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Dev Psychol. Author manuscript; available in PMC 2017 January 01. Published in final edited form as: Dev Psychol. 2016 January ; 52(1): 58–70. doi:10.1037/dev0000066.
Peer Effects on Head Start Children’s Preschool Competency Dawn DeLay, PhD, Laura D. Hanish, PhD, Carol Lynn Martin, PhD, and Richard A. Fabes, PhD Arizona State University
Abstract Author Manuscript
The goals of the present study were to investigate whether young children attending Head Start (N=292; Mage=4.3 years) selected peers based on their preschool competency and whether children’s levels of preschool competency were influenced by their peers’ levels of preschool competency. Children’s peer interaction partners were intensively observed several times a week over one academic year. Social network analyses revealed that children selected peer interaction partners with similar levels of preschool competency and were influenced over time by their partners’ levels of preschool competency. These effects held even after controlling for several child (e.g., sex and language) and family factors (e.g., financial strain and parent education). Implications for promoting preschool competency among Head Start children are discussed.
Keywords
Author Manuscript
Preschool competency; Peers; Peer influence; Preschool children; Social networks
Author Manuscript
In the US, one of the primary interventions designed to help young economically underprivileged children become better prepared for formal schooling is Head Start (Gilford, 2013). Many of the children attending Head Start programs in the US start school at a considerable disadvantage compared to their more economically advantaged peers. Despite this general pattern, there is considerable variation among Head Start children. For example, in a recent Head Start Family and Child Experiences Survey (FACES) report (Klein, Aikens, West, Lekashanets, & Tarullo, 2013), there was significant variability in nonverbal intelligence both across and within Head Start racial/ethnic groups. Similar variability has been reported on social-emotional characteristics (Aikens, Moiduddin, Xue, Tarullo, & West, 2012) and emergent literacy skills (Cabell, Justice, Konold, & McGinty, 2011). Furthermore, there is evidence that Head Start children’s emergent literacy skills change between the fall and spring of the preschool year (Cabell, Justice, Logan, & Konold, 2013), especially for the most at-risk children (Zill & Resnick, 2006). Thus, in a typical Head Start classroom, each child is surrounded by a classroom of peers who represent a wide and changing continuum of social-emotional and academic skill competencies that may, over time, affect her or his own levels of preschool competency (Reid & Ready, 2013).
Correspondence may be directed to Correspondence should be directed to Dawn DeLay, T. Denny Sanford School of Social and Family Dynamics, Arizona State University, PO Box 873701, Tempe, AZ 85287, USA, [email protected], Phone: 480-965-2740, Fax: 480-965-6779.
DeLay et al.
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Author Manuscript Author Manuscript
The goal of the current study was to better understand how preschoolers’ school-related competencies are associated with the peer environment in Head Start classrooms. Specifically, we assess the efficacy of a “peer social exposure” model in which time spent with peers provides opportunities to become more similar to peers through a variety of peer influence processes. Peer influence may arise from observing peers during play and academic tasks and being concurrently rewarded or punished for imitating these behaviors (Bandura, 1977). Peer influence may also arise, in accordance with social motivation theory, through social experiences that motivate both social (peer) and academic (school) engagement or disengagement in the classroom (Juvonen & Wentzel, 1996). To empirically examine how preschool competencies are related to the peer context, we measured changes in Head Start children’s preschool competency – operationalized as the behaviors that underlie, and are predictive of, academic-related skills -- over the course of the school year. Although changes in preschool competency may be accounted for by many different factors, we focus on peer influence as a mechanism for change. Specifically, we contend that young children actively select certain (similar) peers to interact with and that, in the context of peer interaction, there is an opportunity for peer influence to occur. Thus, interactions with some peers may promote, while interactions with other peers may hinder, the development of preschool competency.
Author Manuscript Author Manuscript
In previous empirical research, social network characteristics of the classroom have been used to predict academic and social adjustment both in the short- (e.g., across the academic year) and long- (e.g., from early childhood into adolescence) term (Ahn, Rodkin, Gest, 2013; Garandeau, Ahn, & Rodkin, 2011; Ialongo, Werthamer, Kellam, Brown, Wang & Lin, 1999; Ialongo, Poduska, Werthamer, & Kellam, 2001; Serdiouk, Rodkin, Madill, Logis, & Gest, 2013). Researchers have also used social network theory to address social differences in the school that may impact academic engagement and performance (Cappella, Kim, Neal, & Jackson, 2013; Neal, Cappella, Wagner, & Atkins, 2011). Nevertheless, when scholars use network features as linear predictors of student outcomes, rather than accounting for the socially embedded characteristics of the classroom, information from various levels of analysis is included in the same linear equation. It follows that, by using network features as linear predictors, there is an important loss of classroom variability that may exist between individuals, within social groups, and ultimately within the social network of the classroom (Cascio & Schanzenbach, 2007; Hedges, Laine, Greenwald, 1994). Linear equations also require decisions to be made on how to transform the data to best represent the social composition of the classroom, a decision that may also bias results (Yudron, Jones, & Raver, 2014). Finally, a failure to analyze complete social network data, or the presence of missing data, can also bias the estimates of peer effects in the classroom (Sojourner, 2013). Taken together, these finding suggest that to adequately test peer influence requires a consideration of a complete social network coupled with all individual behaviors in the classroom to account for the individual and group dynamics that lead to social affinities (selection) and behavioral change (influence). In the current study, complete social network and behavioral data were used to examine peer influence within Head Start classrooms.
