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At this site we provide the latest information regarding computing resources for teaching and research in the UTD School of EPPS. Please use the navigation menus to browse the site, or use the site search.

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Research Methods Short Course by Dr. Tse-Min Lin

Spring 2014 Research Methods Short Course 

EVENT COUNT ANALYSIS FOR
SOCIAL SCIENCE DATA

Instructor: Professor Tse-Min Lin
Professor of Political Science
University of Texas, Austin

Friday February 14th, 10 A.M. – 4 P.M.
Saturday February 15th, 10 A.M. – 4 P.M.

Classroom: Green 3.206
Materials: EventCountSlides.pdf

Course Description:  In many social science studies, the dependent variable is an event count, e.g., outbreaks of war in a given year, annual deaths from domestic conflict in a given country, and the number of items a respondent answered correctly in a survey on political knowledge. As an event count, the dependent variable is a non-negative integer which does not follow the normal distribution as required of the classical linear regression model. The estimation of event count models thus requires special techniques. It typically involves choosing an appropriate distribution for the dependent variable as the data generation process and estimating the model with the maximum likelihood method.  This course will introduce these special regression models and discuss their assumptions and limitations. It will also include a comparative analysis of event count models and alternative models based on item response theory (IRT).

Tse-min Lin is Associate Professor of Government at the University of Texas at Austin. His teaching and research interests cover methodology, formal theory, and American and comparative political behavior. He has published in American Journal of Political Science, American Political Science Review, Democratization, Issues & Studies, Journal of Democracy, Journal of Politics, Political Analysis, Political Research Quarterly, Public Choice, Social Science History, Taiwan Journal of Democracy, World Politics, as well as in edited volumes. At UT Austin, he teaches several graduate courses, including Advanced Statistical Analysis, Mathematical Methods for Political Analysis, and Time Series Analysis.

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Research Methods Short Course by Laron Williams, Ph.D.

Spring 2013 Research Methods Short Course
INTERMEDIATE DATA ANALYSIS IN STATA

Instructor: Laron Williams, Ph.D.
Department of Political Science, University of Missouri

Friday Feb 1st 10-4 PM and Saturday Feb 2nd, 9-3 PM
Class Meets in Green Hall (GR) 3.602

Professor Laron Williams is an expert Stata programmer and recent co-author (with Guy Whitten) of a Stata program for analyzing dynamic pooled cross-sectional time series models. The program is described in ‘Dynamic Simulations of Autoregressive Relationships’ in the Stata Journal. Laron also has published in leading peer reviewed journals such as the American Journal of Political Science, the British Journal of Political Science, the Journal of Politics, and International Studies Quarterly.

*For additional information, contact Harold Clarke - hclarke@utdallas.edu

Course Files

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Research Methods Short Course by Guy Whitten, PhD

Introductory Data Management and Analysis Using Stata

The University of Texas at Dallas
September 21-22, 2012

Instructor: Guy D. Whitten, Texas A&M University (g-whitten@pols.tamu.edu).

Overview: The goal of this course is to provide a hand-on introduction to using the Stata program. This particular introduction will focus on data and tools typically employed by social scientists. The course will alternate between lectures on topics and interactive lab sessions where students will put what they are learning to work.

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Research Methods Short Course by David Drukker, Ph.D.

PROGRAMMING IN STATA AND MATA

Instructor

David Drukker, Ph.D.

Director of Econometrics, STATA Corporation

Friday Feb 24th and Saturday Feb 25th, 10-4 PM

Class Meets 10-11:30 AM in Green 3.420 and then moves to Green 3.402 for remainder of Friday class and all day Saturday.

David M. Drukker is the Director of Econometrics at Stata. He obtained a Ph.D. in Economics from the University of Texas at Austin in 2000. David spends most of his time developing new methods for Stata, managing the econometrics group at Stata, and deciding what new econometric methods should be added to Stata. David also gives a number of short courses on econometrics, Stata use, and Stata programming. In addition, he does research in econometrics and recently had been working on new methods for estimating the parameters in spatial-autoregressive models.

