Video – Getting started with the New Statistics and Open Science

This fall I (Bob) was invited to give a talk at Indiana University as part of a series on good science and statistical practice organized by the university’s Social Science Research Commons (SSRC).  The SSRC is like a core facility for getting advice on statistics and experimental design…what a cool thing for a university to have!

I really enjoyed my visit (thanks Emily, Cami, and Patricia)–good conversation with fascinating people in a beautiful setting.  The series has a video archive, so my talk is now posted online as a video and as a powerpoint. Here’s the link–take a look if you want to know more about how to get started using Open Science practices and the New Statistics:  https://media.dlib.indiana.edu/media_objects/gt54kp23k

To what extent do new statistical guidelines change statistical practice?

In 2012 the Psychonomic Society (PS) adopted a set of forward-thinking guidelines for the use of statistics in its journals . The guidelines stressed the use of a priori sample-size planning, the reporting of effect sizes, and the use of confidence intervals for both raw scores and standardized effect-size measures.  Nice!

To what extent did these guidelines alter statistical practice?  Morris & Fritz (2017) report an natural experiment undertaken to help answer this question (Morris & Fritz, 2017) .  They analyzed papers printed before and after the guidelines were released (2013 and 2015; the 2013 data is actually after the guidelines were released, but all the papers analyzed were accepted for publication prior to the release).  The papers analyzed were from journals published by PS or from a journal of similar caliber and content but not subject to new guidelines.  In total, about 1000 articles were assessed (wow!).

What were the findings? Slow, small, but detectable improvement with tremendous room for further improvement:

  • Use of a priori sample size planning increased from 5% to 11% in PS journals but did not increase in the control journal.
  • Effect size reporting increased from 61% to 70%, though this increase was mirrored in the control journal
  • Use of raw score confidence intervals increased from 11% to 18%, with no change in the control journal
  • Confidence intervals for standardized effect sizes were reported in only 2 papers from 2015; an improvement from 0 papers in 2013, but hardly consistent with the PS guidelines to include these.

The authors conclude that more must be done, but don’t offer specifics.  Suggestions?

 

References

Morris, P., & Fritz, C. (2017). Meeting the challenge of the Psychonomic Society’s 2012 Guidelines on Statistical Issues: Some success and some room for improvement. Psychonomic Bulletin & Review. [PubMed]

Now for some good news: SIPS

The Society for the Improvement of Psychological Science (SIPS) held its first meeting last year, with around 100 good folks attending. Working groups have been–would you believe–working hard since then. The second meeting is 30 July to 1 August, in Charlottesville VA. More than 250 are expected to participate.

Today’s announcement:
“The program for the SIPS 2017 conference is up!

“We have lots of great workshops, hackathons, and unconference sessions in the works. Don’t know what that means? Don’t worry, we’re not sure either. Come join us and help us create it!

“There’s still time to register – click here to start!

“-The SIPS program committee
“Alexa Tullett, John Sakaluk, Michèle Nuijten, and Brian Nosek”

It’s so exciting to see so many people working on ways that things can be done better, especially by building tools to make the new ways actually easier.
Geoff

Open Data, Re-usable Code

It feels like every day there is a new development in the Open Science movement.  It’s overwhelming, but exciting.  Here’s a site that I only just stumbled on: Kaggle.  It provides high-quality curated data sets for statistical exploration.  It also allows users to upload R or Python code for data analysis that others can use/adapt/compare.  Really, really cool!  Here’s the site:

https://www.kaggle.com/

Toward cumulative science– the curate science database

One of the themes of the New Statistics is the importance of constantly synthesizing research results.  Putting results together is a form of cumulative science, it helps us weigh all the evidence, provides more precise estimates of effect sizes, and can help identify potential moderators.

The Curate Science project is a pretty amazing effort to do crowd-sourced cumulative science.  Specifically, the site aggregates data on a number of important psychological effects.  It shows the original data, each replication, and provides a cumulative meta-analysis.  Cool!

I’m a bit unclear exactly how effects are selected for curation.  Still, this seems like a glimpse of the future–where we work together to incorporate data from multiple labs to come up with precise estimates of effect sizes.  Check it out:

http://curatescience.org

Draw your own data tool

Sometimes it is nice to be able to make up a set of data to explore.  Here is a cool tool that makes it easy to craft your own data set: just draw the data and you will instantly see how each new data point affects the mean, standard deviation, and Pearson’s r.  You can even download the data you have created.  A nice tool to explore, it could be especially handy for instructors who need to make quiz and test items. The page was developed by stats guru Robert Grant.

http://robertgrantstats.co.uk/drawmydata.html