 1991;Maccoby & Jacklin, 1974; Ruble & Martin, 1988).Currently, it is unclear if parents’ differentiated social-ization of early play activities (e.g., playing house, toyselection) will generalize to elementary school chil-dren’s participation in out-of-school activities.

The findings concerning gender differences in pa-rental socializing behavior are mixed. For example,gender differences did not emerge in parental encour-agement of math and science activities in middle child-hood (Felson & Trudeau, 1991) or computer activitiesin adolescence (Chen, 1986) but Eccles (1993) foundboys received more math and science encouragement

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than girls during both childhood and adolescence.Some evidence also suggests parent–child coactivitymight vary based on children’s gender. Fathers, and insome cases mothers, have more challenging andlengthy science discussions with boys than girls(Crowley, Callanan, Tenenbaum, & Allen, 2001;Tenenbaum & Leaper, 2003). Parents report engagingin more parent–child coactivity on computers withsons than daughters (Eccles, 1993) but no differenceswere found for math and science after-school activities(Eccles & Harold, 1996). Because the number of stud-ies examining gender differences in parents’ behavioris small and the findings are inconsistent, no specifichypotheses were put forward. To fully address the is-sue of gender differences in parents’ behavior, we willtest two ways in which gender differences couldemerge. First, we will test for mean-level differences inparents’ behavior by children’s gender. Second, wewill examine if the associations between parents andchildren significantly vary by children’s gender. Ratherthan testing each individual bivariate association, wewill utilize a more sophisticated approach and testwhether the overall model accounting for the relationsbetween parents’ behavior and children’s participationdiffers for boys and girls.

Method

Participants

The data are from the Michigan Childhood and Be-yond Study (CAB). This longitudinal, school-basedstudy includes families with children in 12 publicschools from three school districts in the Midwest areaof the United States (Eccles, Wigfield, Harold, &Blumenfeld, 1993; Wigfield, Eccles, Mac Iver,Reuman, & Midgley, 1991). A cohort sequential de-sign was used, in which three cohorts of children andtheir families were followed longitudinally. In 1987,children in kindergarten, first grade, and third gradewere recruited along with their parents through chil-dren’s schools. Letters describing the study and per-mission slips were given to families by children’steachers. Seventy-five percent of the families agreed toparticipate.

Data from 448 families were used in this report.Information from one child and both parents in eachfamily were included. Although families weretwo-parent families, not all fathers participated in thestudy. Two hundred eighty-one families had completeinformation from the mother, father, and child; 167families had complete data from the mother and childbut not the father. To understand the pattern of pater-nal missing data, we tested mean differences betweenfathers who participated and those who did not partic-ipate. We tested differences in father’s education;

family income; children’s activity participation as re-ported by the child and mother; children’s mathability; and mothers’ behaviors concerning computer,math, and science activities. Two of the 14 t testswere significant. The fathers who did and did not par-ticipate were significantly different according to theirlevel of education and family income. As one mightexpect, comparisons revealed that fathers who partici-pated had a higher level of education and family in-come than fathers who did not participate. All othercomparisons were not significant.

Ninety-six percent of mothers, 97% of fathers, and94% of children were European American and spokeEnglish. Ninety-eight percent of parents had attained atleast a high school degree. Forty percent of mothersand 54% of fathers had also earned a degree from a4-year college. Families’ 1989 annual household in-come ranged from $20,000 to over $80,000 (Mdn =$60,000 to $69,999).

Data included in this report were collected fromchildren in three different cohorts. One hundredtwenty-five children were in the second grade (ngirls =59, nboys = 66; M age of 8.20 years, SD = .44); 123 chil-dren were in the third grade (ngirls = 61, nboys = 62; Mage of 9.24 years, SD = .43); 200 children were in thefifth grade (ngirls = 99, nboys = 101; M age of 11.16years, SD = .37).

Procedure

The data for this report came from two waves ofCAB. One measure, teachers’ ratings of children’smath ability, was collected during the spring of Wave 2(1988). All other data in this study were collected dur-ing Wave 3 (1989). In Wave 3, children completedquestionnaires in their classroom while being super-vised by several staff members. Questionnaires wereread aloud to the entire class. Children’s data were col-lected at school in the spring. Following the child ques-tionnaires in Wave 3, self-administered parent ques-tionnaires were mailed home with a stamped returnenvelope (70% response rate).

Questionnaires

Children’s reports of their activity participation.Children described how often they participated in com-puter, math, and science activities after school duringthe last year. For each of the three activities, childrenwere first asked whether they participated in severalspecific math, science, or computer activities in the lastyear (1 = yes, 0 = no). Following each of the three listsof specific activities, children rated how often theygenerally participated in those types of activities out-side of school in the last year on a 7-point scale from 0(never) to 6 (almost every day for a lot of time). Thelists of activities were presented before assessing how

