Table of counts extracted from Mislvey (1985). Data the 16 possible response patterns observed for four items from the arithmetic reasoning test of the Armed Services Vocational Aptitude Battery (ASVAB), Form 8A, from samples of white males and females and black males and females.
References
Mislevy, R. J. (1985). Estimation of latent group effects. Journal of the American Statistical Association, 80, 993-997.
Author
Phil Chalmers rphilip.chalmers@gmail.com
Examples
data(ASVAB)
datWM <- expand.table(subset(ASVAB, select=c(Item.1:Item.4, White_Male)))
datWF <- expand.table(subset(ASVAB, select=c(Item.1:Item.4, White_Female)))
datBM <- expand.table(subset(ASVAB, select=c(Item.1:Item.4, Black_Male)))
datBF <- expand.table(subset(ASVAB, select=c(Item.1:Item.4, Black_Female)))
dat <- rbind(datWM, datWF, datBM, datBF)
sex <- rep(c("Male", "Female", "Male", "Female"),
times=c(nrow(datWM), nrow(datWF), nrow(datBM), nrow(datBF))) |> factor()
color <- rep(c("White", "Black"),
times=c(nrow(datWM) + nrow(datWF), nrow(datBM) + nrow(datBF))) |> factor()
group <- sex:color
itemstats(dat, group=group)
#> $`Female:Black`
#> $`Female:Black`$overall
#> N mean_total.score sd_total.score ave.r sd.r alpha SEM.alpha
#> 145 1.462 1.014 0.046 0.087 0.176 0.921
#>
#> $`Female:Black`$itemstats
#> N mean sd total.r total.r_if_rm alpha_if_rm
#> Item.1 145 0.503 0.502 0.659 0.213 -0.078
#> Item.2 145 0.421 0.495 0.550 0.074 0.152
#> Item.3 145 0.283 0.452 0.501 0.064 0.164
#> Item.4 145 0.255 0.437 0.421 -0.011 0.256
#>
#> $`Female:Black`$proportions
#> 0 1
#> Item.1 0.497 0.503
#> Item.2 0.579 0.421
#> Item.3 0.717 0.283
#> Item.4 0.745 0.255
#>
#>
#> $`Female:White`
#> $`Female:White`$overall
#> N mean_total.score sd_total.score ave.r sd.r alpha SEM.alpha
#> 228 2.118 1.255 0.208 0.037 0.512 0.877
#>
#> $`Female:White`$itemstats
#> N mean sd total.r total.r_if_rm alpha_if_rm
#> Item.1 228 0.618 0.487 0.615 0.277 0.464
#> Item.2 228 0.605 0.490 0.642 0.312 0.432
#> Item.3 228 0.487 0.501 0.629 0.284 0.458
#> Item.4 228 0.408 0.493 0.662 0.339 0.408
#>
#> $`Female:White`$proportions
#> 0 1
#> Item.1 0.382 0.618
#> Item.2 0.395 0.605
#> Item.3 0.513 0.487
#> Item.4 0.592 0.408
#>
#>
#> $`Male:Black`
#> $`Male:Black`$overall
#> N mean_total.score sd_total.score ave.r sd.r alpha SEM.alpha
#> 140 1.443 1.027 0.051 0.102 0.18 0.93
#>
#> $`Male:Black`$itemstats
#> N mean sd total.r total.r_if_rm alpha_if_rm
#> Item.1 140 0.443 0.499 0.612 0.158 0.029
#> Item.2 140 0.400 0.492 0.587 0.133 0.071
#> Item.3 140 0.329 0.471 0.426 -0.037 0.304
#> Item.4 140 0.271 0.446 0.521 0.100 0.123
#>
#> $`Male:Black`$proportions
#> 0 1
#> Item.1 0.557 0.443
#> Item.2 0.600 0.400
#> Item.3 0.671 0.329
#> Item.4 0.729 0.271
#>
#>
#> $`Male:White`
#> $`Male:White`$overall
#> N mean_total.score sd_total.score ave.r sd.r alpha SEM.alpha
#> 263 2.475 1.361 0.34 0.075 0.673 0.779
#>
#> $`Male:White`$itemstats
#> N mean sd total.r total.r_if_rm alpha_if_rm
#> Item.1 263 0.741 0.439 0.705 0.475 0.596
#> Item.2 263 0.635 0.482 0.649 0.361 0.667
#> Item.3 263 0.593 0.492 0.734 0.481 0.588
#> Item.4 263 0.506 0.501 0.754 0.507 0.569
#>
#> $`Male:White`$proportions
#> 0 1
#> Item.1 0.259 0.741
#> Item.2 0.365 0.635
#> Item.3 0.407 0.593
#> Item.4 0.494 0.506
#>
#>