RELATIONSHIPS OVER TIME IN A COHORT-BASED MSW PROGRAM NOVEMBER 5, - - PowerPoint PPT Presentation

THE EVOLUTION OF STUDENT RELATIONSHIPS OVER TIME IN A COHORT-BASED MSW PROGRAM

NOVEMBER 5, 2016

REBECCA L. MAULDIN, LMSW LIZA BARROS-LANE, LMSW SARAH C. NARENDORF, PHD

PEER RELATIONSHIPS IN GRADUATE SCHOOL

• Collaborative learning
• Academic success & persistence
• Professional socialization
• Cultural competency
• Well-being, coping with stress

(Casstevens et al. 2012; Collins et al., 2010*; Grady et al, 2014; Hunt et al., 2012; Miller, 2010; Moore, 2011; Oliver 2013; Petrovich & Lowe, 2005; Rau & Heyl, 1990; Rizzuo et al., 2009; Thomas 2000)

COHORT-BASED EDUCATION

• Designed to promote student

relationships

• Students placed into groups

and take classes together as a group

• 10-26 students per cohort

Benefits

• Strong social & academic support
• High levels of academic collaboration
• Sense of belonging
• Better academic success and persistence
• Development of empathy for classmates

Potential problems

• Cliques
• Personality conflicts
• Insular learning environment
• “Merging” with other cohorts

(Lei et al., 2011; Maher, 2005, Swayze & Jakeman, 2014)

COHORT-BASED EDUCATION

COHORTS AND RELATIONSHIP CHARACTERISTICS

Homophily

• Tendency for people to form relationships with others who are similar
• Most common types of homophily are racial/ethnic and age

Multiplexity

• More than one type of interaction or role within relationship
• Associated with ↑ trust and intimacy

Cohorts provide environment for:

• Less homophily
• (Leszczensky & Pink, 2015; Morimoto & Yang, 2013;

Windzio & Bicer, 2013)

• More multiplexity (Kadushin, 2012)

RESEARCH AIMS

• Identify factors that influence the development of

peer relationships in an MSW program that uses cohort-based learning

• Understand student perspectives of their

experiences with the cohort system and peer relationships.

RESEARCH SETTING

2014 – 2016 MSW program in large public university in large metropolitan area Regular standing MSW students (n = 97 in 2014) placed in cohorts for foundation semester

• After 1st semester, students are integrated into

traditionally-scheduled courses with rest of student body

METHODS: SEQUENTIAL EXPLANATORY MIXED METHOD 4 waves of quantitative data collection

• Summer orientation (July/August 2014)
• Middle of 1st semester (Fall 2014)
• End of 1st semester (Fall 2014)
• End of 3rd semester (Fall 2015)

Multi-method qualitative data Open-ended question on 4th survey Three focus groups in September 2016

METHODS: SOCIAL NETWORK DATA

Time 1: “List the names of anyone you know who is an incoming student” Times 2-4: Roster with classmates’ names & check boxes to indicate:

• 1. Academic (I have academic discussions with this person)
• 2. Friendship (I consider this person a personal friend)
• 3. Professional (This person has influenced my professional

development)

METHODS: SOCIAL NETWORK MEASURES

Observed networks: Academic, Friendship, Professional Additional network variables:

• a. General social ties – the existence of any of the three

types of observed ties, values = 0/1

• b. Shared affiliation in student organizations
• c. Same race/ethnicity
• d. Age difference (absolute value)

METHODS: QUALITATIVE DATA

• All cohorts represented
• Data Collection
• Open ended question on survey
• Focus groups
• Analysis
• Semantic thematic analysis (Braun & Clark, 2008)

ENROLLMENT

ENROLLMENT Total Eligible: 97 Enrolled: n = 95 (97.9%) FOCUS GROUP PARTICIPATION 14 students/graduates in 3 focus groups

SAMPLE CHARACTERISTICS: FALL 2014

% (n) Sex Female 89.5 (85) Male 10.5 (10) Race/Ethnicity* Black 30.5 (29) Hispanic 18.9 (18) White 43.2 (41) Other 7.4 (7) Cohort FT, A 27.4 (26) FT, B 25.3 (24) FT, C 25.3 (24) PT, D 22.1 (21) Age, M (SD) 29.5 (9.0) t1 peers “known”, M (SD) .22 (.51)