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much time children participated in each type of activityfor two reasons. First, lists of specific activities pro-vided children with concrete examples of activities ineach domain. Second, the lists of activities providedchildren with an opportunity to reflect on the differenttypes of activities they have done over the last year. Forexample, in regard to computer activities, children re-ported if they had participated in several specific activ-ities during the last year (1 = yes, 0 = no), includingprogram computers (M = .26, SD = .44); play educa-tional games, like learning math, spelling, reading (M= .53, SD = .50); write letters, reports, stories (M = .42,SD = .49); do graphics, drawing (M = .44, SD = .49);play or compose music (M = .16, SD = .37); and playvideo games (M = .71, SD =.45). Following this list,children reported how often they used a computer forthose kinds of activities outside of school in the lastyear on the 7-point scale. For math activities, childrenwere asked if they had participated (1 = yes, 0 = no). inthe following math activities during the last year: flash-cards (M = .25, SD = .43); calculators (M = .34, SD =.47); computer games with math (M = .40, SD = .49);Speak & Math (M = .17, SD = .37); math workbooks,dittos, worksheets (M = .37, SD = .48); math boardgames (M = .24, SD = .42); play school doing mathproblems (M = .26, SD = .43); math puzzles (M = .23,SD = .42); and make up and do problems in math (M =.36, SD = .48). Subsequently, children reported how of-ten they participated in these types of math activities inthe last year using the 7-point scale. To measure chil-dren’s participation in science activities, children wereasked if they had participated (1 = yes, 0 = no) in thefollowing activities during the last year: collectingthings like rocks, insects, leaves, and shells (M = .62,SD = .48); doing experiments like with chemistry sets(M = .33, SD = .47); reading science books (M = .38,SD = .48); going to science museums (M = .41, SD =.49); building erector sets (M = .20, SD = .40); andworking with science kits (M = .32, SD = .46). Follow-ing the list of activities, children stated how often theyparticipated in such science activities in the last year onthe 7-point scale. The three questions assessing tempo-ral participation in the three domains were used in ouranalyses.

Parents’ reports of children’s activity participa-tion. Mothers and fathers separately described theamount of time their child participated in several activ-ities during the last week with a 12-point scale rangingfrom 1 (0 hr) to 9 (12–16 hr) to 12 (over 25 hr). Theydescribed how often their child participated in (a)“math and science activities for pleasure” and (b) used“the microcomputer for activities other than actionvideo games.”

Parents’ encouragement. Parents described theextent to which they generally encouraged their child

to participate in computer, math, and science activities.Each parent rated how much he or she “encouragedtheir child to work on or play with a computer outsideof school” on a 7-point scale from 1 (strongly discour-age) to 7 (strongly encourage). Parents used the samescale to rate how much they encouraged their child todo “math-related (e.g., math-oriented games such asmastermind) or science-related (e.g., chemistry sets)activities at home.”

Parent–child coactivity. Mothers and fathers in-dicated how often they generally participated in theirchild’s daily activities on a 7-point scale ranging from 1(never) to 3 (2–3 times a month) to 7 (every day for 30mins or more). Each parent rated how often they“worked with their child on the computer.” This itemwasmeant toexclude timespentplayingvideogamesoncomputers because a separate item assessed video gamecoactivity. Parents also indicated how often they partici-pated in “math or science activities with their child.”This item was meant to exclude parent–child participa-tion in children’s math and science homework becauseanother item assessed time spent on homework.

Parents’provision of activity-related materials.Mothers reported if they or the child’s father bought orrented activity-related materials for their child in thepast year (1 = yes, 0 = no). Three items assessed com-puter-related materials: a computer, computer gamesor software, and computer books or magazines. Mathand science activity-related materials included twoitems: math-related books, games, toys, or magazines;and science-related books, games, toys, or magazines.Each set of items was summed to create an indicator ofparents’ provision of materials.

Parents’ participation in activities. Parents re-ported how much time they spent at home or after workon several activities without child involvement duringthe previous week. They described how much timethey spent on “math- and science-related activities”with an 8-point scale ranging from 1 (0 hr) to 6 (10–15hr) to 8 (more than 20 hr). Parents also described howmuch time they spent using “a microcomputer for ac-tivities other than action video games” with the same8-point scale.

Parent education. Mothers and fathers reportedtheir highest level of educational attainment on a list ofprecoded responses from 1 (grade school) to 9 (PhD).An index was created to characterize the highest levelof educational attainment across parents (Shumow &Lomax, 2002). The highest level across mothers andfathers in each family was used to characterize parents’level of education for several reasons. First, we usedboth mothers’ and fathers’ educational attainment be-cause our theory and models examine parents as a unit.

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Second, maternal and paternal education are positivelycorrelated, r(279) = .40, p < .001. Third, using thehighest level of education is more intuitive than otheralternatives. For example, characterizing parents’ edu-cational attainment with the average between mothersand fathers was not sensible, because in many casesneither parent actually attained the calculated average.In addition, it is likely that the highest level of educa-tion provides the most accurate indicator of children’sexposure to practices likely to be associated with par-ent education, such as quality of language used in thehousehold, value placed on educational activities, andextent of educational materials in the household.

Family annual income. Parents described theirannual income with a scale listing income brackets in$10,000 increments (minimum = none, maximum =over $80,000). Mothers’ and fathers’ incomes weresummed to create the average family annual income.

Math ability. Teachers described children’smathematics aptitude by rating “how much innate abil-ity or talent does this child have in math” from 1 (verylittle) to 7 (a lot) (M = 5.37, SD = 1.46). Children’smath ability was included in the predictive structuralequation models to control for children’s selection ofactivities based on their mathematics aptitude.

Results

The analyses were completed in five parts. First, weexamined the descriptive statistics of our scales. Sec-ond, gender differences in the mean levels of our indi-

cators were analyzed. Third, Pearson correlations werecomputed to describe bivariate relations between pa-rental behaviors and children’s activity participation.Fourth, analysis of covariance (ANCOVA) was used toexamine the relative predictive power of behaviorsfrom one versus two parents. Last, structural equationmodeling was used to test our model predicting chil-dren’s activity participation and examine gender differ-ences in our models.