Dev Psychol. Author manuscript; available in PMC 2017 January 01.
DeLay et al.
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Young Children’s Preschool Competency
Author Manuscript
Few would dispute the fact that children vary in their readiness to learn because some children have behaviors that help them adjust to school environments and others do not. In the present study, we aim to consider predictors of the variability in preschool competency, a critically important, but often understudied, feature of children’s readiness to learn and success in school (Mendez, Fantuzzo, & Cicchetti, 2002; Murphy, Laurie-Rose, Brinkman, & McNamara, 2007). Preschool competency encompasses the social and learning behaviors that are predictive of young children’s concurrent and longer-term academic-related successes (Blair, 2002; Kaiser, Hancock, Cai, Foster & Hester, 2000; Mashburn, Justice, Downer, & Pianta, 2009; McWayne & Cheung, 2009). This includes successful interactions with peers, focused attention, active participation, and classroom engagement (e.g., Kagan, Moore, Bredekamp, 1995; Raver, 2004). Children who exhibit preschool competencies actively participate in class, attend to teachers, and have good peer relationships. These behaviors are interrelated and positively associated with school readiness, a predictor of later academic success (Coolahan, Fantuzzo, Mendez, & McDermott, 2000; Cuhna & Heckman, 2007). Moreover, these non-cognitive competencies are considered to represent important drivers of academic perseverance and academic behaviors that are essential for long-term academic success (Farrington et al., 2012).
Author Manuscript
However, intervention efforts to promote school readiness typically focus on enhancing children’s cognitive and literacy skills, and little attention is given to how the social ecology of the classroom might be supported in order to build a broader set of school-related competencies. Overlooking the social functioning of peers in a classroom may be a critical oversight (Fabes, Martin, Hanish, Anders, & Madden-Derdich, 2003). We aim to address this concern by directly examining peer influence, or the extent to which children change their own levels of preschool competency to become more similar to the peers with whom they interact most in Head Start classrooms. To be precise, in order to assess the complete social ecology of the classroom; we will analyze peer influence at the level of the classroom peer network rather than at the level of a subset of relationships within the classroom.
Author Manuscript
Most research, to date, on peer influence processes has focused on peers’ influence on learning-related behaviors among older children and adolescents (Altermatt & Pomerantz, 2003; Fonzi, Schneider, Tani, & Tomada, 1997; Kindermann 2007; Slavin, 1983; Wentzel & Watkins, 2002). The lack of attention to how peers potentially influence younger children’s school success can be credited, in part, to an assumption that older children and adolescents are more susceptible to peer influence than younger children (Hartup, 1989). However, several studies suggest that this presumption is inaccurate. There is evidence that peers impact young children’s externalizing behaviors and language skills (Hanish et al 2005; Martin & Fabes, 2001; Mashburn et al., 2009), suggesting peer influence may occur in younger children. Other early childhood research has examined peer collaborations (Hogan & Tudge, 1999), determining that expert-novice interchanges are mutually beneficial because the less skilled peer learns from the more skilled peer and the more skilled peer’s knowledge is strengthened from teaching the less skilled peer (Azmitia, 1988). Achievement abilities of peers are also positively associated with preschool children’s own achievement level (Mashburn, Justice, Downer, & Pianta, 2009; Justice, Petscher, Schatschneider, &
Dev Psychol. Author manuscript; available in PMC 2017 January 01.
DeLay et al.
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Mashburn, 2011). Martin et al. (2013) provided the only direct test of peer selection and peer influence in early childhood using the complete, longitudinal network data we use in the current study and found evidence of peer influence on gender typed activities, after accounting for peer selection effects. The present study extends this previous work by exploring whether preschool peers can also exert influence on school-related outcomes, namely preschool competency, when interacting in unstructured, freely-chosen, peer settings that are typical of the preschool classroom.
