The last decade has seen a massive increase in formality and rigor in quantitative and statistical research methodology in the social scientific study of online communities. These changes have led to higher reliability, increased reproducibility, and increased faith that our findings accurately reflect empirical reality. Unfortunately, these advancements have not come without important costs. When high methodological standards make it harder for scientists to know things, we lose the ability to speak about important phenomena and relationships.
There are many studies that simply cannot be done with the highest levels of statistical rigor. Significant social concepts such as race and gender can never truly be randomly assigned. There are relationships that are rare enough that they can never be described with a p-value of less than 0.05. To understand these phenomena, our methodology must be more relaxed. In our rush to celebrate the benefits of rigor and formality, social scientists are not exploring the ways in which more casual forms of statistical inference can be useful.
To discuss these issues and their impact in social computing research, the Community Data Science Collective will be holding the first ever workshop on Casual Inference in Online Communities this coming October in Evanston, Illinois. We hope to announce specific dates soon.
Although our program remains to be finalized, we’re currently planning to organize the workshop around five panels:
- Panel 1: Relaxing Assumptions
- A large body of work in statistics has critiqued the arbitrary and rigid “p < .05” significance standard and pointed to problems like “p-hacking” that it has caused. But what about the benefits that flow from a standard of evidence that one out of twenty non-effects can satisfy? In this panel, we will discuss some of the benefits of p-value standards that allow researchers to easily reject the null hypothesis that there is no effect.
- For example, how does science benefit from researchers’ ability to keep trying models until they find a publishable result? What do we learn when researchers can easily add or drop correlated measures to achieve significance? We will also talk about promising new methods available to scientists for overcoming high p-values like choosing highly informative Bayesian priors that ensure credible intervals far away from 0. We will touch on unconstrained optimization, a new way of fitting models by “guesstimating” parameters.
- Panel 2: Exputation of Missing Data
- Missing data is a major problem in social research. The most common ways of addressing missing data are imputation methods. Of course, imputation techniques bring with them assumptions that are hard to understand and often violated. How might types of imputation less grounded in data and theory help? How might we relax assumptions to infer things more casually about data—and with data—that we can not, and will not, ever have? How can researchers use their beliefs and values to infer data?
- Our conversation will focus on exputation, a new approach that allows researches to use their intuition, beliefs, and desires to imagine new data. We will touch on multiple exputation techniques where researchers engage in the process repeatedly to narrow in on desired results.
- Panel 3: Quasi-Quasi Experiments
- Not every study can be at the scientific gold standard of a randomized control experiment. The idea of quasi-experiments are designed to relax certain assumptions and requirements in order to draw similar types of inference from non-experimental settings. This panel will ask what might we gain if we were relax things even more.
- What might we learn from quasi-quasi experiments, where shocks aren’t quite exogenous (and might not even be that shocking)? We also hope to discuss superficial intelligence, post hoc ergo propter hoc techniques, supernatural experiments, and symbolic matching based on superficial semantic similarities.
- Panel 4: Irreproducible Results
- Since every researcher and every empirical context is unique, why do we insist that the same study conducted by different researchers should not be? What might be gained from embracing, or even pursuing, irreproducible methods in our research? What might we see if we allow ourselves to be the giants upon whose shoulders we stand?
- Panel 5: Research Ethics
Although we are hardly the first people to talk about casual inference, we believe this will be the first academic meeting on the topic in any field. Please plan to join us if you can!
If you would like to apply to participate, please send a position paper or extended abstract (no more than 1000 words) to email@example.com. We plan to post a list of the best submissions.