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Assessment of the Peer Group:

Identifying Naturally Occurring Social Networks and Capturing their Effects

Thomas A. Kindermann Scott D. Gest

Portland State Pennsylvania State

University University

Kindermann, T. A. & Gest, S. D. (In Press). Assessment of the peer group: Identifying social networks in natural settings and measuring their influences. To appear in: Rubin, K. H., Bukowski, W., & Laursen, B. Handbook of peer interactions, relationships, and groups. New York: Guilford.

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Assessment of the Peer Group:

Identifying Naturally Occurring Social Networks and Capturing their Effects

The notion of a "group" implies a social unit that extends beyond the dyad. In

developmental psychology, there are at least three distinct traditions that study peer group

effects. The first tradition, building on Moreno's (1934) work, focuses on sociometric groups of

individuals who receive similar nominations of liking and disliking from peers in a setting, such

as a classroom (Bukowski & Cillessen, 1998). A second tradition examines social crowds of

youth who share a common reputation among peers for patterns of behavior and values (Brown,

1999). A third tradition focuses on groups of youth whose social ties (friendships or interactions)

are differentially directed toward one another rather than to other peers in the setting (Cairns,

Perrin & Cairns, 1985; Hallinan, 1979). Each approach assumes that knowledge about a person's

group membership has diagnostic value for assessing his or her functioning and that group

membership leads to socialization influences (for review, see Rubin, Bukowski & Parker, 2006).

Figure 1 illustrates the different roles of individuals, relationships, and interactions

(Hinde, 1997). The approaches differ first in how groups are created. Groups that are culturally

assigned or “obligatory” (Laursen, 1997; e.g., classrooms) are defined independently of the

people under study. Sociometric research holds this perspective: The peer group is where people

interact. The other groups are less well defined because they depend on individual preferences.

The approaches also differ in the social ties that define the groups. For sociometric and crowd

researchers, the goal is to identify sub-groups of people who share commonalities. In sociometry,

these are commonalities in social status (e.g., of rejected or popular children; individual ties are

not implied). In crowds, the defining feature is social-behavioral reputation; members of a crowd

(e.g., nerds, jocks) share “ideational ties” and are committed to a shared set of norms (Simmel,

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1950), but they do not necessarily share ties. In contrast, friendship and interaction groups are

defined by “relational ties”. The distinctions have implications for decisions about the social

partners that need to be identified, as well as for expectations about processes within groups.

Purpose of the chapter. In this chapter, we focus on social crowds, friendship groups, and

interaction groups. Our goal is to document the challenges that the approaches are facing and to

outline suggestions for their integration in a way that supports theory building.

The study of peer groups has historically been plagued by two thorny issues: how to

identify groups in natural settings and make sense of the "abstract art" of Moreno-type diagrams

(Cairns, 1983); and how to conceptualize and empirically capture their effects. Many studies

document correlations between the characteristics of individuals and those of their groups, but it

is not clear how the correlations should be interpreted. They can imply that groups influence

individuals, but it is just as likely that they denote that people form groups who are similar to one

another. Experimental studies in which members are randomly assigned to groups demonstrate

that influence is possible, but such studies cannot easily be generalized to the real world in which

people are able to select partners based on social preferences. Both challenges are inextricably

linked: Choices of methods for identifying peer groups imply a conceptualization of the

processes by which groups are likely to shape development; and conceptualizations of group

influences rely on methods to adequately capture the characteristics of peer groups.

Central Issues in the Assessment of Peer Groups

Sources of Information regarding Crowds and Groups

Social crowds are defined as reputational labels assigned to individuals by their peers that

involve stereotypes about personality and behavior (Brown, 1999). Crowds can be identified

with ethnographic methods (e.g., Eckert, 1989), but by definition, peers are the ideal source of

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information. Brown (1999; Brown & Klute, 2003) developed a two-stage process in which first,

an overall account is obtained of the social categories that children agree exist in a setting (e.g.,

jocks, nerds), and then social "type" ratings of peers are used to assign individuals to the

categories. Self-reports ("Which crowd would most of your classmates say you belong to?") may

be sufficient because they correlate with peer-reports and are similarly related to indices of

personal adjustment (Urberg, Degirmencioglu, Tolson, and Halliday-Scher, 2000), but they are

also subject to concerns about self-enhancement biases.

