ci.2x2.stdmean.bs: Computes confidence intervals of standardized effects in a...

In statpsych: Statistical Methods for Psychologists

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

Description

Computes confidence intervals for standardized linear constrasts of means (AB interaction, main effect of A, main efect of B, simple main effects of A, and simple main effects of B) in a 2x2 between-subjects design with a quantitative response variable. Equality of population variances is not assumed. An unweigthed variance standardizer is used, which is the recommended standarizer 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 - bias adjusted estimate of standardized effect

• SE - standard error

• LL - lower limit of the confidence interval

• UL - upper limit of the confidence interval

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)

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

statpsych documentation built on July 9, 2023, 6:50 p.m.