The oTree community has put together a useful oTree app. It allows participants to chat with ChatGPT through OpenAI's API. The app itself uses prompts so ChatGPT takes on a character or personality for participants to chat with. However, the possibilities and use-cases for experimental research are endless.
There is a growing trend of using practical anecdotes to signal relevance and importance of experimental research. However, it's important to be cautious about a hidden danger. Read this post to learn more.
Mediation is widely used in experimental accounting to obtain process evidence. The primary benefits of mediation are its low cost and easy integration. However, it has a hidden cost that weakens its effectiveness as process evidence. This post explains why it's the least effective method and suggests two better alternatives.
In this post, I share simple techniques to filter participants before they take part in your online experiment. These techniques filter bots and participants using automated scripts plus participants who fake their geolocation using VPN/VPS, proxies, and server farms.
The COVID pandemic has increased the popularity of online experiments in the field of accounting, but, at the same time, our criticism of online participants has been mounting up rapidly. Does this mean online experiments are rubbish or are we perhaps a little too harsh?
Do you want to learn how to analyze learning? In this second post of a two-part series, Jake Zureich discusses two approaches when comparing learning curves.
Both replications and practical relevance are awkward discussion topics for most experimental accounting researchers. Yet, replications offer a concrete way to address concerns we may have about the 'practical relevance' of experimental findings.
People in workplace settings can typically communicate freely with each other, but many experiments scale communication down to a restricted form. Should we maintain this status quo or is there room for free-form communication? Read this post by Farah Arshad and Cardin Masselink to find out more.
Do you want to learn how to analyze learning? In this first post of a two-part series, Jake Zureich discusses 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.