Traditionally, most behavioural research is done in laboratory settings. However, in recent years, the popularity of online research has been growing, with more and more departments and labs creating and testing using online platforms.
Researchers are often nervous about collecting experimental data online because unlike lab-based experiments they cannot meet their participants and can’t directly observe their behaviour.
BeOnline2020 – Ensuring Data Quality Online
In her talk at the BeOnline conference this year, Jenni gave a great talk all about data quality, with many tips and hints. She summarises the issues that arise from ‘invisible’ participants, suggesting a framework that helps researchers to (i) maintain data quality and (ii) allows principled decision about when online data collection is (and is not) appropriate. She additionally highlights that this principled approach to data quality control is often missing in lab-based settings and that the lessons we have learned due to running experiments online can be broadly applied to a range of experimental approaches.
Article for the Association for Psychological Science – How to Maintain Data Quality When You Can’t See Your Participants
For more details about maintaining data quality, Jenni also wrote an article, which you can access here.
In the article, Jenni guides you through a five-stage process she proposes to go through prior to collecting data in any specific online experiment.
5-stage process to help ensure data quality:
- Specify your data quality concerns
- Specify the worst case scenario
- Add new within-experiment safeguards
- Design experiment-specific exclusion criteria
- Pre-register your exclusion criteria