Friendship groups are defined by feelings of liking between individuals. As with dyads

(see Chapter 4; Hartup, 1996), self-reports give the best information. Nonetheless, there are

differing perspectives on measurement operations and on the importance of reciprocity. Most

researchers ask youth to identify their friends, but alternative procedures include nominations of

"liked" peers or ratings of "liking". Disagreements about the value of reciprocity fall along

disciplinary lines. Developmentalists see reciprocity as a requirement: A friendship exists when

both individuals agree on the friendship; sociologists tend to view unreciprocated friendships as

key reference-groups or as links that connect larger groups (Bearman, Moody & Stovel, 2004).

Social groups based on frequent interaction can also be identified via self-reports, but

because children tend to exaggerate associations with popular peers (Leung, 1996), alternative

strategies have been developed. One alternative is to observe peers (Hanish, Martin, Fabes,

Leonard & Herzog, 2005; Ladd, 1983; Strayer & Santos, 1996), but such studies are costly and

often constrained by limited access to interaction settings. In contrast, peers are expert

participant-observers with unique access to interactions in social settings. This led Robert Cairns

to develop the Social-Cognitive Map (SCM) procedure, in which peers in a school setting are

asked to identify classmates who "hang around together a lot" (Cairns et al., 1985; Gest, Farmer,

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Xie & Cairns, 2003; Kindermann, 1996). Respondents name or list as many groups as they can

but are not required to classify all peers into groups. The reports are aggregated into a symmetric

co-occurrence (or co-nomination) matrix in which each off-diagonal cell contains the frequency

with which a pair of youth is reported to belong to the same group. This matrix can be analyzed

with various quantitative techniques; because peer reports show typically considerable

consensus, not every student in a class needs to participate to arrive at a reliable map.

Approaches to identifying group structures

Social crowds. A central issue in analyzing crowd data is whether to constrain each

individual to be in only one crowd or whether to permit membership in multiple crowds. In

Brown's original two-stage peer-report approach, each student was classified into a unique

crowd; but later, the coding procedure was refined so that each individual received a separate

score for each crowd label (the proportion of peers who assigned the individual to each crowd;

Brown & Klute, 2003). Similar coding options exist for self-reports of crowd affiliations.

Social groups. Strategies for identifying group structures fall into two approaches and are

equally applicable to data on friendships and interactions (Frank, 1995; Gest, Moody & Rulison,

2007; Moody, 2001). In one approach, individuals are grouped together when their patterns of

ties are similar (i.e., correlated). For example, in the 1950's, network researchers factor analyzed

interaction matrices: Individuals with highly correlated profiles loaded on the same factor and

were seen as members of the same group. Researchers using the SCM data collection method

follow this tradition. Cairns and colleagues (1985) originally identified dyads whose nomination

profiles were reliably correlated and then used decision rules to build groups; later, they used

confirmatory factor analysis (Cairns, Cairns, Neckerman, Gest & Gariepy, 1988). More recently,

SCM researchers have used a computer program that identifies groups comprised of individuals

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whose profiles are correlated (r > .40) with at least 50% of the other group members (Estell,

Farmer, Cairns & Cairns, 2002; Farmer, Estell, Bishop, O’Neal & Cairns, 2003; Rodkin, Farmer,

Pearl, & Van Acker, 2006), whereas Gest and colleagues (2007) used principal component

analysis. Hierarchical cluster analysis also falls in this tradition (Strayer & Santos, 1996).

A second approach is to search directly for groups by applying algorithms that optimize

intuitive notions of internally cohesive yet distinct group structures, such as not splitting apart

triads of mutual friends, or maximizing the density of friendships within groups (e.g., UCINET

by Borgatti, Everett & Freeman, 1999; NEGOPY by Richards, 1995). Other approaches directly

identify groups based on graph theory features such as degree of embeddedness (Moody &

White, 2003). With the same data, different algorithms will often (but not always) yield similar

group structures; there is some indication that variations may be especially consequential when

analyzing friendship nominations rather than SCM groups (Gest et al., 2007).

These approaches aim to divide a social network into largely non-overlapping groups. A

different approach is to identify groups individually for each person, preserving multiple group

overlap. With friendships, this can involve identifying unreciprocated nominations. With SCM

interaction groups, probabilities can be computed from the co-nomination matrix to determine

the peers with whom an individual is co-nominated more often than can be expected by chance.

This strategy gives up on notions that groups exist over and above the level of individuals' ties.

Assessment of Group Characteristics

The second task is to capture the groups' central characteristics. Developmentalists are

highly interested in whether the characteristics of peer groups influence children. We take as a

starting point that the measures characterizing the individual group members must be obtained

independently from the perspective of any other member of the group because individuals'

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descriptions of group-mates are shaped by their own characteristics (Jaccard, Blanton & Dodge,

2005). When individuals’ characteristics are defined independently (by self-, peer- or teacher

report), they can be aggregated across group members to form composite group profiles.

