SSRC Workshop in Methods (WIM): 2012-2013

Spring 2013

Friday, January 11, 2013

Practical Meta-Analysis for the Social Sciences

Dr. Evan J. Ringquist

1:30-4:00pm, Indiana Memorial Union Oak Room

Meta-Analysis encompasses a suite of statistical techniques for drawing generalizable conclusions from a large number of empirical studies that examine the same research question. While used extensively in the experimental sciences, these techniques have only recently begun to be used with some frequency in the social sciences. After providing a brief introduction to meta-analysis, this session will illustrate how techniques common in econometrics can be coupled with the statistics of meta-analysis to generate methods for estimating meta-regression models that can be used to synthesize results from non-experimental research from the social sciences. I demonstrate the validity of these techniques and I illustrate their utility through meta-analyses of research examining (1) inequities in the distribution of environmental risk and (2) the effectiveness of educational vouchers.

Dr. Ringquistis Professor in the School of Public and Environmental Affairs at Indiana University, where he also holds courtesy appointments in the Department of Political Science and The West European Studies Center. Professor Ringquist’s research interests include evaluating the consequences of government actions, democratic influences in the policy making process, bureaucratic behavior, and quantitative methods. Current research projects include evaluating the effectiveness of international environmental agreements, and evaluating the extent to which Congressional candidates keep their campaign promises after elected. His most recent book, Meta-Analysis for Public Management and Policy, was published in January 2013 by Jossey-Bass.


Friday, January 18, 2013

An Introduction to Instrumental Variables Analysis

Dr. Christian B. Hansen

1:30-4:30pm, Indiana Memorial Union Oak Room

Instrumental variables (IV) methods provide an approach to estimating causal or structural relationships from observational data and have historically been widely used in economics. This lecture will provide an overview of the statistical underpinnings of the IV model and related methods. Particular attention will be paid to providing understanding of the assumptions that allow one to use instrumental variables to estimate causal quantities and the interpretation of the quantities estimated by IV methods. Several examples will be discussed.

Dr. Hansen is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. Dr. Hansen studies applied and theoretical econometrics, the uses of high-dimensional statistical methods in economic applications, estimation of panel data models, quantile regression, and weak instruments. Hansen received a PhD in 2004 in economics from the Massachusetts Institute of Technology, where he was a graduate research fellow of the National Science Foundation. He joined the Chicago Booth faculty in 2004. In 2008, Hansen was named a Neubauer Family Faculty Fellow.


Friday, January 25, 2013

Introduction to Spatial Statistics

Dr. Chunfeng Huang

2:30-4:30pm, Woodburn Hall 101

Spatial statistics has gained great interest in various disciplines, such as climatology, environmental science, ecology, economics, political science, and throughout the social sciences. The aim of this presentation is to introduce a variety of statistical methods in the spatial domain. We will introduce and discuss methods for three types of spatial data including geostatistical data, regional data, and spatial point patterns. Major subjects to be covered include spatial covariance functions, variograms, kriging, spatial autoregressive models, and K functions.

Dr. Huang is Assistant Professor of Statistics at Indiana University. He received his Ph. D. in statistics from Texas A&M University in 2001. His research interests include spatial statistics, spline smoothing, survival analysis, and statistics applications.


Friday, February 8, 2013

Statistical Analysis with Missing Data

Dr. Roderick J. A. Little

9:00am-12:00pm and 1:30-4:30pm, Woodburn Hall 101

Please note: This is a six-hour workshop with a morning and an afternoon session, including a break for lunch.

This course covers several methods for analyzing data with missing values. We start with an introduction to the main ideas using several examples. We then cover weighting methods; imputation methods – hot deck, single and multiple imputation; maximum likelihood for incomplete data distinguishing ignorable and non-ignorable missing data mechanisms; selection and pattern missing models for longitudinal studies; and computationally intensive methods including data augmentation and Gibbs sampling methods. The course will draw heavily from the Little and Rubin book Statistical Analysis with Missing Data. The course is designed for those with at least a master’s level background in statistics. Knowledge of complete data methods including likelihood based methods and familiarity with linear and logistic regression models and repeated measure analyses will be assumed. The major emphasis will be on the basic conceptual issues in dealing with missing data, available methods for analyzing such data, and their applications.

