Reflect on the participants' backgrounds and how they might affect the
study
Be aware of problems that arise when experimenters know the users
personally
Prepare for the study carefully (avoid last minute panic)
Select the tasks carefully to be representative and to fit the allotted
time
In general, start with an easier (but not frivolous) task
Write down features of the system that are not being tested as
well as those that are!
Define the start-up state for the study precisely
Define precise rules for when and how users can be helped during the
study
Plan the timing and cut-off procedure (if subject gets stuck) for each
part of the study
Include reasonable provisions for data collection (e.g., notes,
tape or video recorder, keystroke capture where appropriate)
Plan data analysis techniques in advance
Carry out a pilot study (important but often overlooked)
Written materials
Participant release form (if needed)
Questionnaire covering prior experience etc. (if relevant)
Introduction to the study for users, including scenario of use
Checklist for experimenters
Evaluation survey (if relevant)
Carrying out the study
Let users know that complete anonymity will be preserved
Let them know that they may quit at any time
Stress that the system is being tested, not the participant
Indicate that you are only interested in their thoughts relevant to the
system
Demonstrate the thinking-aloud method by acting it out for a simple
task, such as figuring out how to load a stapler, and a computer-related
task
Hand out instructions for each part of the study individually, not all
at once
Maintain a relaxed environment free of interruptions
Encourage users to keep talking using unobtrusive
comments that don't point the user in a particular direction, e.g.
"uh huh", or
"weird?" (after the user says "This is really weird!"),
or
"What do you think is going on?" (Remember Judy Ramey's lecture.)
Debrief each user after the experiment
Improving the study
The pilot study should "debug" the study. This minimizes
changes during the study, allowing quantitative data analysis. But
improvements may be warranted.