n.multiway {easypower}

Sample size calculations for factorial ANOVAs

Description

Sample size calculations for factorial ANOVAs

Usage

n.multiway(iv1 = NULL, iv2 = NULL, iv3 = NULL, iv4 = NULL,
  interaction.eta2 = "small", sig.level = 0.05, power = 0.8,
  result = "all", ...)

Arguments

iv1

The list of data for treatment 1.

iv2

The list of data for treatment 2.

iv3

(optional) The list of data for treatment 3.

iv4

(optional) The list of data for treatment 4.

interaction.eta2

(optional) Either a character string or numeric value of the desired eta squared. Default is set to "small".

sig.level

(optional) Desired significance level. Default value is 0.05.

power

(optional) Desired level of power. Default value is 0.80.

result

The amount of data that will be output to the user (default = "all"). The following are the three output options the user may specify:

• result = "all" - Outputs the sample size recommendations for all treatments and all possible interactions.

• result = "highest" - Outputs the highest recommended sample size.

• result = "select" - Outputs specific results to the user. If there has been previous research on an effect, the user may input a numeric value for the effect size. The output will consist of the highest recommended sample size and the recommendations where the user input a numeric value for the effect size of a treatment.

...

Extra interactions to pass in. In order to change the effect size of a specific interaction an interaction effect may be added to the function. It must take the form: int# = int.eff.#.

Details

Acceptable effect size character string values and their numeric equivalents are: "small" (0.01), "med" (0.06), and "large" (0.14).

Note

Sample size recommendations are rounded up to the nearest integer. More detailed examples on n.multiway can be viewed in the vignette.

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, N.J.: Lawrence Erlbaum Associates.

Examples

# Exercise 8.15, p.400 from Cohen (1988)
# Defining the treatments
main.eff.1 <- list(name = "R", levels = 2, eta.sq = 0.123)
main.eff.2 <- list(name = "C", levels = 4, eta.sq = 0.215)
# Running n.multiway
n.multiway(iv1=main.eff.1, iv2=main.eff.2, interaction.eta2 = 0.079)
# To just view highest
n.multiway(iv1=main.eff.1, iv2=main.eff.2, interaction.eta2 = 0.079, result = "highest")

# Exercise 8.14, p.397 from Cohen (1988)
# Defining the treatments and interaction
main.eff.1 <- list(name = "Sex", levels = 2, eta.sq = 0.0099)
main.eff.2 <- list(name = "Age", levels = 3, eta.sq = 0.0588)
main.eff.3 <- list(name = "Conditions", levels = 4, eta.sq = 0.1506)
# Running n.multiway
n.multiway(iv1=main.eff.1, iv2=main.eff.2, iv3=main.eff.3, interaction.eta2 = 0.0588)