Peer Selection and Influence in Preschool
Author Manuscript
Within the typical preschool classroom, children have ample opportunities to choose with whom they would like to interact. In these free-play peer settings, we may find that two children who spend time together are similar to one another on certain features (e.g., preschool competencies). This similarity may, in part, result from peer influence in the classroom. However, similarity between frequent interaction partners may also arise from selection effects. If peer selection is left unaccounted for in examinations of peer influence, the estimates of peer influence can be drastically inflated (Kandel, 1978). Therefore, it is incumbent that researchers account for peer selection effects before they can draw conclusions about peer influence.
Author Manuscript
A child’s selection of a similar peer as an interaction partner may also contribute to the influence process. For example, having freely selected similar peer interaction partners based on preschool competency, there is an opportunity for influence to occur toward increased similarity on the same school-related competency that fostered the initial formation of the peer relationship. Thus, the initial process of shopping for interaction partners may have a significant long-term impact on child outcomes as children continue to influence and change one another’s school-related behaviors. Children may choose peers based on some initial similarities in preschool competency and these chosen peer affiliates may then influence children toward even greater similarity to chosen peers on preschool competency over time.
The Present Study
Author Manuscript
In the present study, we posed two research questions. The first research question focuses on whether children select their peers based on being similar in preschool competency. Although only a few studies of young children’s peer selection have been conducted (e.g., Farver, 1996; Martin et al., 2013; Santos, Vaughn, Bonnet, 2000; Strayer & Santos, 1996), these studies have shown that even young children select peers based on certain dimensions of similarity (e.g., sex, activity similarity). Thus, there is some evidence to suggest that preschool children are able to detect similarities to peers and use these similarities to select peer interaction partners. It is, however, yet to be determined if children are sensitive to the behavioral cues that might allow preschool children to select peers based on similarity in preschool competency. The second research question focuses on whether children are influenced by peers such that they become more similar to their peers in preschool competency over time. Several studies
Dev Psychol. Author manuscript; available in PMC 2017 January 01.
DeLay et al.
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Author Manuscript
suggest that peer influence occurs in preschool (Haun & Tomasello, 2011; Martin et al., 2013), and, so, there is reason to believe that preschool children will be susceptible to peer influence effects. Preschool children have many opportunities to socially engage each other. Thus, children may grow more similar over time as a consequence of the mutual influence that occurs during these social interactions. The classroom context may also make peer behaviors related to preschool competency (e.g. paying attention to the teacher) particularly salient.
Author Manuscript
In the present study, we used data obtained from a study of children enrolled in Head Start classrooms. The yearlong longitudinal data provides opportunities to explore peer selection and influence effects in young children at a time when peer relationships are beginning to form in the preschool classroom. Understanding the influence of peers in Head Start children’s early preschool competencies, over and above the effects of gender, age, language spoken, financial strain, or cognitive ability, may help educators create classroom environments that enhance the social features of the classroom that will, in turn, promote optimal classroom engagement and student performance in the preschool classroom.
Method Participants
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Participants were preschool-aged children enrolled in 18 Head Start classrooms. Classroom size ranged from 12 to 20 students in 3- to 5-year-old Head Start classrooms. Unique to the current study was sufficient individual variability within these 18 Head Start classrooms to model the effects of important individual differences in the classrooms such as child sex, age, primary language spoken, family financial strain, parent education, and receptive vocabulary. The children were involved in a cross-sequential longitudinal study in which they were observed intensively for a year during preschool, and then followed for 2 additional years. In the current study, we only investigate peer influence during the preschool year. Children were sampled in three cohorts over the first 3 years of the research project. Children were recruited for participation 2–3 weeks into the start of the academic school year. Consent rates were 99% at recruitment (N=308 of a possible 311); 16 participants left the participating Head Start programs and were not included in these analyses due to a low number of observations (fewer than 20 observations). There were no demographic differences greater than chance in children’s rate of permission or attrition.
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Of the 292 children who were part of the present study (boys: n=156, Mage = 51.82 months, SD=5.02, range=38–59; girls: n=136, Mage =51.04 months, SD=5.61, range=37–60), 16 (50% boys) repeated preschool during years 2 and 3 of data collection and were retained in analyses to ensure that we had the complete networks in each classroom. This gave us an effective sample size of N=308. The ratio of girls to boys per classroom ranged from 31% to 62% girls, and 85% of the teachers were women. The majority of children (69%) were Mexican or Mexican American; 60% of the children primarily spoke Spanish. The remaining children were Anglo-American (8%), African American (7%), Asian (2%), Native American (1%), and other or unknown (13%). Consistent with Head Start programs,
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children were from families of low socioeconomic status (82% earned below $30,000 per year). Almost half of the children (45%) came from two-parent married families. Procedures The data for this study were obtained from a larger longitudinal study of Head Start children’s early school adjustment and performance (see Martin et al., 2013). In this study, children were observed across the school year and teacher reports of children’s competencies were obtained at multiple time points.
