A 5-item data set analyzed by Bartholomew (1998). Data contains dichotomous responses (endorsement vs non-endorsement) from 1490 German respondents to five statements on perceptions of social life.
References
Bartholomew, D., J. (1998). Scaling unobservable constructs in social science. Journal of the Royal Statistical Society - Series C, 47, 1-13.
Author
Phil Chalmers rphilip.chalmers@gmail.com
Examples
# \donttest{
# tabular format
data(SLF)
SLF
#> social1 social2 social3 social4 social5 freq
#> 1 0 0 0 0 0 156
#> 2 0 0 0 0 1 26
#> 3 0 0 0 1 0 14
#> 4 0 0 0 1 1 9
#> 5 0 0 1 0 0 127
#> 6 0 0 1 0 1 26
#> 7 0 0 1 1 0 66
#> 8 0 0 1 1 1 16
#> 9 0 1 0 0 0 174
#> 10 0 1 0 0 1 35
#> 11 0 1 0 1 0 36
#> 12 0 1 0 1 1 13
#> 13 0 1 1 0 0 208
#> 14 0 1 1 0 1 65
#> 15 0 1 1 1 0 195
#> 16 0 1 1 1 1 129
#> 17 1 0 0 0 0 8
#> 18 1 0 0 0 1 2
#> 19 1 0 0 1 0 1
#> 20 1 0 0 1 1 3
#> 21 1 0 1 0 0 4
#> 22 1 0 1 0 1 4
#> 23 1 0 1 1 0 18
#> 24 1 0 1 1 1 9
#> 25 1 1 0 0 0 8
#> 26 1 1 0 0 1 2
#> 27 1 1 0 1 0 5
#> 28 1 1 0 1 1 3
#> 29 1 1 1 0 0 19
#> 30 1 1 1 0 1 10
#> 31 1 1 1 1 0 31
#> 32 1 1 1 1 1 68
# full dataset
full <- expand.table(SLF)
itemstats(full)
#> $overall
#> N mean_total.score sd_total.score ave.r sd.r alpha SEM.alpha
#> 1490 2.166 1.324 0.187 0.076 0.536 0.902
#>
#> $itemstats
#> N mean sd total.r total.r_if_rm alpha_if_rm
#> social1 1490 0.131 0.337 0.482 0.251 0.510
#> social2 1490 0.672 0.470 0.550 0.227 0.527
#> social3 1490 0.668 0.471 0.632 0.335 0.458
#> social4 1490 0.413 0.493 0.702 0.420 0.397
#> social5 1490 0.282 0.450 0.578 0.281 0.493
#>
#> $proportions
#> 0 1
#> social1 0.869 0.131
#> social2 0.328 0.672
#> social3 0.332 0.668
#> social4 0.587 0.413
#> social5 0.718 0.282
#>
mod <- mirt(full)
#>
Iteration: 1, Log-Lik: -4186.329, Max-Change: 0.29553
Iteration: 2, Log-Lik: -4158.310, Max-Change: 0.24439
Iteration: 3, Log-Lik: -4144.592, Max-Change: 0.19556
Iteration: 4, Log-Lik: -4137.713, Max-Change: 0.15556
Iteration: 5, Log-Lik: -4134.129, Max-Change: 0.12425
Iteration: 6, Log-Lik: -4132.182, Max-Change: 0.10007
Iteration: 7, Log-Lik: -4129.737, Max-Change: 0.03837
Iteration: 8, Log-Lik: -4129.597, Max-Change: 0.03494
Iteration: 9, Log-Lik: -4129.497, Max-Change: 0.03096
Iteration: 10, Log-Lik: -4129.234, Max-Change: 0.01329
Iteration: 11, Log-Lik: -4129.221, Max-Change: 0.01103
Iteration: 12, Log-Lik: -4129.212, Max-Change: 0.00984
Iteration: 13, Log-Lik: -4129.194, Max-Change: 0.00416
Iteration: 14, Log-Lik: -4129.190, Max-Change: 0.00453
Iteration: 15, Log-Lik: -4129.188, Max-Change: 0.00378
Iteration: 16, Log-Lik: -4129.186, Max-Change: 0.00152
Iteration: 17, Log-Lik: -4129.186, Max-Change: 0.00205
Iteration: 18, Log-Lik: -4129.186, Max-Change: 0.00253
Iteration: 19, Log-Lik: -4129.185, Max-Change: 0.00137
Iteration: 20, Log-Lik: -4129.185, Max-Change: 0.00045
Iteration: 21, Log-Lik: -4129.185, Max-Change: 0.00044
Iteration: 22, Log-Lik: -4129.185, Max-Change: 0.00043
Iteration: 23, Log-Lik: -4129.185, Max-Change: 0.00052
Iteration: 24, Log-Lik: -4129.185, Max-Change: 0.00016
Iteration: 25, Log-Lik: -4129.185, Max-Change: 0.00014
Iteration: 26, Log-Lik: -4129.185, Max-Change: 0.00039
Iteration: 27, Log-Lik: -4129.184, Max-Change: 0.00051
Iteration: 28, Log-Lik: -4129.184, Max-Change: 0.00041
Iteration: 29, Log-Lik: -4129.184, Max-Change: 0.00012
Iteration: 30, Log-Lik: -4129.184, Max-Change: 0.00033
Iteration: 31, Log-Lik: -4129.184, Max-Change: 0.00015
Iteration: 32, Log-Lik: -4129.184, Max-Change: 0.00038
Iteration: 33, Log-Lik: -4129.184, Max-Change: 0.00011
Iteration: 34, Log-Lik: -4129.184, Max-Change: 0.00010
plot(mod, type = 'trace')
# }