About This Software Bundle
This software bundle contains three functions intended to be helpful in the context of MOST. FactorialPowerPlan calculates sample size needed for factorial experiments under various assumptions. RandomAssignmentGenerator generates a list of random numbers for use as condition numbers to which to assign individuals in a factorial experiment. RelativeCosts1 implements a partly customizable form of a figure in the paper “Design of Experiments with Multiple Independent Variables: A Resource Management Perspective on Complete and Reduced Factorial Designs” (Collins et al., 2009) illustrating the efficiency of factorial experiments.
What method does this software implement?
FactorialPowerPlan uses power formulas for cluster-randomized or individual-randomized factorial experiments following “Multilevel factorial experiments for developing behavioral interventions: power, sample size, and resource considerations” (Dziak et al., 2012). The other two functions do not perform computations but are provided for convenience.
Related References
1. Collins, L. M., Dziak, J. J., & Li, R. (2009). Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs. Psychological methods, 14(3), 202.
About This Software Bundle
This software bundle contains three functions intended to be helpful in the context of MOST. FactorialPowerPlan calculates sample size needed for factorial experiments under various assumptions. RandomAssignmentGenerator generates a list of random numbers for use as condition numbers to which to assign individuals in a factorial experiment. RelativeCosts1 implements a partly customizable form of a figure in the paper “Design of Experiments with Multiple Independent Variables: A Resource Management Perspective on Complete and Reduced Factorial Designs” (Collins et al., 2009) illustrating the efficiency of factorial experiments.
What method does this software bundle implement?
FactorialPowerPlan uses power formulas for cluster-randomized or individual-randomized factorial experiments following “Multilevel factorial experiments for developing behavioral interventions: power, sample size, and resource considerations” (Dziak et al., 2012). The other two functions do not perform computations but are provided for convenience.
Related References
1. Collins, L. M., Dziak, J. J., & Li, R. (2009). Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs. Psychological methods, 14(3), 202.