LSAT7 {mirt}

R Documentation

Description of LSAT7 data

Description

Data from Bock & Lieberman (1970); contains 5 dichotomously scored items obtained from the Law School Admissions Test, section 7.

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

References

Bock, R. D., & Lieberman, M. (1970). Fitting a response model for n dichotomously scored items. Psychometrika, 35(2), 179-197.

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06

Examples

## No test: 
dat <- expand.table(LSAT7)
head(dat)

##   Item.1 Item.2 Item.3 Item.4 Item.5
## 1      0      0      0      0      0
## 2      0      0      0      0      0
## 3      0      0      0      0      0
## 4      0      0      0      0      0
## 5      0      0      0      0      0
## 6      0      0      0      0      0

itemstats(dat)

## $overall
##     N mean_total.score sd_total.score ave.r  sd.r alpha
##  1000            3.707          1.199 0.143 0.052 0.453
## 
## $itemstats
##           N  mean    sd total.r total.r_if_rm alpha_if_rm
## Item.1 1000 0.828 0.378   0.530         0.246       0.396
## Item.2 1000 0.658 0.475   0.600         0.247       0.394
## Item.3 1000 0.772 0.420   0.611         0.313       0.345
## Item.4 1000 0.606 0.489   0.592         0.223       0.415
## Item.5 1000 0.843 0.364   0.461         0.175       0.438
## 
## $proportions
##            0     1
## Item.1 0.172 0.828
## Item.2 0.342 0.658
## Item.3 0.228 0.772
## Item.4 0.394 0.606
## Item.5 0.157 0.843

(mod <- mirt(dat, 1))

## 
## Call:
## mirt(data = dat, model = 1)
## 
## Full-information item factor analysis with 1 factor(s).
## Converged within 1e-04 tolerance after 28 EM iterations.
## mirt version: 1.40 
## M-step optimizer: BFGS 
## EM acceleration: Ramsay 
## Number of rectangular quadrature: 61
## Latent density type: Gaussian 
## 
## Log-likelihood = -2658.805
## Estimated parameters: 10 
## AIC = 5337.61
## BIC = 5386.688; SABIC = 5354.927
## G2 (21) = 31.7, p = 0.0628
## RMSEA = 0.023, CFI = NaN, TLI = NaN

coef(mod)

## $Item.1
##        a1     d g u
## par 0.988 1.856 0 1
## 
## $Item.2
##        a1     d g u
## par 1.081 0.808 0 1
## 
## $Item.3
##        a1     d g u
## par 1.706 1.804 0 1
## 
## $Item.4
##        a1     d g u
## par 0.765 0.486 0 1
## 
## $Item.5
##        a1     d g u
## par 0.736 1.855 0 1
## 
## $GroupPars
##     MEAN_1 COV_11
## par      0      1

## End(No test)

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