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Computes confidence intervals of standardized effects in a 2x2 between-subjects design

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

Computes confidence intervals for standardized AB interaction effect, main effect of A, main effect of B, simple main effects of A, and simple main effects of B in a 2x2 between-subjects factorial design with a quantitative response variable. Equality of population variances is not assumed. A square root unweighted average variance standardizer is used, which is the recommended standardizer when both factors are treatment factors.

Usage

ci.2x2.stdmean.bs(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

y12

vector of scores at level 1 of A and level 2 of B

y21

vector of scores at level 2 of A and level 1 of B

y22

vector of scores at level 2 of A and level 2 of B

Value

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

  • Estimate - estimate of standardized effect

  • adj Estimate - bias adjusted estimate of standardized effect

  • 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(14, 15, 11, 7, 16, 12, 15, 16, 10, 9)
y12 <- c(18, 24, 14, 18, 22, 21, 16, 17, 14, 13)
y21 <- c(16, 11, 10, 17, 13, 18, 12, 16, 6, 15)
y22 <- c(18, 17, 11, 9, 9, 13, 18, 15, 14, 11)
ci.2x2.stdmean.bs(.05, y11, y12, y21, y22)
#>             Estimate adj Estimate        SE         LL         UL
#> AB:      -1.44976487   -1.4193502 0.6885238 -2.7992468 -0.1002829
#> A:        0.46904158    0.4592015 0.3379520 -0.1933321  1.1314153
#> B:       -0.75330920   -0.7375055 0.3451209 -1.4297338 -0.0768846
#> A at b1: -0.25584086   -0.2504736 0.4640186 -1.1653006  0.6536189
#> A at b2:  1.19392401    1.1688767 0.5001423  0.2136630  2.1741850
#> B at a1: -1.47819163   -1.4471806 0.4928386 -2.4441376 -0.5122457
#> B at a2: -0.02842676   -0.0278304 0.4820369 -0.9732017  0.9163482

# Should return:
#             Estimate  adj Estimate        SE         LL         UL
# AB:      -1.44976487    -1.4193502 0.6885238 -2.7992468 -0.1002829
# A:        0.46904158     0.4592015 0.3379520 -0.1933321  1.1314153
# B:       -0.75330920    -0.7375055 0.3451209 -1.4297338 -0.0768846
# A at b1: -0.25584086    -0.2504736 0.4640186 -1.1653006  0.6536189
# A at b2:  1.19392401     1.1688767 0.5001423  0.2136630  2.1741850
# B at a1: -1.47819163    -1.4471806 0.4928386 -2.4441376 -0.5122457
# B at a2: -0.02842676    -0.0278304 0.4820369 -0.9732017  0.9163482