Researchers conducting experiments in real-world or field settings face many challenges in establishing internal validity compared to laboratory experiments. In field experiments, it can be difficult to control for the many extraneous variables that could potentially influence the outcome and confound the relationship between the independent and dependent variables. There are some techniques researchers can employ to help account for extraneous variables in field experiments and better establish the internal validity of their research.
One approach is to use random assignment of participants to conditions when possible. Random assignment helps ensure the conditions are equally distributed on both measured and unmeasured extraneous variables that could influence the outcome. For example, if studying the impact of a new teaching method on student test scores, classes of students could be randomly assigned to either receive the new method or continue with traditional instruction. This random assignment increases the likelihood that any pre-existing differences between classes, like inherent academic ability, are evenly distributed across conditions.
True random assignment is often not feasible or ethical in field settings. As an alternative, researchers can employ matching or statistical controls. Matching involves purposefully assigning units, like individual students, to conditions based on known extraneous variables so the conditions are matched on those attributes. For example, students in different classes could be matched based on prior GPA so overall academic ability is balanced across classes receiving different conditions. Statistical controls can be used in the data analysis stage, like including pretest scores as a covariate, to partial out the influence of pre-existing differences on the outcome.
When participants cannot be assigned to conditions, researchers have to rely on quasi-experimental designs like nonequivalent group designs. In these situations, threats to internal validity from selection bias are a particular concern. Researchers can incorporate timing series designs to better account for threats from history, maturation, testing, and statistical regression artifacts. For example, by including multiple pretests and posttests over the course of an experiment rather than just a single pretest and posttest time point. Researchers can also incorporate matching, statistical controls, and other techniques to rule out plausible rival hypotheses for any group differences observed. Natural experiments, where the “conditions” are determined by circumstances outside the researcher’s control, also fall under quasi-experimental designs in field settings.
Field experiments could also introduce testing reactivity as a threat, where merely being in the study affects participant behavior. Researchers can use unobtrusive measures like official records or observations by blinded assessors as dependent variables instead of self-report measures, which are more prone to reactivity. This approach still risks threats from differences in data availability across conditions. Experimenter biases are another concern, since researchers in field settings generally cannot be blinded to condition assignment. Safeguards like standardized administration of conditions according to detailed protocols, objective outcome criteria, and research designs blinding assessors to condition help address experimenter biases.
Locational biases could arise from differences in setting that coincidentally correlate with conditions rather than cause the outcome. For example, if some classrooms receive a new curriculum in wealthier parts of town versus less privileged areas, any observed effects could reflect pre-existing socioeconomic differences rather than the curriculum itself. Researchers can counterbalance conditions across locations or sites to distribute these potential location effects evenly. Historical events coinciding with the start of conditions pose a threat. Incorporating multiple treatment and control groups in a staggered rollout design can help rule out specific historical events as alternative explanations for effects.
Attrition of participants over the course of field experiments threatens internal validity if it differentially impacts conditions. Strategies like tracking participants, reminders to complete follow-ups, scheduling make-up times, and incentive payments can aid participant retention. Intent-to-treat analyses that preserve randomization also mitigate attrition bias concerns compared to analyses only including participants who fully complied with conditions. Measurement reactivity is a threat if outcome criteria differ in their sensitivity to conditions. Incorporating multiple measures of outcomes and process measures during implementation can enhance the internal validity and interpretation of field experiment results.
As this overview illustrates, field experiments introduce a wider variety of threats to internal validity compared to tightly controlled laboratory studies due to reduced experimental control and increased complexity of real-world settings. With careful attention to randomization where possible, accounting for confounding variables through statistical controls and matching, counterbalancing and replication across locations, blinding procedures, robust research designs, and strategies to bolster retention and measurement equivalency, researchers can implement rigorous field experiments capable of establishing plausible causal claims about relationships of interest. No single techniques completely eliminates all validity concerns, but incorporating multiple approaches can help address major alternative explanations for findings from field experiments.