Group profiles are typically calculated as group averages. Such scores pose different

challenges in studies of non-overlapping groups versus individually defined groups. With non-

overlapping groups, researchers commonly compute separate group profile scores for each

individual in the group by excluding his or her own score from the average. So, for a group of

four individuals, each individual’s group average is calculated across the other three members.

This has two advantages: Conceptually, this accounts for the fact that a group is experienced

differently by its members (e.g., a group of one girl and two boys is experienced by the girl as a

group of boys but as a mixed-sex group by each boy); statistically, it makes it possible to

examine individual differences in group contexts. But this approach has also disadvantages:

Conceptually, it denies the possibility that groups have shared qualities beyond the sum of their

members; statistically, it does not fully take into account the non-independence of group scores

for members of the same group. An alternative is to move to the group as the unit of analysis.

Hierarchical Linear Modeling (HLM) can be used to separate individual within-group effects

from between-group effects (Ryan, 2001). This works well when groups are non-overlapping and

have low turnover over time, but to account for the realities of group dynamics, HLM

approaches will need to be further developed to take into account time-varying and overlapping

group structures. The situation is simplified when there is group overlap and groups are

identified separately for each individual (e.g., Kindermann, 2007); group profiles can be

computed individually. Conceptually, this ensures that group averages include all partners who

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have ties with an individual. Statistically, there are fewer problems with non-independence of

group profiles when these include individuals’ ties that bridge across groups.

Relevant Theoretical Models

When identifying natural groups, methodological choices are closely connected to

underlying theoretical perspectives: We begin with a brief summary of theories of group-level

dynamics, including approaches to dyadic processes that have implications for group contexts.

We then discuss their implications for methodological decisions and for studying group selection

and influence processes. The discussion will be highly selective; we only include theoretical

frameworks that pay attention to member selection (thus excluding groups that are not self-

selected, e.g., in the literatures on group dynamics and organizational psychology), and only

include approaches that require researchers to identify the individuals that form a group (thus

excluding purely ideational groups, e.g., social categories from an individual social perception

perspective, and intrapsychic approaches to social identity and in-group out-group comparisons).

Theories and models relevant to group-level processes

Social interaction processes. Proximal processes that occur in frequent interactions are

seen as the "engine" of development (Bronfenbrenner & Morris, 1998) and of individual

differences in adjustment. With regard to groups, two key phenomena are individuals’ influences

on groups (e.g., member selection and group dissolution), and groups’ influences on individuals.

With regard to group selection, Patterson, Littman, and Bricker (1967) proposed a "shopping”

model in which adolescents seek out affiliates according to compatibility of personal

characteristics and interaction patterns. Processes of group influence on individuals include

evaluative feedback, persuasion, coercion, resource control, and a variety of social learning

processes (Altermatt & Pomerantz, 2003; Berndt, Laychak, & Park, 1990; Dishion, Andrews, &

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Crosby, 1995; Hawley, Little, & Pasupathi, 2002). Social learning processes are relevant to

group processes in two ways. A group can be narrowly defined as an individual’s set of direct

interaction partners; these constitute highly overlapping groups of ecological microsystems (Sage

& Kindermann, 1999). However, groups can also be defined more broadly. It is possible that

group contexts amplify the salience of dyadic exchanges that occur within them (Thibaut &

Kelley, 1959), and thus, groups can be defined as mesosystems that also include indirect links.

Theories of close relationships. Close relationship researchers focus on the broader

relational context of social interactions (Hartup & Stevens, 1994; Hinde, 1997). A major focus of

developmental research has been on change in the norms underlying friendship. Laursen and

Hartup (2002) contend that reciprocity norms govern friendships across development, but that

these norms shift over time from a focus on material equality to a focus on needs that allows for

short-term imbalances in each partner’s contributions. This formulation builds upon social-

psychological theories (Thibaut & Kelley, 1959) that frame dyadic and group processes in terms

of the exchange of resources (information, goods). Social exchange dynamics become extremely

complex in group contexts, but can be expected to generate differentiated role and status

structures and to generate pressure for conformity to group norms (Thibaut & Kelley, 1959).

Social comparison theory. According to Festinger (1954), humans have a fundamental

need to compare themselves to others to evaluate their abilities and opinions. This press for

social comparison motivates individuals to affiliate with groups comprised of others similar to

themselves. Within those groups, individuals compare themselves to individuals who are not too

different from themselves. When discrepant evaluations among members of the same group

emerge, individuals will attempt to reduce the discrepancy by changing their own positions or by

attempting to change the positions of other group members. Most research on social comparison

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has focused on adults in laboratory situations (Suls, Martin & Wheeler, 2002), but a significant

literature indicates that many adult-like comparison processes are evident in children by about 4th

grade (Ruble & Dweck, 1995). Social comparison theory provides explanations for the tendency

to affiliate in groups, for selection pressures that create group similarity, and for the development

of shared group norms; but it does not specify the nature of the groups that are most likely to

motivate social comparisons (e.g., crowds, friendship groups, interaction groups).

