plot_scores

This function is a simple plot for diagnostic purposes examining
the performance of the FC scale based on simulated data.

Forced-choice (FC) response has gained increasing popularity
and interest for its resistance to faking when well-designed (Cao &
Drasgow, 2019 <doi:10.1037/apl0000414>). To established well-designed
FC scales, typically each item within a block should measure different
trait and have similar level of social desirability (Zhang et al.,
2020 <doi:10.1177/1094428119836486>). Recent study also suggests the
importance of high inter-item agreement of social desirability between
items within a block (Pavlov et al., 2021 <doi:10.31234/osf.io/hmnrc>).
In addition to this, FC developers may
also need to maximize factor loading differences (Brown &
Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) or minimize item
location differences (Cao & Drasgow, 2019 <doi:10.1037/apl0000414>)
depending on scoring models. Decision of which items should be
assigned to the same block, termed item pairing, is thus critical to
the quality of an FC test. This pairing process is essentially an
optimization process which is currently carried out manually. However,
given that we often need to simultaneously meet multiple objectives,
manual pairing becomes impractical or even not feasible once the
number of latent traits and/or number of items per trait are
relatively large. To address these problems, autoFC is developed as a
practical tool for facilitating the automatic construction of FC tests
(Li et al., 2022 <doi:10.1177/01466216211051726>), essentially
exempting users from the burden of manual item pairing and reducing
the computational costs and biases induced by simple ranking methods.
Given characteristics of each item (and item responses), FC measures can
be constructed either automatically based on user-defined pairing criteria
and weights, or based on exact specifications of each block (i.e., blueprint;
see Li et al., 2024 <doi:10.1177/10944281241229784>). Users can also
generate simulated responses based on the Thurstonian Item Response Theory
model (Brown & Maydeu-Olivares, 2011 <doi:10.1177/0013164410375112>) and
predict trait scores of simulated/actual respondents based on
an estimated model.

Mengtong Li

autoFC

Automatic Construction of Forced-Choice Tests

Tianjun Sun

Bo Zhang

plot_scores function

<dl><dt>x_scores</dt>
<dd>Scores to be plotted on the x axis</dd>
<dt>y_scores</dt>
<dd>Scores to be plotted on the y axis</dd>
<dt>type</dt>
<dd>Which type of plots is plotted? Can be <code>"simple"</code> for simple x-y plot, or <code>"abs.diff"</code>
for plotting absolute difference of (y-x) vs x.</dd>
<dt>...</dt>
<dd>Other parameters used in <code><a href="/link/plot()?package=autoFC&version=0.2.0.1002" data-mini-rdoc="autoFC::plot()">plot()</a></code></dd></dl>

Arguments

Author

Scatter Plot for True vs Estimated Scores, True Score vs Absolute Error, etc. — plot_scores

<dl>

<dt>x_scores</dt>
<dd>Scores to be plotted on the x axis</dd>


<dt>y_scores</dt>
<dd>Scores to be plotted on the y axis</dd>


<dt>type</dt>
<dd>Which type of plots is plotted? Can be <code>"simple"</code> for simple x-y plot, or <code>"abs.diff"</code>
for plotting absolute difference of (y-x) vs x.</dd>


<dt>...</dt>
<dd>Other parameters used in <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot()</a></code></dd>

</dl>

plot_scores: Scatter Plot for True vs Estimated Scores, True Score vs Absolute Error, etc.

Description

Usage

Value

Arguments

Author

Details

Examples