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Field and Practice in American High Schools. James Moody The Ohio State University Population Research Center University of Texas Austin April 23, 2004. Introduction Cause and Consequence in Social Science Put up or shut up: empirically testing an alternative Bourdieu & Field

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slide1

Field and Practice in American High Schools

James Moody

The Ohio State University

Population Research Center

University of Texas Austin

April 23, 2004

slide2

Introduction

    • Cause and Consequence in Social Science
    • Put up or shut up: empirically testing an alternative
  • Bourdieu & Field
    • What is a field?
    • From fields to practice, relations, & risk
  • Analysis
    • Data & Measures
    • How adolescents spend their time
    • Activity Profiles as “systems of practice”
      • Global vs. Local perspectives
      • Case studies of two High Schools
    • Relational position through blockmodels
    • Correspondence of Practice & Position
    • Practice, Position & Youth Risk Behavior
  • Tentative conclusions
  • Future (re)directions
slide3

Cause and Consequence in Social Science

  • Standard view:
    • Science seeks to identify causes of various sorts, with an eye toward manipulation.
    • The standard requirements for causal inference are (1) isolation, (2) association, and (3) direction (Bollen, 1989).
    • The most common methods are variations of the general linear model, either singly or in systems of linked equations.
  • Critiques are common:
    • Within the standard view there are serious debates about drawing causal conclusions from non-experimental data: Lieberson (1989) Making it Count, Perl Causality; Sobel (1989, 1997, 1996). Concerns basically amount to repeated attempts to better specify either isolation, association, or direction; peppered with concerns for endogeneity and selection.
    • Outside the standard view, authors argue that treating cause as a system of intersecting forces misrepresents the fundamental actively embedded nature of social life. Abbott has probably been the most visible critic on this front ().
slide5

What is a Field?

“Field serves as some sort of representation for those overarching social regularities that may also be visualized … as quasi-organisms, systems or structures” J. L. Martin AJS 2003.

Bourdieu’s notion of field – particularly as discussed in Language and Symbolic Power – can be thought of as a space of “organized striving” - a “social topology” composed of various “dimensions of capital” that are relevant in a field.

This implies two related aspects of a field: (1) the dimensions of capital used to construct the field and (2) the practical guides that generate action in a given activity domain.

Examples of fields range from abstract notions of status spaces to concrete examples such as the French academic system.

slide6

Field Position

Habitus

Habitus

Everyday

Practices

Relational

Position

What is a Field?

  • Martin identifies 3 basic dimensions of a (socoiological) definition of field:
    • A topology  a space of/for action, but not simply the distributions of attributes (I.e. not the same as Blau space)
    • A field of forces  thought of as vectors in the space that act on participants in the field. The analogy here is of magnetic or gravitational fields.
    • A “battlefield”  a place of active, organized contestation for the resources (usually some sort of status) at play in the field.
  • We can capture elements of each of these by identifying the relation between position in a social network and the practices that people engage in to signal their position in the field.
slide7

Why Look For Fields?

  • What are some advantages to conceptualizing social spaces as fields, rather than focusing on more ‘normal-science’ explanations for behavior?
  • Simplification. Field concepts provide a way to undstand systemic activity patterns in large collectives ‘at a distance’. Just as iron filings respond to magnetic fields producing a clear pattern relative to each other, people’s activities (theoretically) admit to regularities that cannot be explained by standard variable-centered approaches.
  • Embedded Action ontology. Field concepts allow us to move beyond questions about the essence of variables as active agents to notions of people actively navigating community structures.
  • Relational Focus. Field concepts effectively move the unit of analysis from an individual actor to sets of actors who are positioned relative to each other through social relations and behaviors.
  • There are also costs. Field concepts are notoriously difficult to specify, causal notions do not follow social science standards for “explaining variance” (but in so doing allows an analysis of constants), and some have argued that they slip uncomfortably toward tautology.
slide8

Fields in High School

  • We’ve known since at least Elmtown's Youth and certainly since The Adolescent Society that schools are significant sites for status struggles.
  • “Popularity”, the “Leading Crowd” “Thugs”, and so forth all signal positions that carry status implications.
  • Ethnographic work in schools suggests that youth are actively engaged in exploiting their behaviors and relations to position themselves within this game.
    • Behaviors, dress, etc. signal a particular position
    • Adolescents actively manage their social relations for status concerns.
    • Relations themselves are of key interest to adolescents
  • The logic of practice in such fields suggest that position in the field should correspond to a relational structure and a distinct pattern of how kids spend their time.
slide9

