| ci.2x2.stdmean.bs | R Documentation |
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.
ci.2x2.stdmean.bs(alpha, y11, y12, y21, y22)
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 |
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
Bonett2008statpsych
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: -1.4498 -1.4194 0.68852 -2.7992 -0.1003
# A: 0.4690 0.4592 0.33795 -0.1933 1.1314
# B: -0.7533 -0.7375 0.34512 -1.4297 -0.0769
# A at b1: -0.2558 -0.2505 0.46402 -1.1653 0.6536
# A at b2: 1.1939 1.1689 0.50014 0.2137 2.1742
# B at a1: -1.4782 -1.4472 0.49284 -2.4441 -0.5122
# B at a2: -0.0284 -0.0278 0.48204 -0.9732 0.9163
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