Tuesday, March 31, 2015

2015 Workshops: Longitudinal Data Analysis

In 2015, we are offering four courses on longitudinal data analysis. Longitudinal analysis is the study of short series of observations obtained from many respondents over time and is also referred to as panel analysis (of a cross-section of time series), or repeated measures, or growth curve analysis (polynomials in time), or multilevel analysis (where one level is a sequence of observations from respondents). Longitudinal analysis is used for panel surveys, experiments, and quasi-experiments in health and biomedicine, education and psychology, and the evaluation of prevention and treatment programs.


Second Four-Week Session
Longitudinal Analysis
Dates and Time: July 20-August 14, 10 a.m.-noon
Location: Ann Arbor, MI
Instructor: Michael Berbaum, University of Illinois at Chicago
Description: This course treats the statistical basis and practical application of linear models for longitudinal normal data and generalized linear models for longitudinal binary, count, and ordinal data. The approach involves inclusion of random effects in linear models to reflect within-person cross-time correlation. Techniques for irregularly observed (unequally spaced) data will be covered.
Program Scholar Fee: ICPSR members, $2,300; Non-members, $4,600


Three- to Five-Day Workshops
Analyzing Intensive Longitudinal Data: A Guide to Diary, Experience Sampling, and Ecological Momentary Assessment Methods
Dates and Time: June 9-12, 9 a.m.-5 p.m.
Location: Amherst, MA
Instructors: Niall Bolger, Columbia University, and Jean-Philippe Laurenceau, University of Delaware
Description: Intensive longitudinal methods, often called experience sampling, daily diary, or ecological momentary assessment methods, allow researchers to study people's thoughts, emotions, and behaviors in their natural contexts. Typically they involve self-reports from individuals, dyads, families or other small groups over the course of hours, days, and weeks. Such data can reveal life as it is actually lived and provide insights that are not possible using conventional experimental or survey research methods. Intensive longitudinal data, however, present data analytic challenges stemming from the multiple levels of analysis and temporal dependencies in the data. The multilevel or mixed-effects model for longitudinal data is a flexible analytic tool that can take account of these complexities, and the goal of the 4-day workshop is to provide training in its use.
Fee: ICPSR members, $1,400; Non-members, $2,800

Longitudinal Data Analysis, Including Categorical Outcomes
Dates and Time: June 22-26, 9 a.m.-5 p.m.
Location: Ann Arbor, Michigan
Instructor: Donald Hedeker, University of Chicago
Description: This workshop will focus on the analysis of longitudinal data using mixed models, beginning with models for continuous outcomes and including a description of the multilevel or
hierarchical representation of the model. For dichotomous, ordinal and nominal outcomes, this workshop will focus next on the mixed logistic regression model and generalizations of it. Finally, the workshop will cover missing data issues. In all cases, methods will be illustrated using software, with SAS used for most examples, and augmented with use of SPSS for continuous outcomes and SuperMix for categorical outcomes.
Fee: ICPSR member, $1,500; Non-member, $3,000

Applied Multilevel Models for Longitudinal and Clustered Data 
Dates and Time: July 13-17, 9 a.m.-5 p.m.
Location: Boulder, CO
Instructor: Jonathan Templin, University of Kansas
Description: Multilevel models, also known as hierarchical linear models or general linear mixed models, provide quantification and prediction of random variance due to multiple sampling dimensions (across occasions, persons, or groups). Multilevel models offer many advantages for analyzing longitudinal data, such as flexible strategies for modeling change and individual differences in change, the examination of time-invariant or time-varying predictor effects, and the use of all available complete observations. Multilevel models are also useful in analyzing clustered data (e.g., persons nested in groups), in which one wishes to examine predictors pertaining to individuals or to groups. This workshop will serve as an applied introduction to multilevel models, beginning with longitudinal data and continuing onto clustered data.
Fee: ICPSR member, $1,500; Non-member: $3,000

You can register for all of these course through our portal.


Wednesday, March 18, 2015

2015 Sponsored 3- to 5-Day Workshops

Each year, the Summer Program offers several substantive, or sponsored, 3- to 5-day workshops, which are organized by topical archives within ICPSR and other organizations. Generally, each archive covers the registration fees for their substantive workshop. Participation in a sponsored workshop is limited to 20-25 individuals and is determined by a competitive application process.

