lottiegasp
commented
4 years ago In my study I went with consistently coding novelty effect as positive and familiarity effect as negative. This means that in a habituation/familiarity design "novelty" refers to the non-habituated/familiarised condition heard in the test phase, while in a test-only design, "novelty" refers to the less familiar/expected condition in the participant's everyday environment, i.e. in my case the non-native language. That meant that predictions were opposite for the two design types: we predicted an overall novelty preference (positive effect sizes) for habituation/familiarity tasks, and an overall familiarity preference (negative effect sizes) for test-only tasks. In the stats we separated out the two design types so as to not conflate effect sizes going in two directions.
I had mentioned to Sho that we might want to create more explicit instructions on how to code effect size direction. Do you think the consistent approach of positive=novelty/negative=familiarity could work across all meta-analyses? Then we could provide instructions for how to deal with this in the stats if you have predictions in both directions, as I did in my study. Happy to discuss further.
christinabergmann
shotsuji
I realized recently that we code Habituation studies positive when there is a stronger dishabituation response in the vowels database. This is different from most other types of paradigms (right?) so we either need to make this consistent and flip effect sizes from habituation studies in the code programmatically by checking if something is labelled habituation, or have very clear instructions. @shotsuji and @lottiegasp - Do you have any preference for either? I think each of you solved this in one of the two suggested ways.