Dr. Little is Richard D. Remington Collegiate Professor of Biostatistics, School of Public Health, & Research Professor, Survey Research Center, Institute for Social Research at the University of Michigan. He is also currently Associate Director for Research & Methodology and Chief Scientist at the United States Census Bureau. His research expertise focuses on handling missing data in a variety of statistical analyses, and inference from sample surveys.


Friday, February 15, 2013

Introduction to R

Thomas Jackson

2:30-4:30pm, Wells Library 503 (Computer Lab)

This workshop will introduce the fundamentals of the R programming language. Participants will become familiar with the R user environment, basic data structures, and syntax. Methods for creating and importing data files will be covered, along with basic descriptive statistics. Click here for more information about this workshop: http://www.indiana.edu/~iscc/workshops.html.

Thomas Jackson is a senior consultant at the Indiana Statistical Consulting Center.


Friday, February 22, 2013

Applied Structural Equation Modeling For Dummies, By Dummies

Dr. Joseph J. Sudano and Dr. Adam T. Perzynski

1:30-4:30pm, Woodburn Hall 101

Despite our self-deprecating title, this talk should be understood as an attempt to make Structural Equation Modeling (SEM) accessible to a wide audience of researchers across many disciplines. We present a basic overview of SEM principles, some common nomenclature, a little algebra (with only one or two Greek letters!), a few real world examples, and then a foray into more advanced SEM techniques such as measurement invariance testing and latent growth curve modeling. Whether you just want to know how to read or critique an article that uses SEM in the analysis, or want to engage a couple of "SEM dummies" in some feisty Friday afternoon methods discussions, come to our talk...we'd love to visit with you.

Dr. Sudanois Assistant Professor of Medicine at Case Western Reserve University, where he is also Director of the Behavioral and Social Science unit in the Center for Health Care Research and Policy.

Dr. Perzynski is Senior Instructor of Medicine in the Center for Health Care Research and Policy at Case Western Reserve University.


Tuesday, February 26, 2013

Your Statistical Toolbelt

Stephanie Dickinson

2:30-4:00pm, Ballantine Hall 006

This workshop will give an overview of how to identify what types of data analysis tools to use for a project. We will walk through a map of the most common analysis tools (ANOVA, Regression, Chi-square, etc), and how they should be selected based on the type of data and the types of research questions you have.

Click here for more information about this workshop: http://www.indiana.edu/~iscc/workshops.html.

Stephanie Dickinson is a senior consultant at the Indiana Statistical Consulting Center.

Friday, March 1, 2013

Introduction to Nonparametric and Semiparametric Estimation

Dr. Juan Carlos Escanciano

1:30-4:00pm, Woodburn Hall 120

In this workshop we will review the most popular nonparametric and semiparametric estimation methods, focusing on cross section observations. Standard methods for model estimation in the social sciences rely heavily on functional form assumptions and distributions of unobserved components. Nonparametric and semiparametric methods relax these assumptions, reducing the risk of misspecification errors. These methods are applicable to a wide variety of estimation problems. We will illustrate some of these applications with examples, including density estimation, regression estimation, partially linear models, single-index models, and propensity score methods. Emphasis will not be on math but on how these methods are applied in practice. We will discuss implementation of the procedures in R.

Dr. Escancianois Associate Professor of Economics and Adjunct Associate Professor of Statistics at Indiana University. He received the PhD. In Economics from University Carlos III in Madrid in 2004 and has been at IU since 2006. His research interests fall broadly into the area of econometric theory, with emphasis on specification testing, semiparametric estimation and identification and risk management.


Friday, March 8, 2013

Applications of Linear Models in R

Dr. William Wyatt

2:00-4:00pm, Woodburn Hall 120

This workshop will give a guided tour through using linear models in the R programming language. We will discuss why and how to use linear models along with how to interpret output from R. A wide range of topics will be covered including linear regression, ANOVA, and logistic regression.

Click here for more information about this workshop: http://www.indiana.edu/~iscc/workshops.html.

Dr. William Wyatt is a visiting assistant professor in the Department of Statistics.


Friday, March 22, 2013

Causal inference in the social sciences: Estimating the effects of time-varying treatments in the presence of time-varying confounding: An application to neighborhood effects on high school graduation

Dr. David J. Harding

2:30-4:30pm, Woodburn Hall 120

Conventional regression methods fail when estimating the effects of time-varying treatments in the presence of time-varying confounding. I will discuss methods for causal inference in such situations, focusing on inverse probability of treatment weighting for estimating marginal structural models (Robins et al. 2000) and a two-stage regression with residuals method (Almirall et al. 2010) for estimating time-varying effect moderation in a structural nested mean model. I illustrate the use of these methods in two recent studies of neighborhood effects on high school graduation.