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The data consisted of observations of children’s interactions (used to create the peer interaction network structure), teachers’ reports of children’s preschool competencies (outcome variable), assessment of initial receptive vocabulary (covariate), and demographic information on children and their families (covariates). Data were collected across the preschool academic year (approximately September to May) for the three preschool cohorts. Each year, observational data were divided into 4 waves across the preschool year; wave 1 was the first half of the fall semester, wave 2 was the second half of the fall semester, wave 3 was the first half of the spring semester, and wave 4 was the second half of the spring semester. During Wave 1, all types of data (i.e., observational and teacher data and covariates) were collected. In Waves 2, 3, and 4, only observational and teacher data were collected.
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Observational data—Observational data were collected using a brief-observation protocol (e.g., Martin & Fabes, 2001; Martin et al., 2013) in which children were observed indoors and outdoors during free play (e.g., where children freely decide what to play, with whom, and where to do it) in 10-s periods, multiple times a day, 2–3 times a week for several hours a day over the fall and spring semesters. Classroom observers (8–10 per year; 87% female) were intensively trained for the first 3–4 weeks of each semester. Training of observational coders consisted of several meetings to discuss the coding scheme, practice coding sessions, and testing to ensure coders knew the names of the children and all of the codes. For each day of coding, observers began at the top of a randomized list of children (the list was reordered midway through each semester to prevent order effects), completed the entire list, waited 5 minutes, and then returned to the top of the list again (on average about 4 times per day). Coders were instructed to complete a full rotation before ending their day of observing. Prior to recording data, observers noted whether the child was present and available for coding, present but unavailable for coding (e.g., in the bathroom), or absent. If present and available for coding, the observer would then record the child’s identification number and the identification number of any peer interaction partners (up to five). Additional codes (such as the child’s primary activity) that are not relevant to the purpose of the present study were also obtained. To be coded as engaged with a peer, the focal child and the peer had to either engage in a verbal or physical exchange during the 10-s observation or be playing in the same activity alongside another child. Teacher-child interactions were not included in the present analyses. A total of 38,145 observations were collected for the children in this sample (M=123.55 observations per child, SD=58.95, range = 21–303; 89% with more than 100 observations).
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As previously noted, 16 children with fewer than 20 observations were dropped. The number of observations recorded for each child varied due to differences in attendance and availability during coding. To control for the varying number of observations and presence in the classroom, we calculated classroom presence: the number of times a child was coded as present divided by the total number of observations attempted for that child. To determine reliability, two observers independently coded the same children’s behaviors for approximately 1 hour per week. Reliability assessments varied across coders to prevent bias in the time of day or activities for which reliabilities were conducted. For identification of peer partners, percent agreement across semesters ranged from .87 to .97. Measures
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Network measures—For each wave, the observational data on children’s interactions had to be transformed into classroom networks that represent which children “shared a tie” (i.e., were interactional partners). To do this, we first calculated the numbers of times each target child interacted with peers, providing a proxy for young children’s preferences in interaction partners (Baines & Blatchford, 2009; Schaefer, Light, Fabes, Hanish, & Martin, 2010). Nearly all children interacted with every other child in their classroom, so only when children interacted more often than expected by chance (when compared to any other peer interaction partner in the classroom) were their interactions assumed to represent an underlying relationship (i.e., a tie) (see Schaefer et al., 2010 for additional details on tie creation). As such, ties across children can be asymmetrical. However, because these ties represent interactions between two children, our final step was to remove directionality from these peer interactions so that all interactions were coded as mutual (i.e., a symmetrized network) (Santos, Vaughn, & Bost, 2008). Following these procedures, network ties were calculated separately within each of the 4 waves of data. It was this calculation of network ties that allowed for a test of selection (peer partner choice) as a function of preschool competency and influence (peer partners’ effect on behavioral change) on preschool competency.
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Preschool competency—Teachers completed questionnaires for each participating child in their classroom at four waves throughout the school year—two in the fall semester and two in the spring (corresponding to the middle of the fall semester, end of the fall semester, middle of the spring semester, and end of the spring semester). Teachers were financially compensated for their participation. From these teacher ratings, a 7-item scale of preschool competency was calculated by averaging items together. Items reflected the early noncognitive foundations of academic performance identified by Farrington and colleagues (e.g., 2012) and included: “This child enjoys school and actively participates in activities and games,” “This child is skilled, capable, and effective in interactions with other children,” “This child follows classroom rules and complies with teacher requests,” “This child can focus his/her attention when he or she needs to,” “This child adapts well to change in class schedules and activities,” “This child is not disruptive in class,” “This child has strong academic skills”. Items were rated on a 5-point scale (1 = “Not at all true”; 5 = “Very true”). Internal reliability for the preschool competence scale was acceptable (α=.79 to .90 across waves). In addition, all items were significantly correlated (p
Dev Psychol. Author manuscript; available in PMC 2017 January 01. Published in final edited form as: Dev Psychol. 2016 January ; 52(1): 58–70. doi:10.1037/dev0000066.
