sandra dika, phd assistant research professor office of institutional research and planning

Bonds and bridges:The relative importance of relations

with peers and faculty for college student achievement

Sandra Dika, PhDAssistant Research Professor

Office of Institutional Research and PlanningUniversity of Puerto Rico - Mayaguez

2008 Association for Institutional Research Forum, Seattle, WAMay 28, 2008

Dika, 2008 AIR Forum - Bonds and Bridges

Background and Framework

Influences on student achievement in college What students bring with them (background)

What students do while in college (effort and engagement)

Importance of relationships with institutional agents, especially faculty


Relationships as Social Capital

Bonding social capital – relationships with people in similar situations or of similar status to build solidarity

Bridging social capital – looser relationships that provide access to external assets and information


Research Questions

Are there differences in bonding and bridging social capital based on membership in different student groups: first year, senior, male, female, first generation, continuing generation?

What is the relative importance of bonding and bridging social capital for college student achievement, taking into account previous achievement factors and student study efforts, and does this differ by student group?


University of Puerto Rico Mayaguez

Part of UPR system (11 campuses) Over 12,000 students Bilingual Hispanic Serving Institution STEM focused (60% of undergraduates) High female participation in STEM 71% receive financial aid 35% first generation


Sample and Instrumentation

First year and senior UPRM students participating in the 2005 and 2007 administrations of the Spanish web-based NSSE (N=961) 61% women 62% first year 28% first generation

Little reliability and validity evidence for Spanish NSSE but assumed to be similar to English version; more research necessary


VariablesBackground Study Effort Social CapitalHigh school GPA Hours/wk preparing for

classAcademically focused interaction with peers

English achievement Quality of relationships with other students

Math achievement Interaction with faculty members

Spanish achievement Quality of academic advising

Parental education level

Quality of relationships with faculty members

Quality of relationships with administrative personnel/offices


Dependent variable: college GPA

Data Analysis

Hierarchical multiple linear regression in three blocks Block 1: Five background variables Block 2: Study effort Block 3: Six social capital variables

All statistical tests evaluated at the α=.05 level


Results – Frequency and Quality of Bonding with Other StudentsBonding Social Capital

Highest Levels Lowest Levels

Academically focused interaction

• Seniors • First year students• First generation students

Quality of relationships

• Seniors • First year students• First generation students• Men


Results – Frequency and Quality of Bridging with Institutional AgentsBridging Social Capital

Highest Levels Lowest Levels

Interaction with faculty

• Seniors • First year students

Quality of relationships with faculty

• Women • Men

Quality of academic advising

• First year students • Seniors

Quality of relationships with admin

• First generation students • Continuing generation students


Results - Correlations

Social capital variables were positively correlated, particularly quality of relationships

Most variables positively correlated with college GPA, especially high school GPA

First generation students: Negligible correlation with parental education Quality of relationships with faculty members higher

than standardized test scores


Results – Three Models (N=961)

Model First year Senior Men WomenFirst

generationContinuing generation

Model 1 .27*** .36*** .32*** .25*** .19*** .30***

Model 2 .31*** .36*** .33*** .27*** .22*** .33***

Model 3 .36*** .41*** .37*** .34*** .31*** .38***


Study effort predicts an additional 0 to 4 percent variance in college GPA; social capital variables predict an additional 4 to 9 percent variance

Results – First Year Students (R2=.36)

Six significant predictors: High school GPA (.31) Quality of relationships with faculty (.25) Hours per week preparing for class (.18) English achievement (.14) Math achievement (.10) Maximum parental education (.08)


Results – Senior Students (R2=.41)

Five significant predictors: High school GPA (.34) Quality of relationships with faculty (.22) English achievement (.21) Math achievement (.14) Maximum parental education (.11)


Results – Men (R2=.37)

Eight significant predictors: High school GPA (.34) Quality of relationships with faculty (.17) English achievement (.15) Math achievement (.13) Hours per week preparing for class (.13) Interaction with faculty (.11) Academically focused interaction with peers (-.10) Maximum parental education (.09)


Results – Women (R2=.34)

Eight significant predictors: High school GPA (.28) Quality of relationships with faculty (.26) Hours per week preparing for class (.15) English achievement (.14) Math achievement (.13) Quality of academic advising (-.10) Interaction with faculty (.09) Maximum parental education (.08)


Results – First Generation Students (R2=.31)

Three significant predictors: Quality of relationships with faculty (.34) High school GPA (.29) Quality of academic advising (-.11)


Results – Continuing Generation Students (R2=.38)

Eight significant predictors: High school GPA (.33) Quality of relationships with faculty (.19) English achievement (.17) Hours per week preparing for class (.16) Math achievement (.12) Interaction with faculty (.12) Maximum parental education (.11) Academically focused peer interaction (-.09)


Discussion

Importance of high school GPA as an achievement predictor seen across all groups

Background variables explained notably less variance for first generation students (Hahs-Vaughn, 2004)

Pattern of predictive influence of standardized test performance is not straightforward English vs. Spanish achievement


Discussion

Study effort predicted only 1-4% additional variance beyond background variables

Significant positive effect for first year, men, women, and continuing generation students in final model

Nature of measure may limit interpretation of these effects (5 hour spans of time, self-report)


Discussion

Student-faculty interaction as key social capital factor in predicting college GPA (Anaya & Cole, 2001; Kuh & Hu, 2001)

Quality vs. quantity of interaction with faculty (Lundberg & Schreiner, 2004; Sax, Bryant, & Harper, 2005)


Discussion

Negative coefficients for bonding social capital, quality of academic advising, and quality of relationships with administrative personnel contradict positive correlations

Multicollinearity apparently not an issue (VIF range 1.0-2.0)

Quality of academic advising – need to understand what is measured (who accesses academic advising more frequently?)


Conclusions

Student-faculty interaction is an important element in predicting student achievement, independent of class rank, gender, or first generation status As important as high school GPA for first generation

students Bonding social capital does not significantly predict

academic achievement May predict other forms of engagement and non-

cognitive outcomesDika, 2008 AIR Forum - Bonds and Bridges

Limitations

Sample from a single institution Unexplained negative effects of certain variables

Academic advising Academically focused interaction with peers

Model not inclusive of all variables that may predict student achievement

Other dependent variables not considered (retention, graduation, learning gains)


Continuing Research

Look at individual interaction variables to determine which are more effective in predicting achievement and other outcomes (e.g., discussions outside of class, discussing career plans)

Test model with other dependent variables – retention, perceived learning and development (NSSE variables)


Contact information:[email protected]: 787-265-5465

Paper and presentation will be available for download at

http://oiip.uprm.edu/beams.html


sandra dika, phd assistant research professor office of institutional research and planning

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