Tuesday, December 06, 2016

Big data in psychology: Special issue of Psychological Methods

I just learned of this special issue in Psychological Methods.  I am looking forward to reading many of the articles as the idea of "big data" analysis in psychology is important.  I am particularly looking forward to reading the article co-authored by Jack McArdle on SEM trees.  I am not sure I will understand it, but I know Jack does tremendous work.  He was the first person to introduce me to SEM methods many years ago (during the WJ-R project; he taught me SEM, very gently, with a program called COSAN..and then I graduated to LISREL), and he was an awesome teacher---he could make complex stat methods conceptually clear.  I also then learned of decision-tree methods (CART, MAR) from Jack, and believe they should be used more in psychological research.  This PM issue should be well received by the quantoid readers of this blog.


Update -- Psychological Methods - Volume 21, Issue 4

A new issue is available for the following APA journal:

Big data in psychology: Introduction to the special issue.
Page 447-457
Harlow, Lisa L.; Oswald, Frederick L.

A practical guide to big data research in psychology.
Page 458-474
Chen, Eric Evan; Wojcik, Sean P.

A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research.
Page 475-492
Landers, Richard N.; Brusso, Robert C.; Cavanaugh, Katelyn J.; Collmus, Andrew B.

Mining big data to extract patterns and predict real-life outcomes.
Page 493-506
Kosinski, Michal; Wang, Yilun; Lakkaraju, Himabindu; Leskovec, Jure

Gaining insights from social media language: Methodologies and challenges.
Page 507-525
Kern, Margaret L.; Park, Gregory; Eichstaedt, Johannes C.; Schwartz, H. Andrew; Sap, Maarten; Smith, Laura K.; Ungar, Lyle H.

Tweeting negative emotion: An investigation of Twitter data in the aftermath of violence on college campuses.
Page 526-541
Jones, Nickolas M.; Wojcik, Sean P.; Sweeting, Josiah; Silver, Roxane Cohen

Theory-guided exploration with structural equation model forests.
Page 566-582
Brandmaier, Andreas M.; Prindle, John J.; McArdle, John J.; Lindenberger, Ulman

Finding structure in data using multivariate tree boosting.
Page 583-602
Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.
Page 603-620
Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul R.


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