Andrew Gelman talked about a really old paper I did together with Anton Kühberger ages ago. It was actually the first paper / ‘real’ scientific project I was involved in.
It generated quite the buzz over its 20 year lifespan and was cited a whopping 13 times (stats look good without y-axis) …
Going back to it, I was happy to see that we already talked about replication (and were very reluctant to push the button harder – as we would have not been able to get through the reviews, I guess) … Things have changed.
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.
Recently Ryan Murphy and myself realised that a startup here in Berlin features ideas of our 2011 Flashlight paper.
Well, the guys at attensee.com did a great job taking the idea we had much further we ever thought one would be able to take it …
Here is a feature I totally love – a live heat map of what you are looking at … awesome!
dplyr is the new plyr – and it is awesome!
fast, consistent and easy to read … check out a set of instructional pages, presentation and videos here
Thanks Hadley Wickham
Mal was Längeres zu unseren Lieblingsthemen: Essen und Entscheidungsforschung … Enjoy!
Schulte-Mecklenbeck, M., & Kühberger, A. (2014). Out of sight – out of mind? Information acquisition patterns in risky choice framing. Polish Psychological Bulletin, 45, 21–28.
I teamed up with Anton Kühberger to write about one of our old, favorite topics: framing and process tracing …
Here is the abstract:
We investigate whether risky choice framing, i.e., the preference of a sure over an equivalent risky option when choosing among gains, and the reverse when choosing among losses, depends on redundancy and density of information available in a task. Redundancy, the saliency of missing information, and density, the description of options in one or multiple chunks, was manipulated in a matrix setup presented in MouselabWeb. On the choice level we found a framing effect only in setups with non-redundant information. On the process level outcomes attracted more acquisitions than probabilities, irrespective of redundancy. A dissociation between acquisition behavior and choice calls for a critical discussion of the limits of process-tracing measures for understanding and predicting choices in decision making tasks.
We got a new paper out on how people (consumers) use simple rules to make food choices. This is work in collaboration with the Nestlé Research Center in Lausanne.
Here is the reference:
Schulte-Mecklenbeck, M., Sohn, M., Bellis, E., Martin, N., & Hertwig, R. (2013). A Lack of Appetite for Information and Computation: Simple Heuristics in Food Choice. Appetite, 71, 242–251.
The predominant, but largely untested, assumption in research on food choice is that people obey the classic commandments of rational behavior: they carefully look up every piece of relevant information, weight each piece according to subjective importance, and then combine them into a judgment or choice. In real world situations, however, the available time, motivation, and computational resources may simply not suffice to keep these commandments. Indeed, there is a large body of research suggesting that human choice is often better accommodated by heuristics—simple rules that enable decision making on the basis of a few, but important, pieces of information. We investigated the prevalence of such heuristics in a computerized experiment that engaged participants in a series of choices between two lunch dishes. Employing MouselabWeb, a process-tracing technique, we found that simple heuristics described an overwhelmingly large proportion of choices, whereas strategies traditionally deemed rational were barely apparent in our data. Replicating previous findings, we also observed that visual stimulus segments received a much larger proportion of attention than any nutritional values did. Our results suggest that, consistent with human behavior in other domains, people make their food choices on the basis of simple and informationally frugal heuristics.
It is kind of an odd problem.
For the following pretty straight forward question: How do I randomise questions within a group in Limesurvey? It seems to be really hard to find an answer.
With the help of Jonas I figured out that there is a randomisation option hidden in the ‘Advanced Settings’ section of a question. What you have to do is provide the same number (this is important) for each question in a group that you want to randomise. Limesurvey will then take care of the rest. It did not seem to work if I named the variable with a string, eg, ‘group1’ but only numeric counters work fine.
Thanks Jonas! (I would not have finished at all) …
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 …
Base function Input Output plyr function
aggregate d d ddply + colwise
apply a a/l aaply / alply
by d l dlply
lapply l l llply
mapply a a/l maply / mlply
replicate r a/l raply / rlply
sapply l a laply