statpsych (version 1.7.0)

ci.2x2.stdmean.bs: Computes confidence intervals of standardized effects in a 2x2 between-subjects design

Description

Computes confidence intervals for standardized linear contrasts of means (AB interaction, main effect of A, main effect 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. 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)

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

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

References

Bonett2008statpsych

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  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