Study smarter with Fiveable
Get study guides, practice questions, and cheatsheets for all your subjects. Join 500,000+ students with a 96% pass rate.
Experimental design is the backbone of causal research in communication studies—it's how we move beyond "these two things are related" to "this actually causes that." You're being tested on your ability to distinguish between design types, understand their trade-offs, and recommend the right approach for different research scenarios. The core tensions you'll encounter repeatedly are internal validity vs. external validity, control vs. realism, and statistical power vs. practical constraints.
Don't just memorize design names. Know what problem each design solves, what it sacrifices in the process, and when a researcher would choose one over another. FRQs love asking you to justify design choices or identify threats to validity—so think like a researcher making strategic decisions, not a student reciting definitions.
The most fundamental distinction in experimental research is how much control the researcher has over participant assignment. This determines whether you can make causal claims with confidence or must hedge your conclusions.
Compare: True experimental vs. quasi-experimental design—both manipulate an independent variable, but only true experiments use random assignment. If an FRQ asks about studying workplace communication interventions across existing teams, quasi-experimental is your answer; if it asks about maximizing causal certainty, go with true experimental.
How and when you measure participants shapes what conclusions you can draw. Pre-testing can strengthen your analysis but may also contaminate your results.
Compare: Pre-test/post-test vs. Solomon four-group—both use control groups, but Solomon specifically isolates pre-test sensitization effects. When an exam question mentions concerns about testing effects or reactivity, Solomon is the design that addresses this.
The decision about whether participants experience one condition or multiple conditions affects everything from sample size requirements to the types of bias you'll encounter.
Compare: Between-subjects vs. within-subjects—the trade-off is carryover effects vs. sample size. Between-subjects needs more participants but avoids contamination; within-subjects is more efficient but requires careful counterbalancing. Know which threats each design faces.
Sometimes researchers need to examine how multiple factors work together. These designs allow for more sophisticated questions about interaction effects.
Compare: Factorial vs. single-factor designs—factorial designs answer more complex questions about how variables combine, but they require careful interpretation. If an FRQ describes a study examining how two message features work together, factorial design is the answer.
Where you conduct your experiment creates a fundamental trade-off between the precision of your measurements and the applicability of your findings to the real world.
Compare: Laboratory vs. field experiments—this is the classic internal vs. external validity trade-off. Lab experiments tell you if something can work under ideal conditions; field experiments tell you whether it actually works in the real world. Strong research programs often use both.
| Concept | Best Examples |
|---|---|
| Random assignment (causal claims) | True experimental design |
| Pre-existing groups (real-world constraints) | Quasi-experimental design |
| Controlling for pre-test effects | Solomon four-group design, pre-test/post-test control group |
| Eliminating carryover effects | Between-subjects design |
| Maximizing statistical power | Within-subjects design, repeated measures design |
| Testing interaction effects | Factorial design |
| Maximizing internal validity | Laboratory experiments, true experimental design |
| Maximizing ecological validity | Field experiments |
A researcher wants to study how a new crisis communication training affects employee responses, but company policy requires using intact departments rather than randomly assigning individuals. Which design type is appropriate, and what validity threat should the researcher address?
Compare within-subjects and between-subjects designs: What specific threat does each design face, and how do researchers typically mitigate these threats?
A study finds that a persuasive message only changes attitudes when participants completed a pre-test questionnaire first. Which design would have detected this problem, and how does it work?
If an FRQ asks you to design a study examining how both message framing (gain vs. loss) and messenger identity (celebrity vs. expert) affect health behavior intentions, which design allows you to test these factors efficiently and examine their combined effects?
A communication researcher finds strong effects for a media literacy intervention in the lab but no effects when the same intervention is tested in actual classrooms. Explain this discrepancy using the concepts of internal and external validity.