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Computes confidence intervals of standardized effects in a 2x2 mixed design

Source: R/statpsych1.R
ci.2x2.stdmean.mixed.Rd

Computes confidence intervals for the standardized AB interaction effect, main effect of A, main efect of B, simple main effects of A, and simple main effects of B in a 2x2 mixed factorial design where Factor A is a within-subjects factor, and Factor B is a between-subjects factor. Equality of population variances is not assumed. A square root unweigthed average variance standardizer is used.

Usage

ci.2x2.stdmean.mixed(alpha, y11, y12, y21, y22)

Arguments

alpha

alpha level for 1-alpha confidence

y11

vector of scores at level 1 of A and level 1 of B (group 1)

y12

vector of scores at level 1 of A and level 2 of B (group 2)

y21

vector of scores at level 2 of A and level 1 of B (group 1)

y22

vector of scores at level 2 of A and level 2 of B (group 2)

Value

Returns a 7-row matrix (one row per effect). The columns are:

  • Estimate - estimated standardized effect

  • adj Estimate - bias adjusted standardized effect estimate

  • SE - standard error

  • LL - lower limit of the confidence interval

  • UL - upper limit of the confidence interval

References

Bonett DG (2008). “Confidence intervals for standardized linear contrasts of means.” Psychological Methods, 13(2), 99–109. ISSN 1939-1463, doi:10.1037/1082-989X.13.2.99 .

Examples

y11 <- c(18, 19, 20, 17, 20, 16)
y12 <- c(19, 16, 16, 14, 16, 18)
y21 <- c(19, 18, 19, 20, 17, 16)
y22 <- c(16, 10, 12,  9, 13, 15)
ci.2x2.stdmean.mixed(.05, y11, y12, y21, y22)
#>             Estimate adj Estimate        SE          LL         UL
#> AB:      -1.95153666  -1.80141845 0.5407442 -3.01137589 -0.8916974
#> A:        1.06061775   1.01125934 0.2797699  0.51227884  1.6089567
#> B:        1.90911195   1.76225718 0.5758855  0.78039715  3.0378267
#> A at b1:  0.08484942   0.07589163 0.4650441 -0.82662027  0.9963191
#> A at b2:  2.03638608   1.82139908 0.2995604  1.44925855  2.6235136
#> B at a1:  0.93334362   0.86154796 0.5036429 -0.05377836  1.9204656
#> B at a2:  2.88488027   2.66296641 0.7477246  1.41936706  4.3503935

# Should return:
#             Estimate adj Estimate        SE          LL         UL
# AB:      -1.95153666  -1.80141845 0.5407442 -3.01137589 -0.8916974
# A:        1.06061775   1.01125934 0.2797699  0.51227884  1.6089567
# B:        1.90911195   1.76225718 0.5758855  0.78039715  3.0378267
# A at b1:  0.08484942   0.07589163 0.4650441 -0.82662027  0.9963191
# A at b2:  2.03638608   1.82139908 0.2995604  1.44925855  2.6235136
# B at a1:  0.93334362   0.86154796 0.5036429 -0.05377836  1.9204656
# B at a2:  2.88488027   2.66296641 0.7477246  1.41936706  4.3503935