PLAN: STUDY DESIGNS
Sept 19/20, 2018
Biol/Stat 2244 – Waugh
Plan
Create a plan—including data collection and
analysis—to address the research question(s)
• What is the study population and sampling
strategy?
• What will be measured for the response
variable(s)?
• How will you deal with explanatory variables?
• What statistical procedures do you plan to use?
Biol/Stat 2244 – Waugh
Plan (part 2): Discuss
Question: Does the height of a woman’s
shoe influence male behaviour?
Task:
Describe a study to address this research
question. In the process, consider/identify:
• How will you measure the response variable?
• What variables might influence the response
variable (including heel height)? How will you
deal those variables?
Biol/Stat 2244 – Waugh
Study designs
Issues with observational studies

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Biol/Stat 2244 – Waugh
‘Dealing’ with explanatory variables
Consider all possible variables that might
influence/explain variation in the response
Options:
• Limit to a specific range or value(s);
→ By choice of sampling frame, or by control
• Measure/manipulate as part of the study
design (i.e. use as true factor(s) of interest);
• Ignore (if deemed of negligible importance,
or by ignorance).
Biol/Stat 2244 – Waugh
Principles of experimental design
• Control – limit variation in ‘other’ potential explanatory
variables, to isolate effects of the factor(s) of interest
→blocking
(e.g. matched pairs, repeated measures, blocks)
→blinding (including single and double blinding)
→comparing groups (2+ experimental, control groups, etc.)
• Randomization – random assignment of units to
treatments, OR, the order of treatments experienced
→makes
treatment groups are similar, on average
• Replication – have more than one unit in each treatment
→to
quantify and/or account for variability among units
applet: http://www.rossmanchance.com/applets/Subjects.html
Biol/Stat 2244 – Waugh
Issues with experiments

Biol/Stat 2244 – Waugh
Take home message(s)

observational studies and experimental studies each
have ‘pros’ and ‘cons’
◼ association
versus causation
◼ generalizability/authenticity

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◼ confounding
confounding can be a serious problem that statistical
methods cannot fix after the fact
well-designed experiments (with careful incorporation of
principles of experimental design) can allow claims of
causation
Study design—like sampling strategies—has a
direct impact on the conclusions you can draw
Biol/Stat 2244 – Waugh