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Rachel A.Smith, SIT Conference 2010 Acknowledgements Only Connect: Funded through the 2009-2010 Paul P. Fidler A Mixed Methods Study of How Grant from the National Resource Center for First-Year Students Create The First-Year

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Rachel A.Smith, SIT Conference 2010 1

“Only Connect”:

A Mixed Methods Study of How First-Year Students Create Residential Academic and Social Networks

Rachel A. Smith Baruch College, CUNY rachel.smith@baruch.cuny.edu Conference on Students in Transition November 14, 2010

Acknowledgements

Funded through the 2009-2010 Paul P. Fidler

Grant from the National Resource Center for The First-Year Experience and Students in Transition

Thank you to my participants and staff in

residence life and the learning communities

  • ffice

Introduction

First-year college students: new environment,

need to create academic/social relationships

College administrators structure environments

where students are more likely to create certain relationships (e.g. learning communities) Littl i k b t h th ifi “ t k ”

Little is known about how these specific “networks”

  • f relationships facilitate educational outcomes

Inside the Box

Student Characteristics

Gender

Student Outcomes

Persistence Social Integration Learning Communities

Institution

Race Family Income HS GPA Completion Transfer Learning Involvement Academic Integration

Theoretical Framework

Academic and/or Social Integration (Tinto, 1993)

  • If students are connected to the institution

academically and/or socially, they will be less likely to leave it likely to leave it

“Involvement” & “Engagement” as a proxy for

learning & a measure of student success (Kuh,

Astin, etc.)

Theoretical Framework

Learning Community definitions & research

  • Participation in LCs is generally associated

with positive educational outcomes, although effect sizes may be relatively small

Social Network Analysis (Wasserman & Faust, 1994; Thomas, 2000)

  • Homophily can be a predictor of tie formation

(McPherson, Smith-Lovin, & Cook, 2001)

  • Position in network associated with
  • utcomes
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Rachel A.Smith, SIT Conference 2010 2

Research Questions

What is the structure of students’ residential

academic and social networks? Why? What relationship does structure have with a learning community environment?

What institutional structures influence

network formation?

Are students’ positions in their residential

networks related to educational outcomes?

(first-semester GPA & second-semester involvement)

Methods

Case study: two residential communities at

  • ne institution (mid-size private in the NE)
  • ver 1.5 years
  • Arts-themed learning community (“ProArte”)

R d i t id h ll fl (“T l 2”)

  • Random-assignment residence hall floor (“Tyler 2”)

Mixed methods (“triangulation”)

  • Paper surveys
  • Individual Interviews
  • Participant observation

Methods: Surveys

Two paper surveys

(Fall 2006, Spring 2007)

Based on common

network questions,

  • 1. On average, how many hours

per week have you recently spent socializing with each person?

Name Number of Hours

network questions, roster style

Response rates: 92%

and 85%

76 LC students 64 random-assignment

students

Lastname, Firstname Lastname, Firstname Lastname, Firstname

Methods: Qualitative

Individual Interviews (30-60 minutes each)

  • 45 in Fall 2006
  • 42 first follow-up in Spring 2007

20 d f ll i F ll 2007

  • 20 second follow-up in Fall 2007

Participant Observation

  • Floor meetings, field trips, classes, hanging
  • ut

My Identity

Methods: Research Population

Generally reflected demographics of

institution (Qualitative)

50% men; 50% women (50% / 50%) Race/Ethnicity:

72 1% White (75 1%)

  • 72.1% White (75.1%)
  • 15.7% Asian/Asian-American (15.9%)
  • 6.4% Latino (4.5%)
  • 5.7% Black (4.5%)

80% First-year students (82%)

Methods: Analysis

Quantitative: survey data computerized and

analyzed using Ucinet1, NetDraw2,& SAS

Qualitative:

  • interviews recorded and transcribed
  • field notes typed
  • data categorized into 235 codes
  • analyzed for themes

Brought types of data together for joint

analysis

1 (Borgatti, Everett, & Freeman, 2002) 2 (Borgatti, 2002)

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Rachel A.Smith, SIT Conference 2010 3

“Network”: actors + relations Actors or Nodes Relational tie

Methods: Social Network Analysis

Tie strength/value Directionality Sociogram

Density as an Indicator of Student Integration

Measures the amount of student interaction

in a particular community

Density = reported / possible ties (normalized

y p p ( to account for differential network size)