From Field to Practice

  • Bourdieu seems to suggest that position in a field results in a distinct set of practices that, combined, define relative positions in a given field. These practices are keyed to “everyday” experience, and importantly take place in the real-life constrained time available to participants.
  • I attempt to operationalize positions of practice by identifying characteristic sets of everyday, time-consuming, activities.
  • I use (endogenously determined) sets of activities for 2 reasons:
    • Descriptively, sets might provide a simpler way to characterize ideal types
    • Behaviorally, I suspect that adolescents collapse others into idealized categories (though they probably have fuzzy boundaries).
      • The alternative is to measure distance without creating sets.
  • (Note this might well be contested. Part of the reason I shy away from habitus directly is the link between habitus and dispositions, which (a) are even more difficult to measure than practices and (b) I’m not convinced that actors necessarily are conscious of, and thus able to answer meaningfully about, such dispositions)
slide10

Data & Methods

  • Data:
  • I use the National Longitudinal Survey of Adolescent Health (Add Health). This is a nationally representative survey of adolescents (7th through 12 grade). My sample consists of students who filled out both the in-school and wave-1 in-home survey. I use the two largest “saturated” schools as case studies.
  • Methods
    • Practice Positions are defined using cluster analysis across 17 time-consuming, everyday activities that youth report being involved in.
      • Global versions situate each student relative to all others in the sample
      • Local versions identify clusters by comparing people only to those in their own schools
    • Relational Positions are defined using block models based on regular equivalence
    • The correspondence of “position and practice” is determined through relational (dyad) models.
    • The effect of position and practice on risk-related behaviors uses multivariate regression techniques.
slide13

Adolescent Activities

By Grade in School

To generate activity sets, I cluster over these 17 activity classes.

To avoid simply developing markers for gender and age, I standardize all variables within grade and sex

slide14

Cluster 1: Low Activity, Church Going, Drivers

Cluster 2:

Low Activity, Workers

Cluster 3:

High Activity, Clubs

Cluster 4:

TV & Video Games

Cluster 5:

Non-School Active

Cluster 6:

Church Going, Non-workers

Cluster 7:

Inactive & Uninvolved

Global Cluster Solution

3007

5007

2000

7950

2365

2943

578

901

2413

512

6222

1998

3809

1811

Cluster tree based on a Ward’s minimum variance clustering on 17 variables, standardized by age and sex.

slide15

Global Cluster Solution

Cluster 1:

Low Activity, Church Going, Drivers

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

Arts_club

V.Games

Hang Out

sport_club

HrsWork

Active Sport

Yth. Grps

slife_club

PaidWork

Acad_club

-0.5

-1

-1.5

N=3007 (21%)

slide16

Global Cluster Solution

Cluster 2:

Low Activity, Workers

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

Arts_club

V.Games

Hang Out

sport_club

HrsWork

Active Sport

Yth. Grps

slife_club

PaidWork

Acad_club

-0.5

-1

-1.5

N=2000 (14%)

slide17

Global Cluster Solution

Cluster 3:

High Activity, Clubs

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

Arts_club

V.Games

Hang Out

sport_club

HrsWork

Active Sport

Yth. Grps

slife_club

PaidWork

Acad_club

-0.5

-1

-1.5

N=2943 (21%)

slide18

Global Cluster Solution

Cluster 4:

TV & Video Games

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

Arts_club

V.Games

Hang Out

sport_club

HrsWork

Active Sport

Yth. Grps

slife_club

PaidWork

Acad_club

-0.5

-1

-1.5

N=901 (6%)

slide19

Global Cluster Solution

Cluster 5:

Non-School Active

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

Arts_club

V.Games

Hang Out

sport_club

HrsWork

Active Sport

Yth. Grps

slife_club

PaidWork

Acad_club

-0.5

-1

-1.5

N=512 (3.5%)

slide20

Global Cluster Solution

Cluster 6:

