Differential Knowledge concerning Students
in an Academic Institution

The Relative Effects of Interaction Patterns, Network Position, Status-Role,
Institutional Experience, and Independent Invention

by

John B. Gatewood and Dawn E. Murray

[ Copyright (c) 1999, John B. Gatewood & Dawn E. Murray]

Presented at the 98th meetings of the American Anthropological Association, Chicago, Illinois, November 17-22, 1999 (based on Dawn Murray's M.A. Thesis at Lehigh University).

Abstract
Contemporary cognitive anthropology is increasingly studying the social organization or the distribution of knowledge. Here, we describe a general method for coupling consensus analysis with social network data and illustrate the approach using a close-to-home organizational culture as a case in point. The admissions process in a mid-sized, private university involves various personnel. To ascertain the extent to which administrators and faculty share a common culture regarding their students, we gave 51 "admissions-involved" people a multiple-choice quiz concerning characteristics of the student body. Then, we collected demographic and social network data to test five plausible hypotheses concerning how people learn their institutional culture. (1) People learn the institutional culture dyadically, from those individuals with whom they interact more frequently. (2) Individuals who occupy more central positions in the organization's social network learn the institutional culture better. (3) Different status groups within the university (admissions office/deans/faculty) develop sub-cultural understandings based on their roles vis-a-vis students. (4) Learning the institutional culture is a matter of multi-source, diffuse saturation (measured by variables such as time spent at the university and the number of other people known). And, (5) knowledge of students arises by "independent invention" based on how one interacts with (similar) students. The basic approach could be used to study the organization of knowledge in any bounded social group.


Introduction

Almost forty years ago, Anthony Wallace (1961) and John Roberts (1964) argued that culture is not replicated uniformly, rather it is distributed differentially among individuals. Until the 1980s, anthropologists generally lacked the tools to follow up on Wallace's and Roberts' conceptual point that we should be studying the organization of diversity. Within the last fifteen to twenty years, however, researchers in social network theory and in cognitive anthropology have harnessed computer technology to make the tedious calculations more feasible, and during this same time span, major advances were made in sociometric characterizations of networks as well as in cultural consensus theory.

This paper illustrates how social network theory and cultural consensus theory can be conjoined to study the social organization of knowledge within an institution. The institution we chose to study was our own university, but the general approach could easily be modified for any formal organization or bounded social group.

We are not the first to conjoin these two theoretical/methodological perspectives, but our study differs from others' work in two interesting respects. First, whereas previous researchers have focused on the relation between position in the social network and knowledge of the social network, we are interested in how position in a social network affects knowledge of some substantive domain. Second, our goal is to develop methods for determining the relative effects of network, status, demographic-biographic, and experiential measures with respect to how well each accounts for the distribution of substantive knowledge.
 

Questions and Hypotheses

General Question

What influences the distribution of knowledge in an organization?

Specific Research Questions

In an academic institution (Lehigh University), among those people directly involved in undergraduate admissions ...
A. Do faculty-staff share a single cultural model of the typical Lehigh student, are there sub-cultural models, or are faculty-staff opinions concerning students not standardized at all?

B. If there is a widely shared model of the typical student, what factors predict how well individual faculty-staff learn the prevailing view?

C. Is the model learned primarily from others in the faculty-staff peer group [DIFFUSION], or is it learned primarily through direct experience with students [INDEPENDENT INVENTION]?

Hypotheses

Similarity with respect to models of the typical Lehigh student among admissions-involved faculty and staff is due to:

Research Design (non-experimental, correlational study)

The Setting

The research was done at Lehigh University--an easily accessible, private, coeducational university with about 4400 undergraduate students, about 2000 graduate students, and about 400 full-time faculty. Each year, Lehigh's admissions personnel choose an entering class of about 1000 from among 8000+ applicants. The entering class usually has a combined SAT range from 1150-1330, and are split among the three undergraduate colleges in roughly the following proportions:
ca. 41% enter in the College of Arts and Sciences
ca. 38% enter in the College of Engineering
ca. 21% enter in the College of Business

The Sample

Preliminary. Five informants, known for their routine responsibilities in undergraduate admissions, were asked to FREE-RECALL an exhaustive list of individuals who are "directly involved in undergraduate admissions" at Lehigh. Their lists could include faculty, students, staff, coaches, admission professionals, or anyone else they could think of who had direct involvement in the admission process. The preliminary informants generated a composite list of 63 names (with a large amount of overlap).