Peer Effects on Head Start Children’s Preschool Competency Dawn DeLay, PhD, Laura D. Hanish, PhD, Carol Lynn Martin, PhD, and Richard A. Fabes, PhD Arizona State University
Abstract Author Manuscript
The goals of the present study were to investigate whether young children attending Head Start (N=292; Mage=4.3 years) selected peers based on their preschool competency and whether children’s levels of preschool competency were influenced by their peers’ levels of preschool competency. Children’s peer interaction partners were intensively observed several times a week over one academic year. Social network analyses revealed that children selected peer interaction partners with similar levels of preschool competency and were influenced over time by their partners’ levels of preschool competency. These effects held even after controlling for several child (e.g., sex and language) and family factors (e.g., financial strain and parent education). Implications for promoting preschool competency among Head Start children are discussed.
Keywords
Author Manuscript
Preschool competency; Peers; Peer influence; Preschool children; Social networks
Author Manuscript
In the US, one of the primary interventions designed to help young economically underprivileged children become better prepared for formal schooling is Head Start (Gilford, 2013). Many of the children attending Head Start programs in the US start school at a considerable disadvantage compared to their more economically advantaged peers. Despite this general pattern, there is considerable variation among Head Start children. For example, in a recent Head Start Family and Child Experiences Survey (FACES) report (Klein, Aikens, West, Lekashanets, & Tarullo, 2013), there was significant variability in nonverbal intelligence both across and within Head Start racial/ethnic groups. Similar variability has been reported on social-emotional characteristics (Aikens, Moiduddin, Xue, Tarullo, & West, 2012) and emergent literacy skills (Cabell, Justice, Konold, & McGinty, 2011). Furthermore, there is evidence that Head Start children’s emergent literacy skills change between the fall and spring of the preschool year (Cabell, Justice, Logan, & Konold, 2013), especially for the most at-risk children (Zill & Resnick, 2006). Thus, in a typical Head Start classroom, each child is surrounded by a classroom of peers who represent a wide and changing continuum of social-emotional and academic skill competencies that may, over time, affect her or his own levels of preschool competency (Reid & Ready, 2013).
Correspondence may be directed to Correspondence should be directed to Dawn DeLay, T. Denny Sanford School of Social and Family Dynamics, Arizona State University, PO Box 873701, Tempe, AZ 85287, USA, [email protected], Phone: 480-965-2740, Fax: 480-965-6779.
DeLay et al.
Page 2
Author Manuscript Author Manuscript
The goal of the current study was to better understand how preschoolers’ school-related competencies are associated with the peer environment in Head Start classrooms. Specifically, we assess the efficacy of a “peer social exposure” model in which time spent with peers provides opportunities to become more similar to peers through a variety of peer influence processes. Peer influence may arise from observing peers during play and academic tasks and being concurrently rewarded or punished for imitating these behaviors (Bandura, 1977). Peer influence may also arise, in accordance with social motivation theory, through social experiences that motivate both social (peer) and academic (school) engagement or disengagement in the classroom (Juvonen & Wentzel, 1996). To empirically examine how preschool competencies are related to the peer context, we measured changes in Head Start children’s preschool competency – operationalized as the behaviors that underlie, and are predictive of, academic-related skills -- over the course of the school year. Although changes in preschool competency may be accounted for by many different factors, we focus on peer influence as a mechanism for change. Specifically, we contend that young children actively select certain (similar) peers to interact with and that, in the context of peer interaction, there is an opportunity for peer influence to occur. Thus, interactions with some peers may promote, while interactions with other peers may hinder, the development of preschool competency.
Author Manuscript Author Manuscript
In previous empirical research, social network characteristics of the classroom have been used to predict academic and social adjustment both in the short- (e.g., across the academic year) and long- (e.g., from early childhood into adolescence) term (Ahn, Rodkin, Gest, 2013; Garandeau, Ahn, & Rodkin, 2011; Ialongo, Werthamer, Kellam, Brown, Wang & Lin, 1999; Ialongo, Poduska, Werthamer, & Kellam, 2001; Serdiouk, Rodkin, Madill, Logis, & Gest, 2013). Researchers have also used social network theory to address social differences in the school that may impact academic engagement and performance (Cappella, Kim, Neal, & Jackson, 2013; Neal, Cappella, Wagner, & Atkins, 2011). Nevertheless, when scholars use network features as linear predictors of student outcomes, rather than accounting for the socially embedded characteristics of the classroom, information from various levels of analysis is included in the same linear equation. It follows that, by using network features as linear predictors, there is an important loss of classroom variability that may exist between individuals, within social groups, and ultimately within the social network of the classroom (Cascio & Schanzenbach, 2007; Hedges, Laine, Greenwald, 1994). Linear equations also require decisions to be made on how to transform the data to best represent the social composition of the classroom, a decision that may also bias results (Yudron, Jones, & Raver, 2014). Finally, a failure to analyze complete social network data, or the presence of missing data, can also bias the estimates of peer effects in the classroom (Sojourner, 2013). Taken together, these finding suggest that to adequately test peer influence requires a consideration of a complete social network coupled with all individual behaviors in the classroom to account for the individual and group dynamics that lead to social affinities (selection) and behavioral change (influence). In the current study, complete social network and behavioral data were used to examine peer influence within Head Start classrooms.