In 2015, we will offer the following sponsored workshops:

June 8 - 12
Exploratory Data Mining Via SEARCH Strategies
Location: Ann Arbor, Michigan
Instructor: John J. McArdle, University of Southern California
Description: This workshop provides an overview of current techniques in exploratory data mining for quantitative research in the social and behavioral sciences. Exploratory data mining uses computational methods on large amounts of data in order to construct predictive models of behavior, in contrast to the standard hypothesis testing of many standard statistical techniques. These data mining techniques can be used to model categorical choices, to classify groups, to discover patterns, and to model longitudinal data. Exploratory data mining techniques can be fruitful in most situations where categorical regression or many multivariate analytic techniques are used. This workshop will explore key algorithms, including regression trees and SEM models (CART, SEMtrees, PARTY, etc.). This work was initiated by the SAS algorithm SEARCH (Morgan & Sonnquest, 1963), and the workshop will begin here and then move to use of the free software modules in R that are currently used for exploratory data mining.
Application deadline: April 30, 2015
Sponsor: Institute for Social Research, Development Office


June 10 - 12
Transparency and Reproducibility Methods for Social Science Research
Location: Berkeley, California
Instructors: Katherine E. Casey, Stanford University; Garret Christensen, Clara Cohen, Solomon Hsiang, Edward Miguel, Leif Nelson, and Maya Petersen, University of California at Berkeley; Scott Desposato, University of California at San Diego; Eric Eich, University of British Columbia; and Nicole Janz, University of Cambridge
Description: Participants can expect to finish the program with a thorough overview and understanding of best practices for open, reproducible research, allowing them to remain in the vanguard of new scientific frontiers. They are encouraged to bring existing research questions and ideas based on their own interests, and seek support and feedback from instructors and other attendees.
Application deadline: April 5, 2015
Sponsor: Berkeley Initiative for Transparency in the Social Sciences


June 22 - 24
Secondary Data Analysis and the National Addiction & HIV Data Archive Program (NAHDAP)
Location: Ann Arbor, Michigan
Instructor: Lisa Dierker, Wesleyan University
Description: This workshop will support participants in planning and conducting secondary data analysis using data sets from the National Addiction and HIV Data Archive Program (NAHDAP). Sessions will include brief presentations focused on the nuts and bolts of secondary data analysis followed by hands on workshop sessions that include one-on-one consultation for conducting your own research.
Application deadline: May 3, 2015
Sponsor: NAHDAP


July 20 - 24
Advanced Topics in Using the National Survey of Early Care and Education (NSECE)
Location: Ann Arbor, Michigan
Instructors: Johanna Bleckman, ICPSR, and Rupa Datta, NORC at the University of Chicago
Description: Coming soon
Application deadline: Coming soon
Sponsor: Child Care & Early Education Research Connections


August 3 - 7
Immigration, Immigrants and Health Conditions, Health Status, and Policies: Examining Multilevel and Multidimensional Models and Approaches
Location: Ann Arbor, Michigan
Instructors: Gilbert Gee, University of California, Los Angeles; Krista M. Perreira, University of North Carolina at Chapel Hill; Gabriel Sanchez, University of New Mexico; John A. Garcia, ICPSR
Description: This workshop is designed for individuals who are researching health disparities and conditions, as well as health equity as it pertains to immigrant populations. Topics covered include the following: "the Immigrant (epidemiological) health paradox"; health status and access factors; the role of culture, acculturation, and language; U.S. health policies, socio-political structures, and immigrants; capturing and measuring the multi-dimensional immigrant experience; transnational ties and health; health and countries of origin; data resources and limitations; and analytical approaches incorporating multilevel models.
Application deadline: May 1, 2015
Sponsors: Health and Medical Care ArchiveResource Center for Minority Data, and the Robert Wood Johnson Foundation


Tuesday, March 17, 2015

2015 Courses in Network Analysis

In 2015, the Summer Program is offering several workshops on network analysis.

First Four-Week Session
Network Analysis 
June 22 - July 17, 1-3 p.m. | Ann Arbor, MI | Ann McCranie, Indiana University


Second Four-Week Session
Network Analysis: Advanced Topics
July 20 - August 14, 10 a.m.-Noon | Ann Arbor, MI | Bruce Desmarais, University of Massachusetts


Three- to Five-Day Workshops
A Crash Course in Network Analysis: From Description to Inference
May 4 - 8, 9 a.m.-5 p.m. | Montreal, QC | Skyler Cranmer, University of North Carolina


Network Analysis: An Introduction
June 1 - 5, 9 a.m.-5 p.m. | Ann Arbor, MI | Ann McCranie, Indiana University


Network Analysis: Statistical Approaches
June 8 - 12, 9 a.m.-5 p.m. | Ann Arbor, MI | John Skvoretz, University of South Florida


Social Network Analysis: An Introduction with an Emphasis on Application in R
July 6 - 10, 9 a.m.-5 p.m. | Berkeley, CA | Lorien Jasny, University of Maryland, College Park


Analyzing Social Networks: An Introduction
August 10 - 14, 9 a.m.-5 p.m. | Chapel Hill, NC | Douglas Steinley, University of Missouri

Friday, March 13, 2015

2015 Courses on the R Statistical Computing Environment

Image courtesy of www.r-project.org
The R statistical programming language and computing environment has become the de-facto standard for writing statistical software among statisticians and has made substantial inroads in the social sciences. R is now possibly the most widely used statistical software in the world. R is a free, open-source implementation of the S language and is available for Windows, Mac OS X, and Unix/Linux systems.

