Bias in Statistics > Assignment Bias
What is Assignment Bias?
Assignment bias happens when experimental groups have significantly different characteristics due to a faulty assignment process. For example, if you’re performing a set of intelligence tests, one group might have more people who are significantly smarter. Although this type of bias is usually associated with non-random sampling and assignment, it can occasionally be an issue with random techniques.
Controlling Assignment Bias
Random assignment can help to control assignment bias by ensuring that treatment groups and control groups have an equal spread of characteristics. That said, random assignment is not always possible, especially in the medical fields where it may be unethical to assign patients to control groups. If you are unable to use random sampling and random assignment methods to select participants, alternative methods include:
- Instrumental Variables: A third variable used in regression that helps you to uncover “hidden” variables (other than the independent variables) that cause results.
- Propensity Score Matching: a matching technique that accounts for covariates in the experiment.
- Purposive Sampling: Selecting samples based on your knowledge about the population and the study.
- Randomization Tests: an approach that considers all of the possible ways experimental values could be assigned to all groups.
- Sequential Assignment (assigning the first patient to the first group, the second patient to the second group, the third to the fist group…and so on), followed by Treatment-as-Usual (accepted protocols for treatment).
- Sequential Sampling.
Threats to Validity
Assignment bias can be a threat to internal validity, because it allows two different explanations for differences in treatment results. For example, if you find that a weight loss procedure results in weight loss of more than 50 lbs, it could be that the treatment is actually effective, or it could be that the differences are because people in the experimental group weigh more at the outset (and therefore, have more to lose). In more technical terms, unmeasured extraneous variables (e.g. extra weight) might be interfering with the relationship between the independent variable and dependent variable.
Assignment bias can also be a threat to external validity “…if it affects study results, leading to inaccurate estimates of the relationships between variables in a population” (Dattalo, 2010). In other words, if you take your questionable experimental results and apply them to the broader population, this results in issues with external validity.
Dattalo, P. (201). Strategies to Approximate Random Sampling and Assignment. Oxford University Press, USA.