James N. Morgan Fund for New Directions in Analysis of Complex Interactions


SEARCH Competition

The James N. Morgan Award for New Directions in Analysis of Complex Interactions is no longer making awards. If you are interested in SEARCH, please see the James Morgan Innovation in the Analysis of Economic Behavior Award (under construction).

Objectives & Background

James Morgan. Photo by Linda Stafford.

James Morgan. Photo by Linda Stafford.

The Institute for Social Research and the James Morgan Fund for New Directions in the Analysis of Complex Interactions will provide up to two $15,000 awards to stimulate new and innovative uses of SEARCH Program by senior graduate students and post-doctoral scholars at any accredited university in the U.S. to explore and expand our understanding of complex interactions among explanatory variables in describing behavior.

We know there are complex combinations operating and the availability of large, rich databases provides an opportunity to use a different approach. The ISR program SEARCH was created for analysis of survey data with samples of 1,000 or more, and a clear dependent variable—a behavior or situation to be explained or predicted. And it was designed to look for non-linearities and non-additives (interaction effects) assumed away by multiple regression. When the dependent “variable” is a dichotomy, or a set of categories, one can call the result a decision tree, and change a measure of improvement to a chi square.

SEARCH splits the data sequentially on the best binary split of the best variable for reducing the unexplained variance, or in the case of a categorical dependent variable, for increasing the chi square. With ranks, it tries the first against the rest, then the first two, etc. With non-ranked categories like race or region, it tries each group against all the rest, keeping the best of the best. The reason this works is that a very few splits on any predictor will exhaust its explanatory power (Kalton, 1967, in JDS article). There are stopping rules to avoid overdoing it. SEARCH is a predictive analytics software tools, like CART from Salford Systems. If you’ve used CART or neural networks to analyze your dataset, SEARCH could provide you with more insight into the dependent variable of your choice.

The idea was first promoted by an article in the Journal of the American Statistical Association (June, 1963) and used extensively in a book Productive Americans, 1966. There are already many published articles using multiple regression, and explorations whether SEARCH would produce useful new interpretations is the challenge.

Sample of a tree diagram from the SEARCH program. Click image for larger view.

SEARCH has been widely used in marketing, but is not the same as cluster analysis, data mining, or neural networks, which often do not specify what is to be explained or predicted, and some tend to start with a complete multilevel details and make risky decisions on how to combine. The ISR SEARCH program’s first splits are based on substantial frequencies and hence are safe. As a guide for decision-making or diagnosis, SEARCH can help decide how far down the branching diagram to continue. (For a more extensive overview, see this PDF document.)

The most revealing analysis would take data already analyzed with standard linear additive models and see whether SEARCH provided new and useful findings.

A fuller history and explanation can be found in Morgan, J. N. (2005). History and Potential of Binary Segmentation for Exploratory Data Analysis (PDF). Journal of Data Science, 3, 123-136.

For an explanation of the underlying algebra, see this PDF document. A further description of the mathematics behind the SEARCH Algorithm, please see Chapter II (pgs. 9-53) of Searching for Structure, by Sonquist, Baker and Morgan, here.

For a more detailed discussion about SEARCH, and a step-by-step description of how to access to the program, go to http://bit.ly/SEARCH-ISR.

For instructions on how to run SEARCH in Stata, go to http://bit.ly/SEARCH-Stata.

Form and Scope of the Award

The Morgan Fund will make up two awards of $15,000 each for stipends that may be used to cover salary and/or other expenses that can be suggested by the applicant and funded at the discretion of the Awards Committee.   Funds can also be used to purchase of consulting time with relevant staff at the University of Michigan Institute for Social Research.

$10,000 will be awarded at the time of selection and $5,000 will be awarded when the project is completed and deliverables are submitted. Recipients of the Award have one year from the date of their first award check to send their deliverables to the University of Michigan to be eligible for the $5,000.

Applicants are encouraged to use datasets developed at the Institute for Social Research (e.g. American National Election Study, Panel Study on Income Dynamics, Health and Retirement Study, Surveys of Consumer Attitudes, Monitoring the Future, etc.) or data archived at the Inter-university Consortium for Political and Social Research, but are not limited to these.


These are the criteria for eligibility:

  • Applicants must be admitted to Ph.D. candidacy in a graduate program or post-doctoral scholars at an accredited university in the U.S.
  • Special preference will be given to students using the award to advance their dissertation research.
Chris Antoun. Photo by Eva Menezes/ISR.

Chris Antoun, Cycle 1 Morgan Award Winner. Photo by Eva Menezes/ISR. Read his profile.

For questions about the award, please contact Patrick Shields at peshield@umich.edu or 734.764.8369.

Additional Information
For help in correcting set-ups or advice on combining a data management program with SEARCH are available from Peter Solenberger, who can be reached at pws@umich.edu.

Recipients of Awards from the Morgan Fund

Cycle 1
Christopher Antoun, University of Michigan
Wonjung Oh, University of Michigan
Cycle 2
Seunghyun Hwang, National Collegiate Athletic Association
Cycle 3
Robert Duncan, Oregon State University
Yan Zhou, University of California – Los Angeles
Cycle 4
Pratiti Chatterjee, University of California – Irvine
Emily Hennessy, Vanderbilt University
Dexin Shi, University of Oklahoma
Cycle 5
Turgut Ozkan, University of Texas at Dallas
Scott Van Lenten, Arizona State University
Cycle 6
Taylor Burke, Temple University
Heather McDaniel, University of South Carolina