Symmetrized & dichotomized ties Greater density = greater integration

Results: Social, Fall 2006

ProArte denser than Tyler 2 Densities: ProArte – 0.1740 Tyler 2 – 0.1587 Red: Female Asian American Blue: Male Asian International Black Latino/a White ProArte Tyler 2

Results: ProArte Social Fall 2006

Add networks and

quotes

Most of my friends are people in the [ProArte] learning community, like right now. There was a lot of forced stuff in the beginning, then we all just kind of made friends anyway. … I guess it was a little necessary, when we were first starting out here, It was, I mean at least the girls’ floor was a family. … we were almost like It’s kind of divided I think on the floor at least, between the learning community kids and the other kids. Like I find myself hanging out with the we were first starting out here, everyone was like “Yeah! Yeah! [ProArte]! We don’t know what it means.”

  • Cindi

(November 10, 2006) was a family. … we were almost like a sorority, even though it wasn’t really a sorority, it’s just that we were all there together, doing art. It was cool.

  • Georgina

(November 2, 2007) Like I find myself hanging out with, the

  • nly people I hang out with on the floor

are in the learning community, and everyone else is off doing their own thing.

  • Diego

(October 20, 2006)

  • Group Identity
  • Women’s floor cohesion
  • Men’s floor divisions

Results: Tyler 2 Social Fall 2006

It’s like everyone is each other’s best friend. We have pride in our

  • floor. We’re like “[Tyler 2]”! And,

uh, I tell people, everyone goes to the lounge. I mean so many people, you know, everyone’s friendly with each other Lauren had a “sarcastic sense

  • f humor … that kind of gets

Abigail, as a crew team member, had to go to bed early and spent friendly with each other. Everyone seems to get along. It’s nice. It’s positive energy. I like that. I’ll just walk to the lounge and just hang out with

  • ther people that hang out there.
  • Derek

(September 29, 2006)

  • The “Lounge Group”

lost on people sometimes” (September 29, 2006)

  • Feeling peripheral

had to go to bed early and spent a lot of time off the floor.

Results: Academic, Fall 2006

ProArte denser than Tyler 2 Academic networks less dense than social networks Densities: ProArte – 0.0853 Tyler 2 – 0.0497 ProArte Tyler 2

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Rachel A.Smith, SIT Conference 2010 4 Results: Tyler 2 Academic Fall 2006

I get distracted and I get excited. I’m like, “Yeah! People!” And, then, I’ll like talk about something

  • else. … I don’t think anyone else
  • n this floor is taking any of the

same classes that I’m taking. I know there’s a few [art and Well like last weekend I had to do, all the foundation classes have to do their sketch book of 50 drawings of stuff around campus. So she [Becky] went around with me like taking pictures of all the stuff like helping me find things know there s a few [art and performance] people, like Becky. She, you know, her homework’s doing the color wheel, and like, I don’t really, you know, have a color wheel.

  • Lauren

(September 29, 2006)

  • Studying with others doesn’t work

stuff like helping me find things. And then this weekend I have to make a video for one of my classes and um she’s going to come with me and be in it. And like she had a, she has to do like pictures and I would do stuff and she’d take pictures of it.

  • Kathy

(November 10, 2006)

  • A few with compatible majors

worked together

Results: ProArte Academic Fall 2006

I just walk to their [classmates’] door and we like work things out

  • together. And we actually are

doing group projects in English

  • now. So it’s like I’m with Stacey

and Oriana like they all live We’ll all go in our lounge, and we’ll do it [homework] together, but we’re not like actually working together, like, somebody will be sewing, somebody will be building something for 3-D, somebody will be You work together, you work as a group, you know, because you want to help each other. Because from helping that person, you’re going to grow as a person, you k It’ titi it’ and Oriana, like they all live, Stacey lives right next door and Oriana’s like two doors down, so we can like work in the lounge or in one of our rooms or whatever. It’s a way to get to know people

  • n the floor that I probably

wouldn’t get to know on my own.

  • Elizabeth

(November 15, 2006)

  • Studying via LC courses

g y drawing for foundation, like I’ll be doing my 2-D stuff. So it’s fun. So we’re always in the lounge until like two in the morning, all just there. Because none of us can do art work in our rooms for some reason, it’s such a small cramped space.

