Comparing and contrasting terms
Between- subjects design is an experiment in which the subject is measured in a particular condition. One group of individuals in one treatment condition is compared to another group of individuals in the different treatment conditions. It is a way of avoiding carry-over effects that can arise. The scores are measured in each state are measured. Each subject experiences only one of the experimental conditions. Feedback is independent—only one type, either positive or negative.
Within subjects, the design is an experiment where the same subjects are exposed to more than one-factor level. Within subjects experiments, subjects are exposed to different experimental conditions. This type of experiment has greater statistical power compared to between-subjects design. It also has a smaller error variance than between subjects, Sauro (August 2015).
N refers to the number of participants. Small N is a type of research where it is single-subject research. It entails a few subjects, and it is almost always repeated. Researchers observe how the subjects respond in several conditions. While Large N, the participants are grouped, and data from each group is studied. In Large N, data is analyzed with statistics and represented as group averages, as explained by Graham, Karmarkar, and Ottenbacher (May 2018).
The design would be ideal within-subjects because it has high statistical power in such a case whereby multiple feedback is needed, either positive or negative. Since it gives smaller error variance, it is best when dealing with a group of people. Large N design is effective when we have small samples. It is also ideal when interested in group performance. Large N has higher external validity.
Reference
Jeff Sauro, (August 2015) Comparing between and within-subjects studies retried from Measuring u.
James E. Graham, Amol M. Karmarkar, and Kenneth J. Ottenbacher (May 2018) Small Sample Research Designs for Evidence-based Rehabilitation: Issue and Methods. Retried from PMC