Experimental Design
You want to test whether coffee improves reaction time. You have two options. Option one: split 100 people into two groups - give Group A coffee and Group B water, then compare their reaction times. Option two: give all 100 people water on Monday and coffee on Tuesday, then compare each person’s reaction time across the two days. These two approaches represent the two fundamental experimental designs, and each has trade-offs that the MCAT expects you to know.
Between-Subjects Design
In a between-subjects design, different participants are assigned to each condition. Group A gets the treatment, Group B gets the control, and you compare the two groups.
Advantage: No order effects. Participants only experience one condition, so fatigue, practice, or boredom from the first condition cannot contaminate the second.
Disadvantage: Individual differences. People in Group A might naturally have faster reaction times than people in Group B, regardless of coffee. You need larger sample sizes to overcome this variability, and you must use random assignment to spread individual differences evenly.
Within-Subjects Design (Repeated Measures)
In a within-subjects design, the same participants experience every condition. Each person serves as their own control. You measure the DV under condition A, then again under condition B, and compare.
Advantage: Eliminates individual differences. Since each person is compared to themselves, natural variability between people is removed. You need fewer participants.
Disadvantage: Order effects. Going through one condition first can affect performance in the second condition. If everyone drinks coffee first and water second, maybe their reaction time on the water day is worse because they are tired from the previous session, not because water is inferior to coffee.
Counterbalancing
Counterbalancing is the solution to order effects in within-subjects designs. You vary the sequence of conditions across participants.
Half the participants get coffee first and water second. The other half get water first and coffee second. Any order effect (practice, fatigue, carryover) is spread equally across both conditions, so it cannot systematically favor one over the other.
A crossover design is a specific type of within-subjects design where participants switch between treatment and control, usually with a “washout period” in between to let the first treatment’s effects fade.
Quick Comparison
| Feature | Between-Subjects | Within-Subjects |
|---|---|---|
| Participants per condition | Different people | Same people |
| Individual differences | A concern (need random assignment) | Eliminated (each person is own control) |
| Order effects | Not a concern | A concern (need counterbalancing) |
| Sample size needed | Larger | Smaller |
| Also called | Independent groups design | Repeated measures design |
Random Assignment vs. Random Selection
These two terms sound similar but address completely different problems.
Random assignment means every participant has an equal chance of being placed in any condition (treatment or control). It is used to distribute confounding variables evenly across groups. Random assignment supports internal validity - the confidence that the IV caused the change in the DV.
Random selection (random sampling) means every member of the population has an equal chance of being chosen for the study. It is used to make sure the sample represents the broader population. Random selection supports external validity - the confidence that results generalize beyond the study.
Factorial Designs
A factorial design tests two or more independent variables at the same time. Instead of just testing Drug vs. Placebo, you might test Drug vs. Placebo AND Therapy vs. No Therapy. This creates a 2 x 2 design with four conditions:
| Therapy | No Therapy | |
|---|---|---|
| Drug | Drug + Therapy | Drug + No Therapy |
| Placebo | Placebo + Therapy | Placebo + No Therapy |
The power of factorial designs is that they reveal interaction effects - situations where the effect of one IV depends on the level of the other IV. For example, the drug might work only when combined with therapy but not alone.
Matched-Pairs Design
A matched-pairs design is a hybrid approach. Participants are paired based on a relevant characteristic (age, severity of illness, baseline score), and then one member of each pair is randomly assigned to the treatment and the other to the control.
This reduces individual differences (like within-subjects) while avoiding order effects (like between-subjects). It is especially useful when the characteristic being matched is known to strongly affect the DV.