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Description of Ken Frank’s work
Research
Overview
I have pursued multiple methodological strands, each of which has been motivated by
current needs and demands of social scientists. My clustering algorithm represents crystalized
sociograms of social networks consistent with classic sociological theories (Frank, 1995, 1996).
My index of the sensitivity of statistical inferences to confounding variables informs current
debates regarding the interpretation and use of p-values and causal inferences (Frank, 2000).
My models of organizational culture show that the combination of individual needs for
commonality and diversity generates complex systemic behavior (Frank & Fahrbach, 1999).
My uses of multilevel models capture effects generated by people and the structures they
construct (Wellman & Frank, 2001; Herman, Frank, et al 2000; Frank, 1998; Frank & Yasumoto,
1998; Bidwell Frank and Quiroz, 1997; Seltzer, Frank & Bryk, 1994; Bryk & Frank, 1991).
The methodological strands described above coalesce in the study of the organizational
change, my primary substantive area. Crystalized sociograms help reveal the processes through
which organizational members influence one another, ultimately generating organizational
responses to external conditions. These processes become complex as they transform social
structures, for example as casual conversation between teachers culminates in a collegial
relationship through which multiple influences flow. Multilevel models clearly can disentangle
effects at the organization and individual levels. Finally, though my index of the sensitivity of
statistical inferences has broad potential, it is especially applicable to characterize the robustness
of organizational effects to alternative explanations based on the social composition of
organizations.
My work has direct policy applications because educational reforms and innovations are
implemented through the social processes of schools. Therefore, with knowledge of the social
structures of schools we can not only better implement any given innovation, but we can
cultivate social structures that facilitate the implementation of all changes. This will help schools
adapt to future changes, from student composition to technology to federal policy. Below I
describe each strand of my work in more detail.
Crystalized sociograms: embedding subgroups in a sociogram
I have developed an algorithm (called KliqueFinder) for identifying subgroups of actors
in a network and for embedding subgroup boundaries in a sociogram (Frank, 1995, 1996). The
technique addresses a longstanding deficiency of clustering methods by identifying
non-overlapping cohesive subgroups that theoretically occur in most systems and organizations.
Not surprisingly, my tool can generate empirical and theoretical insights into small systems and
organizations. For example, in Frank (1996) I used the sociograms to infer patterns of influence
among teachers in schools, and Jeff Yasumoto and I described the distribution of social capital
within and between subgroups among members of the French financial elite (Frank & Yasumoto,
1998). The method blended with theory as we found that social capital provided a general
explanation for how people balanced the need for solidarity with subgroup members against the
need for diverse information and resources from outside the subgroup.
The sensitivity of statistical inferences to the impact of confounding variables
I developed an index of the sensitivity of causal inferences to the impact of confounding
variables (Frank, 2000). Skeptics who challenge causal inferences often do so by proposing that
there is an alternative causal mechanism at work. In regression and the general linear model,
their criticism takes the form of a confounding variable. Unfortunately, it is often difficult or
impossible to measure the proposed confounding variable, and researcher and skeptic are left in a
vague and diffuse debate. I quantified the language of this debate by demonstrating that the
impact of a confounding variable on the inference for a regression coefficient can be indexed in
terms of the product of correlations associated with the confounding variable:
impact of confound on inference for predictor of interest= r confound and predictor of interest × r confound and
outcome.
By neatly indexing the impact on inference, one can express what the impact would have to be to alter
an inference (thus extending beyond what is already known about bias in the estimate of the
coefficient). I applied the index to a classic issue in the sociology of education in which Featherman
and Hauser claimed that father’s occupation affects educational attainment. Michael Sobel recently
commented that the effect of father’s occupation could be attributed to father’s education (see Frank,
2000, pages 168-171). I showed that father’s education would have to be correlated with father’s
occupation and with educational attainment at about .47 to alter Featherman and Hauser’s inference.
While not resolving the debate, the index shifts the burden back to the skeptic, Sobel, to establish the
likelihood of observing correlations of such relatively large magnitude associated with father’s
education (note that there were other examples in which Sobel had a stronger case). As I describe in
the conclusion, by contextualizing causal inferences the index directly informs current debates
regarding the interpretation and use of p-values.
Organizational culture as a complex system
Kyle Fahrbach and I combined models of interaction (who talks to whom) and influence (how
people change as a result of interaction) to define the basic processes of organizational culture (Frank
& Fahrbach, 1999). Taking the mathematical models and principles of chaos theory beyond
metaphor, we showed that a system can become chaotic as actors are persuaded by opinions or
information, and as they seek to interact with similar others or those who may have new information.
The models have quite general and powerful implications. For example, we used the models to
simulate systemic responses to external shocks and to develop a longitudinal conceptualization of
centrality of actors in a network.
