Experimental Design
Experimental design is how researchers set up studies to test cause-and-effect relationships. Without a solid design, you can't tell whether your treatment actually caused the result or whether something else was responsible. This section covers the key components of experiments, how to control for unwanted variables, and the ethical rules that keep research honest and safe.
Components of Randomized Experiments
A randomized experiment assigns subjects to groups using a random process. This is the gold standard for establishing cause and effect because it distributes unknown differences across groups, so those differences are less likely to skew your results.
Every randomized experiment has a few core pieces:
- Explanatory variable (independent variable): The factor the researcher deliberately changes. For example, medication dosage or temperature setting.
- Response variable (dependent variable): The outcome you measure to see if the explanatory variable had an effect. For example, patient recovery time or plant growth in centimeters.
- Treatments: The specific conditions applied to subjects. Each treatment represents a different level of the explanatory variable. If you're testing a drug, one treatment might be 10 mg, another 20 mg, and a third might be a placebo.
- Control group: A group that receives no treatment or a placebo. This serves as your baseline. Without it, you have nothing to compare your treatment results against.
Control Methods for Lurking Variables
A lurking variable is a factor you didn't account for that could influence the response variable. For instance, if you're studying whether a new study method improves test scores, students' prior GPA is a lurking variable that could affect results.
There are several ways to keep lurking variables from ruining your experiment:
- Randomization: Randomly assigning subjects to groups. This doesn't eliminate lurking variables, but it spreads them roughly evenly across all groups so they're less likely to favor one group over another.
- Blocking: Grouping subjects by a shared characteristic (like age range or gender) before randomly assigning them to treatments. This reduces variability within each group and makes it easier to detect a real treatment effect.
- Matching: Pairing subjects with similar characteristics across treatment groups. Twin studies are a classic example, where one twin gets the treatment and the other serves as the control.
Researchers also need to control for the power of suggestion:
- Blinding (single-blind): Subjects don't know which treatment they're receiving. This reduces the placebo effect, where people improve simply because they believe they're being treated.
- Double-blinding: Neither the subjects nor the researchers measuring outcomes know who got which treatment. This prevents researchers from unconsciously interpreting results in favor of the treatment.
- Placebo control: A fake treatment (like a sugar pill) given to the control group. This lets you separate the actual drug effect from the psychological effect of just receiving something.
Additional Considerations in Experimental Design
- Sample size: Larger samples generally produce more reliable results. A study with 10 participants is far less convincing than one with 500, because small samples are more easily thrown off by random variation.
- Statistical significance: A result is statistically significant when the observed difference between groups is unlikely to have occurred by chance alone. You'll learn more about how to measure this later in the course.
- Confounding variables: These are similar to lurking variables, but specifically they affect both the explanatory and response variables at the same time. For example, if people who exercise more also eat healthier, and you're studying exercise's effect on heart health, diet is a confounding variable. Confounders can make it look like one variable causes an effect when the real cause is something else entirely.
Ethical Considerations
Statistical research involves real people, so ethical guidelines exist to protect participants and ensure honest results. Most institutions have an ethics committee (often called an Institutional Review Board, or IRB) that reviews and approves research proposals before any data collection begins.
Proper Data Collection
- Informed consent: Before participating, subjects must be told the study's purpose, what they'll be asked to do, and any risks or benefits. Participation must be voluntary, typically confirmed with a signed consent form.
- Confidentiality: Researchers must protect participants' personal information through measures like encrypted data storage and restricted access.
- Anonymity: When possible, data is collected without linking it to individual identities. Using participant ID numbers instead of names is a common approach.
Participant Safety
- Minimizing risks: The study should not cause unnecessary physical, psychological, or social harm. Procedures should be as non-invasive as possible.
- Providing support: If participants experience distress or adverse effects, researchers must offer appropriate resources such as counseling or medical care.
- Right to withdraw: Participants can leave the study at any time, for any reason, without penalty.
Fraud Prevention
- Data integrity: Data must be accurately collected, recorded, and reported. Standardized protocols and data validation checks help prevent errors.
- Replication: Other researchers should be able to reproduce the study using the same methods. Sharing data and methodology openly supports this.
- Peer review: Before publication, research is evaluated by independent experts who check for errors, flawed reasoning, or potential bias.
- Consequences for misconduct: Researchers who fabricate or falsify data face serious penalties, including retraction of published papers, loss of funding, and professional sanctions.