Church Going, Non-workers

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

Arts_club

V.Games

Hang Out

sport_club

HrsWork

Active Sport

Yth. Grps

slife_club

PaidWork

Acad_club

-0.5

-1

-1.5

N=1998 (14%)

slide21

Global Cluster Solution

Cluster 7:

Inactive & Uninvolved

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

Arts_club

V.Games

Hang Out

sport_club

HrsWork

Active Sport

Yth. Grps

slife_club

PaidWork

Acad_club

-0.5

-1

-1.5

N=1811 (13%)

slide22

Local Practices

Case Studies of “Sunshine” & “Jefferson” high schools

Center of the school district. Streets and boundaries are educated guesses. Up is not necessarily North.

slide23

Local Practices

Case Studies of “Sunshine”

& “Jefferson” high schools

Center of the school district. Streets and boundaries are educated guesses. Up is not necessarily North.

slide24

Local Cluster Solution

Jefferson High School

Cluster 1: Non-

Active, Workers

129

324

Cluster 2: Non-

School Active

195

488

106

Cluster 3: Non-working, Non Religions

164

58

Cluster 4:

Video Gamers

98

Cluster 5: Church Going, School Active

108

10

Cluster tree based on a Ward’s minimum variance clustering on 17 variables, standardized by age and sex.

slide25

Local Cluster Solution

Jefferson High School

Cluster 1

Non-active, Workers

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=129 (22%)

slide26

Local Cluster Solution

Jefferson High School

Cluster 2

Non-School, active

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=195 (33%)

slide27

Local Cluster Solution

Jefferson High School

Cluster 3

Non-workers, non-religious

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=106 (18%)

slide28

Local Cluster Solution

Jefferson High School

Cluster 4

Video Gamers

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=58 (10%)

slide29

Local Cluster Solution

Jefferson High School

Cluster 5

Church Going, School Active

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=108 (18%)

slide30

Local Cluster Solution

Sunshine High School

Cluster 1: Active

Non-arts

235

Cluster 2: Non-

School, Active

718

277

483

Cluster 3:

Inactive &

Uninvolved

1064

206

346

Cluster 4:

Workers

Cluster 5: School

Active, Artists

99

Cluster tree based on a Ward’s minimum variance clustering on 17 variables, standardized by age and sex.

slide31

Local Cluster Solution

Sunshine High School

Cluster 1

Active, Non Arts

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=235 (20%)

slide32

Local Cluster Solution

Sunshine High School

Cluster 2

Non-School Active

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=272 (23%)

slide33

Local Cluster Solution

Sunshine High School

Cluster 3

Inactive & Uninvolved

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=206 (18%)

slide34

Local Cluster Solution

Sunshine High School

Cluster 4

Workers

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=346 (30%)

slide35

Local Cluster Solution

Sunshine High School

Cluster 5

School active, Artists

1.5

1

0.5

0

TV

arts

drive

Sport

Work

Cycle

sports

Chores

std_life

Church

Friends

Hobbies

hrswork

Exercise

videogames

Youthgrps

acad_club

-0.5

-1

-1.5

N=91 (8%)

slide36

Comparing Local to Global

The overlap between local and global assignments is statistically significant (adjusted rand of about 15%), but the results suggest a fair amount of room for local variation on the global theme.

This suggests that, while there are some similarities, social relations might admit to patterning via a local embeddedness or a global identity.

slide37

Network Structure

  • Bourdieu’s discussion of relational fields suggests that position in the field’s status structure will correspond to the development of unique habitus, and thus distinct practices. This leaves open (at least) two possible ways to operationalize position in the field.
  • Position as regular equivalence.
    • If position relates to a generalized notion of status, then two actors with similar patterns of relations in the network should have similar behavior profiles. The mechanism here suggests aligning status interests and mimicry between those in “analogous” positions in the field.
  • Position as direct connection between actors.
    • If one learns the relevant action rules through direct interaction with others in the field, then those people who are directly connected should have the most similar behavior profiles. The mechanism here is largely one of communication, peer influence & selection.
slide38

Network Structure

Regular Equivalence through triad distributions

(0)

(1)

(2)

(3)

(4)

(5)

(6)