Dev Psychol. Author manuscript; available in PMC 2017 January 01.
DeLay et al.
Page 3
Author Manuscript
Young Children’s Preschool Competency
Author Manuscript
Few would dispute the fact that children vary in their readiness to learn because some children have behaviors that help them adjust to school environments and others do not. In the present study, we aim to consider predictors of the variability in preschool competency, a critically important, but often understudied, feature of children’s readiness to learn and success in school (Mendez, Fantuzzo, & Cicchetti, 2002; Murphy, Laurie-Rose, Brinkman, & McNamara, 2007). Preschool competency encompasses the social and learning behaviors that are predictive of young children’s concurrent and longer-term academic-related successes (Blair, 2002; Kaiser, Hancock, Cai, Foster & Hester, 2000; Mashburn, Justice, Downer, & Pianta, 2009; McWayne & Cheung, 2009). This includes successful interactions with peers, focused attention, active participation, and classroom engagement (e.g., Kagan, Moore, Bredekamp, 1995; Raver, 2004). Children who exhibit preschool competencies actively participate in class, attend to teachers, and have good peer relationships. These behaviors are interrelated and positively associated with school readiness, a predictor of later academic success (Coolahan, Fantuzzo, Mendez, & McDermott, 2000; Cuhna & Heckman, 2007). Moreover, these non-cognitive competencies are considered to represent important drivers of academic perseverance and academic behaviors that are essential for long-term academic success (Farrington et al., 2012).
Author Manuscript
However, intervention efforts to promote school readiness typically focus on enhancing children’s cognitive and literacy skills, and little attention is given to how the social ecology of the classroom might be supported in order to build a broader set of school-related competencies. Overlooking the social functioning of peers in a classroom may be a critical oversight (Fabes, Martin, Hanish, Anders, & Madden-Derdich, 2003). We aim to address this concern by directly examining peer influence, or the extent to which children change their own levels of preschool competency to become more similar to the peers with whom they interact most in Head Start classrooms. To be precise, in order to assess the complete social ecology of the classroom; we will analyze peer influence at the level of the classroom peer network rather than at the level of a subset of relationships within the classroom.
Author Manuscript
Most research, to date, on peer influence processes has focused on peers’ influence on learning-related behaviors among older children and adolescents (Altermatt & Pomerantz, 2003; Fonzi, Schneider, Tani, & Tomada, 1997; Kindermann 2007; Slavin, 1983; Wentzel & Watkins, 2002). The lack of attention to how peers potentially influence younger children’s school success can be credited, in part, to an assumption that older children and adolescents are more susceptible to peer influence than younger children (Hartup, 1989). However, several studies suggest that this presumption is inaccurate. There is evidence that peers impact young children’s externalizing behaviors and language skills (Hanish et al 2005; Martin & Fabes, 2001; Mashburn et al., 2009), suggesting peer influence may occur in younger children. Other early childhood research has examined peer collaborations (Hogan & Tudge, 1999), determining that expert-novice interchanges are mutually beneficial because the less skilled peer learns from the more skilled peer and the more skilled peer’s knowledge is strengthened from teaching the less skilled peer (Azmitia, 1988). Achievement abilities of peers are also positively associated with preschool children’s own achievement level (Mashburn, Justice, Downer, & Pianta, 2009; Justice, Petscher, Schatschneider, &
Dev Psychol. Author manuscript; available in PMC 2017 January 01.
DeLay et al.
Page 4
Author Manuscript
Mashburn, 2011). Martin et al. (2013) provided the only direct test of peer selection and peer influence in early childhood using the complete, longitudinal network data we use in the current study and found evidence of peer influence on gender typed activities, after accounting for peer selection effects. The present study extends this previous work by exploring whether preschool peers can also exert influence on school-related outcomes, namely preschool competency, when interacting in unstructured, freely-chosen, peer settings that are typical of the preschool classroom.
