Pretest and posttest statistics from El Karkri et al. (2025a)

The posttest sample size, mean, and standard deviation of the four Solomon groups in a classroom study of the Cognitive Acceleration through Science Education programme (El Karkri et al., 2025a, Table 8, p. 11), and the pretest mean and standard deviation of the two pretested groups (Table 7, p. 10). Numbers reported in the publication are reused with citation.

Usage

elkarkri2025a

Format

A data frame with 4 rows, one per Solomon group, and 8 variables:

group: The Solomon group.
pretested, treat: Indicators (1 = yes).
n, mean, sd: Posttest sample size, mean, and standard deviation.
pre_mean, pre_sd: Pretest mean and standard deviation (pretested groups only; the pretest sample sizes equal the posttest ones).

Source

El Karkri, M., Quesada, A., & Romero-Ariza, M. (2025a). The dual impact of pretest sensitisation and the cognitive acceleration through science education programme in the Solomon four-group design. Brain Sciences, 16(1), Article 64. https://doi.org/10.3390/brainsci16010064

Details

Design and caveats. The authors describe the study as quasi-experimental: each Solomon group was one intact class, so class and condition are confounded, and differences between the groups can reflect the classes as well as the treatment and the pretest. validate_solomon() flags this design when class membership is supplied. The pretested classes already differed at pretest (9.61 against 7.86), which baseline_solomon() reports; the unpretested classes have no pretest. The authors reported a significant Pretest x Treatment interaction.

Known result. solomon_from_summary() reproduces the published two-way ANOVA from these numbers within rounding: interaction F(1, 84) = 11.46 against the published 11.482, treatment 6.78 against 6.794, and pretest 0.18 against 0.186 (pp. 11-12).

Examples

with(elkarkri2025a, solomon_from_summary(n, mean, sd))
#> Solomon analysis from summary statistics
#> ----------------------------------------
#> Pooled error variance: 4.640 on 84 df (equal variances assumed)
#> 
#> Two-way ANOVA on the posttest (Type III sums of squares)
#>   Treatment            SS =   31.452  df = 1  F = 6.78  p = 0.011
#>   Pretest              SS =    0.855  df = 1  F = 0.18  p = 0.669
#>   Treatment x Pretest  SS =   53.184  df = 1  F = 11.46  p = 0.001
#>   Error                SS =  389.721  df = 84
#> 
#> Contrasts with 95% confidence intervals
#>   Test A: Pretest x Treatment               3.550 [1.465, 5.635], t(84) = 3.39, p = 0.001
#>   Test B: Treatment | pretested             3.140 [1.475, 4.805], t(84) = 3.75, p < .001
#>   Test C: Treatment | unpretested          -0.410 [-1.665, 0.845], t(84) = -0.65, p = 0.518
#>   Test D: ATE (avg over pretest)            1.365 [0.322, 2.408], t(84) = 2.60, p = 0.011
#>   Pretest main effect                       0.225 [-0.818, 1.268], t(84) = 0.43, p = 0.669
pre <- elkarkri2025a[elkarkri2025a$pretested == 1, ]
baseline_solomon(n = pre$n, mean = pre$pre_mean, sd = pre$pre_sd)
#> Baseline comparison of the pretested arms
#> -----------------------------------------
#>   Pretested, treatment   n = 9, M = 9.61, SD = 2.67
#>   Pretested, control     n = 25, M = 7.86, SD = 2.72
#> 
#> Difference: 1.75, 95% CI [-0.39, 3.89], t(32) = 1.66, p = .106
#> Hedges's g: 0.63, 95% CI [-0.14, 1.42] (noncentral t)
#> 
#> The unpretested arms have no pretest, so their baseline cannot be checked
#> or adjusted for with the study's own data.