About

Felix Thoemmes is an Associate Professor in the Department of Human Development in the College of Human Ecology at Cornell University, with a joint appointment in the Department of Psychology and membership in the graduate field of Statistics. His research broadly focuses on quantitative methods in psychology, with a special emphasis on causal inference and missing data. Thoemmes works on developing and evaluating statistical tools for social scientists and applying these methods to data collected by social scientists. His specific interests include regression-discontinuity design, propensity score matching, and missing data analysis using missingness instruments. He has contributed to the development of web-based interfaces for the analysis of regression-discontinuity designs, including an R package released on CRAN and a Shiny-based GUI. Thoemmes actively publishes in methodological and applied journals and collaborates on projects spanning developmental psychology. He teaches courses on Quantitative Methods and Data Science, incorporating active learning strategies, and has served as the outgoing programming chair for the APA conference, Division 5. His academic background includes a Ph.D. in Quantitative Psychology from Arizona State University, a Fulbright M.A. in Experimental Psychology from Indiana State University, and a department chair position at the University of Landau, Germany.

Research topics

• Psychology
• Sociology
• Computer Science
• Epistemology
• Social psychology
• Econometrics
• Medicine
• Communication
• Cognitive psychology
• Clinical psychology
• Psychiatry
• Internal medicine
• Cognitive science

Selected publications

• Psychological Science in the Wake of COVID-19: Social, Methodological, and Metascientific Considerations

  UNC Libraries · 2025-05-14

  articleOpen access

  The COVID-19 pandemic has extensively changed the state of psychological science from what research questions psychologists can ask to which methodologies psychologists can use to investigate them. In this article, we offer a perspective on how to optimize new research in the pandemic's wake. Because this pandemic is inherently a social phenomenon-an event that hinges on human-to-human contact-we focus on socially relevant subfields of psychology. We highlight specific psychological phenomena that have likely shifted as a result of the pandemic and discuss theoretical, methodological, and practical considerations of conducting research on these phenomena. After this discussion, we evaluate metascientific issues that have been amplified by the pandemic. We aim to demonstrate how theoretically grounded views on the COVID-19 pandemic can help make psychological science stronger-not weaker-in its wake.

  PublisherDOI

• Causal Assumptions of the Two-Wave Longitudinal Mediation Model

  Multivariate Behavioral Research · 2025-01-02 · 1 citations

  articleSenior author
  PublisherDOI

• Causal Inference for Dummies: A Tutorial on Directed Acyclic Graphs and Balancing Weights

  Social Cognition · 2025-06-01 · 3 citations

  articleOpen accessSenior author

  Traditionally, causal claims in social cognition research have been reserved for experimental designs. However, restricting causal claims to experimental research limits the type of questions that can be answered satisfactorily—including questions about geographical differences or changes over time recently popularized in the field of social cognition. In this tutorial, we outline a principled approach to causal inference for nonexperimental designs. We describe how researchers can use directed acyclic graphs to make their causal model explicit and discuss one strategy to estimate causal effects: balancing weights. We show how researchers can use balancing weights to obtain unbiased causal effects from nonexperimental designs. We provide detailed R Code to implement balancing weights analyses and provide readers with resources to delve deeper into the field of causal inference.

  PublisherOA PDFDOI

• Causal Inference for Dummies: A Tutorial on Directed Acyclic Graphs and Balancing Weights

  2024-08-01

  preprintOpen accessSenior author

  Traditionally, causal claims in social cognition research have been reserved for experimental designs. However, restricting causal claims to experimental research limits the type of questions that can be answered satisfactorily – including questions about geographical differences or changes over time recently popularized in the field of social cognition. In this tutorial, we outline a principled approach to causal inference for non-experimental designs. We describe how researchers can use Directed Acyclic Graphs to make their causal model explicit and discuss one strategy to estimate causal effects: Balancing weights. We show how researchers can use balancing weights to obtain unbiased causal effects from non-experimental designs. We provide detailed R Code to implement balancing weights analyses and provide readers with resources to delve deeper into the field of causal inference.

  PublisherOA PDFDOI

• Effects of SES on Executive Attention in Malay–English bilingual children in Singapore – ADDENDUM

  Bilingualism Language and Cognition · 2023-04-19 · 1 citations

  article

  An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

  PublisherDOI

• Bias and sensitivity analyses for linear front-door models

  Methodology · 2023-09-28

  articleOpen access1st authorCorresponding

  <p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">The front-door model allows unbiased estimation of a total effect in the presence of unobserved confounding. This guarantee of unbiasedness hinges on a set of assumptions that can be violated in practice. We derive formulas that quantify the amount of bias for specific violations, and contrast them with bias that would be realized from a naive estimator of the effect. Some violations result in simple, monotonic increases in bias, while others lead to more complex bias, consisting of confounding bias, collider bias, and bias amplification. In some instances, these sources of bias can (partially) cancel each other out. We present ways to conduct sensitivity analyses for all violations, and provide code that performs sensitivity analyses for the linear front-door model. We finish with an applied example of the effect of math self-efficacy on educational achievement.

  PublisherDOI

• Review for "These Are Not the Effects You Are Looking for: Causality and the Within-/Between-Persons Distinction in Longitudinal Data Analysis"

  2022-04-07

  peer-review1st authorCorresponding
  PublisherDOI

• Review for "A Primer on Structural Equation Model Diagrams and Directed Acyclic Graphs: When and How to Use Each in Psychological and Epidemiological Research"

  2022-04-05

  peer-review1st authorCorresponding
  PublisherDOI

• Derailment and depression in college: Tests of 3-year predictive capacity and moderation by self-reflection, brooding, perfectionism, and cognitive flexibility.

  Journal of Counseling Psychology · 2022-11-17 · 9 citations

  articleSenior author

  While rich with opportunities for self-exploration, the transition to and through college is stressful, often associated with the onset or exacerbation of mental illness. Attending to these characteristics, this preregistered study asked whether derailment-or difficulties reconciling perceived identity change-in freshman year predicts senior depressive symptoms, and how individual risks for depression relate to this association. Derailment and depressive symptoms evidenced significant 3-year stability, and these constructs had positive cross-sectional associations in both freshman and senior year. Freshman derailment failed to predict senior depressive symptoms for the average student, but individual differences in self-reflection moderated the association: freshman derailment positively predicted senior depression among those lowest in self-reflection. Together, this study suggests derailment and depressive symptoms are consistently related at critical points of transition, and some individual differences in cognition may help predict their long-term association. While useful for understanding nuances between derailment and depression, these findings also inform ways of attending to and supporting college students through periods of transition. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

  PublisherDOI

• Review for "A Primer on Structural Equation Model Diagrams and Directed Acyclic Graphs: When and How to Use Each in Psychological and Epidemiological Research"

  2022-08-01

  peer-review1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Johannes Textor

  52 shared

• Yves Rosseel

  Ghent University

  51 shared

• Howard Tennen

  University of Connecticut

  36 shared

• Alex Zautra

  36 shared

• Mary C. Davis

  Oregon Health & Science University

  36 shared

• Patrick H. Finan

  University of Virginia

  36 shared

• Kaylin Ratner

  University of Illinois Urbana-Champaign

  18 shared

• Anthony L. Burrow

  Cornell University

  15 shared