Dr. Harding is Associate Professor of Sociology and Public Policy at the University of Michigan. He studies urban poverty and inequality, incarceration and prisoner reentry, adolescence, and statistical methods for causal inference. His book, Living the Drama (University of Chicago Press, 2010), examines the role of neighborhoods in adolescent outcomes related to education and romantic and sexual behavior, focusing on exposure to violence and the cultural context of poor communities. Harding is currently working on projects on prisoner reentry, the effects of community context on adolescent and young adult romantic relationships, and for-profit colleges and educational inequality.


Tuesday, March 26, 2013

Choose Your Own Statistical Adventure

Stephanie Dickinson

2:30-4:00pm, Cedar Hall 112

This workshop will be an adventure through the path of the data analysis process: from the open fields of formulating your research questions, through the forest of selecting the appropriate analysis tools, wading through the necessary model assumptions and diagnostic tools, investigating relevant plots and tables, and crossing the final bridge for interpreting results and reporting conclusions.

Click here for more information about this workshop: http://www.indiana.edu/~iscc/workshops.html.

Stephanie Dickinson is a senior consultant at the Indiana Statistical Consulting Center.

Friday, March 29, 2013

Group-Based Trajectory Models: An Overview

Dr. Daniel Nagin

10:00-11:30am: Presentation, Woodburn Hall 120
1:00-2:30pm: Hands-on Workshop, Ballantine 006

A developmental trajectory describes the course of a behavior over age or time. This lecture will provide an overview of a group-based method for analyzing developmental trajectories. The method provides the capability to (1) identify rather than assume distinctive groups of trajectories, (2) estimate the proportion of the population following each such trajectory group, (3) relate group membership probability to individual characteristics and circumstances, (4) relate trajectories to subsequent outcomes, and (5) analyze the interconnection of trajectories of different behaviors. The lecture will be followed by a workshop that demonstrates the use of a Stata plugin for estimating group-based trajectory models. For the workshop please bring your laptop w/Stata with the proc traj plugin installed.

Dr. Naginis Teresa and H. John Heinz III University Professor of Public Policy and Statistics in the Heinz College, Carnegie Mellon University. He is an elected Fellow of the American Society of Criminology and of the American Society for the Advancement of Science and is the 2006 recipient of the American Society of Criminology’s Edwin H Sutherland Award. His research focuses on the evolution of criminal and antisocial behaviors over the life course, the deterrent effect of criminal and non-criminal penalties on illegal behaviors, and the development of statistical methods for analyzing longitudinal data.


Friday, April 5, 2013

Visualizing Sequences in the Social Sciences: Relative Frequency Sequence Plots

Dr. Tim Liao

1:30-3:00pm: Presentation, Woodburn Hall 120
3:15-4:45pm: Hands-on Workshop, Woodburn Hall 120

Visualization is a potentially powerful tool for exploration and complexity reduction of categorical sequence data. This presentation discusses currently available sequence visualization against established criteria for graphical excellence in the visual display of quantitative information. Existing sequence graphs fall into two groups: they either represent categorical sequences or summarize them. We discuss in the presentation relative frequency sequence plots as an informative way of graphing sequence data and as a bridge between data representation graphs and data summarization graphs. The efficacy of the proposed plot is assessed by the R2 and the F-statistics. The applicability of the proposed graphs is demonstrated using data from the German Life History Study (GLHS) on women’s family formation. For the workshop please bring your laptop with R installed.

Dr. Liao is Professor of Sociology and Statistics at the University of Illinois at Urbana-Champaign. He is Editor of Sociological Methodology and former editor of Sage’s Quantitative Applications in the Social Sciences series, and serves on the editorial board of Demography. His current methodological research focuses on inequality measurement, estimation of social stratification, and visualization of social science sequences.


Friday, April 19, 2013

Analyzing Count Data

Dr. Pravin K. Trivedi

Postponed until next academic year

Dr. Trivedirecently retired from Indiana University, where he was Distinguished Professor and J. H. Rudy Professor of Economics.