  • Sarah

(November 11, 2006)

  • Role of space
  • know. It’s very competitive, it’s

intense, but you, you make the best

  • f it.
  • Juan

(December 1, 2006)

  • Competition / feedback in arts majors

Results: Social Networks Spring 2007

ProArte denser than Tyler 2

ProArte – 0.1449 Tyler 2 – 0.1215

Both networks less dense than their respective Fall social

networks (Tyler 2 not significant difference)

ProArte Tyler 2

Results: Academic Networks Spring 2007

ProArte density decreased (0.0505), while Tyler 2

density increased (0.0600, not significant)

ProArte Tyler 2

Results: Change Over Time According to Students

Social change:

Set group of

friends—“real friends real friends”

Too busy to make

new friends

Larger groups

fractured

Results: Change Over Time According to Students

Academic change: P A t d t f

ProArte—need to focus

  • n profession/major

Tyler 2—understand

academic expectations, need to study more use friends on floor

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Rachel A.Smith, SIT Conference 2010 5 Results: Change Over Time ProArte Multiplex Ties

Fewer multipurpose (red) ties More divided by academic (green) and social (blue)

107 113 117 119 121 124 125 128 132 133 134 136 139 140 142 143 145 148 149 153 154 155 167 109 111 117 131 132 134 135 140 141 142 143 145 148 149 153 154 155 163 164 166 167 182 101 102 103 104 105 106 108 109 110 111 112 114 115 116 118 120 122 123 126 127 129 130 131 135 137 138 141 143 144 146 147 150 151 152 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 172 174 176 178 180

Fall 2006 Spring 2007

101 102 103 104 105 106 107 108 110 112 113 114 115 116 118 119 120 121 122 123 124 125 126 127 128 129 130 133 134 136 137 138 139 144 145 146 147 150 151 152 156 157 158 159 160 161 162 165 168 169 170 172 174 176 178 180

Results: Change Over Time Tyler 2 Multiplex Ties

More multipurpose (red) ties More academic only (green) in spring than in fall

203 215 217 222 225 231 233 241 242 243 245 248 254 207 208 209 211 215 223 225 228 234 236 244 246 250 253 263 265 269

Fall 2006 Spring 2007

201 202 203 204 205 206 207 208 209 210 211 212 213 214 216 218 219 220 221 223 224 226 227 228 229 230 232 234 235 236 237 238 239 240 244 246 247 248 249 250 251 252 253 255 257 259 261 263 265 201 202 203 204 205 206 210 212 213 214 216 217 218 219 220 221 224 226 227 229 230 231 232 233 234 235 237 238 239 240 241 242 244 245 247 248 249 251 252 254 255 257 261 265 256 267 258

Discussion

In this study, LC (even in a simple form)

seemed to influence the speed of academic integration

  • LC: academic & social ties in first semester

LC: academic & social ties in first semester, then emphasis on major in second semester (no LC courses)

  • Tyler 2: social ties in first semester, turn to

multiplex ties in second semester, emphasis

  • n major in second year

Possible Role of Homophily in Network Formation

“Homophily” = being “like” someone

(e.g. same gender, race, major, class year)

Do students create academic or social Do students create academic or social

relationships based on homophily? If so, what kind(s) of homophily?

Analysis: MRQAP regression

(matrices predict a resulting matrix)

Results: Homophily

Variable b

β

b

β

Gender 0.15*** 0.20 0.10*** 0.17 Race 0.05* 0.07 0.01 0.01 Income 0.01 0.01

  • 0.00
  • 0.00

M j 0 19*** 0 14 0 18*** 0 19 MRQAP Analysis Using Homophily to Predict Existence of Network Ties in the Learning Community, Fall 2006 Social Network Academic Network Major 0.19*** 0.14 0.18*** 0.19 Class Year 0.15*** 0.19

  • 0.01
  • 0.01

Learning Community Member 0.11*** 0.15

  • Enrollment in LC Music Appreciation
  • 0.02
  • 0.03

Enrollment in LC Writing

  • 0.04**

0.07 R2 0.13*** 0.07*** *p<.05; **p<.01; ***p<.001.

Summary of Results: Homophily

Social Ties Academic Ties Learning Community Gender, major, class year, LC membership Gender, major, LC writing course Race class year Major class year Random- Assignment Floor Race, class year Major, class year, gender

