Welcome to the thirteenth online lecture in the Spring 2015 Introduction to Sociology course at the University of Maine at Augusta. This week, we look back at what we have learned during the semester and apply it to one particular context: education. Social structures of interaction, of institutions, of population and of stratification affect your experience as a university student as an individual and the fate of educational institutions. In this lecture we’ll focus on network structures and stratification in education — but the possibilities are immense beyond these limited bounds. Sociology has such great relevance for the study of education that an entire section of the American Sociological Association is dedicated to the subfield.
For students in the online section of SOC 101, your expectations this week are to complete all of the following tasks by Sunday, April 26 at 11:59 PM:
- Read You May Ask Yourself Chapter 13
- Review the online lecture that lies below. You are responsible for all the material in it, including material in embedded videos.
- Complete Problem Set #11, on Blackboard, covering Chapter 13 in the Conley text
- Complete the In-Course Activity Situating UMA Socially, available in the course Blackboard page’s “Discussions” area. In this activity, I’m asking you to describe some aspects of your experience as a student at the University of Maine at Augusta in the light of social structure.
Don’t Forget! Get Ready for Exam II (May 4 – May 8)
This section only pertains to students in my online course…
The end of the semester is close — remember that as our syllabus mentions you need to arrange to take Exam II between Monday, May 4 and Friday, May 8. Particular times are subject to the availability of proctors, so be sure to schedule a time and date for taking the exam as early as possible. This means it would be a good idea to call your local center or (in the case of out-of-state students) getting in touch with your designated proctors now.
To reiterate, your final exam must be completed with a certified proctor between May 4 and May 8 in the Course Schedule section of our syllabus. As your syllabus also notes, it is your responsibility to make time available to take the exams and to complete your exam within the necessary time frame. It is also your responsibility to contact the location you’ve chosen, before each exam, to arrange a specific day and time which is practical for both you and the site staff. If you need help contacting the site to arrange a specific day for your exam, please let me know by e-mail (email@example.com) and I’ll be glad to help — but arranging for an exam is your primary responsibility.
Exam Review Opportunity
Although this is the last online lecture for our Introduction to Sociology course, I am eager to assist you in your exam review. That review, however, should be shaped by your collective needs as students, and it’s up to you to tell me what those needs are. The second exam is structured in the same way as the first exam — it will be a mix of multiple choice, short-answer and definition questions covering all course material we’ve covered since the first exam. If you send me a question about that material by 11:59 pm on the night of Friday, May 1 via e-mail (firstname.lastname@example.org), I will be sure to answer your question by Sunday, May 3 in a video that I will assemble from all provided questions. I’m looking forward to those questions, and I’ll post that review video with an announcement on our course Blackboard page.
Group and Network Structures in Education
The Class Size Paradox and Students’ Experience of Education
In 1991’s “Why Your Friends Have More Friends Than You Do,” Sociologist Scott Feld described the Class Size Paradox and its effects on friendship networks. You should be familiar with this sociological paradox from our discussion at the beginning of the semester. But that’s not all — tucked away in the latter half of his paper is an underappreciated section on other class size effects, some of which profoundly affect the way our educational system is experienced by students. Feld describes the impact of social structure on the lives of institutions and individuals — and that’s what sociology is all about. This video reviews Feld’s insight and its application to university class sizes, advisor pools, and wait lists.
Social Networks and University Drop-Outs
The impact of networks on education is not limited to the Class Size Paradox. Your ultimate fate as a university student — do you finish your degree or drop out? — is partially related to your academic performance, but it is also strongly related to your place in the social network structure of that university. A generation ago, Alexander W. Astin (1975) and Tinto (1993) discovered in separate but related research that the students most likely to persist in their studies and make it through to graduation are those who join groups and who forge friendships with their fellow students. These ties do not need to be academically meaningful to keep students going at their university. Miller (2011) studied student attachment to an on-campus recreation center and found that users were reluctant to leave the university in part because they didn’t want to lose their recreation center community membership. Fitness may not be the primary point of an undergraduate education, but the group membership engendered by participation in a fitness center can help students stay on campus, stay on track and graduate on time.