Influences from outside the group. Members of a group can differentiate themselves from

other groups partly by fostering within-group conformity, but also by pressuring members of

other groups to adhere to different sets of norms (Brewer & Miller, 1996). Moreover, stereotypes

held by out-group members can lead to biases in the treatment of groups. Such external pressures

are most applicable to crowds, which are defined as peer-assigned social categories (Brown,

1999) that represent "normative guideposts" for adolescents' behavior and give "directives on

attending to and interpreting messages from the broader youth culture" (p. 76). Similar outside

influences may also exist for relationship or interaction groups. Teachers can treat members of a

group similarly, or members can share experiences with adults, institutions, or other groups. A

key distinction, however, is that youth are free to leave these groups when external pressures are

excessive, but because crowd membership is defined by the peer culture rather than by the

individual, external pressures on members of a crowd may be impossible to avoid.

Structural balance theory. Generalizing from Heider’s (1946) work on attitude formation,

Cartwright and Harary (1956) described a press for structural balance in social networks. The

basic notion is that every triad in a network can be described in terms of whether the ties among

members are in balance. For example, if mutual friendships exist between AB, AC and BC, then

triad ABC is in balance and there is no press for change. In contrast, if AB and AC are friends

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but B and C are not, a state of imbalance exists, resulting in pressure for BC to form a friendship

or for AB or AC to dissolve their friendship. Empirical studies have validated the proposition

that states of structural imbalance can predict network changes over time; developmentally, it

highlights that the formation and dissolution of individual ties can result from larger structural

features of a network. Because structural balance is defined with respect to direct ties among

individuals (friendship, interaction), it is not applicable to the study of social crowds.

Individual status in a network context. Developmental researchers measure individual

status with “impact”-type indices (nominations for friendship, liked most, perceived popularity,

"coolness" or social group membership) that capture the number of nominations directed to an

individual but not their patterning. Alternative indices focus on the degree to which individuals

occupy strategic positions of influence within the network of ties (Wasserman & Faust, 1994).

For example, two individuals may receive an equal number of friendship nominations, but the

one who represents the only "bridge" between two otherwise distinct groups can occupy a more

important position for the transmission of ideas or behaviors across groups (Granovetter, 1983).

This is related to the study of "contagion" processes in which direct ties can mediate or amplify

information from indirect affiliates (Bearman et al., 2004). Borgatti (2005) discusses several

“topological” indices of network centrality and the theoretical notions they embody. Working

within this tradition, Moody and White (2003) found that after controlling for the number of

friendship nominations youth received, those whose friendships were tightly interconnected in

highly cohesive subgroups reported stronger feelings of school connectedness.

Implications for methodological choices

The theoretical processes described differ in the types of social ties they implicate.

Friendship and interaction groups emphasize close relationships and social learning processes,

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respectively, but both types of ties are subject to similar group-level dynamics (involving social

comparisons, social exchange, structural balance, and variations in topological network position).

In contrast, the lack of ties within crowds suggests that only imitation and social comparison

dynamics are necessarily in play within them, and that social processes occur in interchanges

with the outside. Theories also differ in whether they require the researcher to (a) identify each

individual’s direct interaction partners, (b) identify non-overlapping groups, or (c) consider the

entire network without identifying subgroups. Contingency learning (including evaluative

discourse), persuasion, and power assertion all require identification of the direct partners who

exert influence. In contrast, imitation, social comparison and social exchange theories highlight

the importance of structures that entail direct as well as indirect ties. Finally, concepts of

structural balance and topological importance can be explored without identifying subgroups.

Implications for Studying Peer Group Selection and Influence

The most challenging questions for peer research are about developmental processes.

Historically, the main interest has focused on processes that produce similarities among group

members: initial selection of members; similarities that members share with regard to their

experiences outside of their group; and mutual influences among group members.

