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

How to write your own R package and publish it on CRAN

R is a great resource for data management, statistics, analysis, and visualization — and it becomes better every day. This is to a large part because of the active community that continuously creates and builds extensions for the R world. If you want to contribute to this community, writing a package can be one way. That is exactly what we intended with our package overviewR. While there exist many great resources for learning how to write a package in R, we found it difficult to find one all-encompassing guide that is also easily accessible for beginners. Continue reading

LaTeX and Overleaf

LaTeX is a high quality typesetting system that that facilitates the production of well-formatted document. It has become highly popular in academic settings as an alternative to common typewriting systems (e.g., Word). This Methods Bites Tutorial by our team member Cosima Meyer and Dennis Hammerschmidt walks you through your first steps in LaTeX (using Overleaf) and provides you with a hands-on guide for writing scientific papers using an easily accessible template. Continue reading

Efficient Data Management in R

The software environment R is widely used for data analysis and data visualization in the social sciences and beyond. Additionally, it is becoming increasingly popular as a tool for data and file management. Focusing on these latter aspects, this Methods Bites Tutorial by Marcel Neunhoeffer, Oliver Rittmann and our team members Denis Cohen and Cosima Meyer illustrates the workflow and best practices for efficient data management in R. Continue reading

Applied Bayesian Statistics Using Stan and R

Whether researchers occasionally turn to Bayesian statistical methods out of convenience or whether they firmly subscribe to the Bayesian paradigm for philosophical reasons: The use of Bayesian statistics in the social sciences is becoming increasingly widespread. However, seemingly high entry costs still keep many applied researchers from embracing Bayesian methods. Next to a lack of familiarity with the underlying conceptual foundations, the need to implement statistical models using specific programming languages remains one of the biggest hurdles. In this Methods Bites Tutorial, Denis Cohen provides an applied introduction to Stan, a platform for statistical modeling and Bayesian statistical inference. Continue reading