Final. Weeding out coaches, secretaries, and a few faculty, the final sample of 51 "admissions-involved" people was composed as follows:

10 Admissions Office staff
7 Other Administrators (Registrar, Financial Aid, etc.)
6 Deans (two from each undergraduate college)
28 Faculty (12 Arts and Sciences, 11 Engineering, 5 Business)

Data Collections (see Appendix A for the forms themselves)

Demographic and Biographic characteristics of the informants themselves ... age, gender, years at Lehigh, years in current position, and so forth.

"Knowledge Quiz" concerning Lehigh students ... kinds of students who apply to Lehigh, kinds of students who accept admission offers, other schools with whom Lehigh competes for students, attributes of Lehigh that influence (positively and negatively) students decisions to apply and/or accept, and so forth.

Social Network Data among the "admissions-involved" group ... who interacts with whom and how often.

Informants' Familiarity with Students ... average number of students seen one-on-one per week, number of students' hometowns known, number of students' summer plans known, and so forth.

Analytical Steps and Procedures

STEP 1. Do a consensus analysis of the "knowledge quiz" data for the whole sample.

STEP 2. IF THERE IS AN OVERALL CONSENSUS, then the hypotheses can be tested easily, and the relative strengths of the factors can be compared using a 'proportion of variance explained' logic.

For Hypothesis A. Construct an actor-by-actor similarity matrix based on matches in the "knowledge quiz," then correlate that matrix with the actor-by-actor frequency of interaction matrix. If relation is significant, calculate r2.

For Hypothesis B. For each actor, calculate his/her network centrality (degree, betweenness, etc.), then correlate those scores with actors' cultural competence scores from the consensus analysis. If relation is significant, calculate r2.

For Hypothesis C. Compare cultural competence scores for different status groups via t-tests or oneway analysis of variance. If the group-group differences are significant, calculate estimated- omega2.

For Hypothesis D. Correlate actors' "experience at Lehigh" measures (years at Lehigh, percentage of other actors known, level of involvement with admissions issues, etc.) with their cultural competence scores. If relation is significant, calculate r2.

For Hypothesis E. Correlate actors' "familiarity with students" measures with their cultural competence scores. If relation is significant, calculate r2.
 

STEP 2'. IF THERE IS NO OVERALL CONSENSUS, then much of the analytical effort shifts to identifying the different sub-cultural groups, and the relative strengths of the hypothesized factors cannot be assessed.

For Hypothesis A. Construct an actor-by-actor similarity matrix based on matches in the "knowledge quiz," then QAP-correlate that matrix with the actor-by-actor frequency of interaction matrix [same as Step 2B, above]. If relation is significant, calculate r2.

For Hypothesis B. The expected relation between network centrality and knowledge changes when there is no overall consensus. High centrality individuals will be atypical of any given sub-group, i.e., such individuals 'know too much' to be typical of any one group because of their mediating role. Under these conditions, network centrality and sub-group cultural competence scores should be inversely related; and the main analytical task is to identify sub-group boundaries: (1) For each actor, calculate measures of his/her network centrality. (2) Identify sub-groups in the sample based on clique, k-plex, or component analyses of the social network data. (3) For each sub-group identified, and including high centrality individuals who connect different groups to one another, perform a separate consensus analysis. [High betweenness centrality individuals are likely to be included in more than one such analysis.] (4) For each identified sub-group, correlate actors' overall network centrality scores with their sub-group's "knowledge quiz" consensus scores. If relations are significant, calculate r2 (but conclusions will be for each sub-group separately).

