Computes generalized eta-square estimates in a two-factor design where one or both factors are classification factors. If both factors are treatment factors, then partial eta-square estimates are typically recommended. Use the estimates from this function in the etasqr.adj function to obtain bias adjusted estimates.

etasqr.gen.2way(SSa, SSb, SSab, SSe)

Arguments

SSa

sum of squares for factor A

SSb

sum of squares for factor B

SSab

sum of squares for A x B interaction

SSe

error (within) sum of squares

Value

Returns a 3-row matrix. The columns are:

  • A - estimate of eta-squared for factor A

  • B - estimate of eta-squared for factor B

  • AB - estimate of eta-squared for A x B interaction

Examples

etasqr.gen.2way(12.3, 15.6, 5.2, 7.9)
#>                                           A         B        AB
#> A treatment, B classification:     0.300000 0.5435540 0.1811847
#> A classification, B treatment:     0.484252 0.3804878 0.2047244
#> A classification, B classiciation: 0.300000 0.3804878 0.1268293

# Should return:
#                                           A         B        AB
# A treatment, B classification:     0.300000 0.5435540 0.1811847
# A classification, B treatment:     0.484252 0.3804878 0.2047244
# A classification, B classiciation: 0.300000 0.3804878 0.1268293