Selection is a defining feature of all natural groups and a key process by which children

can influence the contexts to which they are exposed. Social learning based “shopping” models

(Patterson et al., 1967), social comparison theory (Festinger, 1954) and close relationship

research (Laursen & Hartup, 2002; Thibaut & Kelley, 1959) all assume that peers form groups

based on similarity. In contrast, it is the specific merit of crowd approaches and theories of

between-group differentiation that they highlight group-external influences. Finally, within-

group influences are conceived of as socialization processes. This concept dominated the

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literatures three decades ago, but research has declined because of sobering conclusions about

the extent to which correlational studies allow researchers to make causal interpretations (Harris

1995). For research on peers, the concept needs to be reconceptualized. The traditional

understanding was that influence was unidirectional and that it would "make" a child similar to a

socialization ideal. In peer groups, the socialization agents are also children, and influences occur

from each member of a group towards all other members. This perspective has two implications.

First, conformity may not be the best indicator to study. Experimental and naturalistic

studies have traditionally taken conformity as the indicator of group socialization (Asch, 1955;

Sherif, et al., 1961). Although member convergence can indicate influence, it is just one of many

processes. In natural groups, similarity exists in many features when groups are formed (Kandel,

1978). In addition, such groups are highly fluid, and it is hard to disentangle changes that are due

to member turnover from changes due to member convergence. Analyses are typically restricted

to stable groups ("after selection has occurred,” Urberg, Degirmencioglu, & Pilgrim, 1997, p.

835) but stable groups may be special groups. Finally, the focus on group convergence may lead

researchers to "reify" groups as discrete units. Treating groups as segregated spheres is warranted

with experimental groups and justifiable with crowds. However, because relational groups tend

to be fuzzy, and people are members of many groups at the same time, there is risk in adopting

group-level analytic strategies and potential value in studying individually defined groups.

Second, natural groups may not always produce uniform outcomes. Socialization implies

change; a focus on change broadens the scope of peer influences and suggests to consider a range

of outcomes (in addition to convergence) and change in various directions (Kindermann, 2003;

Mounts & Steinberg, 1995). In groups of friends, questions about change have been examined in

terms of onset of deviant behaviors (Ennett & Bauman, 1994; Urberg, et al., 1997) and in terms

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of academic attitudes, behavior, and motivation (Berndt & Keefe, 1995; Berndt, Hawkins & Jiao,

1999). With interaction groups, analyses of change have been used to study school motivation

(Kindermann, 2003; 2007). Group interactions were expected to make children increasingly

different from one another: Students who were "rich" in terms of motivation and motivation of

affiliates were expected to become "richer" over time, whereas "poor" children would decline.

Theoretically, it is hard to reconcile beliefs that intimate relationships provide mutual support

with expectations that they produce uniform outcomes in the form of a social "mold" (Cairns &

Cairns, 1994). Instead, social groups may make children enjoy activities that they would not

enjoy (as much) if they were not with a group (Furrer & Skinner, 2003) and they may also foster

increasing autonomy and differentiation (Deci & Ryan, 1985; Altermatt & Pomerantz, 2003).

Key Studies

Studies clarifying the reliability and validity of group measurement approaches

Group Homogeneity. All approaches share an interest in the extent to which members of

groups are similar to one another. This is also called "homophily" (Kandel, 1978), "mesh"

(Hinde, 1997), "assortativeness" (Kindermann 2003), or "synchrony" among members (Cairns,

Neckerman, & Cairns, 1989). Often, homogeneity of a group is taken as validity information that

a group exists. Virtually all studies from all approaches find homophily; the pervasive finding is

that "good" groups tend to consist of well-adjusted members (and vice versa) and that

membership in such groups is predictive of various kinds of positive developmental pathways

(e.g., Brown, 1999; Cairns & Cairns, 1994; Epstein, 1989).

Assessments of social crowds (Brown, 1999) have been explicitly developed with the

goal to detect within-group homogeneity of members' social reputation. Prinstein and La Greca

(2002) showed that adolescents' self-nominations for most kinds of crowds overlapped with

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peer-assigned crowd designations. Crowds of popular students have been shown to match

perceptions of teachers (Rodkin, Farmer, Pearl, & van Acker, 2000). Groups of friends also tend

to be homogeneous (Kandel, 1978). An important question is about aggregation of dyads into

larger groups. Urberg, Degirmencioglu, Tolson, and Halliday-Scher (1995) report that over 90%

of adolescents' best friends were members of their friendship groups, but concordance dropped to

70% when all friends were considered. Questions also extend to the definitions of groups and

raise caution about whether friendship groups should include isolated dyads, "loose groups" (in

which not every member can be reached by every other member via a direct or indirect path), or

"liaisons" (children who are not members of a group but have friends in cliques and ties bridging

across cliques). Finally, interaction networks show similar homophily in a variety of variables

(Cairns & Cairns, 1994; Kindermann, 2007), and they have also been examined with regard to

whether they capture ‘real’ groups. Overall, SCM maps tend to converge with group affiliations

identified in independent observations (Gest et al., 2003).