Descriptive Statistics

Means and standard deviations of children’s activityparticipation and parents’ behaviors are presented sep-arately for boys and girls in Table 1. The means of chil-dren’s reports indicated that children believed theygenerally participated in math, science, and computeractivities about once a week. Parents’ reports of chil-dren’s participation were comparable to children’s re-ports of their participation and indicated that they per-ceived children, on average, engaged in these activitiesless than 2 hr last week. The means of parents’ encour-agement of math, science, and computer activities fellaround the midpoint of the 7-point scale (i.e., 4). Themeans of parent–child coactivity were lower for chil-dren’s computer participation than their participationin math and science activities. Parents and children en-gaged in math and science activities together about 2 to3 times a month; in contrast, parents and childrenparticipated in computer activities together occasion-ally, which was less often than 2 to 3 times per month.Parents, on average, bought or rented one piece of ac-tivity-related materials in the previous year. The means

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Table 1. Descriptive Statistics of Children’s Activities and Parents’ Socializing Behavior

Computers Math and Science

Girls Boys Girls Boys

Variable M SD M SD M SD M SD

Child participationChild report 2.13 1.95 2.88 2.25 2.69 1.67a 2.93 1.84a

2.76 1.67b 2.39 1.90b

Maternal report 1.56 0.99 1.86 1.33 1.91 1.18 2.15 1.31Paternal report 1.77 1.42 1.93 1.36 2.12 1.25 2.20 1.22

EncouragementMothers 3.50 1.53 4.02 1.72 4.06 1.59 4.41 1.54Fathers 3.89 1.53 4.06 1.70 4.06 1.50 4.29 1.45

CoactivityMother–child 1.75 1.02 1.75 0.95 3.45 1.41 3.31 1.35Father–child 2.11 1.15 2.05 1.09 3.24 1.20 3.24 1.21

Provision of materialsMothers 0.77 0.98 1.16 1.01 1.04 0.83 1.22 0.76

Parental participationMothers 0.71 1.59 0.82 1.54 1.41 1.49 1.37 1.40Fathers 1.37 1.87 0.92 1.63 1.69 1.78 1.47 1.69

aChildren’s report of science participation. bChildren’s report of math participation.

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of parents’ participation suggested that parents, on av-erage, spent 0 to 3 hr on these activities last week.

Gender Differences in Children’sParticipation and Parents’ Behavior

Children’s activity participation. Independentt tests were computed to examine the differences inboys’ and girls’ participation (see Table 1 for means).Child-reported computer use was significantly differ-ent for boys and girls, t(446) = 3.75, p < .001, in whichboys reported higher computer use than girls. Maternalreports of children’s computer use also revealed thatboys used computers significantly more often thangirls, t(446) = 2.58, p < .01. Paternal reports of chil-dren’s computer use were not significantly different forboys and girls.

Gender differences in children’s math and scienceactivities were found for children’s report of their mathactivities, t(446) = 2.12, p < .05. Children reported thatgirls participated in math activities more often thanboys. Parental report of children’s math and science ac-tivities and children’s report of their science activitiesdid not differ for boys and girls.

Parents’ socializing behavior. Gender differ-ences in parents’ behavior concerning computer activi-ties and math and science activities were tested with in-dependent t tests. Maternal encouragement ofchildren’s computer activities, t(446) = 3.27, p < .001,and paternal use of computers, t(279) = 2.23, p < .05,significantly differed based on children’s gender.Mothers of boys encouraged computers more oftenthan mothers of girls and fathers of boys used comput-ers less often than fathers of girls. Provision of com-puter items also significantly differed for boys andgirls, t(446) = 4.06, p < .001, in which boys were pro-vided more computer-related materials than girls. All

other gender comparisons of parents’ behavior con-cerning computers were not significantly different.

Two significant gender differences emerged con-cerning parents’ socializing behavior for math and sci-ence activities. Mothers encouraged math and scienceactivities for boys more often than girls, t(446) = 2.28,p < .05. Math and science materials were bought orrented for boys more often than girls, t(446) = 2.30, p <.05.

Bivariate Relations Between Parents’Behavior and Children’s Participation

Bivariate correlations were computed to examinerelations between parents’ behaviors and children’s ac-tivity participation. As discussed in the Method sec-tion, more mothers completed questionnaires than fa-thers. Thus, correlations involving maternal data werecomputed on a larger sample (i.e., n = 448) than corre-lations with paternal data (i.e., n = 281). An additionalset of correlations was computed with maternal datafrom only those families with both maternal and pater-nal data (i.e., n = 281). Because the pattern of correla-tions was similar between the larger and smaller mater-nal samples, correlations based on the larger sample ofmaternal data are presented along with the correlationsbased on the maximum sample of paternal data (Tables2 and 3).

Maternal and paternal encouragement of computeractivities was positively related to all three reports ofchildren’s computer use (Table 2). Parental encourage-ment of math and science activities was positively re-lated to parents’ reports of children’s participation inthose activities (Table 3). Children’s reports of theirparticipation in math activities were significantly asso-ciated with maternal but not paternal encouragement.Therefore, with the exception of children’s reports oftheir science participation, children who spent more

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Table 2. Correlations Between Children’s Computer Activities and Parents’ Socializing Behavior

Variable 1 2 3 4 5 6 7 8 9

Children’s activityChild reportMaternal report .40***Paternal report .32*** .51***

EncouragementMothers .26*** .38*** .38***Fathers .32*** .40*** .41*** .42***

Co-activityMother–child .26*** .50*** .41*** .38*** .40***Father–child .30*** .40*** .51*** .41*** .49*** .46***

Provision of materialsMothers .40*** .42*** .32*** .42*** .36*** .42*** .40***

Parental participationMothers .17*** .23*** .13* .24*** .13* .34*** .15** .17***Fathers .21*** .17** .19*** .25*** .27*** .23*** .38*** .13* .09

*p < .05. **p < .01. ***p < .001.

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time in activities had parents who were more likely toshow encouragement of these activities.

