The choice of research design greatly affects statistical power. There are certain types of research designs that maximize statistical power while minimizing sample size.
Between-subjects research designs where independent groups are compared on an outcome will decrease statistical power and increase the needed sample size because more observations are needed to detect differences between independent groups.
Within-subjects designs, by far, are the most statistically powerful type of research design. This is because each participant serves as their own control.
Multivariate designs, by default, will always require larger sample sizes in order to detect any interactions or confounding effects associated with the relationship between predictor and outcome variables. At least 20-40 outcomes must be collected for each parameter (or variable) that is entered into a multivariate model.
Between-subjects research designs where independent groups are compared on an outcome will decrease statistical power and increase the needed sample size because more observations are needed to detect differences between independent groups.
Within-subjects designs, by far, are the most statistically powerful type of research design. This is because each participant serves as their own control.
Multivariate designs, by default, will always require larger sample sizes in order to detect any interactions or confounding effects associated with the relationship between predictor and outcome variables. At least 20-40 outcomes must be collected for each parameter (or variable) that is entered into a multivariate model.
Between-subjects designs are focused on comparing independent groups or levels of a categorical variable on an outcome.
Within-subjects designs are focused on assessing how observations of an outcome change across time or within-subjects.
Multivariate designs adjust outcome variables for demographic, clinical, prognostic, confounding, between-subjects (fixed) effects, within-subjects (random) effects, and mixed-effects.