For Hypothesis C. Dis-aggregate the sample into homogeneous status-role groups (e.g., admissions staff vs. faculty vs. administrators), and perform separate consensus analyses (one for each status group) of their "knowledge quiz" data. If status-role is a relevant factor, then each group should show a consensus of its own. If status groups do not show consensus among themselves, then status would be rejected as a relevant factor.

For Hypothesis D. For each sub-group, correlate actors' "experience at Lehigh" measures (years at Lehigh, percentage of other actors known, level of involvement with admissions issues, etc.) with their cultural competence score. If relations are significant, calculate r2 (but conclusions will be for each sub-group separately).

For Hypothesis E. For each sub-group, correlate actors' "familiarity with students" measures with their cultural competence scores. If relations are significant, calculate r2 (but conclusions will be for each sub-group separately).
 

Findings

PRELIMINARY ISSUE

Is there a single cultural model of the typical Lehigh student? Is there a consensus among the sample of 51 admissions-involved faculty and staff?

Test: Consensus analysis of the 30-item, multiple choice "knowledge quiz" data.

Findings:
 
Eigenvalues:
Factor Value Percent Cum % Ratio
1: 19.144 76.2 76.2 4.992
2: 3.835 15.3 91.5 1.798
3: 2.133 8.5 100.0
25.111 100.0

Average Competence Score = 0.601
Std Dev of Scores = 0.121

Conclusion: YES... The patterning of agreement among the 51 admissions-involved informants indicates that they deviate randomly around a single model of culturally "correct" answers concerning the typical Lehigh student.
 
 

HYPOTHESIS A

DYADIC INTERACTIONS WITHIN THE ADMISSIONS NETWORK: faculty-staff's beliefs resemble those in their peer group with whom they interact more often.

Test: QAP-correlation between the actor-by-actor similarity matrix based on matches in the "knowledge quiz" and the frequency of interaction matrix.

Findings:
 
(Significance) QAP-r = .137 p = .028
(Strength) QAP-r2 = .019

Conclusion: TRUE... Dyadic interactions within the admissions network have a significant but very weak effect in explaining shared understandings of the typical Lehigh student.
 
 

HYPOTHESIS B

POSITION IN THE ADMISSIONS NETWORK: Faculty-staff in the organization through whom more information passes tend to have more "accurate" (more representative of their group) beliefs than people who are peripheral in the group.

Test: Correlation between measures of network centrality and cultural competence scores.

Findings:
 
(Significance) Consensus Factor 1
Information centrality .284 p < .05
Degree centrality .171 n.s.
Flow-Betweenness centrality .140 n.s.
Betweenness centrality .090 n.s.
(Strength) Information centrality: r2 = .081

Conclusion: TRUE... Position in the admissions network has a significant effect on one's grasp of the 'typical Lehigh student' model, at least when the measure is "information centrality" (Stephenson & Zelen, 1989). This measure of one kind of Diffusion accounts for 8% of the variance in cultural competence scores.
 
 

HYPOTHESIS C

STATUS AND ROLE IN THE ORGANIZATION: Faculty-staff occupying similar structural roles in the organization develop similar understandings (and these contrast from the understandings of other such groups).

Test: Compare mean cultural competence scores of status-groups using oneway analysis of variance.

Findings:
 
(Significance)
ANOVA with Four Groups: F = .061, df = 3 / 47, p = .980
10 Admissions Staff .6120
7 Other Administrators .6114
6 College Deans .5900
28 Faculty .5982
ANOVA with Two Groups: F = .051, df = 1 / 49, p = .822
23 Administrators & Deans .6061
28 Faculty .5982
(Strength) Irrelevant, since not significant.

Conclusion: FALSE... Status-role within the organization has no effect with respect to one's grasp of the 'typical Lehigh student' model.
 
 

HYPOTHESIS D

SATURATION IN THE ORGANIZATION'S MILIEU: Faculty-staff learn the organizational culture through diffuse, multi-source saturation based on their total experiences in the organization.

