About

The Quantitative Methods Lab in the Department of Psychology is directed by Felix Thoemmes.

Our lab is interested in developing and exploring new quantitative tools to improve data analysis of social science research.  We use statistical theory, simulation studies, and applied examples in our work.  Particular research interests are the use of modern tools of causal inference, problems of missing data, and advanced structural equation models.

The lab has opportunities to become involved in research projects on quantitative methods. Projects include work on causal inference, missing data, and structural equation models.  If you are a Cornell undergraduate or graduate student and are interested in getting involved in the lab, please contact Felix Thoemmes at fjt36 [at] cornell.edu (fjt36[at]cornell[dot]edu).

People

Lab director

Felix Thoemmes, Associate Professor
Director, Quantitative Methods Lab
G62A Martha Van Rensselaer Hall | Cornell University
Ithaca, NY 14853 | Phone: (607) 254-6411 | Email: fjt36 [at] cornell.edu (fjt36[at]cornell[dot]edu)

Graduate students

Jiwoo Kim

Undergraduate students

Kelly Kim

Graduate lab alumni

  • Ze Jin
  • Wang Liao
  • Sarah Moore
  • Shira Mingelgrin

Undergraduate lab alumni

  • Marina Yamasaki
  • Tianwang Liu
  • Janice Lee

Research

View a full list of Felix Thoemmes' publications.

Causal inference is at the heart of many questions in social sciences, yet the study of it is in some sense still a novel and currently thriving field. In our lab we draw on the classic Campbellian tradition, the potential outcomes framework, and causal graphical models. In particular we work on extending methods of propensity score matching to complex data situations and improve model testing in causal graphical models.

Missing data are a prevalent problem in nearly all studies conducted in psychology, education, and other fields. Principled methods to deal with missing data are slowly being adapted by these fields. However, there are still a host of unresolved issues concerning selection of so-called auxiliary variables, or methods to deal with missing data in complex data analytic situations, e.g. those involving clustered data or latent heterogeneity.

Structural equation modeling is a flexible statistical tool to model (among other things) latent factors and direct and indirect effects. Recently there has been more interest in the causal underpinning of such models, in particular the causal assumptions involving indirect effects. Our lab researches methods that strengthen causal conclusions of such models in the presence of causal heterogeneity. Furthermore, we are interested in exploring rigorous tests of models that allow eliminating alternative causal pathways.

Software and replication code

Download a program to conduct propensity score analysis in SPSS written by Felix Thoemmes.
View the accompanying paper
 

Thoemmes is currently developing an extension of the program to conduct propensity score matching in multi-level models, following the approach suggested by Thoemmes & West, 2011.

The paper "Continuously Cumulating Meta-Analysis and Replicability" by Braver, Thoemmes, and Rosenthal (2014) presented ways to use CCMA to address issues of replicability.

Available for download are an R program that provides a template for a CCMA analysis, and an Excel spreadsheet that performs fixed-effects CCMA.
ccmatemplate.R
ccmatemplate.xlsx

Thoemmes,F. (2015). Empirical evaluation of directional-dependence tests. International Journal of Behavior Development
R Code

Also available at:
Harvard Dataverse
Open Science Framework