Parent–child coactivity in computer and in mathand science activities was positively related to chil-dren’s participation in these activities. Parent–childcomputer coactivity was positively associated with allreports of children’s participation in computer activi-ties (Table 2). Associations including coactivity andchildren’s math and science activities were less consis-tent than associations including computer activities.Mother–child coactivity was significantly related toboth parents’ reports of children’s participation in mathand science activities (Table 3). Father–child coactivitywas significantly related to fathers’ reports of chil-dren’s participation but not to maternal or child reportsof children’s participation.

Provision of activity-related materials was posi-tively correlated with most indicators of children’s par-ticipation. Provision of computer-related items waspositively associated with all reports of children’s par-ticipation in computer activities (Table 2). All reportsof children’s participation in math and science activi-ties, except children’s reports of science activities,were positively related to provision of math and sci-ence materials (Table 3).

Associations between parents’ participation (i.e.,modeling) and children’s participation in activitieswere modestly associated. Both maternal and paternaluse of computers was positively related to all reportsof children’s computer use (Table 2). On the otherhand, children’s reports of participation in math andscience activities were only significantly associatedwith maternal participation in math and science activ-ities (Table 3). Parents’ reports of participation inmath and science activities were significantly corre-lated with their own reports of children’s participa-tion in those activities.

Behaviors From One Versus BothParents

In the previous section, bivariate correlations de-scribed relations between each parental behavior andchildren’s participation in activities. The next set ofanalyses address whether behavior from both parents isa more powerful predictor of children’s participationthan behavior from one parent. ANCOVA was used toexamine differences in children’s participation de-pending on whether neither, one, or both parents re-ported high levels of each behavior. Three groups werecreated based on mothers’ and fathers’ behaviors: (a)both parents low, (b) one parent low one parent high,and (c) both parents high. The first group, both parentslow, included families in which both mothers’ and fa-thers’ behavior fell below the median. The secondgroup, one parent low one parent high, included par-ents where one parent’s behavior fell below the medianand the other’s behavior fell above the median. Thethird group included parents that both fell above themedian of the sample. A separate ANCOVA was com-puted for each parental behavior reported by bothmothers and fathers (i.e., encouragement, modeling,and coactivity). A total of 21 ANCOVAs were esti-mated (12 ANCOVAs predicted math and science par-ticipation, 9 ANCOVAs predicted computer participa-tion). Each analysis included the following controlvariables: family income, parent education, child gradelevel, child sex, and child math ability. Children’smeans and the significant pairwise comparisons fromthese analyses are presented in Table 4.

We also completed a set of ANCOVAs in which wecompared differences in children’s participation basedon four parenting groups instead of three. The fourgroups were created in the same manner but they dif-ferentiated parent gender. The groups included both

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Table 3. Correlations Between Children’s Math and Science Activities and Parents’ Socializing Behavior

Variable 1 2 3 4 5 6 7 8 9 10

Children’s activityChild scienceChild math .38***Maternal report .12* .12*Paternal report .08 .06 .39***

EncouragementMothers .05 .10* .42*** .27***Fathers .00 .04 .23*** .33*** .31***

Co-activityMother–child .00 .08 .26*** .21*** .29*** .10Father–child –.08 –.08 .06 .16** .16** .22*** .18**

Provision of materialsMothers .07 .09* .32*** .19*** .32*** .20*** .23*** .05

Parental participationMothers .05 .13** .22*** .02 .14** .03 –.08 .18*** .15***Fathers –.06 –.04 .04 .25*** .01 .19*** .06 .22*** –.01 .08

*p < .05. **p < .01. ***p < .001.

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Table 4. Means (and Standard Deviations) of Children’s Participation and Significant Pairwise Comparisons From the Analysis of Covariances

Encouragement Modeling Coactivity

Participation Both Low One High Both HighPairwise

Comparisons Both Low One High Both HighPairwise

Comparisons Both Low One High Both HighPairwise

Comparisons

Computer activitiesChild report 1.93 (1.91) 2.26 (2.00) 3.33 (2.12) BL***,

OH*** < BH1.86 (1.94) 2.93 (2.13) 3.57 (1.86) BL < OH***,

BH***1.76 (1.95) 2.39 (2.05) 3.17 (2.05) BL***,

OH** < BHMaternal report 1.21 (0.60) 1.44 (1.77) 2.30 (1.50) BL***,

OH*** < BH1.30 (0.76) 1.71 (1.02) 2.57 (1.64) BL***,

OH** < BH;BL*** < OH

1.09 (0.47) 1.54 (0.95) 2.15 (1.37) BL***,OH** < BH;BL*** < OH

Paternal report 1.23 (0.63) 1.59 (1.09) 2.63 (1.63) BL***,OH*** < BH

1.42 (1.07) 1.99 (1.21) 2.65 (1.75) BL***,OH*** < BH;BL** < OH

1.13 (0.56) 1.82 (1.23) 2.32 (1.55) BL***,OH*** < BH;BL*** < OH

Math & science activitiesChild science 2.80 (1.71) 2.72 (1.86) 2.86 (1.82) 2.79 (1.85) 2.70 (1.77) 2.84 (1.83) 2.94 (1.75) 2.81 (1.84) 2.73 (1.80)Child math 2.69 (1.78) 2.34 (1.73) 2.66 (1.83) 2.23 (1.75) 2.53 (1.71) 2.59 (1.83) 2.89 (1.93) 2.52 (1.87) 2.45 (1.65)Maternal report 1.38 (0.63) 1.83 (1.11) 2.62 (1.44) BL***,

OH*** < BH;BL* < OH

1.58 (0.99) 1.85 (1.19) 2.23 (1.32) BL*,OH* < BH

1.63 (0.85) 1.92 (1.13) 2.26 (1.44) BL*,OH* < BH

Paternal report 1.46 (0.82) 2.19 (1.31) 2.70 (1.52) BL***,OH* < BH;BL** < OH

1.97 (1.37) 1.86 (1.15) 2.49 (1.46) BL*** < BH 1.66 (1.02) 2.03 (1.12) 2.57 (1.61) BL***,OH** < BH

Note: BL = both parents low; OH = one parent high; BH = both parents high.*p < .05. **p < .01. ***p < .001.