Group Stability. All peer relationships are fluid over time. Relatively little is known

about the stability of crowds (Brown, 1999). Stability of individual friendships has been studied

extensively, but because there is no consensus on how to quantify group stability, estimates from

studies using different metrics are difficult to interpret. For example, Hallinan (1979) reports that

18% to 30% of groups in middle school kept at least three members during the year, whereas

Cohen (1977) found that 76% of high school friendship groups kept 50% of their members

across a school year. Estimates for interaction groups vary similarly, ranging from 40%

(Kindermann, 1993) to 60% (Cairns, Leung, Buchanan, & Cairns, 1995) within a school year.

The most informative studies use the same metric to examine variations in stability in different

contexts. Neckerman (1996) found that interaction group stability was low when students were

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divided over multiple classrooms across years, but remained moderate when classrooms were

kept together. However, high fluidity does not need to imply low stability of the psychological

group environment: Even when member turnover is high, selection processes can occur such that

the psychological composition of the groups remains fairly consistent (Kindermann, 1993).

Studies that clarify overlap among crowds, friendship groups and interaction groups

Structural similarity. Gest and colleagues (2007) applied three group-detection

algorithms (one similarity-based, two graph-theoretic) to two types of data (self-reported

friendships; peer-reported interaction groups). Results indicated that the similarity of solutions

varied for the different data types. For self-report friendships data, agreement across grouping

strategies was modest, suggesting that the strategies are not interchangeable. For peer-nominated

interaction groups, the grouping solutions were much more concordant. A valued co-nomination

matrix provided the most robust basis for identification, making the choice of algorithms less

consequential. For both types of data, homophily was strongest for groups identified by principal

components analysis (a similarity-of-ties algorithm).

There are also indications that members of the different kinds of groups are not the same.

Kindermann (2007), in a cohort of 6th graders in a small town, found that although most students

were members of interaction networks (80%) and about the same number had reciprocal friends

(in class, in school, and outside school), groups of friends were not identical with interaction

groups: Slightly more than half of children's friends were members of their interaction groups

(others were friends that were not publicly known), but 70% of the members of interaction

groups were not friends. Most children without an interaction group had reciprocal friends, and

most children without friends had an interaction group. To gauge crowd structures, children were

also asked to give names to nominated groups. Many names converged with crowd designations

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reported in the literature (e.g., popular kids, nerds, brains, jocks; Brown, 1999; Eckert, 1989), but

students with large networks were typically members of several crowds. Large groups of "cool"

students or "nerds" bridged between separate interaction groups; among the six separate crowds

of "nerds" or "geeks", two provided the connection between the otherwise separate crowds of

"popular" students (the map can be viewed at www.psy.pdx.edu/~thomas/Research).

Similarity in composition. The different identification methods also converge in their

findings on specific target phenomena, even across different kinds of groups. One of the main

research areas is aggression. At least some aggressive children tend to affiliate with one another;

some are popular as a crowd, with aggressive cliques or with their own interaction group (e.g.,

Cairns, et al., 1998; Chen, Chang & He, 2003; Estell, et al., 2002; Farmer, et al., 2002; 2003;

Rodkin, et al., 2006). Homophily also exists in deviant and problem behaviors (e.g., Ennett &

Bauman, 1994; Urberg, et al., 1995, 1997, 2000). Finally, a large body of research also indicates

homophily in children's school motivation, academic aspirations, performance, and school drop-

out. Findings on crowds (Clasen & Brown, 1985) converge with those on friendship groups

(Berndt, et al., 1999; Berndt & Keefe, 1995; Hallinan & Williams, 1990) and interaction groups

(Cairns & Cairns, 1994; Chen et al., 2003; Kindermann, 2007; Ryan 2001).

Functional differences. The consistency of findings across different kinds of groups is

striking and invites suspicions that the respective assessments may be measures of the same

underlying phenomenon. However, there are indications that the functions of the different kinds

of groups can be different. For example, Urberg and colleagues (2000; see also Brown, 1999)

argue that although friendship groups appear to be uniquely predictive of adolescents' problem

behavior over and above crowd associations, relationship ties and interactions can "channel" or

modify norms of the peer culture. Thus, mutual influences in relationships and social interactions

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may occur differently when adolescents belong to different crowds.

Studies clarifying peer selection effects

Studies of selection typically focus on a dyadic level, but the processes have implications

for the development of group structures (Epstein, 1989). At the group level, peer selection has

traditionally been seen as a methodological "problem" (Kandel, 1978; Urberg, et al., 1997) and

has not been studied extensively. This contrasts with key studies of dyadic selection of friends

(Gottman, 1983; Hallinan, 1979; Parker & Seal, 1993; for review, see Hartup & Stevens, 1997).