003

012

102

111D

201

210

300

021D

111U

120D

Intransitive

Transitive

021U

030T

120U

Mixed

021C

030C

120C

slide39

120D_S

003

021C_S

120D_E

021C_B

012_S

021C_E

120U_S

012_E

120U_E

111D_S

012_I

120C_S

111D_B

102_D

120C_B

111D_E

102_I

030T_S

120C_E

111U_S

030T_B

021D_S

210_S

111U_B

030T_E

021D_E

210_B

111U_E

021U_S

210_E

030C

021U_E

201_S

300

201_B

Network Structure

Regular Equivalence through triad distributions

slide40

Block Model: School Network Structure

Jefferson High School

Sunshine High School

School provides a good boundary for social relations

School does not provide a good boundary for social relations

slide41

Block Models

Jefferson High School

Sunshine High School

4%

34%

43%

32%

52%

33%

Image networks. Width of tie is proportional to the ratio of cell density to mean cell density.

slide42

Block Model Characteristics

Jefferson High School

Sunshine High School

slide43

Block Model: Sunshine High School

Jefferson High School

Sunshine High School

slide44

Correspondence of Field & Practice

Jefferson High School

  • Being in the same block significantly increases the likelihood of being the same behavioral cluster
    • “Locally” defined: OR = 1.13
    • “Globally” defined: OR = 1.12
  • The effect is differential across blocks:
  • Being adjacent in the network has a consistent positive effect:
    • Local: OR = 1.21
    • Global: OR = 1.35
  • SES Similarity increases the odds of being in the same behavior profile

Coefficients based on a dyad-level logistic regression model. Models control for grade and gender.

slide45

Correspondence of Field & Practice

Sunshine High School

  • Being in the same block barely increases the likelihood of being the same behavioral cluster
    • Locally defined: OR = 1.03
    • Globally define” OR = 1.02
  • The effect is differential across blocks:
  • Being adjacent in the network has a weaker, but still positive effect:
    • Local: 1.13
    • Global: 1.08
  • SES Similarity increases the odds of being in the same behavior profile

Coefficients based on a dyad-level logistic regression model. Models control for grade, race and gender.

slide46

Field, Practice & Adolescent Risk Behavior

Delinquency

Scale ranges from ‘never’ to “6 or more times” and is highly skewed. I dichotomize to identify the 25% who are most delinquent

slide47

Field, Practice & Adolescent Risk Behavior

Sexually Active

Proportion who report having had sex

Grade in school

slide48

Field, Practice & Adolescent Risk Behavior

School Attachment

Mean of 3 items:

- Happy to be at school

- Feel a part of the school

- Feel close to others at school

slide49

Field, Practice & Adolescent Risk Behavior

Jefferson High School: Delinquency

  • Core blocks in Jefferson are much more likely to be delinquent than either of the “insider” periphery blocks.
  • Periphery Insiders are 0.32 times as likely to be delinquent
  • Semi-Periphery Insiders are 0.53 times as likely to be delinquent

Models control for SES, Sex, grade in school and family structure, values are odds rations for being delinquent.

slide50

Field, Practice & Adolescent Risk Behavior

Jefferson High School: Delinquency

  • Based on the Local activity profile, students in the “Non-active” working group are significantly less likely (OR = 0.309) to be delinquent.
  • Based on the Global activity profile, students in the “Non-School Active” group are significantly more likely to be delinquent.
  • Combining block membership and behavior profiles suggest that the three sources are largely independent, with each making significant contributions to the likelihood of delinquency.
    • The substantive effect of the network position variables seems highest
    • Followed by the effect of local behavior profile
    • The global behavior effects are the least powerful.

Models control for SES, Sex, grade in school and family structure

slide51

Field, Practice & Adolescent Risk Behavior

Jefferson High School: Sexual Activity

Core block members are more likely to be sexually active than outsiders, with members of block 1 being particularly unlikely to have had sex.