Test: Correlations between measures of "experience with Lehigh" and cultural competence scores.

Findings:
 
(Significance) Consensus Factor 1
Gender -.130 n.s.
Admissions activity level .126 n.s.
Age -.083 n.s.
Child(ren) attended L.U. .082 n.s.
Years in current position -.012 n.s.
Years at L.U. .006 n.s.
Attended L.U. -.003 n.s.
(Strength) Irrelevant, since not significant.

Conclusion: FALSE... Saturation in the organization's milieu has no discernible effects on one's grasp of the 'typical Lehigh student' model. Varying degrees of "exposure" to Lehigh do not account for degrees of cultural competence.
 
 

HYPOTHESIS E

FAMILIARITY WITH (SIMILAR) STUDENTS: Faculty-staff's models of students resemble one another because the students they deal with are similar.

Test: Correlations between measures of "familiarity with students" and cultural competence scores.

Findings:
 
(Significance) Consensus Factor 1
Knowing students' hometowns .387 p < .05
Knowing students' summer plans .184 n.s.
Average hours per week with students .094 n.s.
Maximum hours per week with students .093 n.s.
Students known: faces & names .083 n.s.
Maximum number of students seen per week .031 n.s.
Average number of students seen per week .021 n.s.
(Strength)
Knowing students' hometowns: r2 = .150

Conclusion: TRUE... Familiarity with (similar) students has a significant effect on one's grasp of the 'typical Lehigh student' model. In particular, the more students' hometowns one knows, the more likely he/she is to share the consensus model of Lehigh students. This Independent Invention measure accounts for 15% of the variance in cultural competence scores.
 

Summary

A combination of data collection methods and analytical procedures were used to study the organization of knowledge within an academic institution. The particular domain of knowledge investigated was "characteristics of the typical undergraduate student," but it could have been any topic about which most members have opinions. And for that matter, the general approach could be applied to virtually any bounded social group.

Because the sample of 51 admissions-involved faculty and staff did show consensus around a single cultural model, testing the five specific hypotheses was greatly simplified. All things considered, it would appear that the way in which faculty-staff interact one-on-one with students is the single biggest factor affecting how well they "learn" the cultural model concerning the typical Lehigh student. Faculty-staff who get to know students' personal histories (know many students' hometowns) tend to better exemplify the consensus model of students. For this reason, we suggest that INDEPENDENT INVENTION is more responsible for the distribution of the cultural model than is diffusion, i.e., firsthand experience with students themselves is more important than hearsay from one's peers. On the other hand, sharing of information within the social network of admissions-involved people also has significant effects. In particular, the higher an individual's "information centrality," the better he or she exemplifies the cultural model concerning students. And, people in the network do tend to resemble those others with whom they interact more frequently. Still, INDEPENDENT INVENTION accounts for about 15% of the variance in cultural competence, whereas DIFFUSION factors account for only about 10% (network centrality's 8%, plus dyadic resemblance's 2%).

It is interesting to contemplate whether a similar study of a larger and more diverse university would turn out differently. Lehigh's students, or so faculty often say, are remarkably similar to one another. Thus, as individual faculty and staff deal with similar students, they come to similar conclusions. But at a large school with a much more diverse undergraduate population, faculty-student interactions could very well lead faculty to dissimilar conclusions regarding the 'typical student.' In such settings, perhaps the DIFFUSION factors (social learning from one's peers) might better explain faculty-staff views of students.

Overall, the case study illustrates how to research the 'organization of diversity' in systematic fashion. By conjoining the tools of social network analysis and consensus analysis, it is possible to determine the degrees to which various factors explain the distribution of knowledge in a variety of social contexts. In so doing, one hopes to advance the theoretical insights of Wallace and Roberts through fine-grained empirical research.



Cited References

Roberts, J. M. 1964. The Self-Management of Cultures. In W. Goodenough, (Ed.), Explorations in Cultural Anthropology: Essays in Honor of George Peter Murdock. Pp. 433-454. New York: McGraw-Hill.

