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The second edition of RECSM Winter Methods School is comprised of two courses. Each course lasts for three days, with four-hour long lectures per day. 

The courses are open to academics (students, professors, individuals employed by colleges and universities, and other institutions of higher education) and non-academics (consultants and other practitioners from the private sector or national and international organizations).

The RECSM Winter Methods School 2021 will be held online due to the situation of Covid-19, from February 17th to 26th 2021. 

Course fees: 250€. 10% discount when registering in both courses. 
​Cancellation Policy: All refund/cancellation requests must be provided via email to [email protected] and received prior to February 10th, 2021. From February 11th cancellation will not be refunded.

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The course offers three four-hour lectures on the design and implementation of online and survey experiments taught by experts in this type of experiments.

 

February 17, 2021

14:00 - 18:00 Introduction to experimental designs

Luis Miller, CSIC

This introductory section discusses the main features of online and survey experiments, especially how they differ from offline laboratory and field experiments.

References:

  • Gërxhani, Klarita, and Luis Miller (2021). Experimental sociology. In Gërxhani, K., N.D. de Graaf, and W. Raub (eds.) Handbook of rigorous theoretical and empirical sociology. Research Handbook in Sociology Series, Edward Elgar.
  • Morton, Rebecca, and Kenneth Williams (2010) Experimental Political Science and the Study of Causality. Cambridge: Cambridge University Press.

 

February 18, 2021

14:00 - 18:00 Online and lab-in-the-field experiments

Amalia Álvarez, MPI Bonn

This second section will be devoted to the design of web experiments. The focus will be on the implementation of online treatments and the measure of attitudinal and behavioral variables online. The section will emphasize the potentiality of web experiments for large-scale data collection and on implementing lab-in-the-field type of experiments.

References:

  • Álvarez-Benjumea, A., & Winter, F. (2018). Normative change and culture of hate: An experiment in online environments. European Sociological Review, 34(3), 223-237.
  • Álvarez-Benjumea, A., & Winter, F. (2020). The breakdown of antiracist norms: A natural experiment on hate speech after terrorist attacks. Proceedings of the National Academy of Sciences.  2020 117 (37) 22800-22804; Dimant, E. (2020). Hate Trumps Love: The Impact of Political Polarization on Social Preferences. Available at SSRN.
  • Keuschnigg, M., Bader, F., & Bracher, J. (2016). Using crowdsourced online experiments to study context-dependency of behavior. Social science research, 59, 68-82.
  • Munger, K. (2017). Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior, 39(3), 629-649.

 

February 19, 2021

14:00 - 18:00 Survey experiments

Sofia Breitenstein, UAB

This part of the course will review some of the most important survey experimental designs in public opinion research, including vignette and list experiments and focusing especially on the implementation of conjoint experiments.

References:

  • Sniderman, P. M. (2018). Some Advances in the Design of Survey Experiments. Annual Review of Political Science, 21(1), 259–275. https://doi.org/10.1146/annurev-polisci-042716-115726
  • Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2014). Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments. Political Analysis, 22(1), 1–30. https://doi.org/10.1093/pan/mpt024
  • Hainmueller, Jens, Dominik Hangartner, and Teppei Yamamoto. 2015. “Validating Vignette and Conjoint Survey Experiments against Real-World Behavior.” Proceedings of the National Academy of Sciences of the United States of America 112 (8):2395–2400.
  • Leeper, T. J., Hobolt, S. B., & Tilley, J. (2019). Measuring Subgroup Preferences in Conjoint Experiments. Political Analysis, 1–15. https://doi.org/10.1017/pan.2019.30

Course description

What is the effect of smoking on health? Does having an additional child increase the risk of poverty? Are development policies targeted on small firms effective in increasing investments?

Most studies in the social sciences are motivated by questions that are causal in nature.

However, in these areas experiments are not always possible because of ethical or practical reasons and the estimation of causal effects often has to rely on observational studies. The validity of inference will then strictly depend on the plausibility of the assumptions underlying the employed statistical techniques.

Particularly challenging are those situations where one or more important confounding factors are not observed and those characterized by reverse causality. This course will cover some of the most popular techniques for estimating causal effects with observational data in these situations: difference-in-difference methods, instrumental variable regression, and regression discontinuity designs. Special emphasis will be placed during the course on discussing the plausibility of the identifying assumptions, the data requirements and other practical and theoretical challenges for the implementation of each method.

This short course will offer to participants theoretical and applied perspectives on the covered topics. Examples will be drawn from political science, sociology, economics, public health and policy evaluation. Lab sessions will demonstrate the implementation of the covered techniques using the software STATA. Syntax in R will be also provided.

  • Prerequisites: Familiarity with regression analysis and basic statistical concepts. Familiarity with basic concepts in causal inference is welcome but not necessary.
  • Software: STATA will be used in the lab sessions. Although familiarity with STATA is preferable, this is not strictly necessary. Easy to follow do-files and dataset to replicate the analyses discussed during the lectures will be provided.

February 24, 2021

14:00 - 18:00

  • Introduction and course overview
    • Causation vs association
    • Good Causal Inference is Theory + Data + Methods
    • Randomized experiments versus observational studies
    • Unobserved confounding and reverse causality
    • Overview of statistical methods corresponding to different sets of assumptions
  • Difference-in-Difference (DiD) methods:
    • Assumptions
    • Implementation
    • Examples from published research
    • Pros and cons
  • Hands on!
    • Guided lab session on DiD methods with STATA
  • Summary

February 25, 2021

14:00 - 18:00 

  • Instrumental Variable Regression
    • Assumptions
    • Implementation
    • Examples from published research
    • Pros and cons
  • Hands on!
    • Guided lab session on Instrumental Variable Regression
  • Summary

 

February 26, 2021

14:00 - 18:00

  • Regression Discontinuity Designs
    • Assumptions
    • Implementation
    • Examples from published research
    • Pros and cons
  • Hands on!
    • Guided lab session on Regression Discontinuity Designs
  • Summary
    • Key take home messages
    • Directions on current and future developments in causal inference for the social sciences