Models control for SES, Sex, grade in school and family structure, values are odds ratios for being sexually active.

slide52

Field, Practice & Adolescent Risk Behavior

Jefferson High School: Sexual Activity

  • Based on the local activity profile, students in the “Non-active” working group are significantly more likely (OR = 2.13) to report having had sex.
    • Note that this correlation is opposite the general positive relation between sexual activity and delinquency in Add Health as a whole.
  • The global activity profile neatly sorts groups according to how likely they are to have sex:
    • “Low activity workers” (Or=3.56) and “Inactive & Uninvolved” (1.75) are much more likely to report being sexually active.
    • “Church going non-workers” are much less likely to report being sexually active (OR = 0.19).
  • Combining block membership and behavior profiles suggest that the three effects are largely independent, each making significant contributions to sexual activity, though”
    • The effect of global activity profile seems the strongest
    • Followed by block position
    • Then by local activity level

Models control for SES, Sex, grade in school and family structure

slide53

Field, Practice & Adolescent Risk Behavior

Jefferson High School: School Attachment

Periphery “Insiders” feel the least attached to school and core groups 6 & 7 are most attached, net of other factors.

School Attachment

4

3.8

3.6

Mean School Attachment

3.4

3.2

Semi- Periphery “Outside”

Semi- Periphery “Inside”

Periphery “Outside”

Periphery “Inside”

Core ‘2nd string”

Core Popular

Core Aloof

3

2.8

1

2

3

4

5

6

7

Block

Models control for SES, Sex, grade in school and family structure

slide54

Field, Practice & Adolescent Risk Behavior

Jefferson High School: School Attachment

School Attachment

4.1

4

3.9

3.8

3.7

3.6

3.5

3.4

3.3

3.2

3.1

3

1: Non active

Workers

2: Non-School

Active

3: Non-working

Non Religious

4: Video

Gamers

5: Church Going

School Active

  • Adding block position removes the effect of being a “gamer”
  • Global position shows that:
    • Group 1 (Low –activity church going) are more attached
    • Group 5 (Non-school active) are less attached
  • The network position effects and local cluster effects are the strongest

Models control for SES, Sex, grade in school and family structure. Cross color comparisons are statistically significant.

slide55

Field, Practice & Adolescent Risk Behavior

Sunshine High School: Delinquency

Core & Periphery members are both delinquent, though I suspect they are involved in different activities …

Models control for SES, Sex, grade in school and family structure, values are odds ratios for being delinquent.

slide56

Field, Practice & Adolescent Risk Behavior

Sunshine High School: Delinquency

Local activity effects are weak, but those in the “Non-School Active” cluster (2) are slightly more likely to be delinquent (OR = 1.32).

The global activity profile suggests a somewhat more differentiating effect:

Local behavior profile effects dissolve after adding global effects and network position, though they remain largely intact, with periphery actors having the strongest network effect.

Models control for SES, Sex, grade in school and family structure, values are odds ratios for being delinquent

slide57

Field, Practice & Adolescent Risk Behavior

Sunshine High School: Sexual Activity

Core members are the least likely to be sexually active. Those on the periphery are most likely.

Models control for SES, Sex, grade in school and family structure, values are odds ratios for being sexually active.

slide58

Field, Practice & Adolescent Risk Behavior

Sunshine High School: Sexual Activity

Students in the “Worker” cluster are more likely to report being sexually active than any of the other clusters (OR = 1.45 respectively).

The global activity profile suggests that those in “Non-School Activities” are much more likely (OR=3.28) to be sexually active, while “high-activity club” students and those who are “Inactive & uninvolved” are much less likely to be sexually active (OR = 0.46 & 0.68 respectively). Global profile behavior account for “Receiving Periphery” position effects.

Combined, the global effects seem to supercede local effects, but network position remains strong. Core members are somewhat less likely to be sexually active & sending periphery members are more likely to be sexually active, net of behavior activities.

Models control for SES, Sex, grade in school and family structure

slide59

Field, Practice & Adolescent Risk Behavior

Sunshine High School: School Attachment

School Attachment

4.3

4.2

4.1

4

3.9

Mean Attachment

3.8

3.7

3.6

3: Semi-Periphery

Receive Periph.

5: Popular Core

4: Lieutenants

Send Periph.

3.5

3.4

3.3

Cross-color comparisons are statistically significant

Models control for SES, Sex, grade in school and family structure

slide60

Field, Practice & Adolescent Risk Behavior

Sunshine High School: School Attachment

Local activity profiles suggest that those who are “Inactive & Uninvolved” & “Workers” are significantly less attached to school. This effect of local position are independent of the network position effects and largely additive.