Stephenson, K. & Zelen, M. 1989. Rethinking centrality: Methods and examples. Social Networks, 11, 1-37.

Wallace, A. F. C. 1961. Introduction. In Culture and Personality. Pp. 1-44. New York: Random House.

Implicit References

Borgatti, S. P. 1996. ANTHROPAC 4.0 Methods Guide. Natick, MA: Analytic Technologies.

Borgatti, S. P. 1998. ANTHROPAC Version 4.95X. Natick, MA: Analytic Technologies. [computer software]

Borgatti, S. P., Everett, M., & Freeman, L. C. 1996. UCINET IV Reference Manual. Natick, MA: Analytic Technologies.

Borgatti, S. P., Everett, M., & Freeman, L. C. 1996. UCINET IV Version 1.66x. Natick, MA: Analytic Technologies. [computer software]

Boster, J. S. & Johnson, J. C. (1989). Form or function: A comparison of expert and novice judgments of similarity among fish. American Anthropologist, 91, 866-889.

Boster, J. S., Johnson, J. C., & Weller, S. C. 1987. Social position and shared knowledge: Actors' perceptions of status, role, and social structure. Social Networks, 9, 375-387.

Freeman, L. C. 1978/79. Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-239.

Freeman, L. C., Borgatti, S. P., & White, D. R. 1991. Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13, 141-154.

Friedkin, N. E. 1982. Information flow through strong and weak ties in intraorganizational social networks. Social Networks, 3, 273-285.

Johnson, J. C. 1994. Anthropological contributions to the study of social networks: A review. In S. Wasserman & J. Galaskiewicz, (Eds.), Advances in Social Network Analysis. Pp. 113-151. Thousand Oaks, CA: Sage Publications.

Krackhardt, D., Blythe, J., & McGrath, C. 1996. Krackplot 3.01G. Natick, MA: Analytic Technologies (distributor). [computer software]

Romney, A. K., Weller, S. C., & Batchelder, W. H. 1986. Culture as consensus: A theory of culture and informant accuracy. American Anthropologist, 88, 313-338.

Acknowledgment

The authors are especially grateful to Jeffrey C. Johnson and Stephen P. Borgatti for their patient counseling concerning the nuts-and-bolts of social network analysis.


Appendix A

INITIAL QUESTIONNAIRE
(data collected in March-April)

Part I. Demographic-Biographic Questions

  1. Age: _____
  2. Sex: _____
  3. Position at Lehigh: ___________________________________
  4. College: Arts & Sciences / Business & Economics / Engineering & Applied Science / None
  5. Time/years at Lehigh University: _____
  6. Time/years in current position: _____
  7. Did you attend Lehigh for any of your education? YES / NO
  8. Do you have children? YES / NO (If no, then skip question 9)
  9. Have any of your children attended Lehigh? YES / NO (If yes, how many? _____)
  10. Would or did you encourage any of your children to attend Lehigh? YES / NO ... Why, or why not?
  11. Admissions activities? Please explain what you do...

  12.  
Part II. Admission Knowledge Quiz

PROSPECTIVE STUDENTS: Questions pertaining to students interested in applying to Lehigh University.

  1. Prospective students to Lehigh University are most often from?

  2. a. Large Cities
    b. Suburbs of Large Cities
    c. Small Towns
    d. Rural Areas
  3. Most students are attracted to Lehigh because of the?

  4. a. Academic Reputation
    b. Campus Layout
    c. Geographic Location
    d. Social Atmosphere
  5. The combined SAT scores of Lehigh's prospective students are most often?

  6. a. Less than 1000
    b. 1000 - 1100
    c. 1101 - 1200
    d. More than 1200
  7. Which adjective best describes a prospective's students economic situation?

  8. a. Unpredictable
    b. Struggling
    c. Comfortable
    d. Wealthy
  9. Which adjective best describes a student interested in Lehigh?

  10. a. Goal Oriented
    b. Hardworking
    c. Highly Motivated
    d. Intellectual
  11. What academic programs are most prospective Lehigh students interested in?

