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

In statpsych: Statistical Methods for Psychologists

Computes confidence intervals of standardized effects in a 2x2 mixed design

Description

Computes confidence intervals for the 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 mixed factorial design where Factor A is a within-subjects factor, and Factor B is a between-subjects factor. Equality of population variances is not assumed. A square root unweighted average variance standardizer is used.

Usage

ci.2x2.stdmean.mixed(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 (group 1)
y12	vector of scores at level 1 of A and level 2 of B (group 2)
y21	vector of scores at level 2 of A and level 1 of B (group 1)
y22	vector of scores at level 2 of A and level 2 of B (group 2)

Value

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

• Estimate - estimated standardized effect

• adj Estimate - bias adjusted standardized effect estimate

• SE - standard error

• LL - lower limit of the confidence interval

• UL - upper limit of the confidence interval

References

\insertRef

Bonett2008statpsych

Examples

y11 <- c(18, 19, 20, 17, 20, 16)
y12 <- c(19, 16, 16, 14, 16, 18)
y21 <- c(19, 18, 19, 20, 17, 16)
y22 <- c(16, 10, 12,  9, 13, 15)
ci.2x2.stdmean.mixed(.05, y11, y12, y21, y22)

# Should return:
#          Estimate adj Estimate      SE      LL      UL
# AB:       -1.9515      -1.8014 0.54074 -3.0114 -0.8917
# A:         1.0606       1.0113 0.27977  0.5123  1.6090
# B:         1.9091       1.7623 0.57589  0.7804  3.0378
# A at b1:   0.0848       0.0759 0.46504 -0.8266  0.9963
# A at b2:   2.0364       1.8214 0.29956  1.4493  2.6235
# B at a1:   0.9333       0.8615 0.50364 -0.0538  1.9205
# B at a2:   2.8849       2.6630 0.74772  1.4194  4.3504

statpsych documentation built on Jan. 13, 2026, 1:07 a.m.