Implements the Kang & Chen (2007) polytomous extension to
S statistic of Orlando & Thissen (2000). Rows with
missing data are ignored, but see the omit
option.
SitemFit1(
grp,
item,
free = 0,
...,
method = "pearson",
log = TRUE,
qwidth = 6,
qpoints = 49L,
alt = FALSE,
omit = 0L,
.twotier = TRUE
)
a list containing the model and data. See the details section.
the item of interest
the number of free parameters involved in estimating the item (to adjust the df)
Not used. Forces remaining arguments to be specified by name.
whether to use a pearson or rms test
whether to return p-values in log units
lifecycle::badge("deprecated")
lifecycle::badge("deprecated")
whether to include the item of interest in the denominator
number of items to omit or a character vector with the names of the items to omit when calculating the observed and expected sum-score tables
whether to enable the two-tier optimization
A model, or group within a model, is represented as a named list.
list of response model objects
numeric matrix of item parameters
logical matrix of indicating which parameters are free (TRUE) or fixed (FALSE)
numeric vector giving the mean of the latent distribution
numeric matrix giving the covariance of the latent distribution
data.frame containing observed item responses, and optionally, weights and frequencies
factors scores with response patterns in rows
name of the data column containing the numeric row weights (optional)
name of the data column containing the integral row frequencies (optional)
width of the quadrature expressed in Z units
number of quadrature points
minimum number of non-missing items when estimating factor scores
The param
matrix stores items parameters by column. If a
column has more rows than are required to fully specify a model
then the extra rows are ignored. The order of the items in
spec
and order of columns in param
are assumed to
match. All items should have the same number of latent dimensions.
Loadings on latent dimensions are given in the first few rows and
can be named by setting rownames. Item names are assigned by
param
colnames.
Currently only a multivariate normal distribution is available,
parameterized by the mean
and cov
. If mean
and
cov
are not specified then a standard normal distribution is
assumed. The quadrature consists of equally spaced points. For
example, qwidth=2
and qpoints=5
would produce points
-2, -1, 0, 1, and 2. The quadrature specification is part of the
group and not passed as extra arguments for the sake of
consistency. As currently implemented, OpenMx uses EAP scores to
estimate latent distribution parameters. By default, the exact same
EAP scores should be produced by EAPscores.
This statistic is good at finding a small number of misfitting items among a large number of well fitting items. However, be aware that misfitting items can cause other items to misfit.
Observed tables cannot be computed when data is missing. Therefore, you can optionally omit items with the greatest number of responses missing relative to the item of interest.
Pearson is slightly more powerful than RMS in most cases I examined.
Setting alt
to TRUE
causes the tables to match
published articles. However, the default setting of FALSE
probably provides slightly more power when there are less than 10
items.
The name of the test, "S", probably stands for sum-score.
Kang, T. and Chen, T. T. (2007). An investigation of the performance of the generalized S-Chisq item-fit index for polytomous IRT models. ACT Research Report Series.
Orlando, M. and Thissen, D. (2000). Likelihood-Based Item-Fit Indices for Dichotomous Item Response Theory Models. Applied Psychological Measurement, 24(1), 50-64.
Other diagnostic:
ChenThissen1997()
,
SitemFit()
,
multinomialFit()
,
rpf.1dim.fit()
,
sumScoreEAPTest()