Global activity profiles suggest that those who are in the “high activity club” profile and “TV / Video Gamers” profile are more likely to be attached to school, while those who are in “Non-school active” profile are least likely to be attached to school.

All of these effects are independent of each other and remain strong when entered simultaneously into the model.

slide61

Field, Practice & Adolescent Risk Behavior

Tentative Conclusions: Local or Global Practice Profiles?

  • For Jefferson, where the school seems to capture student social life, local behavior profiles fit much better than in Sunshine, where the school does not effectively capture student social relations.
  • This is more true for delinquency & school attachment than for sexual activity, which seems more affected by position in the global profile.
  • Looking at the global profiles, three stand out as having particularly interesting effects: High-Activity Clubs, Non-School but Active, and Inactive & Uninvolved. Interestingly, the effects differ across the two schools.
slide62

Cluster 3:

High Activity, Clubs

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

HrsWork

Arts_club

V.Games

Yth. Grps

slife_club

Hang Out

PaidWork

sport_club

Acad_club

Active Sport

-0.5

-1

-1.5

Field, Practice & Adolescent Risk Behavior

Tentative Conclusions: Local or Global Practice Profiles?

In Jefferson:

Low Delinquency

In Sunshine

Low Delinquency

Low Sexual Activity

Most Attached to school

N=2943 (21%)

slide63

Cluster 5:

Non-School Active

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

HrsWork

Arts_club

V.Games

Yth. Grps

slife_club

Hang Out

PaidWork

sport_club

Acad_club

Active Sport

-0.5

-1

-1.5

Field, Practice & Adolescent Risk Behavior

Tentative Conclusions: Local or Global Practice Profiles?

In Jefferson:

Most likely to be delinquent

Least attached to school

In Sunshine:

Most delinquent

Most sexually active

Largely Unattached

N=512 (3.5%)

slide64

Cluster 7:

Inactive & Uninvolved

1.5

1

0.5

0

TV

Drive

Chores

Church

Cycling

Hobbies

Exercise

HrsWork

Arts_club

V.Games

Yth. Grps

slife_club

Hang Out

PaidWork

sport_club

Acad_club

Active Sport

-0.5

-1

-1.5

Field, Practice & Adolescent Risk Behavior

Tentative Conclusions: Local or Global Practice Profiles?

In Jefferson:

Most likely to be sexually active

Least likely to be delinquent

In Sunshine:

Unlikely to be sexually active

Least likely to be delinquent

N=1811 (13%)

slide65

Field, Practice & Adolescent Risk Behavior

Tentative Conclusions: How do networks matter?

In both schools, the networks have a basic status hierarchy structure with a clear distinction between core and periphery, which seems important for risk outcomes.

slide66

Field, Practice & Adolescent Risk Behavior

Tentative Conclusions: Correspondence between relations and behavior

  • The weak between relational position and behavior profiles is there, but it’s weak.
    • That both relations and behaviors affect risk outcomes suggests that I might be missing a key aspect of the behavior profile. “Risk behaviors” should probably be considered as part of the profile.
    • The strength of the distinction between core & periphery AND the that adjacency is a better predictor of behavior suggests that we should conceive of network position in a way that accounts for connectivity.
      • Cohesive Blocking (Moody & White) seems a good possibility
    • -Should reconsider the meaning of a friendship nomination:
    • - Distinguish ties within and between sex and grade
    • - Account for ties that include activity values
slide67

Field, Practice & Adolescent Risk Behavior

Future (re)directions

  • The analysis thus far suggests a couple of potential avenues that might be more profitable:
  • The current model tries to ‘split the difference’ between a relational approach and a more standard social science model. Instead, it might make sense to use a completely relational model.
    • Suggests treating the entire exercise as modeling distances between actors in behavior space and relational space. This drops the problems associated with cluster analysis, but makes it harder to talk directly to policy.
  • Expand the definition of behaviors to include ‘dispositions’. This raises more measurement concerns, but fits with Bourdieu’s general frame.
  • Use the physical distance as a marker for unmeasured field factors: relate the geographical distance directly to the social distance.
  • Incorporate a model of trajectories. The current model looks at a cross-section of both relations and behaviors, but adolescents are surely moving through both of these spaces at different clips. Is there a satisfactory way to model the correspondence in trajectories through these two spaces?