• Note. *Race/Ethnicity

categories based on those used in academic records

METHODS: SOCIAL NETWORK ANALYSIS

Analysis in UCINET (Borgatti et al., 2002) Visualization using NetDraw (Borgatti et al., 2002) Quadratic Assignment Procedures (QAP):

• Multiple Regression
• Logistic Regression

RANOVAs – Significant increases for all types Academic: F(1.411, 135.434) = 28.507, p < .001; post hoc Bonferroni comparisons t3 sig. ↑ than t2 & t1 Friendship: F(1.018, 97.689) = 5.986, p = .016; post hoc Bonferroni comparisons t3 sig. ↑ than t2 Professional: F(2, 192) = 6.770, p = .001; post hoc Bonferroni comparisons t2 and t3 sig. ↑ than t1

AVERAGE NUMBER OF TIES PER STUDENT

GENERAL SOCIAL TIES: 1ST SEMSTER, MID-SEMESTER

GENERAL SOCIAL TIES: END OF 1ST SEMESTER

GENERAL SOCIAL TIES: END OF 3RD SEMESTER

Odds Ratios from QAP Logistic Regression Analyses Predicting Friendship Ties at the end of the 3rd semester (n = 97)

• Note. *p ≤ .05, ***p ≤ .001. p-values were determined by 10,000 permutations

Model 1 Model 2 Model 3 Difference in age 1.00 .98 .98 Same race/ethnicity 1.40** 2.03*** 1.62* Same cohort 11.99*** 2.85*** Joint Affiliation in Student Orgs 1.95** 1.71* 1st semester Academic 2.36*** 1st semester Friendship 9.36*** 1st semester Professional 1.76*** Intercept

• 1.90
• 3.41
• 3.33

LL

• 3565.14
• 1585.98
• 1297.03

R2 .003** .18*** .36***

Odds Ratios from QAP Logistic Regression Analyses Predicting Friendship Ties at the end of the 3rd semester (n = 97)

• Note. *p ≤ .05, ***p ≤ .001. p-values were determined by 10,000 permutations

Model 1 Model 2 Model 3 Difference in age 1.00 .98 .98 Same race/ethnicity 1.40** 2.03*** 1.62* Same cohort 11.99*** 2.85*** Joint Affiliation in Student Orgs 1.95** 1.71* 1st semester Academic 2.36*** 1st semester Friendship 9.36*** 1st semester Professional 1.76*** Intercept

• 1.90
• 3.41
• 3.33

LL

• 3565.14
• 1585.98
• 1297.03

R2 .003** .18*** .36***

Odds Ratios from QAP Logistic Regression Analyses Predicting Academic Ties at the end of the 3rd semester (n = 97)

• Note. *p ≤ .05, ***p ≤ .001. p-values were determined by 10,000 permutations

Model 1 Model 2 Model 3 Difference in age .99 .99 .99 Same race/ethnicity 1.17* 1.23* 1.07 Same cohort 8.71*** 4.85*** Joint Affiliation in Student Orgs 1.97*** 1.86*** 1st semester Academic 1.78*** 1st semester Friendship 2.20*** 1st semester Professional 1.67** Intercept

• 1.22
• 2.08
• 2.02

LL

• 4789.02
• 2612.55
• 2424.09

R2 .001* .20*** .25***

Odds Ratios from QAP Logistic Regression Analyses Predicting Academic Ties at the end of the 3rd semester (n = 97)

• Note. *p ≤ .05, ***p ≤ .001. p-values were determined by 10,000 permutations

Model 1 Model 2 Model 3 Difference in age .99 .99 .99 Same race/ethnicity 1.17* 1.23* 1.07 Same cohort 8.71*** 4.85*** Joint Affiliation in Student Orgs 1.97*** 1.86*** 1st semester Academic 1.78*** 1st semester Friendship 2.20*** 1st semester Professional 1.67** Intercept

• 1.22
• 2.08
• 2.02

LL

• 4789.02
• 2612.55
• 2424.09

R2 .001* .20*** .25***

Odds Ratios from QAP Logistic Regression Analyses Predicting Professional Ties at the end of the 3rd semester (n = 97)

• Note. *p ≤ .05, ***p ≤ .001. p-values were determined by 10,000 permutations

Model 1 Model 2 Model 3 Difference in age 1.02 1.02 1.02 Same race/ethnicity .98 1.02 .97 Same cohort 3.38*** 2.39*** Joint Affiliation in Student Orgs 1.16*** 1.11 1st semester Academic 1.04 1st semester Friendship 3.59*** 1st semester Professional 1.27 Intercept

