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<h1 class="title toc-ignore">Multiverse Analysis in R</h1>
<h3 class="subtitle">Workshop</h3>
</div>
<div style="padding: 2em;">
<p><img src="https://github.com/masurp/specr/raw/master/man/figures/README-unnamed-chunk-2-1.png" width = 380 align="right" /></p>
</div>
<p>Welcome to the workshop on <em>Multiverse Analysis in R</em>. This
page will be used to distribute the slides, handouts, and other
materials for this course. All material is free to re-use under the <a
href="https://github.com/masurp/workshop_bayes/blob/main/LICENSE.md">CC-BY</a>
license.</p>
<p>Empirical results depend on the conceptual and analytical choices
made by the researcher. For example, researchers need to decide how
variables should be operationalized and estimated, which variables to
control for, which observations to exclude, what to do with missing
values, which functional form to assume, which subgroups to analyse,
what model to choose and so on. When interpreting study outcomes, we
seek genuine insights into the underlying relationships under scrutiny.
Achieving this goal hinges on the premise that reported analyses
accurately represent the pool of valid analyses that could have been
conducted – a condition frequently unmet. One issue is that published
results may hinge upon arbitrary decisions made by researchers. Another
concern is the inclination of researchers to favor evidence that aligns
with their assumptions rather than presenting contrary evidence.</p>
<p>Multiverse-style methods (also known as specification curve analysis
or vibration of effects) have been proposed as a possible solution to
this dilemma. The idea is to estimate an effect across the entire set of
possible specifications to expose the impact of hidden degrees of
freedom and/or obtain robust, less biased estimates of the effect of
interest. However, this does not come with potential pitfalls: if the
investigated specifications are not truly arbitrary, multiverse-style
analyses can produce misleading results, potentially hiding meaningful
effects within a mass of poorly justified alternatives.</p>
<p>The workshop aims to equip participants with the knowledge to
meaningfully apply multiverse/specification curve analyses to their own
research. The 1-day workshop covers (1) a theoretical introduction to
the concept of researcher’s degrees of freedom and the problem of
undisclosed flexibility in data analyses, (2) an in-depth discussion of
how multiverse approaches may help to establish better practices and
more transparency in this regard, and (3) practical tutorial sessions
illustrating how to use the package <code>specr</code> to conduct
multiverse/specification curve analyses in R.</p>
<p>The workshop will be held by <a
href="https://www.philippmasur.de">Dr. Philipp K. Masur</a> (Vrije
Universiteit Amsterdam), who is the developer of the R package
<code>specr</code>. As we will run practical analyses in R, prior
programming experience in R and familiarity with RStudio is an
asset.</p>
<p><br><br></p>
<div id="preparation" class="section level2">
<h2>Preparation</h2>
<p>All materials and slides can be found on this webpage: <a
href="https://masurp.github.io/workshop_specr/"
class="uri">https://masurp.github.io/workshop_specr/</a>.</p>
<p>For this 1-day workshop, I expect you…</p>
<ol style="list-style-type: decimal">
<li>…to have R and RStudio installed. If you haven’t done so yet, have a
look at this <a
href="https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/R_basics_1_getting_started.md">getting
started</a> tutorial, which walks you through the installation and helps
you get some first hands-on experience using R. If possible, try to
install the R package <code>specr</code>, which we will use to run
multiverse analyses:</li>
</ol>
<pre class="r"><code>install.packages("specr")</code></pre>
<ol start="2" style="list-style-type: decimal">
<li><p>…to have some basic knowledge of R and particularly data
wrangling skills (at best, some knowledge about the
<code>tidyverse</code>). If this is still new to you, we have several
videos and tutorials on our <a
href="https://github.com/ccs-amsterdam/r-course-material">R course
material GitHub page</a> that can help you getting started. I would
suggest to check out the tutorials on transforming, summarizing,
visualizing and reshaping data in the “data wrangling with the
tidyverse” category.</p></li>
<li><p>…some basic knowledge of regression models (e.g., linear
regression, logistic regression, multilevel linear regression).</p></li>
</ol>
<p><strong>Note:</strong> The workshop will NOT provide an introduction
to R!</p>
<p><br><br></p>
<div id="morning" class="section level3">
<h3>Morning</h3>
<table>
<thead>
<tr class="header">
<th align="left">Time</th>
<th align="left">Topic</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">09:00 - 09:30</td>
<td align="left">Statistical Fortune-Telling</td>
</tr>
<tr class="even">
<td align="left">09:30 - 10:00</td>
<td align="left">A Garden of Forking Paths</td>
</tr>
<tr class="odd">
<td align="left">10:00 - 11:00</td>
<td align="left">R: Exercise I: Basic Mechanisms of Multiverse
Analysis</td>
</tr>
<tr class="even">
<td align="left">11:00 - 12:00</td>
<td align="left">Into the Multiverse</td>
</tr>
</tbody>
</table>
</div>
<div id="afternoon" class="section level3">
<h3>Afternoon</h3>
<table>
<thead>
<tr class="header">
<th align="left">Time</th>
<th align="left">Topic</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">13:00 - 13:45</td>
<td align="left">Arbitrary Decisions</td>
</tr>
<tr class="even">
<td align="left">13:45 - 14:45</td>
<td align="left">R: Exercise II: Multiverse Analysis with
<code>specr</code></td>
</tr>
<tr class="odd">
<td align="left">14:45 - 15:30</td>
<td align="left">A Mass of Poorly Justified Alternatives</td>
</tr>
<tr class="even">
<td align="left">16:00 - 16:30</td>
<td align="left">R: Exercise III: Advanced Specifications</td>
</tr>
</tbody>
</table>
<br><br>
<hr>
<p>This course is published under the following <a
href="https://github.com/masurp/workshop_specr/blob/main/LICENSE">license</a>.</p>
</div>
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