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(This article was first published on <strong><a href="http://www.r-exercises.com/2017/06/14/exploratory-factor-analysis-exercises/?utm_source=rss&utm_medium=rss&utm_campaign=exploratory-factor-analysis-exercises"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>This set of exercises is about exploratory factor analysis. We shall use some basic features of <code>psych</code> package. For quick introduction to exploratory factor analysis and <code>psych</code> package, we recommend <a rel="nofollow" href="http://personality-project.org/r/psych/HowTo/factor.pdf" target="_blank">this</a> short “how to” guide.</p>
<p>You can download the dataset <a rel="nofollow" href="http://r-exercises.com/wp-content/uploads/2017/06/efa.zip" target="_blank">here</a>. The data is fictitious.</p>
<p>Answers to the exercises are available <a rel="nofollow" href="http://r-exercises.com/2017/06/14/exploratory-factor-analysis-solutions/" target="_blank">here</a>.</p>
<p>If you have different solution, feel free to post it.</p>
<p>
<strong>Exercise 1</strong>
</p>
<p>Load the data, install the packages <code>psych</code> and <code>GPArotation</code> which we will use in the following exercises, and load it. Describe the data.</p>
<p>
<strong>Exercise 2</strong>
</p>
<p>Using the parallel analysis, determine the number of factors.</p>
<p>
<strong>Exercise 3</strong>
</p>
<p>Determine the number of factors using Very Simple Structure method.</p>
<p>
<strong>Exercise 4</strong>
</p>
<p>Based on normality test, is the Maximum Likelihood factoring method proper, or is OLS/Minres better? <i>(Tip: Maximum Likelihood method requires normal distribution.)</i></p>
<p>
<strong>Exercise 5</strong>
</p>
<p>Using oblimin rotation, 5 factors and factoring method from the previous exercise, find the factor solution. Print loadings table with cut off at 0.3.</p>
<p>
<strong>Exercise 6</strong>
</p>
<p>Plot factors loadings.</p>
<p>
<strong>Exercise 7</strong>
</p>
<p>Plot structure diagram.</p>
<p>
<strong>Exercise 8</strong>
</p>
<p>Find the higher-order factor model with five factors plus general factor.</p>
<p>
<p>
<strong>Exercise 9</strong>
</p>
<p>Find the bifactor solution.</p>
<p>
<strong>Exercise 10</strong>
</p>
<p>Reduce the number of dimensions using hierarchical clustering analysis.</p>
<div>
<h3>Related exercise sets:</h3>
<ol>
<li><a rel="nofollow" href="http://www.r-exercises.com/2016/11/29/repeated-measures-anova-in-r-exercises/" title="Repeated measures ANOVA in R Exercises" target="_blank">Repeated measures ANOVA in R Exercises </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/2017/04/21/data-science-for-doctors-cluster-analysis-exercises/" title="Data science for Doctors: Cluster Analysis Exercises" target="_blank">Data science for Doctors: Cluster Analysis Exercises </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/2015/12/28/factor-exercises/" title="Factor exercises" target="_blank">Factor exercises </a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/start-here-to-learn-r/" target="_blank">Explore all our (>1000) R exercises</a></li>
<li><a rel="nofollow" href="http://www.r-exercises.com/r-courses/" target="_blank">Find an R course using our R Course Finder</a> directory</li>
</ol>
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