Program

Session 1: 8:30–9:45  Statistical Models I

Analysis of multiple source/multiple informant data in Stata (20 minutes)

Nicholas Horton, Smith College

Garrett Fitzmaurice, Harvard University

gologit2: Generalized logistic regression models for ordinal dependent variables (20 minutes)

Richard A. Williams, University of Notre Dame

L-statistics, especially L-moments, for fun and profit (20 minutes)

Nicholas J. Cox, Durham University

Break: 9:45–10:15

Session 2: 10:15–11:45  Data Management Using Stata

Integrating Stata with database management systems (20 minutes)

Ed Bassin, ProfSoft, Inc.

Mass producing appendices using Stata and word processor mail merge (10 minutes)

Michael Blasnik, M. Blasnik & Associates

Reproducible research using Stata (20 minutes)

L. Philip Schumm, University of Chicago

Ronald A. Thisted, University of Chicago

Collaborative data management for longitudinal studies (20 minutes)

Stephen F. Brehm, University of Chicago

L. Philip Schumm, University of Chicago

Lunch: 11:45–1:00

Session 3: 1:00–2:15  Statistical Models II

Creating valid and effective measures: Using -optifact- to create better summated rating scales (20 minutes)

Paul Millar, University of Calgary

Using and teaching Stata in a semester-length introduction to biostatistics course (10 minutes)

Clinton Thompson, University of Utah

Stephen C. Alder, University of Utah

Justin Brown, University of Utah

Laurie Johnson, University of Utah

Using Stata graphics as a method of understanding and presenting interaction effects (20 minutes)

Joanne Garrett, UNC at Chapel Hill

cron, perl, and Stata: automated production and presentation of a business-daily index (10 minutes)

Kit Baum, Boston College

Atreya Chakraborty, University of Massachusetts, Boston

Break: 2:15–2:40

Session 4: 2:40–4:00  Biostatistics

Selecting the appropriate statistical distribution for the primary analysis: a case study (20 minutes)

Peter A. Lachenbruch, US FDA

Using Stata 9 to model complex nonlinear relationships with restricted cubic splines (20 minutes)

William D. Dupont, Vanderbilt University School of Medicine

Dale Plummer, Vanderbilt University School of Medicine

Adjusting for unequal selection probability in multilevel models: A comparison of software packages (20 minutes)

Kim Chantala, Carolina Population Center, UNC at Chapel Hill

CM Suchindran, Carolina Population Center, UNC at Chapel Hill

Dan Blanchette, Carolina Population Center, UNC at Chapel Hill

Break: 4:00–4:15

Wishes and Grumbles: 4:15–5:30

Optional dinner: 7:00

Day 2   Featured training courses

Training course 1: 9:00–12:00 (includes 30-minute break)

Mata — matrix programming language

William Gould, President and Head of Development at StataCorp, will provide training in Mata—Stata's new matrix programming language. Mata is both an interactive environment for manipulating matrices and a full development environment that produces compiled and optimized code. Bill will cover both applications, with an emphasis on how you can use Mata to quickly program solutions and how you can easily create new Stata commands with Mata. (Mata is fully integrated with Stata). As you learn how to use Mata, it will become clear why Stata developers chose to implement some of the major new features in Stata 9 using Mata, including linear mixed models and multinomial probit.

Lunch: 12:00–1:15

Training course 2: 1:15–5:00 (includes 30-minute break)

Analysis of survey data and correlated data

Jeff Pitblado, Senior Statistician at StataCorp and principal architect of the new survey-analysis features in Stata 9, will discuss and demonstrate Stata's features for analyzing survey data and correlated data. Jeff will explain how and when to use the three major variance estimators for survey and correlated data—the linearization estimator, balanced repeated replications, and the clustered jackknife (the latter two having been added in Stata 9). He will also discuss sampling designs and stratification, including Stata's new features for estimation with data from multistage designs and for applying poststratification. A theme of the seminar will be how you can make inferences with correct coverage from data collected by single stage or multistage surveys or from data with inherent correlation, such as data from longitudinal studies.

Special needs:
If you have any special needs (vegetarian, handicapped, etc.), please contact Chris Farrar, cfarrar@stata.com, or Gretchen Farrar, gfarrar@stata.com.

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