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parents low, mother high father low, mother low fatherhigh, and both parents high. These parent groups plusthe main effect of child gender allowed us to test differ-ences based on parent and child gender. The interactionbetween the parent grouping and child gender was onlystatistically significant in 2 of the 21 cases. In addition,further differentiating the one parent high group intotwo groups, namely mother high father low and motherlow father high, did not provide further insight than theresults presented in Table 4 based on three parentalgroups.

As shown in Table 4, parents’ behaviors signifi-cantly predicted children’s participation in computeractivities. In all comparisons, children spent more timeusing computers if both parents reported high behaviorthan if only one parent was high or if both parents werelow, with the exception of children’s report of com-puter participation and parental modeling. In addition,children’s computer activities were higher when oneparent reported high behavior than when neither parentreported high behavior. Overall, children spent moretime on computer activities if both parents reportedhigh behaviors than if only one or neither parent re-ported high behaviors.

Group differences were found in parental but notchild report of children’s math and science activities(Table 4). Parental report of children’s math and sci-ence activities was higher when both parents’ reportedhigh behaviors than when both parents reported lowbehaviors. In five of the six comparisons, children’sparticipation was higher if both parents reported highbehavior than if one parent reported high behavior.Across these ANCOVAs, parents’ reports of children’smath and science activities were consistently highestwhen both parents reported high math and science-re-lated behaviors.

Models Predicting Children’sParticipation

Structural equation modeling was used to test rela-tions between parents’ behaviors and children’s activ-ity participation. To fully test our predictive models,we first tested the measurement models of parents’ so-cializing behavior. Separate measurement and predic-tive models were specified for children’s computer ac-tivities and their math and science activities.

Analysis of Moment Structures (AMOS) was thestructural equation modeling program used in theseanalyses. Researchers with missing data have two gen-eral options on how to handle missing data in AMOS.They can either deal with the missing data before esti-mating the models (e.g., listwise deletion, imputation)or use full information maximum likelihood (FIML)when estimating models. As discussed in the Methodsection, fathers with missing data attained a lower levelof education and had a lower family income in compar-

ison to fathers who had complete data. These two sig-nificant differences between fathers who did and didnot participate suggest that data are not missing com-pletely at random (MCAR). If the data had beenMCAR, listwise deletion methods would have been ap-propriate. AMOS assumes the data are missing at ran-dom (MAR) which is not as strict of an assumption asMCAR (Arbuckle, 1996; Byrne, 2001). When data areMAR, FIML yields results that are more accurate thanother methods used to handle missing data, such aspairwise deletion, listwise deletion, or imputation(Arbuckle, 1996). Simulation studies have shown thateven if the data are not MAR, AMOS computes rea-sonable parameter and model estimates. Given the na-ture of the missing data, FIML was used to estimate themodels.

Measurement models of parents’ behavior. Thetwo measurement models consisted of a confirmatoryfactor analysis in which parents’ socializing behaviorwas represented by seven indicators: parents’provisionof materials and maternal and paternal encouragement,participation, and coactivity. Because we proposed tomodel the influence of multiple behaviors from bothmothers and fathers and results from the previous sec-tion suggest that behaviors from both parents is a stron-ger predictor than behaviors from one parent, we speci-fied that all seven of these behaviors were representedby one latent variable. Separate models were estimatedfor behaviors concerning computers and behaviorsconcerning math and science.

Both measurement models were a good fit to thedata according to several model fit indexes. Althoughthe model chi-squares were significant in both mod-els—computer, χ2(14) = 37.63, p < .001; math and sci-ence, χ2(14) = 45.86, p < .001—this statistic provides ahigh rate of false negatives when the model actually fitsthe data well (Hu & Bentler, 1996; Klein, 1998;Schumacker & Lomax, 1996). As a result, theTucker-Lewis index (TLI), comparative fit index(CFI), and root mean square error of approximation(RMSEA) were also used to understand how well eachmodel fit the data. These indicators all suggest that ourmodels fit the data well (computer model, TLI = .98,CFI = .99, RMSEA = .06; math and science model, TLI= .98, CFI = .99, RMSEA = .07; Hu & Bentler, 1999).All of the path coefficients for parents’ behavior con-cerning computers were significant (.36 to .72, p <.001). In the math and science measurement model, allparent behavior significantly loaded on the overall con-struct of parents’ socializing behavior (.24 to .63, p <.001) except fathers’ participation in math and scienceactivities (.12, ns).

Predictive models. Each predictive model in-cluded a measurement model of parents’ behavior anda path model predicting children’s participation (Fig-

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Figure 1. This figure includes the structural equation model of children’s computer use. Standardized path coefficients are presented.Significant paths have solid lines. *p < .05. **p < .01. ***p < .001.

Figure 2. This figure includes the structural equation model of children’s participation in math and science activities. Standardizedpath coefficients are presented. Significant paths have solid lines. *p < .05. **p < .01. ***p < .001.