  12. a. Engineering
    b. Business / Management
    c. Sciences / Mathematics
    d. Social Sciences / Humanities
  13. Which of the following types of schools are Lehigh prospectives most often interested in?

  14. a. Ivy League Institutions (such as Harvard, Yale, Pennsylvania, )
    b. Science and Engineering Institutions (such as RPI, MIT, Cal Tech, )
    c. Small Private Institutions (such as Bucknell, Dickinson, Williams, )
    d. State Affiliated Institutions (such as Temple, Rutgers, Penn State, )
  15. Most of the students interested in Lehigh University would probably spend the summer before college?

  16. a. In Academic Programs
    b. Hanging Out with Friends
    c. Traveling / Vacationing
    d. Working / Saving for School
  17. What about Lehigh is sometimes unattractive to prospective students?

  18. a. Academic Reputation
    b. Campus Community
    c. Geographic Location
    d. Social Atmosphere
  19. Most prospective students to Lehigh are usually concerned about?

  20. a. Athletic Programs
    b. Career / Academic Support Services
    c. Campus Life
    d. Financial Aid
ACCEPTED STUDENTS: Questions pertaining to students accepted for admission to Lehigh University.
  1. Students who are accepted to Lehigh usually have Math SAT scores in which range?

  2. a. Less than 400
    b. 400 - 500
    c. 501 - 600
    d. More than 600
  3. Students who are accepted to Lehigh usually have Verbal SAT scores in which range?

  4. a. Less than 400
    b. 400 - 500
    c. 501 - 600
    d. More than 600
  5. Students who are accepted to Lehigh have combined SAT scores in which range?

  6. a. Less than 1000
    b. 1000 - 1100
    c. 1101 - 1200
    d. More than 1200
  7. Students who are accepted to Lehigh usually have class ranks in which range?

  8. a. Top 10%
    b. Top Quarter
    c. Top Third
    d. Top 50%
  9. The majority of students accepted to Lehigh have also been accepted to which type of institution?

  10. a. Ivy League Institutions (such as Harvard, Yale, Penn, )
    b. Science and Engineering Institutions (such as RPI, MIT, Cal Tech, )
    c. Small Private Institutions (such as Bucknell, Dickinson, Williams, )
    d. State Affiliated Institutions (such as Temple, Rutgers, Penn State, )
  11. Which adjective best describes a student accepted to Lehigh?

  12. a. Achiever
    b. Disciplined
    c. Intellectual
    d. Social
  13. What type of High School do most students accepted to Lehigh attend?

  14. a. Private High Schools
    b. Public High Schools
    c. Equally Attend Private and Public High Schools
    d. Alternative schools, Magnet schools, Performing or Arts Schools
  15. Students accepted to Lehigh spend the majority of their time in which type of extracurricular activity during High School?

  16. a. Athletics / Cheerleading
    b. Drama & Music Activities
    c. Service Organizations
    d. Social Organizations
  17. Students accepted to Lehigh most often have questions about

  18. a. Athletic Programs & Other Activities
    b. Campus Life and Housing
    c. Career and Academic Support Services
    d. Academics and Majors
  19. Most of the students accepted to Lehigh would spend their summer before college?

  20. a. In Academic Programs
    b. Hanging Out with Friends
    c. Traveling / Vacationing
    d. Working / Saving for School
MATRICULATED STUDENTS: Questions pertaining to students who decide to attend Lehigh University.
  1. The majority of students who decide to attend Lehigh University have chosen which of the following areas of study?

  2. a. Engineering
    b. Business / Management
    c. Sciences / Mathematics
    d. Social Sciences / Humanities
  3. Which of the following is usually the main attraction to enrolled Lehigh students?

  4. a. Academic Reputation
    b. Campus Layout
    c. Geographic Location
    d. Financial Aid Package
  5. Which of the following adjectives best describes students who decide to attend Lehigh?

