sirt (version 4.1-15)

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 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 )
}