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ures 1 and 2). Four control variables were included ineach model: family income, parent education, chil-dren’s grade level (0 = second grade, 1 = third grade, 2= fifth grade), and math ability. Therefore, there werepaths from each of these control variables to all reportsof children’s activity participation and the latent vari-able of parents’ behavior. Correlations were also esti-mated between all four control variables. In addition tothe paths and variables depicted in Figures 1 and 2, dis-turbance terms were also included in each model. Eachdisturbance term is a latent variable, which accountsfor variance not explained by other indicators in themodel including error. Disturbance terms were esti-mated for each of the endogenous variables (10 in thecomputer model and 11 in the math and science model)and the latent variable of parents’socializing behavior.

Multigroup analysis in AMOS was used to test gen-der differences in the models (Byrne, 2001). Two mod-els were estimated to examine gender differences.First, a model in which all paths were free to varyacross the two groups was estimated. In the secondmodel, several paths and correlations were constrainedto have the same value in each of the two groups, in-cluding (a) correlations between the control variables(6 correlations), (b) paths from the controls to the par-ent latent variable and children’s participation (16paths for the computer model, 20 paths for the mathand science model), (c) paths in the measurement por-tion of the model (7 paths), and (d) paths from the par-ent latent variable to children’s participation (3 pathsfor the computer model, 4 paths for the math and sci-ence model). In this restricted model, each of the pathsand correlations had to be the same value for boys andgirls. Gender differences in the models were tested bycalculating the change in the chi-square associatedwith the two models. The degrees of freedom are cal-culated by taking the difference in the degrees of free-dom from the two models. If the change in thechi-square between the two models is significant, thenthe models are significantly different between the twogroups. A nonsignificant chi-square means that there isnot a significant difference between the two models, orthe same model fits both boys and girls. Thesemultigroup analyses that examine gender differencesin the model were examined first to test whether sepa-rate models needed to be estimated for boys and girlsor whether one model fit both groups.

Computer predictive model. First, we exam-ined gender differences in the model for computer ac-tivities. The change in the chi-square between the re-stricted model and the one that was free to vary acrossgroups was not significant, ∆ χ2(31) = 36.11, ns. Thissuggests that a similar model fit both boys and girls. Asa result, we estimated one model for the entire sample.

The model predicting children’s computer use was agood fit to the data according to several fit indexes. Al-

though the model chi-square was significant, χ2(59) =145.38, p < .001, other fit indexes suggest that themodel fit the data well, TLI = .98, CFI = .99, andRMSEA = .05. Significant standardized path coeffi-cients for this model are presented in Figure 1. Parents’behavior was a strong predictor of children’s computeruse. The variance explained in maternal and paternalreports of children’s computer use (R2 = .55 and .44, re-spectively) were larger than the variance explained inchildren’s reports (R2 = .27). It is likely that the R2 washigher for parental reports due to shared variance be-tween parents reporting on both their behavior andchildren’s computer use.

A handful of the associations including the controlvariables was significant. Children’s grade level posi-tively predicted maternal and paternal reports of chil-dren’s computer use, suggesting parents believed olderchildren used computers more often than younger chil-dren. Parents’ income negatively predicted paternal re-ports of children’s computer use. In addition to thesesignificant paths, two correlations between the controlvariables were significant. Parent education and in-come were significantly positively correlated. Lastly,children’s grade level was positively correlated withparents’ income.

Math and science predictive model. Gender dif-ferences in the model for math and science were notsignificant, ∆ χ2(36) = 33.30, ns. Because the modelswere similar for boys and girls, one model was esti-mated for the entire sample.

The model predicting children’s math and scienceparticipation was a good fit to the data according toseveral fit indexes (Figure 2). The model chi-squarewas significant, χ2(67) = 181.53, p < .001, but other fitindexes indicated that our model fit the data well: TLI= .98, CFI = .99, and RMSEA = .06. Parents’ socializ-ing behavior significantly predicted all reports of chil-dren’s participation in math and science activities, ex-cept children’s report of their science activities. Onceagain, the variance explained in parental reports ofchildren’s participation was higher than the amount ex-plained in children’s reports of their participation.

Four of the 20 paths from control variables to otherindicators were significant. Children’s grade level neg-atively predicted children’s reports of their participa-tion in math and science activities. Thus, according tochildren, older children were less likely to participatein math and science activities than younger children.Children’s grade level also significantly predicted thelatent variable of parents’ behavior. The negative pathcoefficient between child grade and parent behaviorsindicates that parents’ socializing behaviors decreaseas children age. Parent income negatively predictedchildren’s report of their participation in science activi-ties. As in the model of computer activities, parent in-

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come was positively correlated with parent educationand children’s grade level.

Alternative models of child effects. Alternativepredictive models were tested to examine the fit ofmodels in which children’s activity participation pre-dicted parents’ behavior. Separate models were testedfor computer use and for math and science activities. Inthese models, the paths between parents’ behavior andchildren’s participation were reversed, so that theywere drawn from maternal, paternal, and child reportsof children’s activities to parents’ behavior. All otherpaths in the models remained unchanged. The fit indi-ces for the child influence model of children’s com-puter use were slightly worse than the parent influencemodel, χ2(59) = 298.38, p < .001, TLI = .95, CFI = .97,and RMSEA = .09. The child influence model for chil-dren’s math and science activities, however, provided acomparable fit to the data as the parental influencemodel, χ2(67) = 230.03, p < .001, TLI = .97, CFI = .98,and RMSEA = .07.

