Scott Gest Human Development & Family Studies Penn State … · 2020. 2. 27. · Human Development & Family Studies Penn State University Prevention Research Seminar ... Emphases

Scott Gest

Human Development & Family Studies

Penn State UniversityPrevention Research Seminar

November 17, 2010

A network perspective on peer norms

Intervention effects on peer norms in middle/high school (PROSPER Peers)

Correlates of classroom-level peer norms

Everyday teaching practices and peer norms

The promise/hope of setting level program impact◦ Diffusion, mutual reinforcement, continuation

Prevention programs often seek to reorganize social systems◦ Dividing schools into smaller sub-units

Other programs attempt setting-level change via individual-level change◦ Seek to improve classroom climate by teaching

individual social skills

In Theory◦ Prominent in major explanations of deviance◦ Differential association, social learning, problem

behavior theory, peer cluster theory

In Research◦ Friends’ deviance highly reliable predictor

In Prevention/Intervention Practice◦ Emphases on refusal skills, friendship choice,

parent monitoring about friends

Public health emphasis on social networks

Descriptive norms◦ what most people do (“what is”)

◦ Setting-level measure: average across individuals

Injunctive norms◦ what people are expected to do (“what ought to be”)

◦ Setting-level measure: average across individuals

Norm salience ◦ the extent to which a behavior is associated with

positive or negative social sanctions

◦ Setting-level measure: correlation between level of behavior and centrality in peer network

Youth In-Degree Reach

A 5 8

B 5 10

C 3 8

D 3 4

Deviant behavior associated with less centralposition.

Deviant behavior associated with more centralposition.

Contrasts with norm as attribute mean over indivs.

Reduced selection of deviant friends◦ Students encouraged to select prosocial peers who

will help them meet positive goals

◦ Parents encouraged to monitor students’ activities and peer affiliations

Link to network structure◦ Altered friendship selection tendencies will place

antisocial youth in less central/influential structural positions

School based programs targeting substance

use, community partnerships with university

extension:

◦ Random assignment of communities

26 communities, 2 grade cohorts, 5 waves

◦ Iowa & Pennsylvania, Small towns

◦ 10,000+ Students per wave; 14,000+ total

◦ 368 school/cohort/wave networks

Questionnaires assess friendships

◦ Fall of 6th grade & Spring of 6th, 7th, 8th, & 9th

◦ Also assess variety of attitudes and behaviors

Lead Investigators

◦ Wayne Osgood, Mark Feinberg, Scott Gest, Jim Moody (Duke Univ.), Karen Bierman

Other Investigators

◦ Derek Kreager, Sonja Siennick (Florida State Univ.), Kelly Rullison (UNC Greensboro), Michael Cleveland, Suellen Hopfer

Graduate Assistants

◦ Wendy Brynildsen, Robin Gautier, Lauren Molloy, Dan Ragan, Debra Temkin, April Woolnough

PROSPER Study Lead Investigators

◦ Dick Spoth, Mark Greenberg, Cleve Redmond, Mark Feinberg

Funders

◦ National Institute of Drug Abuse, Division of Epidemiology, Services and Prevention Research, Prevention Research Branch

◦ William T. Grant Foundation

Family-focused intervention – 6th Grade◦ Strengthening Families Program◦ Seven 2-hr sessions◦ Overarching theme: “Love & Limits”◦ Prior results suggest diffusion of effects

School-focused intervention – 7th grade◦ Life Skills Training, Project Alert, or All Stars◦ 11 to 18 sessions◦ Relevant focus: Selecting prosocial peers, helping

peers make good choices, and resisting negative influence from peers

Who are your best and closest friends in your grade?

First name Last Name

(or if you don’t

know their last

name, . . . )

How often do you “hang out”

with this person outside of

school, (without adults

around)?

