I'll be presenting a poster tomorrow afternoon. In the meantime, I've been sitting in interesting presentations today.
First up this morning was a panel on psychometric approaches. There was a lot of attention given to Bayesian approaches, and this just signals to me something I've suspected for a while - I should learn Bayesian statistics. I'll probably write more about this approach in a future Statistics Sunday post, but to briefly summarize, Bayesian statistics deal with probability differently than traditional statistics, mostly in the use of "priors" - prior information we have about the thing we're studying (such as results from previous studies) or educated guesses on what the distribution might look like (for very new areas of study). This information is combined with the data from the present study to form a "posterior" distribution. There are some really interesting combinations of Bayesian inference with item response theory (a psychometric approach, which I've blogged about before and should probably discuss in more detail at some point). One great thing about Bayesian approaches is that they don't require normally distributed data.
The panel was devoted to the benefits and drawbacks of different kinds of psychometric models and the research situations in which you should use special models - here's one of my favorite slides of the panel:
I also attended a presentation for a brand new journal, Advances in Methods and Practices in Psychological Science, which will be publishing its first issue early next year:
The journal publishes a range of article types, including empirical articles that exemplify best practices, articles that discuss current research methods and practices in an accessible manner, and tutorials that teach researchers how to use new tools in their own research programs. An explicit part of the journal’s mission is to encourage discussion of methodological and analytical questions across multiple branches of psychological science and related disciplines. Because AMPPS is a general audience journal, all articles should be accessible and understandable to the broad membership of APS—not just to methodologists and statisticians. The journal particularly encourages articles that bring useful advances from within a specialized area to a broader audience.I already have an idea for a paper I'd like to submit.
The last session of the day I attended was on implicit bias, and how they impact real-world interactions between police and community members, doctors and patients, and employers and employees.
All that's left is a reception tonight. At the moment, I'm relaxing in my hotel room before heading out to try a German restaurant with an excellent beer selection.
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