Confidence interval for a standardized linear contrast of means in a between-subjects design

Source: R/statpsych1.R

Computes confidence intervals for a population standardized linear contrast of means in a between-subjects design. The unweighted standardizer is recommended in experimental designs. The weighted standardizer is recommended in nonexperimental designs with simple random sampling. The group 1 standardizer is useful in both experimental and nonexperimental designs. Equality of variances is not assumed.

ci.lc.stdmean.bs(alpha, m, sd, n, v)

Arguments

alpha

alpha level for 1-alpha confidence

m

vector of estimated group means

sd

vector of estimated group standard deviation

n

vector of sample sizes

v

vector of between-subjects contrast coefficients

Value

Returns a 3-row matrix. The columns are:

  • Estimate - estimated standardized linear contrast

  • adj Estimate - bias adjusted standardized linear contrast 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

m <- c(33.5, 37.9, 38.0, 44.1)
sd <- c(3.84, 3.84, 3.65, 4.98)
n <- c(10,10,10,10)
v <- c(.5, .5, -.5, -.5)
ci.lc.stdmean.bs(.05, m, sd, n, v)
#>                           Estimate adj Estimate        SE        LL         UL
#> Unweighted standardizer: -1.301263    -1.273964 0.3692800 -2.025039 -0.5774878
#> Weighted standardizer:   -1.301263    -1.273964 0.3514511 -1.990095 -0.6124317
#> Group 1 standardizer:    -1.393229    -1.273810 0.4849842 -2.343781 -0.4426775

# Should return:
#                           Estimate  adj Estimate        SE        LL         UL
# Unweighted standardizer: -1.301263     -1.273964 0.3692800 -2.025039 -0.5774878
# Weighted standardizer:   -1.301263     -1.273964 0.3514511 -1.990095 -0.6124317
# Group 1 standardizer:    -1.393229     -1.273810 0.4849842 -2.343781 -0.4426775