1) Never . . . 5) Almost Every Day

YOUR BEST FRIEND or FRIENDS

OTHER CLOSE FRIENDS

Questionnaire response rate: 87.2% Usable friendship choices, Overall: 81.9%

◦ Of respondents: 93.9%

Name matching, Overall: 81.5%◦ Inter-rater agreement, 98%◦ Non-matches primarily out of school

Reciprocation rate◦ Overall: 48%, ◦ 1st choice: 76%

Regression Coefficient

◦ Within-school association across individuals

◦ Between a measure of deviant behavior (IV) and a network measure of influence potential (DV)

◦ (Mean effect later standardized for effect size)

Degree: Number of Friendships

Closeness Centrality: Mean distance to reach others

Reach: Number of Direct & Indirect Friendships

Bonacich Centrality: Links to well connected others

Information Central: Harmonic mean dist to others

Betweenness Central: Import. in connecting others

Composite: Standardized sum of above

◦ All for both incoming and total friendships

◦ Transformed to normalize distributions and make independent of network size

Substance Use:◦ 30 days, 4 substances, IRT scoring

Substance Use Attitudes:◦ Composite of 4 scales, 22 items

Delinquency:◦ Past year, 12 items, IRT scoring

Composite antisocial◦ Sum of other 3, standardized

No significant pretest diffs btwn trt & ctrl

Waves 2 – 5 as outcome

◦ Pooled test (impact doesn’t vary over time)

Multilevel model:

◦ 5 levels: Comm pairs for random assignment,

Community, School, Cohort, Wave

School & Cohort crossed

Level 1 variance heterogeneous by s. e. of b

◦ Controls for wave, state, Network size (Log,

quadratic), and pretest

◦ Estimated by Bayesian MCMC in MLwiN

◦ BDIC criterion for variance comps & controls

Antisocial

Behavior &

Attitudes

Diff.,

Std b

Std.

Error z p

Composite -0.052 0.022 -2.38 0.017

Substance Use -0.034 0.018 -1.93 0.053

Subst Use Attitudes -0.048 0.023 -2.07 0.039

Delinquency -0.046 0.022 -2.05 0.040

Composite -0.045 0.025 -1.84 0.066

Composite -0.056 0.115 -0.49 0.625

Composite -0.041 0.019 -2.15 0.032

Composite -0.080 0.039 -2.03 0.043

Composite -0.063 0.055 -1.15 0.252

Composite -0.021 0.067 -0.31 0.760p < .05, 2 tail p < .10, 2 tail N = 253 - 256 networks

Betweenness Central.

PROSPER Program Effects on Behavioral Norms

Total Friends

Closeness Centrality

Direct & Indirect Frnds

Bonacich Central.

Composite

Composite

Composite

Composite

Measures Defining Relationship

Trtent Vs. Control

Comparison

Social Status

(Undirected)

Information Central.

-0.20

-0.15

-0.10

-0.05

0.00

0.05

Pretest -6th Fall

6th Grade Spring

7th Grade Spring

8th Grade Spring

9th Grade Spring

Std

. b for

Com

pos. S

ocia

l Sta

tus

with C

om

pos. A

ntisocia

l

Control Treatment

Clear evidence of beneficial intervention impact on behavioral norms ◦ Reduced social status of antisocial relative to

prosocial youth

◦ Most consistent for composite measures

◦ Non-signif often equally strong (lower power)

Modest effect size◦ Approximately .05 difference in correlation

Implications Solid initial support for social network

approach to setting level intervention◦ Setting level impact distinct from individual effects

Next Steps Simulations needed to determine implied

reductions in deviance◦ Networks are complex systems and influence

processes are reciprocal and dynamic

Assess evidence for mediation◦ More deviance reductions where bigger network

effects?

Are positive peer norms associated with better youth experiences & outcomes?

How might everyday teaching practices shape peer norms?