Peer Selection and Influence in Preschool
Author Manuscript
Within the typical preschool classroom, children have ample opportunities to choose with whom they would like to interact. In these free-play peer settings, we may find that two children who spend time together are similar to one another on certain features (e.g., preschool competencies). This similarity may, in part, result from peer influence in the classroom. However, similarity between frequent interaction partners may also arise from selection effects. If peer selection is left unaccounted for in examinations of peer influence, the estimates of peer influence can be drastically inflated (Kandel, 1978). Therefore, it is incumbent that researchers account for peer selection effects before they can draw conclusions about peer influence.
Author Manuscript
A child’s selection of a similar peer as an interaction partner may also contribute to the influence process. For example, having freely selected similar peer interaction partners based on preschool competency, there is an opportunity for influence to occur toward increased similarity on the same school-related competency that fostered the initial formation of the peer relationship. Thus, the initial process of shopping for interaction partners may have a significant long-term impact on child outcomes as children continue to influence and change one another’s school-related behaviors. Children may choose peers based on some initial similarities in preschool competency and these chosen peer affiliates may then influence children toward even greater similarity to chosen peers on preschool competency over time.
The Present Study
Author Manuscript
In the present study, we posed two research questions. The first research question focuses on whether children select their peers based on being similar in preschool competency. Although only a few studies of young children’s peer selection have been conducted (e.g., Farver, 1996; Martin et al., 2013; Santos, Vaughn, Bonnet, 2000; Strayer & Santos, 1996), these studies have shown that even young children select peers based on certain dimensions of similarity (e.g., sex, activity similarity). Thus, there is some evidence to suggest that preschool children are able to detect similarities to peers and use these similarities to select peer interaction partners. It is, however, yet to be determined if children are sensitive to the behavioral cues that might allow preschool children to select peers based on similarity in preschool competency. The second research question focuses on whether children are influenced by peers such that they become more similar to their peers in preschool competency over time. Several studies
Dev Psychol. Author manuscript; available in PMC 2017 January 01.
DeLay et al.
Page 5
Author Manuscript
suggest that peer influence occurs in preschool (Haun & Tomasello, 2011; Martin et al., 2013), and, so, there is reason to believe that preschool children will be susceptible to peer influence effects. Preschool children have many opportunities to socially engage each other. Thus, children may grow more similar over time as a consequence of the mutual influence that occurs during these social interactions. The classroom context may also make peer behaviors related to preschool competency (e.g. paying attention to the teacher) particularly salient.
Author Manuscript
In the present study, we used data obtained from a study of children enrolled in Head Start classrooms. The yearlong longitudinal data provides opportunities to explore peer selection and influence effects in young children at a time when peer relationships are beginning to form in the preschool classroom. Understanding the influence of peers in Head Start children’s early preschool competencies, over and above the effects of gender, age, language spoken, financial strain, or cognitive ability, may help educators create classroom environments that enhance the social features of the classroom that will, in turn, promote optimal classroom engagement and student performance in the preschool classroom.
Method Participants
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Participants were preschool-aged children enrolled in 18 Head Start classrooms. Classroom size ranged from 12 to 20 students in 3- to 5-year-old Head Start classrooms. Unique to the current study was sufficient individual variability within these 18 Head Start classrooms to model the effects of important individual differences in the classrooms such as child sex, age, primary language spoken, family financial strain, parent education, and receptive vocabulary. The children were involved in a cross-sequential longitudinal study in which they were observed intensively for a year during preschool, and then followed for 2 additional years. In the current study, we only investigate peer influence during the preschool year. Children were sampled in three cohorts over the first 3 years of the research project. Children were recruited for participation 2–3 weeks into the start of the academic school year. Consent rates were 99% at recruitment (N=308 of a possible 311); 16 participants left the participating Head Start programs and were not included in these analyses due to a low number of observations (fewer than 20 observations). There were no demographic differences greater than chance in children’s rate of permission or attrition.
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Of the 292 children who were part of the present study (boys: n=156, Mage = 51.82 months, SD=5.02, range=38–59; girls: n=136, Mage =51.04 months, SD=5.61, range=37–60), 16 (50% boys) repeated preschool during years 2 and 3 of data collection and were retained in analyses to ensure that we had the complete networks in each classroom. This gave us an effective sample size of N=308. The ratio of girls to boys per classroom ranged from 31% to 62% girls, and 85% of the teachers were women. The majority of children (69%) were Mexican or Mexican American; 60% of the children primarily spoke Spanish. The remaining children were Anglo-American (8%), African American (7%), Asian (2%), Native American (1%), and other or unknown (13%). Consistent with Head Start programs,
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children were from families of low socioeconomic status (82% earned below $30,000 per year). Almost half of the children (45%) came from two-parent married families. Procedures The data for this study were obtained from a larger longitudinal study of Head Start children’s early school adjustment and performance (see Martin et al., 2013). In this study, children were observed across the school year and teacher reports of children’s competencies were obtained at multiple time points.