Social Networks and Horizons of Observability at a University
The effect of social networks on education is not limited to students. Professors at a university carry out ongoing research as a vital part of their careers, and the division of universities into “colleges” is a reflection of the idea that professors will be collegial with one another — that is, share their work with one another in the spirit of community. But how much of a community do your professors share with one another? Network analysis has been used to address this question, and the answer is not reassuring.
In 1983, Noah Friedkin published a paper in the journal Social Forces establishing the notion of a “Horizon of Observability” in social networks. A horizon in the physical world is a line between ground and sky; the ground we see that touches the sky establishes the distance beyond which we cannot see. Even before the actual horizon line, the detail of the information we can collect about the physical world quickly declines with distance. Friedkin’s paper not only argues but empirically establishes that in social networks, a similar horizon effect exists across network distance — one of the measures we have learned about in our earlier work this semester with social networks.
Studying professors in different departments of Columbia University and the University of Chicago, Friedkin defined a tie between two professors as existing if and only if the two professors “have been in face-to-face communication about one or the other’s current research.” An network of academic communication results from the pattern of communication and non-communication between professors. In the hypothetical network depicted below, for example, Professor DeMint communicates regarding current research projects with Professors Thomas and Davis, but not with Professors Jones, Snodgrass or Anwar. The network distance between Professor DeMint and Professor Thomas is 1. The distance between Professor DeMint and Professor Jones is 2. The distance between Professor DeMint and Professor Anwar is 4.
With each increasing point of distance between two Professors, information about one of the Professors’ work must be communicated indirectly through an increasingly large number of other Professors in order for the other Professor to become aware of that research. How often does information about Professors’ work travel across increasing network distances? Put in other words, at what distance does a network horizon for information about academic work appear?
Table 4 below reports results from Friedkin’s study. Probability multiplied by 100 represents the percent of the time that an event can be expected to occur. At a network distance of two, one biological science professor is aware of another biological science professor’s work only 9.2% of the time at the University of Chicago and only 6.5% of the time at Columbia University. At a network distance of three, such awareness occurs less than 2% of the time. At a network distance of four, such awareness happens less than one time in a hundred. In academic networks at least, information does not travel far; the horizon of observability is both close and steep. Most of your professors are not, in fact, aware of what most of your other professors are doing!
(A quick note on the operationalization of the dependent variable of this study: How is it possible for the probability Professor U is aware of the work of Professor V to be less than 1 at a network distance of 1? After all, a network distance of 1 between U and V means they’re directly tied, and didn’t Noah Friedkin define a tie between two professors as the discussion of current work between them? The answer is that these ties can be directed. Professor V may report discussion with Professor U about Professor U‘s work, but Professor V‘s own work might not come up at all in such discussion. Indeed, as the first row of results in Table 4 shows, the reciprocation of discussion ties only happens roughly a fifth to a quarter of the time.)
Stratification in the Educational System
To review research discussed in our previous lecture, sociologist S. Michael Gaddis recently utilized the structure of an audit study to uncover stratification in higher education. Gaddis sent out 1,008 fake job applications in which two features varied: the college or university from which an applicant graduated and the name an applicant used. Names were identified as “racialized” if they were strongly associated with black identity (DaQuan, Ebony, Jalen, Lamar, Nia, and Shanice) or white identity (Aubrey, Caleb, Charlie, Erica, Ronny and Lesly). The fake applicants’ alma maters were grouped into two categories: high-prestige universities such as Duke, Harvard or Stanford and “second-tier universities” that are respected but not as well-ranked (University of California-Riverside and University of North Carolina-Greensboro were two such universities). The quality of applicants’ records, and of the applications themselves, were held equal within pairs; only names and university names varied (Gaddis 2015).