Standard finding in peer relations literature: aggression is negatively correlated with acceptance (being “liked most”)

But this masks considerable variation across classrooms

normsagainst aggression

norms supporting aggression

TRAILS study (Siegwart Lindenberg, PI)◦ Dijkstra, Gest, Lindenberg, Sentsa & Veenstra (in prep)

N = 3,231 youth in 164 classrooms

Mage = 13.60

Peer nominations◦ acceptance/rejection, behavior

Student reports◦ fun, boredom, excitement at school

Teacher reports◦ student achievement

Overall, there were weak norms against academic achievement, but considerable variability.◦ Mr (164) = -.11, SDr = .28

◦ 10th %ile r = -.42 90th %ile r = .27

Similar variability in norms for prosocial behavior (weakly supporting) and bullying (moderately against)

Norm for achievement, prosocial and bullying were intercorrelated, so overall classroom norms in each classroom were categorized as positive or negative

In classrooms categorized as having a positive profile of peer norms:

◦ Students reported

more fun & excitement

less boredom

more emotional and practical support from peers

less peer rejection

◦ Teachers reported

better academic adjustment

◦ Median d = .15

Peer norms may serve as useful marker of classroom social atmosphere & student experience

What teaching practices may be related to these norms?

Goal: Identify teaching practices associated with emerging peer network features (including peer norms) and links to student outcomes

1st, 3rd & 5th grade classrooms◦ Assessed three times within same school year

Each assessment includes measures of…◦ Teaching practices (Observations, Teacher report)

◦ Peer-reported networks

◦ Youth behavior (peer & teacher report)

◦ Youth perceptions of classroom & school

Sample

◦ N = 39 classrooms from pilot study year (cross-sectional)

◦ Final sample will include >150 classrooms

Investigators: Scott Gest, Phil Rodkin (U of Illinois), Tom Farmer

PA Lab◦ Grad students: Deborah Temkin, Rebecca Madill,

Kathleen Zadzora, Rachel Abenavoli◦ Project Coordinator: Gwen Kreamer◦ Data manager: Larissa Witmer◦ Undergraduate assistants: Kristen Granger, Kara

McKee

Funding◦ William T. Grant Foundation & Spencer Foundation◦ Institute of Education Sciences

1930s – Lewin

◦ teachers can and must influence “social atmosphere” of the classroom

1940s-50s – Gronlund (1959)

◦ Teachers should try to prevent “cliques & cleavages” in “social fabric”. Based on clinical wisdom, mostly from descriptive case studies.

1970s – Hallinan

◦ Network analysis to show that reading groups foster friendships

1980s-90s – Cairns & Cairns

◦ “invisible hand” of the teacher in shaping peer networks

2000s – Farmer

◦ Elaboration on how teachers can deliberately influence social related to status, affiliations and aggression

Now

◦ potential for network concepts & methods to strengthen and test theories about teacher influence on peer networks

• Generally weak associations (little power with N=39)• Emotional support associated with less peer support for aggression• Instructional support associated with less peer support for prosocial behavior and achievement

Causality unclear:

Peer norms teaching?

Teaching peer norms?

Longitudinal design (Sep/Nov/May) will help some

There is striking diversity in teachers’ use of grouping strategies, even within the same school – what do teachers think their strategies are accomplishing? Sparse empirical literature to guide practice.

Might peer norms partially mediate any associations between teaching practices and student experiences of the classroom (e.g., perceptions of support, achievement motivation)?

What do we really know about “desirable” and “undesirable” features of peer networks?◦ How are peer norms related to student experiences

and academic learning over time?

◦ Are more tight-knit networks always better?

◦ Are cliques always bad?

◦ Are hierarchies always bad?

◦ Might the desirability of these features depend on which behaviors are valued in the network?

What are teachers (and schools) already doing that may have systematic effects on peer networks?◦ Decades of clinical wisdom but little data◦ Tremendous variation in very basic practices◦ Variation in teacher awareness of peer dynamics,

goals for network, and planful action◦ Building an evidence base could clarify processes

governing emerging network features and provide a stronger foundation for professional training & development

How can we apply this knowledge to advance “prevention science” in schools?◦ There is value in articulating hypotheses about how

school-based interventions may impact peer networks at the setting level Many “universal” interventions target all students in the

school setting

Implications for peer network are sometimes explicit, but more often implicit

◦ Network concepts can sharpen program theories and indices can permit strong tests

◦ Consideration of network dynamics quickly leads to recognize of potential tradeoffs in intervention effects