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The data consisted of observations of children’s interactions (used to create the peer interaction network structure), teachers’ reports of children’s preschool competencies (outcome variable), assessment of initial receptive vocabulary (covariate), and demographic information on children and their families (covariates). Data were collected across the preschool academic year (approximately September to May) for the three preschool cohorts. Each year, observational data were divided into 4 waves across the preschool year; wave 1 was the first half of the fall semester, wave 2 was the second half of the fall semester, wave 3 was the first half of the spring semester, and wave 4 was the second half of the spring semester. During Wave 1, all types of data (i.e., observational and teacher data and covariates) were collected. In Waves 2, 3, and 4, only observational and teacher data were collected.
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Observational data—Observational data were collected using a brief-observation protocol (e.g., Martin & Fabes, 2001; Martin et al., 2013) in which children were observed indoors and outdoors during free play (e.g., where children freely decide what to play, with whom, and where to do it) in 10-s periods, multiple times a day, 2–3 times a week for several hours a day over the fall and spring semesters. Classroom observers (8–10 per year; 87% female) were intensively trained for the first 3–4 weeks of each semester. Training of observational coders consisted of several meetings to discuss the coding scheme, practice coding sessions, and testing to ensure coders knew the names of the children and all of the codes. For each day of coding, observers began at the top of a randomized list of children (the list was reordered midway through each semester to prevent order effects), completed the entire list, waited 5 minutes, and then returned to the top of the list again (on average about 4 times per day). Coders were instructed to complete a full rotation before ending their day of observing. Prior to recording data, observers noted whether the child was present and available for coding, present but unavailable for coding (e.g., in the bathroom), or absent. If present and available for coding, the observer would then record the child’s identification number and the identification number of any peer interaction partners (up to five). Additional codes (such as the child’s primary activity) that are not relevant to the purpose of the present study were also obtained. To be coded as engaged with a peer, the focal child and the peer had to either engage in a verbal or physical exchange during the 10-s observation or be playing in the same activity alongside another child. Teacher-child interactions were not included in the present analyses. A total of 38,145 observations were collected for the children in this sample (M=123.55 observations per child, SD=58.95, range = 21–303; 89% with more than 100 observations).
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As previously noted, 16 children with fewer than 20 observations were dropped. The number of observations recorded for each child varied due to differences in attendance and availability during coding. To control for the varying number of observations and presence in the classroom, we calculated classroom presence: the number of times a child was coded as present divided by the total number of observations attempted for that child. To determine reliability, two observers independently coded the same children’s behaviors for approximately 1 hour per week. Reliability assessments varied across coders to prevent bias in the time of day or activities for which reliabilities were conducted. For identification of peer partners, percent agreement across semesters ranged from .87 to .97. Measures
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Network measures—For each wave, the observational data on children’s interactions had to be transformed into classroom networks that represent which children “shared a tie” (i.e., were interactional partners). To do this, we first calculated the numbers of times each target child interacted with peers, providing a proxy for young children’s preferences in interaction partners (Baines & Blatchford, 2009; Schaefer, Light, Fabes, Hanish, & Martin, 2010). Nearly all children interacted with every other child in their classroom, so only when children interacted more often than expected by chance (when compared to any other peer interaction partner in the classroom) were their interactions assumed to represent an underlying relationship (i.e., a tie) (see Schaefer et al., 2010 for additional details on tie creation). As such, ties across children can be asymmetrical. However, because these ties represent interactions between two children, our final step was to remove directionality from these peer interactions so that all interactions were coded as mutual (i.e., a symmetrized network) (Santos, Vaughn, & Bost, 2008). Following these procedures, network ties were calculated separately within each of the 4 waves of data. It was this calculation of network ties that allowed for a test of selection (peer partner choice) as a function of preschool competency and influence (peer partners’ effect on behavioral change) on preschool competency.
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Preschool competency—Teachers completed questionnaires for each participating child in their classroom at four waves throughout the school year—two in the fall semester and two in the spring (corresponding to the middle of the fall semester, end of the fall semester, middle of the spring semester, and end of the spring semester). Teachers were financially compensated for their participation. From these teacher ratings, a 7-item scale of preschool competency was calculated by averaging items together. Items reflected the early noncognitive foundations of academic performance identified by Farrington and colleagues (e.g., 2012) and included: “This child enjoys school and actively participates in activities and games,” “This child is skilled, capable, and effective in interactions with other children,” “This child follows classroom rules and complies with teacher requests,” “This child can focus his/her attention when he or she needs to,” “This child adapts well to change in class schedules and activities,” “This child is not disruptive in class,” “This child has strong academic skills”. Items were rated on a 5-point scale (1 = “Not at all true”; 5 = “Very true”). Internal reliability for the preschool competence scale was acceptable (α=.79 to .90 across waves). In addition, all items were significantly correlated (p
