Digital Marketing - Study Notes:
How data is analyzed
Biases can make their way into research, either on the part of the participant, for instance, when offering responses to interview questions, or on the part of the researcher when analyzing the data gathered.
Here are some example biases:
- Functional fixedness is the inability to realize that something known to have a particular use may also be used to perform other functions.
- A lack of foresight or imagination on the part of an interviewee may mean that there are opportunities for innovation missed.
- The Illusion of Validity is a situation where a researcher over estimates their ability to accurately interpret a set of data. In particular, when the data analyzed suggests a very consistent pattern. That is, when the data tells a coherent story. Put simply, research is interpreted as fitting in with a predetermined narrative, simply backing up what was already believed.
- The Von Restorff Effect suggests that things that stand out from the others are more likely to be remembered. This might affect, for instance, focus groups, where a particularly vocal member of the group can make their opinions more strongly made than other members.
- Information bias is the incorrect belief that more information, even irrelevant information, must always be acquired before making a decision. This can be countered with a coherent learning plan.
Rick Monro
Rick Monro is UX Director at Fathom. He has extensive experience in user research, interaction design, user-centered design, and design strategy with private and public sector organisations throughout the UK and Ireland.
By the end of this topic, you should be able to:
- Appraise practices for planning UX research
- Critically evaluate the roles of innovation and users in User Experience (UX) research
- Evaluate cognitive biases that can affect research data