For additional information, contact Harold Clarke – hclarke@utdallas.edu

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Research Methods Short Course by Professor Fred Boehmke

AUTUMN 2011
RESEARCH METHODS SHORT COURSE

FROM INTRODUCTORY TO ADVANCED
PROGRAMMING IN STATA 

Instructor:
Professor Fred Boehmke
Department of Political Science
University of Iowa

Thursday October 27th and Friday October 28th
10 AM – 4 PM Green 3.402

Download Course Materials

Fred Boehmke is Associate Professor of Political Science at the University of Iowa and a Social Science Scholar in Residence at the Iowa Public Policy Institute. He received his PhD from the California Institute of Technology in 2000 and from 2005-2007 he was a Robert Wood Johnson Scholar in Health Policy Research at the University of Michigan. His research examines the direct and indirect effect of political institutions on representation, often with an eye to the American states. He does research on and teaches a variety of advanced statistical methodologies, including survival analysis, spatial analysis, selection bias, and MLE. He regularly teaches a graduate course on statistical computing in Stata at the University of Iowa and the Essex Summer School in Social Science Data Analysis.

For Further Information, contact Harold Clarke
email: clarke475@msn.com

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Methodology Short Course by Professor Allan McCutcheon, April 28th-29th

Full announcement by Professor Harold Clarke:

Course Materials

I am very pleased to announce that the Spring 2011 Research Methods Course on LATENT CLASS MODELS will be taught by Professor Allan McCutcheon (please see bio sketch below). Latent class models are widely used to study heterogeneity in causal processes of interest to social scientists and market researchers. The course meets Thurs April 28th and Friday April 29th, 10 AM – 4 PM in Green 3.206.

If you have any questions, please contact me.

Thanks,

Harold

About Professor McCutcheon

Allan L. McCutcheon holds the Donald O. Clifton Chair of Survey Science, and is Professor of Statistics and Survey Research & Methodology at the University of Nebraska at Lincoln; he is also the founding Director of the Gallup Research Centre. He has taught workshops on categorical data analysis and public opinion polling at the ICPSR Summer Program at the University of Michigan; the Essex Summer School in the UK; the Central Archive for Empirical Social Research at the University of Cologne; the Central European University in Budapest; the Catholic University of Brussels and the Institute for Advanced Studies in Vienna. He is, with Jacques Hagenaars, co-editor of Applied Latent Class Analysis (Cambridge University Press. 2002), and author of Latent Class Analysis (Sage 1986). He has also published several articles applying log-linear and latent class models to substantive issues in social, political, and cross-cultural research. He is an elected fellow of the American Statistical Association and the Royal Statistical Society.

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Methodology Short Course by Professor Alan Acock, Nov. 18th-19th

Full announcement by Professor Harold Clarke:

Course Materials | Example

The autumn 2010 research methods short course is “Structural Equations and Latent Growth Curve Models.” The instructor is Professor Alan Acock, former Dean of the School of Family Studies, Oregon State University. Allan is a leading social science methodologist and a pioneer in the use of structural equation methods for studying attitudinal and behavioral dynamics with panel survey data. The course will be Thursday, November 18th and Friday, November 19th 10-4 pm. We will meet on Thursday at 10 am in Green Hall 3.206. Free pizza lunch both days. All course materials including data and program files are available at Statistics lab computers (c:\temp\Acock2010).

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Methodology Short Course by Professor William Jacoby, April 8th – 9th

4/8 Graphics in R | 4/9 Multidimensional scaling

Full announcement by Professor Harold Clarke:

I am very pleased to announce that Professor William Jacoby, Director of the ICPSR Research Methods Summer Program at the University of Michigan will be doing the spring short course on April 8th and April 9th. All EPPS and UTD faculty, graduate students and staff are welcome. The course has two topics: day 1 will focus on doing simple and advanced graphics with R; day 2 will focus on contemporary multidimensional scaling methods for social science data. Details to follow.
Harold Clarke
Asbel Smith Professor
EPPS

STATISTICAL GRAPHICS FOR VISUALIZING DATA WITH R
Course materials

This short course will cover methods for obtaining visual displays of quantitative information. We will discuss ways to, quite literally, look at your data. This is important because graphical representations avoid some of the restrictive assumptions and simplistic models that are often encountered in empirical analyses. The material presented in the workshop should be useful for people at varying levels of technical sophistication. Virtually everybody who is exposed to these ideas and methods seems to agree with a variant of the old cliché: A picture is worth a thousand numbers.

The course will begin with a discussion of some basic ideas, concepts, and examples. We will examine the advantages of graphical displays relative to tabular presentations, the situations in which graphical displays are most useful, the principles underlying effective graphical displays, and a tour of some specific graphical methods for univariate, bivariate, and multivariate data.

Next, we will learn how to produce visual displays of quantitative information, using the lattice package within the R statistical computing environment. Lattice displays are very easy to produce and they can be customized in many ways. We will examine the R functions for constructing various kinds of graphs (including histograms, smoothed histograms, dot plots, scatterplots, and trellis displays). We also will cover many “tips and tricks” for customizing graphs to fit specific data analysis situations.

