Extracting Emotions from Faces with Face++ (and Microsoft Azure)
Images are an increasingly used data source in the social sciences. One application is to extract features from human faces using machine learning algorithms. This blog post provides a guide on using APIs for this task, specifically how to access the services offered by Face++ and the Microsoft Face API. The post walks you through (1) how to gain API access credentials, (2) how to call the Face++ API from R, and (3) how to handle the output. It is based on the talk by Theresa Küntzler, who introduced the participants of the MZES Social Science Data Lab on May 12, 2020, to Extracting Emotions (and more) from Faces with Face++ and Microsoft Azure. Continue reading
regplane3D: Plotting 3D regression predictions in R
The interpretation and presentation of empirical findings from (generalized) linear models has come a long way in the social sciences. Researchers increasingly visualize substantively meaningful quantities of interest such as expected values, first differences, and average marginal effects and consistently include uncertainty estimates in the form of analytical, simulation-based, or bootstrapped confidence intervals.
However, existing interpretations and presentations are typically restricted to bivariate patterns which show (changes in) expected values as function of a single predictor, holding all else constant. This can be a significant limitation, especially when substantive inquiries focus on the interplay of two variables in predicting an outcome. To interpret and visualize such applications effectively, researchers must extend their presentations to include a third dimension.
In this Methods Bites Tutorial, Denis Cohen and Nick Baumann introduce and showcase the regplane3D
package, a tool for plotting 3D regression predictions in R.
Continue reading
Teaching Quantitative Social Science in Times of COVID-19: How to Generate and Distribute Individualized Exams with R and RMarkdown
The COVID-19 pandemic has forced universities around the globe to switch from on-site teaching to online teaching. As a consequence, quantitative social science classes that previously relied on closed-book in-class exams now have to administer open-book take-home exams. A downside of this switch is that it becomes impossible to monitor compliance with no-collaboration rules. Individualizing exam prompts can prevent students from sharing digital answers while taking the exam. Yet generating, distributing, and correcting individualized exams can be highly time consuming unless the procedure is automated. In this Methods Bites Tutorial, Denis Cohen, Marcel Neunhoeffer and Oliver Rittmann present an approach for the automated generation of individualized exam prompts and solution sheets, along with their automated distribution via email, using R and RMarkdown. Continue reading
new events added
Virtual Event Schedule for Spring 2021 Released
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new events added
Virtual Event Schedule for Fall 2020 Released
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