Skip to contents

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')


# }