Patterson and colleagues' (1967) "shopping" model was adapted by Kindermann (1993) with the

assumption that over time, children would optimize the composition of their interaction groups.

In the one classroom of 4th graders that was examined, students’ levels of motivation at the

beginning of a school year predicted how the motivational composition of their peer groups

changed over the year when students' own scores were weighted with the number of newly

gained peers. The effect was weak but suggests that when children include many new members

in their groups, they tend to do so in a way that matches their own motivation levels.

Studies clarifying group influence effects

Two research programs have examined details of group influence processes on children's

school motivation. Berndt and colleagues (Berndt et al., 1995; 1999) studied groups of friends,

and Kindermann (1993; 2007) examined interaction groups. Both followed similar strategies to

come as close to causal interpretations as possible. In terms of design, both examined change in

children's own characteristics over time as a consequence of the composition of their earlier peer

groups; individuals served as their own controls with regard to selection processes. Studies from

both programs indicate that groups can influence children's development. Berndt and colleagues

(1999) showed that the characteristics of friendship groups predicted changes in 6th graders'

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adjustment over a school year. Kindermann's (2007) study focused on interaction groups, and

included controls for a range of selection factors (group size, stability, homogeneity), as well as

for involvement from parents and teachers. Despite membership changes, group motivation

profiles were moderately stable, and group profiles predicted changes in students' motivation

over the school year, even when selection and group-external adult influences were controlled.

Studies clarifying intra-group processes. Correlational studies with rigorous controls

suggest that group influences are possible, but they do not explain how they can come about. A

second step is to examine whether the mechanisms theoretically postulated to cause change do

actually occur in the real world. This requires natural observations. Several such studies have

documented that a variety of interactions (e.g., discussions, evaluative discourse, prosocial

interchanges, learning contingencies) can be mechanisms of group influence (Adler & Adler,

1995; Dishion, et al., 1995; Hawley, et al., 2002; Wentzel, McNamara-Barry & Caldwell, 2004).

One study (Sage & Kindermann, 1999) examined learning contingencies in a classroom of 5th

graders. Sequential analyses showed that children's behaviors had different consequences when

they were members of different interaction groups: The more engaged students were, the more

they experienced contingent support for their (frequent) on-task behavior from group members,

while non-members responded at random. For low motivated students, positive responses from

group members were rare. These patterns can explain differential changes in children and are

consistent with the changes found in correlational studies. The study followed Bronfenbrenner's

ecological model for identifying the sources of peer influences in school, and used SCM

assessments to identify all of children's interaction partners as direct socialization agents. Note

that a group-level analysis would likely have required to assign each child to one specific group

(i.e., excluding multiple group memberships), to exclude ties with others who were not members

20

of this main group (i.e., liaisons or dyadic ties), and to include indirect connections (in analogy to

“friends-of friends”) even if those themselves were not identified as direct interaction partners.

Future Directions

In this chapter, we reviewed and compared three approaches to peer groups that have

evolved from over 70 years of research. All three aim to examine group influences. However,

each approach holds different assumptions about the nature of groups and their developmentally

active ingredients, and each has developed its own assessment strategy. Nevertheless, each finds

group homogeneity in many characteristics. At the same time, it is not really clear what this

means. Is this diagnostic information about individuals, the functioning of social systems, or

selection processes? Or, is this information about the developmental effects of groups? A key

problem is how to design studies that allow us to make causal inferences.

Advances in the modeling of social network dynamics should allow us to reconceptualize

group experiences so that the model of a socialization "mold" can be overcome and bi-directional

models can be developed. The characteristics of self-selected groups that we can identify in

natural settings can guide us towards models of their developmental functions, and free us from

relying on analogies that can only be created in a lab. The social-psychological theories

described above can be elaborated, integrated and translated into sophisticated models of

network dynamics. Most promising are models of the “co-evolution” of networks and individual

characteristics (Handcock, Hunter, Butts, Goodreau & Morris, 2003; Snijders, Steglich &

Schweinberger, 2007). They are not comprehensive (e.g., social ties need to become scalable;

subgroup differentiations need to be incorporated), but they allow investigators to specify a range

of dynamics that are linked to social-psychological theories (Espelage, Green & Wasserman,

2007; Light & Dishion, 2007).