Workshop participants will get hands-on experience in constructing and modifying graphs using the Lattice system. Some prior experience with the R statistical computing environment would be helpful, but it is definitely not required. In fact, the workshop material will assume that participants have no prior experience with, or exposure to, R.

MULTIDIMENSIONAL SCALING: AN INTRODUCTION
Course materials

Would you like to draw pictures of your data, in ways that reveal structures which are not obvious from inspection of the data values, alone? Multidimensional scaling (MDS) tries to accomplish exactly that objective. To be more precise, MDS produces “map” of stimuli, based upon information about the “proximities” among those stimuli.

Multidimensional scaling methods have many potential applications in empirical research. They can be used to: simplify the contents of large, complex datasets; model similarities among sets of objects; estimate the cognitive structures underlying survey responses; and optimize the measurement characteristics of qualitative observations. MDS can be generalized to show individual differences across distinct data sources (e..g, subsets of survey respondents or data collected at different time points). It also can be adapted to represent respondent preferences among a set of stimuli (so-called “ideal points” models).

This short course will provide a basic introduction to multidimensional scaling. Specific topics to be covered include: The basic idea of MDS; types of data that might be input to MDS; the general estimation procedure; interpretation of results; different varieties of MDS; and software options for performing MDS analyses. The course will consist of two parts. The first (and longer) part will occur in the classroom, to introduce the basic concepts and ideas. The second part will occur in the computer laboratory, to give course participants hands-on experience with the MDS routines in one or more of the major statistical software packages.

This short course is intended for a general audience. It does not assume any prior experience with MDS or familiarity with advanced statistical methods (i.e., beyond basic regression analysis). Participants should be able to perform basic data processing tasks with a statistical software package (e.g., Stata, SAS, SPSS), but no special knowledge of MDS software is assumed or necessary.

Professor Jacoby’s biographical sketch:

William G. Jacoby is a Professor in the Department of Political Science at Michigan State University. He is also a Research Scientist at the University of Michigan, where he serves as Director of the Inter-University Consortium for Political and Social Research (ICPSR) Summer Training Program in Quantitative Methods of Social Research.
Professor Jacoby received his Ph.D. from the University of North Carolina, Chapel Hill in 1983 and his main professional interests are mass political behavior (public opinion, political attitudes, voting
behavior) and quantitative methodology (measurement theory, scaling methods, statistical graphics, modern regression). Professor Jacoby’s current research focuses on citizen ideology and belief system organization, value choices and their implications for subsequent political orientations, measuring policy priorities in the American states, the consequences of measurement assumptions for statistical models, and graphical strategies for data analysis. His most recent book (with Michael Lewis-Beck, Helmut Norpoth and Herbert Weisberg) is The American Voter Revisited (Ann Arbor: University of Michigan Press, 2008).

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Multilevel Models in Quantitative Research: Bayesian Perspectives on Inference and Computation, 11/6/09-11/7/09

The autumn 2009 Research Methods short course is “Multilevel Models in Quantitative Research: Bayesian Perspectives on Inference and Computation.” I am very pleased to announce that the instructor will be Professor Jeff Gill. Professor Gill holds a Ph.D. in Statistics, and is the author of numerous publications, including influential books on Bayesian statistics. He is Director of the Center for Applied Statistics at Washington University, St. Louis, and currently is President of the Society for Political Methodology. Professor Gill regularly teaches courses on applied statistics in the Medical School at Washington University and at the Essex Summer School in Data Collection and Analysis. His course will be held on Friday November 6th and Saturday November 7th. Friday’s class begins at 10 AM in Green 3.606. Saturday we start at 10 AM in Green 3.206. Free pizza lunch will be provided both days! All are welcome. Teaching materials are available at the following website:

http://jgill.wustl.edu/short.course.html

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Methodology Short Course, March 27th – 28th

Professor B. Dan Wood, Department of Political Science, Texas A & M University will be doing the spring short course in research methods. The title of the course is: Maximum Likelihood Estimation, Count and Duration Models. Formerly an engineer with NASA, Professor Wood is a distinguished political scientist who regularly teaches advanced methods courses at the University of Essex Summer School and his home university. His many publications include articles in the American Journal of Political Science and numerous other leading journals. Click here to download a zipped file that contains the lecture notes (in PDF and Word formats), data for exercises, and programs in STATA, R, Excel, and Maple.

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