Design Based Inference
- Estimates how a treatment or intervention affects a population by using study design rather than indirect measures.
- Commonly implemented via randomized controlled trials or natural experiments.
- Randomization or use of real-world events helps control confounding and improve representativeness.
Definition
Section titled “Definition”Design based inference is a statistical methodology that aims to understand the effects of a certain treatment or intervention on a given population. This approach is often used in social science research, where the goal is to understand how a particular policy or program affects a group of individuals or a community.
Explanation
Section titled “Explanation”Design based inference draws conclusions about the effect of an intervention by comparing outcomes across groups defined by the study design. In randomized designs, individuals in a sample are assigned to treatment or control groups and outcomes are compared between those groups. In natural experiments, researchers exploit real-world events or policy changes to form comparisons between affected and unaffected populations. Using randomization or natural variation helps control for potential confounding variables and supports results that are representative of the overall population.
Examples
Section titled “Examples”Randomized controlled trial (RCT)
Section titled “Randomized controlled trial (RCT)”In an RCT, a sample of individuals is selected and randomly assigned to either a treatment group or a control group. The treatment group receives the intervention being studied, while the control group does not. Researchers then compare the outcomes of the two groups to determine the effectiveness of the intervention.
Example: A study looking at the effects of a new parenting program on children’s academic performance randomly assigns a group of parents and their children to either the treatment group (which receives the parenting program) or the control group (which does not receive the program). The researchers then compare the academic performance of the two groups to see if the parenting program had a positive effect.
Natural experiment
Section titled “Natural experiment”A natural experiment is a research design that uses a real-world event or policy change to study the effects of a certain intervention on a population. Researchers compare outcomes in a population affected by the event or policy to outcomes in a similar, unaffected population.
Example: A study looking at the effects of a minimum wage increase on employment rates compares employment rates in a state that increased its minimum wage with employment rates in a similar state that did not increase its minimum wage. By comparing the two states, the researchers can infer the effects of the minimum wage increase on employment rates.
Use cases
Section titled “Use cases”- Frequently used in social science research to measure the effects of policies or programs on groups or communities.
Notes or pitfalls
Section titled “Notes or pitfalls”- Randomization or natural experiments are used to control for potential confounding variables.
- Design based inference aims to produce results that are representative of the overall population rather than relying on self-reported or indirect measures.
Related terms
Section titled “Related terms”- Randomized controlled trial (RCT)
- Natural experiment
- Randomization
- Control group
- Treatment group
- Confounding variables