As mentioned some days ago our Handbook of Process Tracing Methods is out in the wild …
Here is a bit of an overview of what is going on inside :)
The book has 390 pages divided into 24 chapters. There are 202014 words in there including everything (references, thanks, hello, goodbye …).
Ignoring the chapters and that they have reference lists, that mess up things a bit, first an overview of frequency for highly frequent words:
So, here we go - new blogdown site … thanks to Dan (https://twitter.com/dsquintana) to kicked me over the edge actually doing this …
Things are fine, the site is up - pictures are still linked back to my old wordpress site … will figure this out eventually … but this is live now - for your reading pleasure :)
Often, when we run process tracing studies (e.g., eye-tracking, mouse-tracking, thinking-aloud) we talk about cognitive processes (things we can’t observe) in a way that they are actually and directly observable. This is pretty weird – which becomes obvious when looking at the data from the paper below. In this paper we simply instruct participants to follow a strategy when making choices between risky gamble problems. Taking the example of fixation duration we see that there is surprisingly litte difference between calculating an expected value, using a heuristic (priority heuristic) and just making decisions without instructions (no instruction) … maybe we should rethink our mapping of observation to cognitive processes a bit?
This is one of the fastest papers I have ever written. It was a great collaboration with Tomás Lejarraga from the Universitat de les Illes Balears. Why was it great? Because it is one of the rare cases (at least in my academic life) where all people involved in a project contribute equally and quickly. Often, the weight of a contribution lies with one person which slows down things – with Tomás this was different – we were often sitting in front of a computer writing together (have never done this before, thought it would not work).
Before there was R, there was S. R was modeled on a language developed at AT&T Bell Labs starting in 1976 by Rick Becker and John Chambers (and, later, Alan Wilks) along with Doug Dunn, Jean McRae, and Judy Schilling. Here is a talk by Rick Becker telling the story of R. Good Stuff!
I gave the R package exams a shot for my decision making lecture. Here is what it does:
“Automatic generation of exams based on exercises in Sweave (R/LaTeX) or R/Markdown format, including multiple-choice questions and arithmetic problems. Exams can be produced in various formats, including PDF, HTML, Moodle XML, QTI 1.2 (for OLAT/OpenOLAT), QTI 2.1, ARSnova, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format is provided that can be printed, scanned, and automatically evaluated.
The friendly people from RStudio recently started a webinar series with talks on the following topics (among others):
Data wrangling with R and RStudio
The Grammar and Graphics of Data Science (both dplyr happiness)
RStudio and Shiny
… and many more.
Our friend Dr. Nathaniel D. Philipps also started a cool R course with videos, shiny apps and many other new goodies.
Here is an excellent stackoverflow post on how *apply in all its variations can be used.
One of the followups points at plyr (from demi-R-god Hadley Wickham) which provides a consistent naming convention for all the *apply variations. I like plyr a lot, because like ggplot, it is easy to grasp and relatively intuitive to find an answer to even tricky problems.
Here is the translation from *apply to plyr …