When designing an experimental study, a researcher must decide between utilizing a between-subjects design or a within-subjects design. The main difference between the two is that a within-subjects design involves the use of the same subjects participating within each level of treatment condition, while in between subject designs different groups of participants are placed into separate treatment condition’s (Myers & Hansen, 2012). As between-subject designs require the use of different groups of people per treatment condition, researchers must be aware that they come with their own lists of advantages and disadvantages.
Most importantly between subject designs prioritize external validity (Egele, Kiefer & Stark, 2021). This is a benefit of the overall design of between-subject experiments. As each of the treatment conditions is made up of different individuals, the experimenter has a better chance of capturing a wider number of individual differences, or subject variables. If the researcher utilizes proper random sampling, they can ensure that their subject variables selected are representative of the population of interest. As random assignment is meant to equally divide these individual differences of this random sample across multiple treatment conditions, it is more likely that the results found will generalize to the overall public (Myers & Hansen, 2012). On the other hand, within-subjects’ designs utilize the same groups of individuals which may lower the number of subject variables, which will subsequently lower the level of the external validity (Myers & Hansen, 2012).
However, this focus on external validity can also be a double-edged sword and result in a few disadvantages of between-subject designs. Between-subject designs require that each treatment condition is filled with different groups of individuals. This requirement leads to the need for more individual volunteers compared to within-subject designs which utilize the same participants among multiple condition’s (Myers & Hansen, 2012). Additionally, the inclusion of more individuals and more subject variables means that randomization becomes extremely important. Without proper randomization, the results of these types of experimental designs can easily be confounded (Egele, Kiefer, & Stark, 2021; Myers & Hansen, 2012).