Changes in second semester:

  • For LC, gender and class year became negative in academic model
  • For RA Floor, gender not significant in academic model

Sm all r 2 value

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Rachel A.Smith, SIT Conference 2010 6 Summary of Results: Institutional Factors

Major socialization & decision-making

  • personal & professional identity
  • anxiety
  • other influences (family, culture, finances)
  • navigating major & other interests

Some evidence institutional regulations

constrained ties with non-majors

Results: Network Position and Fall 2006 GPA

Variable b

β

Academic Closeness Centrality

  • 0.00
  • 0.10

(0.01) Learning Community 0.03 0.04 (0.27) Student Characteristics (%) Gender Female 0.15* 0.17 (0.07) Race/Ethnicity Students of Color

  • 0.09
  • 0.10

(0.08) High School GPA Below 3.5

  • 0.27***
  • 0.32

(0.07) Class Year Second-year and above 0 26** 0 24 OLS Regression Predicting Fall 2006 GPA

Academic closeness

centrality not predictive of GPA

Second year and above 0.26 0.24 (0.09) Academic Major Arts

  • 0.05
  • 0.06

(0.07) Sciences

  • 0.17
  • 0.13

(0.10) Enrolled in LC Writing Course 0.24* 0.19 (0.11) Intercept 3.34*** (0.09) F value 4.48*** R2 0.25

  • Adj. R2

0.19 N 134 Notes : Standard errors are shown in parentheses. OLS = ordinary least squares; Adj. = Adjusted Reference groups are: Gender (male); Race/Ethnicity (White) High School GPA (3.5 and above); Class Year (First-year); Academic Major (Other) *p<.05; **p<.01; ***p<.001.

Higher GPA:

female, second-year and above, LC writing course

Lower GPA:

HS GPA below 3.5

Results: Network Position and Spring 2007 Campus Involvement

Social closeness centrality

predictive of campus involvement

Variable b

β

Social Closeness Centrality 0.21* 0.33 (0.09) Learning Community

  • 2.20
  • 0.16

(1.94) Student Characteristics (%) Gender Female 1.37 0.10 (1.12) Race/Ethnicity Students of Color

  • 1.40
  • 0.09

(1.22) High School GPA Below 3.5

  • 3.50**
  • 0.27

(1.12) Class Year OLS Regression Predicting Spring 2007 Semester Campus Involvement

involvement

Greater involvement:

second-year and above, sciences

Less involvement:

HS GPA below 3.5

Second-year and above 4.25** 0.26 (1.42) Academic Major Arts 0.78

  • 0.06

(1.19) Sciences 3.39* 0.17 (1.68) Intercept

  • 1.11

(3.37) F value 3.71*** R2 0.19

  • Adj. R2

0.14 N 134 Notes : Standard errors are shown in parentheses. OLS = ordinary least squares; Adj. = Adjusted Reference groups are: Gender (male); Race/Ethnicity (White) High School GPA (3.5 and above); Class Year (First-year); Academic Major (Other) *p<.05; **p<.01; ***p<.001.

Conclusions

The learning community appears to

facilitate a greater number of academic and social ties among students during the first semester

Some evidence suggests restrictive major

i t i i requirements increase in-group interactions at the expense of out-group connections

Having an initially central network position

is related to having higher second- semester campus extracurricular involvement

Limitations & Future Directions

Not generalizable Student-reported outcomes; not able to use

persistence as a dependent variable

Future study with other student populations,

i tit ti t l i it t institution types, learning community types, and other student communities; administrative structures

Student interactions across diversity Development of SNA as an assessment tool

References

Borgatti, S.P. (2002). NetDraw: Graph Visualization Software. Harvard, MA: Analytic Technologies. Borgatti, S.P., Everett, M.G. & Freeman, L.C. (2002). Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies. McPherson, M., Smith-Lovin, L., & Cook, J.M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415-444. Sociology, 27, 415 444. National Survey of Student Engagement. http://nsse.iub.edu/ Thomas, S.L. (2000). Ties that bind: A social network approach to understanding student integration and persistence. The Journal of Higher Education, 71(5), 591-615. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition, 2nd Ed. Chicago: The University of Chicago Press. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. New York: Cambridge University Press.