A statistical software "package," such as SPSS, is primarily oriented toward combining instructions with rectangular case-by-variable datasets to produce (often voluminous) output. Such packages make it easy to perform routine data analysis tasks, but they make it relatively difficult to do things that are innovative or nonstandard or to extend the built-in capabilities of the package. In contrast, a good statistical computing environment makes routine data analysis easy and also supports convenient programming. R fulfills both of these requirements, and users can readily write programs that add to its already impressive facilities. R is also particularly capable in the area of statistical graphics.

In 2015, the ICPSR Summer Program is offering several courses on R:

FOUR-WEEK LECTURES

Session I: June 23 - July 3, 5:30 p.m.-7:30 p.m.
Introduction to the R Statistical Computing Environment
Location: Ann Arbor, MI
Instructor: John Fox, McMaster University
Description: These lectures provide an introduction to the R statistical computing environment. The first four lectures present a basic overview of and introduction to R, including statistical modeling in R -- in effect, using R as a statistical package. The following five sessions pick up where the basic lectures leave off and are intended to provide the background required to use R seriously for data analysis and presentation, including an introduction to R programming and to the design of custom statistical graphs, unlocking the power in the R statistical programming environment.

Session II: July 21 - July 31, 5:30 p.m.-7:30 p.m.
Introduction to the R Statistical Computing Environment
Location: Ann Arbor, MI
Instructor: Kerem Ozan Kalkan, Eastern Kentucky University
Description: These lectures provide a basic introduction to the R environment. Specific topics will include: Elements and rules of the R language; R functions and objects; statistical models in R, including linear and generalized linear models; data manipulation; constructing statistical graphs; and using R packages. The overall objective is to provide some facility in the use of R, to a level that enables participants to employ this software for assignments and projects in other Summer Program courses.


THREE- TO FIVE-DAY WORKSHOPS

May 20 - 22, 9 a.m.-5 p.m.
R: Learning by Example
Location: Montreal, QC
Instructor: Kelly Gleason, University of Wisconsin at Milwaukee
Description: This course will introduce users to R by working through a number of example analyses commonly encountered in the social sciences. We use these examples as ways to motivate rudimentary topics such as data management, graphics, and finding help and other useful routines in R. We will cover linear models, generalized linear models, discrete choice models, and models for multivariate data. We will have ample time for participants to ask questions and work through examples themselves. Participants should leave the course with a good understanding of R and how to put it to use in their own research.

June 8 - 10, 9 a.m.-5 p.m.
R: Learning by Example
Location: Boulder, CO
Instructor: David Armstrong, University of Wisconsin at Milwaukee
Description: This course will introduce users to R by working through a number of example analyses commonly encountered in the social sciences. We use these examples as ways to motivate rudimentary topics such as data management, graphics and finding help and other useful routines in R. We will cover linear models, generalized linear models, discrete choice models, and models for multivariate data. We will have ample time for participants to ask questions and work through examples themselves. Participants should leave the course with a good understanding of R and how to put it to use in their own research.


Thursday, March 5, 2015

2015 Summer Program Courses in Chapel Hill, North Carolina

In 2015, we will hold the following three- to five-day workshops at the Odum Institute at the University of North Carolina at Chapel Hill.

May 18 - 22
Latent Growth Curve Models (LGCM): A Structural Equation Modeling Approach 
Instructor: Kenneth Bollen, University of North Carolina at Chapel Hill

May 27 - 29
Growth Mixture Models: A Structural Equation Modeling Approach
Instructor: Sarah Mustillo, University of Notre Dame

June 1 - 5
Multilevel Models: Pooled and Clustered Data
Instructor: Thomas M. Carsey, University of North Carolina at Chapel Hill

June 8 - 10
Introduction to Spatial Regression Analysis
Instructor: Elisabeth Root, University of Colorado at Boulder

August 3 - 5
An Applied Introduction to Bayesian Methods
Instructor: Jeffrey Harden, University of Colorado at Boulder

August 5 - 7
Introduction to Mixed Methods Research
Instructor: Kathleen M. T. Collins, University of Arkansas at Little Rock

August 10 - 12
Qualitative Research Methods
Instructor: Paul Mihas, University of North Carolina at Chapel Hill

August 10 - 14
Analyzing Social Networks: An Introduction
Instructor: Douglas Steinley, University of Missouri

For additional information, including travel and lodging information for visitors, check out the Odum Institute's website.