Workshop on Advanced Research Methods (WARM)

Sharing innovations or innovative uses of research methods, Thursdays 8:30-9:30 PM (US ET)
Organizing Committee: Weihua An, Shawn Bauldry, Yan Long, Ian Lundberg, Xi Song, Jun Xu
Join the mailing list to stay updated and click here to propose speakers.

2023 Schedule

Zoom Meeting ID: 973 6768 9353; Passcode: warm
Add to Google Calendar    Outlook Calendar (.ics)

1. Causal Network Analysis

Time: February 23, 8:30-9:30 PM (US ET)

Speaker: Weihua An, Emory University

Abstract: Fueled by recent advances in statistical modeling and the rapid growth of network data, social network analysis has become increasingly popular in sociology and related disciplines. However, a significant amount of work in the field has been descriptive and correlational, which prevents the findings from being more rigorously translated into practices and policies. This talk provides a review of the popular models and methods for causal network analysis, with a focus on causal inference threats (such as measurement error, missing data, network endogeneity, contextual confounding, simultaneity, and collinearity), potential solutions (such as instrumental variables, specialized experiments, and leveraging longitudinal data), and future directions for causal network analysis. [Related Paper]

2. Bayesian Analysis: An Overview

Time: March 23, 8:30-9:30 PM (US ET)

Speaker: Jun Xu, Ball State University

Abstract: Previously portrayed as a heretical paradigm and subjective doctrine, Bayesian inference has emerged from this abject oblivion to a tidal wave to sweep through the world of statistics and data science. This talk begins with the origin of Bayesian statistics, the Bayes theorem, and recounts how and (possibly) why this framework was created. Previously called the inverse probability approach, and probably more appropriately—Laplacian statistics—Bayesian statistics has undergone the nadir and zenith of its practice, due in part to its computational inconvenience and subjective assignment of priors. With the computational breakthroughs, especially those in the 1980s and early 1990s, several seemingly unrelated dots were connected to create the Markov chain Monte Carlo (MCMC) methods. This has completely changed the landscape in the field and revolutionized the estimation methods for Bayesian statistics. Unlike the classical frequentist statistics with the null hypothesis significance testing (NHST), Bayesian statistics usually uses Bayes factors, probabilities (not the confusing and problematic p-values), and credible intervals (not confidence intervals) to make inferences. Along with prior information integrated into the current iteration of estimation, the Bayesian approach dovetails well with how information is processed and updated epistemologically. This talk is based on the introductory sections of this recently published book.  

3. Structural Equation Models: Applications and Frontiers

Time: April 20, 8:30-9:30 PM (US ET)

Speaker: Shawn Bauldry, Purdue University


4. Sequence Analysis: Past, Present, and Future

Time: September 21, 8:30-9:30 PM (US ET)

SpeakerTim Liao, State University of New York at Stony Brook 


5. Machine Learning for Social Science 

Time: October 19, 8:30-9:30 PM (US ET)

Speaker: Ian Lundberg, Cornell University 

Abstract: This talk is based on a paper with Jennie Brand and Nanum Jeon. Computational power and big data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention to further social science research. We aim to reduce perceived barriers to machine learning by summarizing several fundamental building blocks and their grounding in classical statistics. We present a few guiding principles and promising approaches where we see particular potential for machine learning to transform social science inquiry. We conclude that machine learning tools are increasingly accessible, worthy of attention, and ready to yield new discoveries for social research.

6. Text Analysis for Social Science

Time: November 17, 8:30-9:30 AM (US ET). Note this is on Friday morning.

Speaker: Ana Macanovic, Utrecht University 

 Abstract: The emergence of big data and computational tools has introduced new possibilities for using large-scale textual sources in social science research. We discuss five computational text analysis methods that can help researchers analyze large quantities of textual data and discuss exemplary applications in recently published research. First, we show how dictionary methods can assist the quantification of concepts of interest in texts; then, we summarize the potential of using semantic text analysis to extract information on social actors, social actions, and relationships between them. We move on to explore how unsupervised machine learning clustering methods assist inductive exploration of underlying meanings and concepts present in texts and how supervised machine learning classification methods support replication of manual coding onto new data. Finally, we discuss how powerful language models can help us map complex meanings, explore the evolution of meanings over time, and follow the emergence of new concepts in texts. We conclude by emphasizing the important implications of using large datasets and computational methods to infer complex meaning from texts in social sciences. This talk builds on this recently published paper.