Experience Sampling 7.2

Question 1

Experience Sampling

Answer 1

Procedure for studying what people do, feel, and think during their
daily lives
Method: Ask people for self-reports at random occasions throughout the day

Question 2

Experience sampling procedure

Answer 2

Cue ppts throughout the day (e.g., text message or pager)
•Signaling cues need to be temporally representative / unbiased

Ppts respond at that moment (or ASAP if can’t)
•Report their objective situation and subjective state in the moment

Question 3

Properties of the data

Answer 3

• Self report data is always prone to bias
• Single subject data
• Collecting data from many subjects

Question 4

Advantages of experience sampling

Answer 4

•Doesn’t rely on recollection and reconstruction of memories
•Ecologically valid
•Doesn’t rely on a single assessment (like most surveys and interviews) but
collects repeated measurements across many occasions

Question 5

Limitations of experience sampling

Answer 5

•Potential self-selection biases
•Not all types of people would participate in such a study
•Not all experiences are captured
•Useful to have a debriefing interview to find out about significant unsampled events
•Potential biases: over-reporting (positive things) and under-reporting
(negative and sensitive things)
•reactivity concerns

Question 6

Diary Studies

Answer 6

•Participants keep a diary and log specific info about activities being
studied
-Qualitative, longitudinal data (a few days to months)
-To help ppts remember to fill out diaries, they are sometimes sent reminders
-Can be applied to UX research
-Focus of research can be broad or narrow

Question 7

Logging period

Answer 7

•Provide a simple framework for ppts to log their data
-Be as specific as possible with what they should log

•In-situ logging
-Log info when the relevant events occur

•Snippet technique

• Less intrusive
• record notes in the moment, fill in the details later

Question 8

Analyze your data

Answer 8

• There will be lots of qualitative, longitudinal data
• Review and explore the data
• Figure out what you will code
• Code your data & analyze it
• Represent and visualize your data