Last chance! 50% off unlimited learning
Sale ends in
plink with the exception of
the multiple-choice model.equate(x, method=c("TSE", "OSE"), true.scores, ts.low, base.grp=1, score=1,
startval, weights1, weights2, syn.weights, exclude, max.tse.iter, ...) ## S4 method for signature 'list'
equate(x, method, true.scores, ts.low, base.grp, score, startval,
weights1, weights2, syn.weights, exclude, max.tse.iter, ...)
## S4 method for signature 'irt.pars', 'ANY'
equate(x, method, true.scores, ts.low, base.grp, score, startval,
weights1, weights2, syn.weights, exclude, max.tse.iter, ...)
"TSE" and "OSE".score = 1, score responses for the true-score equating
method with zero for the lowest category and k-1 for the highest, k, category
for each item. If score = 2, score responses with one for the
lowest category and k for the highest, k, category for each item. A vector or
list of scoring weights for each response category can be supplied, but this is
only recommended for advanced users.true.scoresyn.weights is a list, there should be k-1 elements for
k groups.weights1 can be a list or a list of lists. The purpose of this object is to specify
the theta values for population 1 to integrate over in the observed score equating as well as
any weights associated with the theta values. The function as.weight
can be used to facilitate the creation of this object. If weights1 is missing, the
default is to use equally spaced theta values ranging from -4 to 4 with an increment of 0.05
and normal density weights for all groups. To better understand the elements of weights1, let us assume for a moment that x
has parameters for only two groups. In this instance, weights1 would be a single list
with length two. The first element should be a vector of theta values corresponding to points
on the base scale. The second list element should be a vector of weights corresponding the
theta values. If x contains more than two groups, a single weights1 object can
be supplied, and the same set of thetas and weights will be used for all adjacent groups.
However, a separate list of theta values and weights for each adjacent group in x can be
supplied. The specification of weights2 is the same as that for weights1, although the
theta values and weights for this object correspond to theta values for population 2.
This argument is only used when the synthetic weight associated with population 2 is greater
than zero. If weights2 is missing, the same theta values and weights used for
weights1 will be used for weights2. For both equating methods, response probabilities are computed using the functions drm,
grm, gpcm, and nrm for the associated models respectively. Various
arguments from these functions can be passed to equate. Specifically, the argument
incorrect can be passed to drm and catprob can be passed to grm.
In the functions drm, grm, and gpcm there is an argument D
for the value of a scaling constant. In plink, a single argument D can be passed
that will be applied to all applicable models, or arguments D.drm, D.grm, and
D.gpcm can be specified for each model respectively. If an argument is specified for D
and, say D.drm, the values for D.grm and D.gpcm (if applicable) will be
set equal to D. If only D.drm is specified, the values for D.grm and
D.gpcm (if applicable) will be set to 1. There are instances where certain items should not be included in the computation of total scores
(e.g., when the common items correspond to an external anchor test or when using field test items)
The exclude argument can be used to remove these items prior to conducting the equationg.
exclude can be specified as a character vector or a list. In the former case, a single value
"all.common" can be used to remove all common items or a vector of model names (i.e., "drm", "grm",
"gpcm", "nrm", "mcm") can be supplied, indicating that any item on any test associated with the given
model(s) would be excluded. If the argument is specified as a list, exclude should have as
any elements as groups in x. Each list element can include model names and/or item numbers
corresponding to the items on each test that should be excluded. If no items need to be excluded for
a given group, the list element should be equal NA. For example, say we have two groups and we would
like to exclude the GRM items and item 23 from the first group, we would specify exclude as
exclude <- list(c("grm",23),NA).# IRT true score and observed score examples from
# Kolen & Brennan (2004, ch. 6)
pm <- as.poly.mod(36)
x <- as.irt.pars(KB04$pars, KB04$common,
cat=list(rep(2,36),rep(2,36)), poly.mod=list(pm,pm))
out <- plink(x, rescale="MS", base.grp=2, D=1.7, exclude=list(27,NA))
# Create the quadrature points and weights
wt <- as.weight(
theta=c(-5.2086,-4.163,-3.1175,-2.072,-1.0269,0.0184,
1.0635,2.109,3.1546,4.2001),
weight=c(0.000101,0.00276,0.03021,0.142,0.3149,0.3158,
0.1542,0.03596,0.003925,0.000186))
# Conduct the equating
equate(out,weights1=wt, synth.weights=c(1,0),D=1.7)
# Conduct true score equating for specific true scores
equate(out, true.scores=7:15, ts.low=FALSE, D=1.7)
# Exclude all common items (assume they correspond to an external anchor)
equate(out, D=1.7, exclude="all.common")
# Observed score equating for mixed-format tests
pm1 <- as.poly.mod(55,c("drm","gpcm","nrm"),dgn$items$group1)
pm2 <- as.poly.mod(55,c("drm","gpcm","nrm"),dgn$items$group2)
x <- as.irt.pars(dgn$pars,dgn$common,dgn$cat,list(pm1,pm2))
out <- plink(x, rescale="HB")
OSE <- equate(out, method="OSE", score=2)
# Display the equated scores
OSE[[1]]
# Multiple group equating
pars <- TK07$pars
common <- TK07$common
cat <- list(rep(2,26),rep(2,34),rep(2,37),rep(2,40),rep(2,41),rep(2,43))
pm1 <- as.poly.mod(26)
pm2 <- as.poly.mod(34)
pm3 <- as.poly.mod(37)
pm4 <- as.poly.mod(40)
pm5 <- as.poly.mod(41)
pm6 <- as.poly.mod(43)
pm <- list(pm1, pm2, pm3, pm4, pm5, pm6)
x <- as.irt.pars(pars, common, cat, pm, grp.names=paste("grade",3:8,sep=""))
out <- plink(x, rescale="SL")
# True score equating
equate(out, method="TSE")
# True score equating with the base group changed to 3
equate(out, method="TSE", base.grp=3)
# Observed score equating (These data are for non-equivalent groups, but
# this example is included to illustrate the multigroup capabilities)
OSE <- equate(out, method="OSE", base.grp=3)
# Display the equated scores for each group
OSE[[1]]
Run the code above in your browser using DataLab