Datasets from Borg and Staufenbiel (2007)

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

Usage

data(data.bs07a)

Format

The dataset data.bs07a contains the data Gefechtsangst (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

if (FALSE) {
#############################################################################
# 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 )
}