The dependent variable in Gaddis’ research was whether these fictitious job applications would receive a response. Here are the rates of positive employer responses from employers in Gaddis’ study:
Gaddis describes a kind of stratification as students exit the educational system, but what about stratification while students are trying to enter the system? Katherine Milkman and her colleagues (2012) also used audit study techniques of matching equally-qualified, similarly-communicating students to uncover stratification in graduate school admission. In order to get into graduate school, you must communicate effectively with that graduate school to obtain necessary information. Do graduate schools treat prospective students differently according to irrelevant characteristics? Do graduate schools discriminate? To find out, Milkman’s research team sent out messages from fictional students seeking to apply to graduate schools, e-mail messages that were sent to the professors acting as directors of 6,300 graduate programs in the United States. Names were selected that strongly indicated applicants’ race and gender. The request from applicants: could we meet in one week’s time? Milkman and her colleagues wanted to find out how quickly professors would respond to these requests. The results:
That disparity is discrimination.
Finally, Corrine Moss-Racusin and colleagues (2012) asked natural science faculty to rate the applications of students seeking a job as a lab manager. These applications were fictional and matched according to qualifications, as is typical in audit studies, varying only in the gender of the names used by fictional applicants. The 127 faculty participating in the study were told, however, that the applications were real and that the “real” applicants would receive their feedback. The researchers’ lie gave the exercise apparently “real” consequences.
To cut to the chase, faculty declared male applicants to be more competent, worth a greater starting salary, and suggested more mentoring for masculine-gendered applicants; feminine-gendered applicants were rated lower in all of these characteristics. This is true despite the fact that applicants possessed identical qualifications on paper, varying only in their gender:
Yet again, that disparity qualifies as discrimination. Scholars may study the world, but apparently they are not immune to the biases in the world.
Stratification in Education — What is the Capacity for Exploitation? Where Does UMA Fit?
As the previous section establishes, students can be treated differently within a single university according to their individual-level status. It is also possible that different universities treat their students differently, setting up a system of stratification between high-service universities, middling universities and poorly-serving universities. In recent years, there has been growing concern at trends in for-profit universities that some call downright exploitative. When making money off of student enrollment becomes the primary objective of a university, do students do well?
The Frontline documentary College, Inc. takes about 56 minutes to address this question through investigations and interviews with students and administrators in the for-profit university sector. Please watch the entire documentary, available online at http://www.pbs.org/wgbh/pages/frontline/collegeinc/view/.
Think about the standards by which the investigators at Frontline decide whether a for-profit university is exploiting its students (acting as what some would call a “diploma mill”). Now take a look at some limited data provided by the United States Government regarding some of the universities operating in the state of Maine. Let’s start with UMA:
Compare those scores to the scores for the University of Maine in Orono:
… for the University of Southern Maine:
… and finally for the for-profit Kaplan University as it operates in the state of Maine:
How does the University of Maine at Augusta compare? Which of these institutions are most like the others, and in which ways? What do you see?
Astin, Alexander W. 1975. Preventing Students From Dropping Out. San Francisco: Jossey-Bass.
Feld, Scott L. 1991. “Why Your Friends Have More Friends than You Do.” American Journal of Sociology 96(6): 1464-1477.
Friedkin, Noah E. 1983. “Horizons of Observability and Limits of Informal Control in Organizations.” Social Forces 62(1): 54-77.
Milkman, Katherine L., Modupe Akinola, and Dolly Chugh. 2012. “Temporal Distance and Discrimination: An Audit Study in Academia.” Psychological Science 23(7): 710-717.
Miller, John J. 2011. “Impact of a University Recreation Center on Social Belonging and Student Retention.” Recreational Sports Journal 35(2): 117-129.
Moss-Racusin, Corinne A., John F. Dovidio, Victoria L. Brescoll, Mark J. Grahama and Jo Handelsman. 2012. “Science Faculty’s Subtle Gender Biases Favor Male Students.” Proceedings of the National Academy of Sciences 109(41): 16474-16479.
Tinto, Vincent. 1993. Leaving College: Rethinking the Causes and Cures of Student Attrition, 2nd edition. Chicago, Illinois: The University of Chicago Press.