This function conducts IRT true score and observed score equating for unidimensional
single-format or mixed-format item parameters for two or more groups. This function
supports all item response models available in
plink with the exception of
the multiple-choice model.
1 2 3 4 5 6 7 8 9 10
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, ...)
an object of class
character vector identifying the equating method(s) to use.
Values can include
numeric vector of true score values to be equated
logical value. If TRUE, interpolate values for the equated true scores in the range of observed scores from one to the value below the lowest estimated true score (a rounded sum of guessing parameters)
integer identifying the group for the base scale
integer starting value for the first value of
list containing information about the theta values and weights to be used in the observed score equating for population 1. See below for more details.
list containing information about the theta values and weights to be used in the observed score equating for population 2. See below for more details.
vector of length two or a list containing vectors of length two
with synthetic population weights to be used for each pair of tests for populations
1 and 2 respectively. If missing, weights of 0.5 will be used for both populations
for all groups. If
character vector or list identifying common items that should be excluded when estimating the linking constants. See below for more details.
maximum number of iterations to identify the theta value associated with each true score. The default is 50.
further arguments passed to or from other methods. See below for details.
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
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
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
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
For both equating methods, response probabilities are computed using the functions
nrm for the associated models respectively. Various
arguments from these functions can be passed to
equate. Specifically, the argument
incorrect can be passed to
catprob can be passed to
In the functions
gpcm there is an argument
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.gpcm can be specified for each model respectively. If an argument is specified for
D.drm, the values for
D.gpcm (if applicable) will be
set equal to
D. If only
D.drm is specified, the values for
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)
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 <- list(c("grm",23),NA).
Returns a matrix of equated true scores and/or a list of equated observed scores with associated marginal distributions or a list combining these two objects. The output for the observed-score equating also includes EAP scores and SDs for each of the observed scores (Thissen & Orlando, 2001).
Jonathan P. Weeks [email protected]
Kolen, M. J. (1981). Comparison of traditional and item response theory methods for equating tests. Journal of Educational Measurement, 18(1), 1-11.
Kolen, M. J. & Brennan, R. L. (2004) Test Equating, Scaling, and Linking (2nd ed.). New York: Springer
Thissen, D. & Orlando, M. (2001) Item response theory for items scored in two categories. In D. Thissen & H. Wainer (Eds.) Test Scoring (p. 23 - 72). Hillsdale, NJ: Lawrence Erlbaum Associates.
Weeks, J. P. (2010) plink: An R package for linking mixed-format tests using IRT-based methods. Journal of Statistical Software, 35(12), 1–33. URL http://www.jstatsoft.org/v35/i12/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
# 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$cat,list(pm1,pm2)) out <- plink(x, rescale="HB") OSE <- equate(out, method="OSE", score=2) # Display the equated scores OSE[] # Multiple group equating pars <- TK07$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[]
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.