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Quick-start guide to experimental design
PSYC 11: Laboratory in Psychological Science
Jeremy R. Manning
Dartmouth College
Spring 2026
What is the purpose of running an experiment?
Understand or explore how something works
Distinguish between several potential alternatives
Get more information (data!)
Two big design philosophies
Classic (maximize control):
simplify the phenomenon, carefully manipulate specific factors across conditions, measure behavioral differences
Naturalistic (maximize realism):
create a rich, realistic scenario, measure as much as possible, mine the data for patterns
Most studies fall somewhere on this spectrum -- where does yours?
The key ingredients
Independent variable (IV):
what you
manipulate
or compare across groups
Dependent variable (DV):
what you
measure
Controls:
what you keep the
same
so you can isolate the effect of your IV
Participants:
who are you studying, and how many?
Discussion: what's your IV/DV?
In your project groups, identify:
What is your
IV
? (What are you manipulating or comparing?)
What is your
DV
? (What are you measuring?)
What needs to be
controlled
?
If you are doing an observational study, what are your key
variables of interest
?
Be ready to share with the class
Common pitfalls
Confounds:
something other than your IV that differs between conditions
Demand characteristics:
participants guess what you expect and change their behavior
Too many variables at once:
keep it simple -- one clear comparison is better than five murky ones
Forgetting a baseline:
what does "normal" look like without your manipulation?
Implementation tools
Low-tech:
notebooks, audiovisual recordings, Google Forms
Mid-tech:
slideshows, Qualtrics surveys
High-tech:
PsychoPy, jsPsych, Google Colaboratory
Use what you already know -- simplicity is your friend
Practical advice
Simplicity: the art of maximizing the amount of work
not
done
Pilot test early -- run your study on a friend before collecting real data
Work together and ask for help
You have
3--4 weeks
-- scope accordingly!