Discussion

As predicted, parents’ behaviors are powerful pre-dictors of children’s participation in computer, math,and science activities. The analyses in this study de-scribed two complementary portraits of associationsbetween parents’ behaviors and children’s activities.The structural equation models speak to the cumulativepredictive value of mothers’ and fathers’ socializingbehavior treated as a single latent construct. In con-trast, the correlational analyses provide strength esti-mates of associations between each socializing behav-ior and children’s activity participation. Taken togetherthese findings suggest that an overall indicator of par-ent socializing behavior is related to children’s activityparticipation but within that indicator, parental partici-pation in activities was not as strongly associated withchildren’s activity participation as other parental be-haviors.

Our results suggest parents are likely to use multiplestrategies and that treating these behaviors as an over-all indicator, rather than as independent constructs, is apowerful and valid predictor of children’s activity par-ticipation. These findings extend work by others in thearea of sociocultural psychology (e.g., Rogoff, 1990),motivation (e.g., Grolnick & Ryan, 1989), the HOMEenvironment (e.g., Bradley et al., 2001; Elardo &Bradley, 1981), and family management (e.g.,Furstenberg et al., 1999; Parke & Ladd, 1992) by ex-amining multiple parental behaviors geared towardspecific activity domains in relation to children’s par-ticipation in those activity domains. Many of these re-search areas highlight the importance of parental ar-rangement of children’s environment and direct

parent–child interaction on children’s development. Inthis study, arrangement of children’s environment waslargely characterized by provision of materials andparents’ engagement in activities. Parent–childcoactivity and encouragement represented compo-nents of parent–child interactions. The four behaviorsutilized in our analyses may have made such a power-ful combination because they incorporate many of theways parents promote youth’s activities, motivation,and overall development (Eccles, 1993; Furstenberg etal., 1999; Grolnick & Ryan, 1989). This work supportsresearch on the importance of multiple contextualcomponents and extends prior research by includingmultiple behaviors from both parents in one study. Ourresults suggest that researchers examining parentalcorrelates of children’s activity participation need toinclude multiple indicators of parents behaviors to ac-curately reflect the multidimensional influences onchildren. Furthermore, particular analytic strategiesmust be used to capture these complicated processes—examples include the latent variables used in this study,the HOME inventory (e.g., Bradley et al., 2001; Elardo& Bradley, 1981), and cumulative risk factor ap-proaches (e.g., Sameroff, Bartko, Baldwin, Baldwin, &Seifer, 1998). Regression strategies, which describethe unique variance attributed to each socializationstrategy, for instance, cannot adequately explain howall of these strategies as a whole are related to chil-dren’s activity participation.

Of the four types of parental behaviors, parentalparticipation in activities (i.e., parental modeling) evi-denced the weakest links with children’s participation.Although parent participation in their own math, sci-ence, and computer activities is a component of chil-dren’s environments and has the potential to exposechildren to these different domains (Rogoff, 1990),parents’ participation does not explicitly include chil-dren by definition. Many children may not have oppor-tunities to observe parents. Even if parents engaged inthese activities around their children, several processesmust transpire for observational learning to occur.Children need to attend to parents’ activity, retain in-formation about the activity, and have the motivationand ability to reproduce the activity (Bandura, 1997).Reproducing parents’ math, science, and computer ac-tivities may be particularly challenging because manyof these activities are complex, which make it more dif-ficult for elementary school children to learn these ac-tivities through observations than many other activi-ties, such as kicking a ball or drawing. Math, science,and computer activities typically require direct instruc-tion and appropriate structure for children to gain theknowledge and skills necessary to successfully partici-pate and develop interest in the activity. Providing ade-quate structure along with parental involvement in ac-tivities and support for child autonomy are theorized tobe critical for children’s internalization processes

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(Grolnick & Ryan, 1989). Of the four types of parentbehavior included in this study, parent modeling in-volves the least amount of instruction, support, struc-ture, and immediate positive reinforcement for chil-dren. Parental encouragement, coactivity, andprovision of activity-related materials may be more ap-propriate for children in elementary school than mod-eling as a way of acquiring such complex behavior(Crowley, Callanan, Jipson, et al., 2001). Althoughmodeling may not be the most appropriate method toincrease young children’s activity participation, it maybe very suitable for older children who already under-stand the activities. Future studies should focus on theutility of these socialization methods across changes inchildren’s skill level and at different ages.

Computer Versus Math and ScienceActivities

Parents’ behaviors were more powerful predictorsof children’s participation in computer activities thanmath and science activities. This difference in the pre-dictive power in these domains may reflect the differ-ence between computer and math and science instruc-tion in school. Although many elementary schoolsincorporate computers in children’s learning, childrenare still not likely to spend as much time at school oncomputers as they spend on math and science topics.Parent influences may be more critical and powerfulfor computers than for math and science activities dueto an imbalanced exposure at school. A next step is toexamine the interplay between parents’ behaviors,children’s participation in these activities, and chil-dren’s exposure to these activities in school. Re-searchers, such as Epstein (1995), have shown thepower that coordinated family and school influencescan have on children’s achievement. In addition, find-ings concerning computer activities are easier to inter-pret than the results on math and science because all ofthe parental indicators combined math and science.Thus, we were unable to examine math and scienceseparately.

Gender Differences

Gender differences in parents’ socializing behaviorand children’s activity participation were moderate.Three significant gender differences emerged in chil-dren’s activity participation: boys reported spendingmore time in computer activities (child and maternalreport) and less time in math activities (child report)than girls. Our results concerning computer activitiesare consistent with and build on previous research byfocusing on younger children and their out-of-schoolactivities rather than their course selection (Chen,1986; Culley, 1988; Rocheleau, 1995; Shashaani,1994a). Gender differences in math and science activ-

ity participation may not have consistently emergedwith our elementary school students because the fre-quency of participation in these activities for boys andgirls was generally low. In addition, both boys and girlshave similar interest in these activities despite girlshaving lower math self-competence beliefs (Eccles etal., 1992; Fredricks & Eccles, 2002; Jacobs, Lanza,Osgood, Eccles, & Wigfield, 2002; Wigfield et al.,1991).