  6. a. Academic
    b. Athletic
    c. Individualistic
    d. Social
  7. The majority of Lehigh admitted students have career aspirations to work for?

  8. a. Government Agencies
    b. Large Corporations
    c. Non Profit Organizations
    d. Small Companies or Firms
  9. Most of the students who attend Lehigh spend the summer before matriculation?

  10. a. In Academic Programs
    b. Hanging Out with Friends
    c. Traveling / Vacationing
    d. Working / Saving for School
  11. Students who decide to attend Lehigh are from families who can be described economically as?

  12. a. Deprived
    b. Unstable
    c. Comfortable
    d. Well off
  13. Students who decide to matriculate to Lehigh are most often from?

  14. a. Large Cities
    b. Suburbs of Large Cities
    c. Small Towns
    d. Rural Areas
  15. Students who enroll at Lehigh University usually have second choice schools in which category?

  16. a. Ivy League Institutions (such as Harvard, Yale, Penn, )
    b. Science and Engineering Institutions (such as RPI, MIT, Cal Tech, )
    c. Small Private Institutions (such as Bucknell, Dickinson, Williams, )
    d. State Affiliated Institutions (such as Temple, Rutgers, Penn State, )
  17. Most students who enroll at Lehigh will be involved in?

  18. a. Athletics / Cheerleading
    b. Drama & Music Activities
    c. Service Organizations
    d. Social Organizations
  19. What percent of students who attend Lehigh University Receive Financial Aid?

  20. a. 10% - 20%
    b. 21% - 30%
    c. 31% - 40%
    d. 41%+
Part III. Network Questions
  1. During the typical month, how often do you communicate or interact with the following 51 individuals? [Note: Communication and interactions include letters, e-mails, phone conversations, person-to-person contact, and meetings, both professional and social.]

  2. ..... NAMES OF THE 51 ADMISSIONS-INVOLVED PE0PLE .....
  3. During the typical month, how often do you interact socially or have non-work related communication with the following 51 individuals? [Note: Communication and interactions include letters, e-mails, phone conversations, person-to-person contact, and meetings.]

  4. ..... NAMES OF THE 51 ADMISSIONS-INVOLVED PE0PLE .....
  5. During the typical month, how often do you communicate or interact with the following 51 people concerning undergraduate admissions? [Note: Communication and interactions include letters, e-mails, phone conversations, person-to-person contact, and meetings, both professional and social.]

  6. ..... NAMES OF THE 51 ADMISSIONS-INVOLVED PE0PLE .....
  7. During the typical month, how often do you communicate or interact with the following 51 people concerning prospective, accepted, or enrolled/current students? [Note: Communication and interactions include letters, e-mails, phone conversations, person-to-person contact, and meetings, both professional and social.]

  8. ..... NAMES OF THE 51 ADMISSIONS-INVOLVED PE0PLE .....
Possible Responses For All Four Network Questions:
__ Don't know them
__ 0
__ 1
__ 2 - 5
__ 6 - 15
__ 16 or more
 
 

FOLLOW-UP QUESTIONNAIRE
(data collected during the summer)

1. During the past academic year, how much one-on-one interacting with undergraduate students did you do?
a. Hours per week: _____ average / _____ maximum
b. Number of students per week: _____ average / _____ maximum

2. Before classes ended this spring, approximately how many undergraduates were you able to both recognize when you saw them and remember their names (i.e., match faces with names, and vice versa)?
__ 0 to 9
__ 10 to 19
__ 20 to 29
__ 30 to 39
__ 40 to 49
__ 50 or more

3. Do you know where any of our current undergraduates (including graduating seniors) are from, e.g., their hometowns?
___ No ___ Yes

If yes, for approximately how many individual students do you know this?
__ none
__ 1 or 2
__ 3 to 5
__ 6 to 10
__ 11 to 20
__ more than 20

4. Do you know what any of our current undergraduates (including graduating seniors) plan to do this summer?
___ No ___ Yes

If yes, for approximately how many individual students do you know this?
_____ [estimate]


Number of visitors to this page: