SLF {mirt}

R Documentation

Social Life Feelings Data

Description

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.

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

References

Bartholomew, D., J. (1998). Scaling unobservable constructs in social science. Journal of the Royal Statistical Society - Series C, 47, 1-13.

Examples

## No test: 
# 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)
plot(mod, type = 'trace')

## End(No test)

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