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README.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
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code .at { color: #008080; }
code .do { color: #969896; }
code .an { color: #008080; }
code .cv { color: #008080; }
code .in { color: #008080; }
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box-sizing: border-box;
min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
padding-top: 0px;
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<!-- README.md is generated from README.Rmd. Please edit that file -->
<h1 id="latent2likert-">latent2likert
<a href="https://lalovic.io/latent2likert/"><img role="img" aria-label="Package logo" src="data:image/png;base64,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" align="right" height="138" alt="Package logo" /></a></h1>
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<p><a href="https://github.com/markolalovic/latent2likert/actions/workflows/R-CMD-check.yaml"><img role="img" aria-label="R-CMD-check" src="data:image/svg+xml; 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<h2 id="overview">Overview</h2>
<p>The <strong>latent2likert</strong> package is designed to effectively
simulate the discretization process inherent to Likert scales while
minimizing distortion. It converts continuous latent variables into
ordinal categories to generate Likert scale item responses. This is
particularly useful for accurately modeling and analyzing survey data
that use Likert scales, especially when applying statistical techniques
that require metric data.</p>
<h2 id="installation">Installation</h2>
<p>You can install the released version from CRAN:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"latent2likert"</span>)</span></code></pre></div>
<p>Or the latest development version from GitHub:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>devtools<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"markolalovic/latent2likert"</span>)</span></code></pre></div>
<h2 id="dependencies">Dependencies</h2>
<p>To keep the package lightweight, <strong>latent2likert</strong> only
imports <a href="https://cran.r-project.org/package=mvtnorm">mvtnorm</a>, along
with the standard R packages stats and graphics, which are typically
included in R releases. An additional suggested dependency is the
package <a href="https://cran.r-project.org/package=sn">sn</a>, which is
required only for generating random responses from correlated Likert
items based on a multivariate skew normal distribution. The package
prompts the user to install this dependency during interactive sessions
if needed.</p>
<h2 id="features">Features</h2>
<ul>
<li><code>rlikert</code>: Generates random responses to Likert scale
questions based on specified means and standard deviations of latent
variables, with optional settings for skewness and correlations.</li>
<li><code>estimate_params</code>: Estimates latent parameters from
existing survey data.</li>
</ul>
<h2 id="structure">Structure</h2>
<figure>
<img role="img" aria-label="Overview of inputs and outputs" 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" width="80%" alt="Overview of inputs and outputs" />
<figcaption>
<p><em>Overview of inputs and outputs.</em></p>
</figcaption>
</figure>
<h2 id="using-rlikert">Using <code>rlikert</code></h2>
<p>You can use the <code>rlikert</code> function to simulate Likert item
responses.</p>
<p>To generate a sample of random responses to one item on a 5-point
Likert scale, use:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">library</span>(latent2likert)</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">rlikert</span>(<span class="at">size =</span> <span class="dv">10</span>, <span class="at">n_items =</span> <span class="dv">1</span>, <span class="at">n_levels =</span> <span class="dv">5</span>)</span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="co">#> [1] 1 3 3 3 2 4 1 3 3 1</span></span></code></pre></div>
<p>To generate responses to multiple items with specified
parameters:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">rlikert</span>(<span class="at">size =</span> <span class="dv">10</span>,</span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a> <span class="at">n_items =</span> <span class="dv">3</span>,</span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a> <span class="at">n_levels =</span> <span class="fu">c</span>(<span class="dv">4</span>, <span class="dv">5</span>, <span class="dv">6</span>),</span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a> <span class="at">mean =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="sc">-</span><span class="dv">1</span>, <span class="dv">0</span>), </span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a> <span class="at">sd =</span> <span class="fu">c</span>(<span class="fl">0.8</span>, <span class="dv">1</span>, <span class="dv">1</span>),</span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a> <span class="at">corr =</span> <span class="fl">0.5</span>)</span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a><span class="co">#> Y1 Y2 Y3</span></span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a><span class="co">#> [1,] 2 1 3</span></span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a><span class="co">#> [2,] 2 2 2</span></span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a><span class="co">#> [3,] 4 3 2</span></span>
<span id="cb4-11"><a href="#cb4-11" tabindex="-1"></a><span class="co">#> [4,] 3 3 4</span></span>
<span id="cb4-12"><a href="#cb4-12" tabindex="-1"></a><span class="co">#> [5,] 4 5 6</span></span>
<span id="cb4-13"><a href="#cb4-13" tabindex="-1"></a><span class="co">#> [6,] 2 1 4</span></span>
<span id="cb4-14"><a href="#cb4-14" tabindex="-1"></a><span class="co">#> [7,] 1 2 3</span></span>
<span id="cb4-15"><a href="#cb4-15" tabindex="-1"></a><span class="co">#> [8,] 3 1 6</span></span>
<span id="cb4-16"><a href="#cb4-16" tabindex="-1"></a><span class="co">#> [9,] 3 3 4</span></span>
<span id="cb4-17"><a href="#cb4-17" tabindex="-1"></a><span class="co">#> [10,] 3 2 6</span></span></code></pre></div>
