Do you want to learn how to analyze learning? In this first post of a two-part series, Jake Zureich discussed common pitfalls when comparing learning curves using an illustrative example.
Developing theory after collecting data is problematic because the theoretical predictions are post hoc. However, does that imply that all exploratory analyses are pointless? In this post, Jeremy Bentley explains that exploratory analyses can still add value even when researchers prefer to pre-commit to ex-ante theoretical predictions.
With the increasing popularity of online experiments, many have asked us for advice on how to conduct experiments on Amazon's Mechanical Turk. In this post, Christian Peters provides a hands-on guide.
Experiments that recruit from online participants pools such as MTurk and Prolific have become increasingly popular over the past two decades. However, since scholars have referred to such experiments as both laboratory and field experiments, which classification should we use?
Choosing the right participant pool for your experiment is challenging. Which experiments require professional participants? Does it matter whether you recruit students or online participants? In this post, Jeremy Bentley explains his approach to participant pool selection.