Multilevel models
I have worked extensively with multilevel models (Wellman & Frank, 2001; Herman, Frank, et
al 2000; Frank, 1998; Frank & Yasumoto, 1998; Bidwell Frank and Quiroz, 1997; Seltzer, Frank &
Bryk, 1994; Bryk & Frank, 1991) having done my dissertation with Tony Bryk and been a close
colleague of Steve Raudenbush. Multilevel models have a natural application to the study of
organizations, disentangling effects of the organization from those of the individual. Relatively
recently, I reviewed the application of multilevel models in educational research (Frank, 1998), and
applied multilevel models to separate effects of individuals and networks on the provision of support
(Wellman & Frank, 2001). Combining effects across levels, Barry Wellman and I found that people
were more likely to provide support when both provider and receiver were embedded in a network of
similar others, suggesting that support is given as much to the network as to the individual.
Current work and extensions
Funded in part by a National Academy of Education/Spencer postdoctoral fellowship, I am
currently studying the diffusion of computer technology in elementary schools. I have chosen
computer technology because there is currently a strong demand to integrate technology into schools.
But the diffusion processes, especially the social aspects, are applicable across organizations. As I
analyze the diffusion of innovations in schools I am generating crystalized sociograms and testing
models of influence and interaction to study the relationship between teacher networks and their
changes in beliefs and behaviors regarding technology use. This work also draws on my theoretical
interest in social capital (e.g., Frank & Yasumoto) and the evolution of organizational culture. As I
explore the diffusion process across multiple schools I am applying multilevel models, examining how
characteristics of schools and districts affect the diffusion process. I currently have drafts of three
papers (presented at conferences and colloquia), one on the general dynamics of social capital with the
diffusion of innovations as an example, a second on the diffusion process within schools, and a third
on the role of subgroups in structuring the diffusion process.
I am part of a team of investigators (headed by Chandra Muller) who have been funded by
NICHD and NSF to augment the Add Health data and generate our own scholarly analyses. The Add
Health data set features longitudinal social network data on students in 80 nationally representative
schools. In a multi-year, $4 million-plus project, we will link the extant Add Health data with
educational indicators and outcomes. Ultimately, we will be able to explore the social contexts of
students as defined by friends, classmates and those in similar tracks. We will then explore the
effects of these contexts on outcomes during and after high school.
I am now extending my ideas about the sensitivity of causal inferences to issues regarding the
representativeness of a sample. The motivating question here is: “What would an association in an
unmeasured sample have to be such that if the unmeasured sample were combined with an observed
sample it would alter an inference?” As one application, this tool can be applied to aggregate results
over time. For example, some critics would argue that the Featherman/Hauser/Sewell analyses are
dated. One could then ask, “What would the correlation have to be in a current sample such that if
combined with the historical sample our inference regarding the general relationship between father’s
occupation and educational attainment would change?” The index I have developed quantifies the
answer to the question.
I have also extended my index for the impact of confounding variables theoretically and in
applications. In his thesis, my student Wei Pan approximated the distribution of the product of two
dependent correlation coefficients to better make inferences about the likelihood of observing impacts
of a given size. Charles Bidwell, Aaron Pallas and I have begun to apply the indexes to several
sociological issues that have appeared in recent years in major sociological journals. These efforts
will culminate in publications in one or two years.
Teaching
My teaching follows my research interests. The courses in my repertoire include:
•Introduction to research methods for masters or doctoral students;
•General linear model and causal inference;
•Multilevel models, including extensions to generalized linear models and cross-nested
designs.
•Multivariate methods (eigen value based procedures such as factor analysis, canonical
correlation and discriminant analysis, as well as multidimensional scaling and cluster analysis);
•Social network tools, methods and theories.
• Causal Inference and the Counterfactual
In each of these courses I draw on sociological theory, from organizational theory to social capital to
stratification. I incorporate recent data sets from my own research well as from national sources such
as NELS.
My students come from a diverse set of backgrounds. For example, in my most recent course
on multilevel models I had students from Counseling, Fisheries and Wildlife, Kinesiology,
Psychology, and my own program. They come with diverse experiences in statistics, some struggling
with the basic linear model and others having mastered some aspects of theoretical statistics. And yet
my students rate me quite high (I was nominated by my department for an award as an outstanding
young scholar and teacher -- full documentation available upon request). I am typically rated above
average to superior on items regarding instructor involvement and student-instructor interaction, in
spite of the fact that my courses are considered very demanding. I embrace the challenge of these
courses by presenting material for diverse learners (using equations, text, examples, graphs, literature,
etc) and structuring discussion exercises for students to learn from each other.