Experimental: Field, Laboratory30th January 2021
They randomly assign subjects (or other sampling units) to either treatment or control groups in order to test claims of causal relationships. Random assignment helps establish the comparability of the treatment and control group, so that any differences between them that emerge after the treatment has been administered plausibly reflect the influence of the treatment rather than pre-existing differences between the groups. The distinguishing characteristics of field experiments are that they are conducted real-world settings and often unobtrusively. This is in contrast to laboratory experiments, which enforce scientific control by testing a hypothesis in the artificial and highly controlled setting of a laboratory. Field experiments have some contextual differences as well from naturally-occurring experiments and quasi-experiments. While naturally-occurring experiments rely on an external force (e.g. a government, nonprofit, etc.) controlling the randomization treatment assignment and implementation, field experiments require researchers to retain control over randomization and implementation. Quasi-experiments occur when treatments are administered as-if randomly (e.g. U.S. Congressional districts where candidates win with slim-margins, weather patterns, natural disasters, etc.).
Field experiments encompass a broad array of experimental designs, each with varying degrees of generality. Some criteria of generality (e.g. authenticity of treatments, participants, contexts, and outcome measures) refer to the contextual similarities between the subjects in the experimental sample and the rest of the population. They are increasingly used in the social sciences to study the effects of policy-related interventions in domains such as health, education, crime, social welfare, and politics.
Under random assignment, outcomes of field experiments are reflective of the real-world because subjects are assigned to groups based on non-deterministic probabilities. Two other core assumptions underlie the ability of the researcher to collect unbiased potential outcomes: excludability and non-interference. The excludability assumption provides that the only relevant causal agent is through the receipt of the treatment. Asymmetries in assignment, administration or measurement of treatment and control groups violate this assumption.
There are limitations of and arguments against using field experiments in place of other research designs (e.g. lab experiments, survey experiments, observational studies, etc.). Given that field experiments necessarily take place in a specific geographic and political setting, there is a concern about extrapolating outcomes to formulate a general theory regarding the population of interest. However, researchers have begun to find strategies to effectively generalize causal effects outside of the sample by comparing the environments of the treated population and external population, accessing information from larger sample size, and accounting and modeling for treatment effects heterogeneity within the sample. Others have used covariate blocking techniques to generalize from field experiment populations to external populations.
Noncompliance issues affecting field experiments (both one-sided and two-sided noncompliance) can occur when subjects who are assigned to a certain group never receive their assigned intervention. Other problems to data collection include attrition (where subjects who are treated do not provide outcome data) which, under certain conditions, will bias the collected data. These problems can lead to imprecise data analysis; however, researchers who use field experiments can use statistical methods in calculating useful information even when these difficulties occur.
Using field experiments can also lead to concerns over interference between subjects. When a treated subject or group affects the outcomes of the nontreated group (through conditions like displacement, communication, contagion etc.), nontreated groups might not have an outcome that is the true untreated outcome. A subset of interference is the spillover effect, which occurs when the treatment of treated groups has an effect on neighboring untreated groups.
Participants are randomly allocated to each independent variable group. An example is Milgram’s experiment on obedience or Loftus and Palmer’s car crash study.
A laboratory experiment is an experiment conducted under highly controlled conditions (not necessarily a laboratory), where accurate measurements are possible.
The researcher decides where the experiment will take place, at what time, with which participants, in what circumstances and using a standardized procedure.
- Strength: It is easier to replicate (i.e. copy) a laboratory experiment. This is because a standardized procedure is used.
- Strength: They allow for precise control of extraneous and independent variables. This allows a cause and effect relationship to be established.
- Limitation: The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e. low ecological validity. This means it would not be possible to generalize the findings to a real life setting.
- Limitation: Demand characteristics or experimenter effects may bias the results and become confounding variables.