sirt (version 1.5-0)

data.bs: Datasets from Borg and Staufenbiel (2007)

Description

Datasets of the book of Borg and Staufenbiel (2007) Lehrbuch Theorien and Methoden der Skalierung.

Usage

data(data.bs07a)

format

• The datasetdata.bs07acontains the dataGefechtsangst(p. 130) and contains 8 of the original 9 items. The items are symptoms of anxiety in engagement. GF1: starkes Herzklopfen,GF2: flaues Gefuehl in der Magengegend,GF3: Schwaechegefuehl,GF4: Uebelkeitsgefuehl,GF5: Erbrechen,GF6: Schuettelfrost,GF7: in die Hose urinieren/einkoten,GF9: Gefuehl der Gelaehmtheit The format is 'data.frame': 100 obs. of 9 variables: $ idpatt: int 44 29 1 3 28 50 50 36 37 25 ... $ GF1 : int 1 1 1 1 1 0 0 1 1 1 ... $ GF2 : int 0 1 1 1 1 0 0 1 1 1 ... $ GF3 : int 0 0 1 1 0 0 0 0 0 1 ... $ GF4 : int 0 0 1 1 0 0 0 1 0 1 ... $ GF5 : int 0 0 1 1 0 0 0 0 0 0 ... $ GF6 : int 1 1 1 1 1 0 0 0 0 0 ... $ GF7 : num 0 0 1 1 0 0 0 0 0 0 ... $ GF9 : int 0 0 1 1 1 0 0 0 0 0 ...
• MORE DATASETS

References

Borg, I., & Staufenbiel, T. (2007). Lehrbuch Theorie und Methoden der Skalierung. Bern: Hogrefe.

Examples

#############################################################################
# EXAMPLE 07a: Dataset Gefechtsangst
#############################################################################

data(data.bs07a)
dat <- data.bs07a
items <- grep( "GF" , colnames(dat)  , value=TRUE )

#************************
# Model 1: Rasch model
mod1 <- TAM::tam.mml(dat[,items] )
summary(mod1)
IRT.WrightMap(mod1)

#************************
# Model 2: 2PL model
mod2 <- TAM::tam.mml.2pl(dat[,items] )
summary(mod2)

#************************
# Model 3: Latent class analysis (LCA) with two classes
tammodel <- "
ANALYSIS:
  TYPE=LCA;
  NCLASSES(2)
  NSTARTS(5,10)
LAVAAN MODEL:
  F =~ GF1__GF9
  "  
mod3 <- TAM::tamaan( tammodel , dat )
summary(mod3)

#************************
# Model 4: LCA with three classes
tammodel <- "
ANALYSIS:
  TYPE=LCA;
  NCLASSES(3)
  NSTARTS(5,10)
LAVAAN MODEL:
  F =~ GF1__GF9
  "  
mod4 <- TAM::tamaan( tammodel , dat )
summary(mod4)

#************************
# Model 5: Located latent class model (LOCLCA) with two classes
tammodel <- "
ANALYSIS:
  TYPE=LOCLCA;
  NCLASSES(2)
  NSTARTS(5,10)
LAVAAN MODEL:
  F =~ GF1__GF9 
  "  
mod5 <- TAM::tamaan( tammodel , dat )
summary(mod5)

#************************
# Model 6: Located latent class model with three classes
tammodel <- "
ANALYSIS:
  TYPE=LOCLCA;
  NCLASSES(3)
  NSTARTS(5,10)
LAVAAN MODEL:
  F =~ GF1__GF9 
  "  
mod6 <- TAM::tamaan( tammodel , dat )
summary(mod6)

#************************
# Model 7: Probabilistic Guttman model
mod7 <- sirt::prob.guttman( dat[,items] )
summary(mod7)

#-- model comparison
IRT.compareModels( mod1, mod2 , mod3 , mod4 , mod5 , mod6 , mod7 )