21

As a first step, we need to overcome the idea that there is only one valid representation of

peer groups. This pertains to different analysis techniques, even when studying the same kind of

group, as well to theorizing across approaches. There is a tension between the desire to settle on

a small set of "standard" approaches and the need to document the consequences of differing

measurement strategies. Given the current state of knowledge, a realistic recommendation is that

researchers should be urged to provide explicit theoretical rationales for their choices; and, if

possible, to compare the results of multiple measurement strategies within the same study.

A second task will be to discover more about how natural peer groups emerge and about

diverse patterns of socialization. The most informative studies will consider how processes of

selection and socialization work together in the real world. On the one hand, the openness of

natural groups can shape social influences in complex ways. For example, negative influence

processes (e.g., coercion) may not be found that frequently; if such interactions dominate, people

are likely to leave or the group may break up. On the other hand, if people select others similar to

them, interaction processes do not have as great an opportunity to increase similarity. Rather,

socialization may produce maintenance or even increased diversity. For example, children may

select peers who like them just the way they are and may stay with them as long as they remain

supportive; the role of groups may be to support or amplify pre-existing characteristics. It

appears possible that peer groups can create diversity: Once similar children join together in a

group, the group may socialize them into different roles or members may strive to express their

individuality. Such processes would not lead to increased homogeneity but to patterns in which

groups and the individuals within them become more different from each other over time.

The third task will be integration that remains truthful to the strengths of the theoretical

assumptions about the different kinds of peer groups. A rough sketch could look like the

22

following: For decades, peer researchers have aimed to show that relationships influence

development. Because of their reliance on the experimental model, they have mainly looked at

similarity among friends and convergence over time, and have largely ignored peer selection.

Convergence can occur, but it is hard to detect if friends are similar to one another at the outset.

In groups of friends with high-quality relationships, it is questionable whether a social mold

model (in which friends attempt to change each other) makes so much sense at all. Instead,

friendship may be what holds people, and perhaps society, together; a sense of relatedness may

be what helps children and adolescents make sense of the world and of themselves. Friendship

groups could be the equivalent of a "safe haven" in social settings.

In contrast, social crowds (and, similarly, sociometric categories) may say more about

structure, order, access to resources, and minimization of friction in a social system (e.g.,

Merton, 1957); crowd affiliations may be what helps adolescents orient themselves within the

larger peer culture. Both kinds of groups and their associated processes should co-exist at the

same time, in cultures as well as in smaller social systems like schools, and the patterns of

overlap in affiliations that people show across both kinds of groups may denote how they define

themselves. Finally, if Bronfenbrenner and Morris (1998) are correct that social interactions are

the "engine" of development, interaction networks would be the place where groups of people

share activities that they enjoy, perhaps influence each others' behavior (directly as well as

indirectly), and where people are instrumental for each other in acquiring competencies.

Decisions about the kinds of groups to be identified will need to be informed by

expectations about the kinds of peers who provide the arena for the characteristics under study.

Groups that are publicly known tend to engage in public activities; when public behavior is of

interest (e.g., in classrooms; Kindermann, 1993, 2007), public interactions with groups may be

23

most influential. In contrast, personal relationships may have strongest effects on private

characteristics that may or may not be displayed in public. If private perceptions or goals are

targets of examination (e.g., academic or social goals; Berndt & Keefe, 1995; Wentzel, et al.,

2004), close friends, because of their intimate relationships, should be most influential. As a

consequence, it may be less important to construct separate theories about the different kinds of

groups that we can identify, and more important to provide theoretical accounts and models of

their interconnections and combined functions for development. Empirically, it may not matter

much whether the variables derived from a specific approach have more power in predicting a

specific outcome than another approach. Instead, what may be needed most are studies exploring

how multiple identifiable aspects of children's peer relationships work together over time.

Empirical information about the interconnections of the different kinds of groups may be

necessary before we can attempt to build theories about their individual or combined influences.

We began this chapter by highlighting the different traditions of studying social crowds,

friendship groups and interaction groups (Figure 1). In reviewing the methods and theories

associated with these traditions, it should be clear that crowds, which do not necessarily involve

direct ties among crowd-mates, implicate a very distinct set of influence processes, and require a

very different set of measurement strategies, than the study of friendship groups or interaction

groups. And while both friendship groups and interaction groups involve direct relational ties

among their members, there is surprisingly little empirical research that documents the degree to

which they overlap in structure or function across different periods of childhood and

adolescence. Research that is grounded in the rich tradition of social-psychological theorizing

about groups and close relationships may play a key role in motivating studies that address such

questions and contribute to broader theoretical integration in the study of peer groups.

24

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Figure Captions

Figure 1. Schematic overview over types of peer groups, with attention to the roles of

individuals, groups, relationships, and interactions.

34