lordif (version 0.1-1)

montecarlo: performs Monte Carlo simulations for empirical cutoff thresholds

Description

Performs Monte Carlo simulations using multiple datasets without DIF.

Usage

montecarlo(obj, alpha = 0.01, nr = 100)

Arguments

obj

an object returned from lordif

alpha

desired significance level (e.g., .01)

nr

number of replications

Value

• Returns a data frame with the following components:
• chi12
  threshold for the prob associated with the LR Chi-square test comparing Model 1 vs. 2
• chi13
  threshold for the prob associated with the LR Chi-square test comparing Model 1 vs. 3
• chi23
  threshold for the prob associated with the LR Chi-square test comparing Model 2 vs. 3
• pseudo12.CoxSnell
  threshold for the Cox & Snell pseudo R-square change from Model 1 to 2
• pseudo13.CoxSnell
  threshold for the Cox & Snell pseudo R-square change from Model 1 to 3
• pseudo23.CoxSnell
  threshold for the Cox & Snell pseudo R-square change from Model 2 to 3
• pseudo12.Nagelkerke
  threshold for the Nagelkerke pseudo R-square change from Model 1 to 2
• pseudo13.Nagelkerke
  threshold for the Nagelkerke pseudo R-square change from Model 1 to 3
• pseudo23.Nagelkerke
  threshold for the Nagelkerke pseudo R-square change from Model 2 to 3
• pseudo12.McFadden
  threshold for the McFadden pseudo R-square change from Model 1 to 2
• pseudo13.McFadden
  threshold for the McFadden pseudo R-square change from Model 1 to 3
• pseudo23.McFadden
  threshold for the McFadden pseudo R-square change from Model 2 to 3
• beta12
  threshold for proportional beta change from Model 1 to 2
• alpha
  significance level
• nr
  number of replications

Details

The simulated datasets have the same dimensions as the empirical data. Group differences (impact) in theta between groups are preserved in simulated datasets. Returns empirical thresholds for various statistics and effect size measures.

References

Choi, S. W., Gibbons, L. E., & Crane, P. K. (April, 2009). Development of freeware for an iterative hybrid ordinal logistic regression/IRT DIF. Paper Presented at the National Council of Measurement in Education. San Diego, CA.

See Also

lordif

Examples

##run lordif first
age.dif <- lordif(Anxiety[paste("R",1:29,sep="")],Anxiety$age)
##the following takes a long time
mc1 <- montecarlo(age.dif,alpha=0.05,nr=500)