<p>You can also provide a correlation matrix:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a>corr <span class="ot"><-</span> <span class="fu">matrix</span>(<span class="fu">c</span>(<span class="fl">1.00</span>, <span class="sc">-</span><span class="fl">0.63</span>, <span class="sc">-</span><span class="fl">0.39</span>, </span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a> <span class="sc">-</span><span class="fl">0.63</span>, <span class="fl">1.00</span>, <span class="fl">0.41</span>, </span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a> <span class="sc">-</span><span class="fl">0.39</span>, <span class="fl">0.41</span>, <span class="fl">1.00</span>), <span class="at">nrow=</span><span class="dv">3</span>)</span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>data <span class="ot"><-</span> <span class="fu">rlikert</span>(<span class="at">size =</span> <span class="dv">10</span><span class="sc">^</span><span class="dv">3</span>,</span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a> <span class="at">n_items =</span> <span class="dv">3</span>,</span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a> <span class="at">n_levels =</span> <span class="fu">c</span>(<span class="dv">4</span>, <span class="dv">5</span>, <span class="dv">6</span>),</span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a> <span class="at">mean =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="sc">-</span><span class="dv">1</span>, <span class="dv">0</span>), </span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a> <span class="at">sd =</span> <span class="fu">c</span>(<span class="fl">0.8</span>, <span class="dv">1</span>, <span class="dv">1</span>),</span>
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a> <span class="at">corr =</span> corr)</span></code></pre></div>
<p>Note that the correlations among the Likert response variables are
only estimates of the actual correlations between the latent variables,
and these estimates are typically lower:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">cor</span>(data)</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="co">#> Y1 Y2 Y3</span></span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="co">#> Y1 1.0000000 -0.5774145 -0.3838274</span></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="co">#> Y2 -0.5774145 1.0000000 0.3856997</span></span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="co">#> Y3 -0.3838274 0.3856997 1.0000000</span></span></code></pre></div>
<h2 id="using-estimate_params">Using <code>estimate_params</code></h2>
<p>Given the data, you can estimate the values of latent parameters
using:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="fu">estimate_params</span>(data, <span class="at">n_levels =</span> <span class="fu">c</span>(<span class="dv">4</span>, <span class="dv">5</span>, <span class="dv">6</span>), <span class="at">skew =</span> <span class="dv">0</span>)</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="co">#> items</span></span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="co">#> estimates Y1 Y2 Y3</span></span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co">#> mean -0.0526979746 -0.9696916596 -0.0009229545</span></span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="co">#> sd 0.8163184862 1.0533629380 1.0381389630</span></span></code></pre></div>
<h2 id="transformation">Transformation</h2>
<p>To visualize the transformation, you can use
<code>plot_likert_transform()</code>. It plots the densities of latent
variables in the first row and transformed discrete probability
distributions below:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">plot_likert_transform</span>(<span class="at">n_items =</span> <span class="dv">3</span>,</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a> <span class="at">n_levels =</span> <span class="dv">5</span>,</span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a> <span class="at">mean =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="sc">-</span><span class="dv">1</span>, <span class="dv">0</span>), </span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a> <span class="at">sd =</span> <span class="fu">c</span>(<span class="fl">0.8</span>, <span class="dv">1</span>, <span class="dv">1</span>), </span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a> <span class="at">skew =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">0</span>, <span class="fl">0.5</span>))</span></code></pre></div>
<figure>
<img role="img" aria-label="Transformation of latent variables to Likert response variables" src="data:image/png;base64,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width="80%" alt="Transformation of latent variables to Likert response variables" />
<figcaption>
<p><em>Transformation of latent variables to Likert response
variables.</em></p>
</figcaption>
</figure>
<br>
<p>Note that, depending on the value of the skewness parameter, the
normal latent distribution is used if skew = 0, otherwise the skew
normal distribution is used. The value of skewness is restricted to the
range -0.95 to 0.95, that is</p>
<blockquote>
<p><code>skew >= -0.95</code> and <code>skew <= 0.95</code>.</p>
</blockquote>
<h2 id="further-reading">Further Reading</h2>
<ul>
<li>For more detailed information and practical examples, please refer
to the package <a href="https://lalovic.io/latent2likert/articles/using_latent2likert.html">vignette</a>.</li>
<li>The implemented algorithms are described in the functions <a href="https://lalovic.io/latent2likert/reference/index.html">reference</a>.</li>
</ul>
<h2 id="related-r-packages">Related R Packages</h2>
<p>Alternatively, you can simulate Likert item responses using the
<code>draw_likert</code> function from the <a href="https://CRAN.R-project.org/package=fabricatr">fabricatr</a>
package. This function recodes a latent variable into a Likert response
variable by specifying intervals that subdivide the continuous range.
The <strong>latent2likert</strong> package, however, offers an advantage
by automatically calculating optimal intervals that minimize distortion
between the latent variable and the Likert response variable for both
normal and skew normal latent distributions, eliminating the need to
manually specify the intervals.</p>
<p>There are also several alternative approaches that do not rely on
latent distributions. One method involves directly defining a discrete
probability distribution and sampling from it using the
<code>sample</code> function in R or the <code>likert</code> function
from the <a href="https://CRAN.R-project.org/package=wakefield">wakefield</a>
package. Another approach is to specify the means, standard deviations,
and correlations among Likert response variables. For this, you can use
<a href="https://CRAN.R-project.org/package=LikertMakeR">LikertMakeR</a>
or <a href="https://CRAN.R-project.org/package=SimCorMultRes">SimCorMultRes</a>
to generate correlated multinomial responses.</p>
<p>Additionally, you can define a data generating process. For those
familiar with item response theory, the <a href="https://CRAN.R-project.org/package=mirt">mirt</a> package allows
users to specify discrimination and difficulty parameters for each
response category.</p>
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