• .72
• 1.77
• 1.91

LL

• 5840.52
• 2968.85
• 2613.95

R2 .006** .06*** .11***

Odds Ratios from QAP Logistic Regression Analyses Predicting Professional Ties at the end of the 3rd semester (n = 97)

• Note. *p ≤ .05, ***p ≤ .001. p-values were determined by 10,000 permutations

Model 1 Model 2 Model 3 Difference in age 1.02 1.02 1.02 Same race/ethnicity .98 1.02 .97 Same cohort 3.38*** 2.39*** Joint Affiliation in Student Orgs 1.16*** 1.11 1st semester Academic 1.04 1st semester Friendship 3.59*** 1st semester Professional 1.27 Intercept

• .72
• 1.77
• 1.91

LL

• 5840.52
• 2968.85
• 2613.95

R2 .006** .06*** .11***

Regression coefficients from Double Dekker Semi- Partialling QAP Multiple Regression Analyses Predicting Multiplex Relationships at the end of the 3rd semester

(n = 97)

• Note. *p ≤ .05, ***p ≤ .001. p-values were determined by 10,000 permutations

Model 1 Model 2 Model 3 Difference in age

• .005*
• .005
• .003

Same race/ethnicity .11*** .13*** .06* Same cohort .93*** .42*** Joint Affiliation -Student Organization .25*** .19*** 1st semester Academic .34*** 1st semester Friendship .99*** 1st semester Professional .32*** Intercept .36*** .16*** .18*** R2 .007*** .25*** .39***

Regression coefficients from Double Dekker Semi- Partialling QAP Multiple Regression Analyses Predicting Multiplex Relationships at the end of the 3rd semester

(n = 97)

• Note. *p ≤ .05, ***p ≤ .001. p-values were determined by 10,000 permutations

Model 1 Model 2 Model 3 Difference in age

• .005*
• .005
• .003

Same race/ethnicity .11*** .13*** .06* Same cohort .93*** .42*** Joint Affiliation -Student Organization .25*** .19*** 1st semester Academic .34*** 1st semester Friendship .99*** 1st semester Professional .32*** Intercept .36*** .16*** .18*** R2 .007*** .25*** .39***

QUALITATIVE RESULTS

The cohort effect held because of friendships developed during their time together. “…My cohort created strong bonds, relationships. We all tend to flock to one another in classes outside of foundation.” “We really bonded. As foundation progressed, we felt more comfortable sharing & discussing topics amongst each other. Even into our second year , I still feel excited to see people from my cohort in my foundation semester. There is a special bond

• there. When people from my cohort are in my classes now, I

feel more at ease in those classes.”

QUALITATIVE RESULTS

Cohort effect held because it was difficult to “un-cohort”. “It worked well during the semester , but in spring when we "un-cohorted" it was difficult only knowing 20 other of the 150 students. Usually only knew 2-3 people per class.” “I liked the community feel that I got from my cohort; but

• nce classes started mixing cohorts I felt like I didn't know

any of them. It would have been nice to have more

• pportunities to meet all of our entering class.”

QUALITATIVE RESULTS

Personal relationships were the most important. Used those to grow the other types of relationships

“All three types of networks helped me grow. However , without the personal commitment, it would not have been as great” “Personal relationships were most important because grad school was just so hard. The encouragement from our peers kept us going.”

DISCUSSION

• Cohort Effects remained, even 2 semesters after

classes were no longer taken by cohort

• Students perceived cohort particularly important for

development of initial friendships.

• Friendships are an important foundation for other

types of relationships to form

• Initial cohort membership difficult to transcend in

developing new peer relationships

LIMITATIONS

• Generalizability
• No comparison program
• Focus groups smaller than the recommended 8-12
• Students who returned to participate in the focus groups

may have stronger ties to the school

IMPLICATIONS

• Our research supports the use of cohorts as a part of the

implicit curriculum to enhance peer relationships

• One semester was sufficient to produce lasting effects
• Student supports for transition from cohort-based to

traditional learning models are indicated

• Personal relationships should be acknowledged as

important to the development of academic and professional support

• Network approach as assessment tool for MSW

programs

ACKNOWLEDGEMENTS

Special thanks to : Zuniga y Rivero Foundation University of Houston Graduate of Social Work Graduate students who assisted with data entry