Significant gender differences occurred in a handfulof parents’ behaviors. Maternal reports of her encour-agement and parental provision of activity-related ma-terials in computer, math, and science activities werehigher for boys than girls. These results support andextend findings for encouragement of adolescents’math and science activities (Eccles, 1993). Mothersmay vary their general encouragement and provisionof materials in response to perceived differences inchildren’s interest and participation in these activities.Future research needs to examine if these gender dif-ferences are the result of a response to perceived differ-ences in boys’ and girls’ interests or due to differencesin maternal value of these activities for boys and girls.Gender differences, however, did not emerge concern-ing parent–child coactivity or fathers’ encouragement.Although the frequency of these behaviors may not dif-fer based on gender, there may be differences in quali-ties of the parent–child interactions, such as length andcomplexity of speech (Crowley, Callanan, Tenebaum,et al., 2001; Tenebaum & Leaper, 2003).

In addition to mean differences between boys andgirls, we also examined whether the model accountingfor relations between parents’ behaviors and children’sparticipation were different for boys and girls. This is asignificant extension over previous research examiningdifferences in means or bivariate relations. The samemodel fit both groups for all activity domains. In otherwords, relations between parental behavior and the timechildren spend in each activity are similar for boys andgirls. What is still unclear is whether the same processesaccount for the relationsbetweenparents’behaviorsandchildren’s activities. For example, increases in chil-dren’s self-concept of abilities may account for the rela-tions between parental behaviors and children’s activi-ties for girls, boys, or both. A reasonable next step is toexamine gender differences and similarities in variousprocesses thataccount forassociationsbetweenparents’behaviors and children’s activities.

Applied Implications

This study has several implications for children’sparticipation in informal math, science, and computeractivities. First, our findings suggest that in two-parentfamilies, mothers and fathers’ behaviors are related tohow children use their time after school. In addition,mothers’ and fathers’ behaviors are not independent of

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each other; rather, parents’ behaviors work in tandem.Parents may have a larger impact on children if theirbehaviors are coordinated rather than contradictory.Second, a variety of behaviors and various combina-tions of behaviors can lead to the same outcome—inthis case, children’s participation in activities. The be-haviors that matter most are likely to depend on the ac-tivity, the child, and the parent–child relationship. Forexample, our results suggest that for mentally chal-lenging activities, such as math, science, and computeractivities, behaviors that include direct parent–child in-teraction are more strongly related to young children’sengagement in these activities. Finally, our studyshowed that parents’ behaviors are associated withboys and girls activity participation even when genderbiases may be present. For example, although parentsseem to buy more materials related to math, science,and computer activities for boys, their provision of ma-terials, along with their other behaviors, have similarinfluences on boys’ and girls’ activity participation.

Future Directions and Limitations

Even though this study contributes to our under-standing regarding parenting behaviors and activities,there are limitations to this work. It is unclear from thecurrent results whether parents, children, or both areaccountable for the associations between children’s ac-tivity participation and parents’ behaviors. It may bethat parents who place importance on math, science,and computer activities socialize their children forthese activities, which in turn leads to children’s partic-ipation in these activities. However, children who dem-onstrate an interest in math, science, and computer ac-tivities may influence their parents’ behaviors. It islikely that this process is bidirectional and parents andchildren influence one another (Eccles, 1993; Jacobs &Eccles, 2000). Early analyses suggest the causal direc-tion likely runs from parents to children’s self-beliefsand values in early years and then become more recip-rocal over the elementary school years (Eccles, Jacobs,et al., 1993).

A second limitation of this study concerns thegeneralizability of our findings. Families who partici-pated in this study were two-parent, European Ameri-can families with middle-class incomes. We specifi-cally selected such a sample because we wanted tostudy family-level differences in parents’ behavior in-dependent of the families’ financial resources. In otherwords, our sample enabled us to examine relations be-tween parents’ behavior and children’s activity partici-pation, which were not largely explained by families’monetary resources. The lack of relations between par-ent education and income with parents’ behaviors andchildren’s activities was not surprising given that oursampling procedure restricted variation on these vari-ables. Additional research is needed to determine if the

relations we have found replicate in other social classand ethnic group samples. Given the growing preva-lence of computers in children’s homes from differentethnic and social class groups (Rocheleau, 1995), webelieve our findings concerning children’s activitiesare likely to replicate in a wide range of families. Re-cent advances in computer programs and platformshave also made computers more accessible to people ofvarying education levels. In the case of math and sci-ence activities, family educational background, ratherthan financial constraints, is likely to be a better predic-tor of the likelihood that parents will actively socializetheir children’s interest in math and science. But evenin this case it is likely that the basic relations of parents’socialization practices to children’s activity participa-tion will replicate.

Another direction for future research is studying as-sociations among parent behaviors and children’s ac-tivity participation across development. Children’sparticipation, parents’ behaviors, and the associationsbetween them are likely to change with development.There is some cross-sectional evidence in our resultsindicating that math and science activities decreasethrough the elementary school period. In addition,children’s beliefs concerning both their competence inand value of math and science decrease through ele-mentary school (Eccles, 1993; Jacobs et al., 2002). Re-searchers should examine more extensive periods ofdevelopment to map changes in these constructs. Forinstance, preliminary results suggest parents may playa larger role in children’s activity participation in ele-mentary school than during high school or college(Eccles, Jacobs, et al., 1993).

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Received November 12, 2003Final revision received May 27, 2004Accepted May 27, 2004

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