Partial least squares structural equation modeling (PLS-SEM) enables researchers to model and estimate complex cause-effects relationship models

Images contain information absent in text, and this extra information presents opportunities and challenges. It is an opportunity because one image can document variables with which text sources (newspaper articles, speeches or legislative documents) struggle or on datasets too large to feasibly code manually. Learn how to overcome the challenges.

Learn about research with online Experiments from Dr. Giuseppe Veltri.

What is the difference between experimental and non-experimental research design? We look at how non-experimental design distinguishes itself from experimental design and how it can be applied in the research process with open-access examples.

Download the free report from the project, “Fostering Data Literacy: Teaching with Quantitative Data in the Social Sciences.” This report explores two key challenges to teaching with data: helping students overcome anxieties about math and synchronizing the interconnected methodological, software, and analytic competencies.

In the day-to-day of political communication, politicians constantly decide how to amplify or constrain emotional expression, in service of signalling policy priorities or persuading colleagues and voters. We propose a new method for quantifying emotionality in politics using the transcribed text of politicians’ speeches. This new approach, described in more detail below, uses computational linguistics tools and can be validated against human judgments of emotionality.

Institutions — rules that govern behavior — are among the most important social artifacts of society. So it should come as a great shock that we still understand them so poorly. How are institutions designed? What makes institutions work? Is there a way to systematically compare the language of different institutions? One recent advance is bringing us closer to making these questions quantitatively approachable. The Institutional Grammar (IG) 2.0 is an analytical approach, drawn directly from classic work by Nobel Laureate Elinor Ostrom, that is providing the foundation for computational representations of institutions. IG 2.0 is a formalism for translating between human-language outputs — policies, rules, laws, decisions, and the like. It defines abstract structures precisely enough to be manipulable by computer. Recent work, supported by the National Science Foundation (RCN: Coordinating and Advancing Analytical Approaches for Policy Design & GCR: Collaborative Research: Jumpstarting Successful Open-Source Software Projects With Evidence-Based Rules and Structures ), leveraging recent advances in natural language processing highlighted on this blog, is vastly accelerating the rate and quality of computational translations of written rules.

What is case study methodology? It is unique given one characteristic: case studies draw from more than one data source. In this post find definitions and a collection of multidisciplinary examples.

Learn about experimental research designs and read open-access studies.

In the field of artificial intelligence (AI), Transformers have revolutionized language analysis. Never before has a new technology universally improved the benchmarks of nearly all language processing tasks: e.g., general language understanding, question - answering, and Web search. The transformer method itself, which probabilistically models words in their context (i.e. “language modeling”), was introduced in 2017 and the first large-scale pre-trained general purpose transformer, BERT, was released open source from Google in 2018. Since then, BERT has been followed by a wave of new transformer models including GPT, RoBERTa, DistilBERT, XLNet, Transformer-XL, CamemBERT, XLM-RoBERTa, etc. The text package makes all of these language models and many more easily accessible to use for R-users; and includes functions optimized for human-level analyses tailored to social scientists.

This year’s lockdown challenged the absolute core of higher education and accelerated or rather imposed the adoption of digital tooling to fully replace the interactivity of the physical classroom. And while other industries might have suffered losses, the edtech space flourished, with funding for edtech almost doubling in the first half of 2020 vs 2019. Even before the pandemic, lecturers were starting to feel overwhelmed by the amount of choice to support their teaching. More funding just meant more hype, more tools, and more tools working on similar or slightly improved solutions, making it even harder and more time-consuming to find and adapt them in a rush.

Below, we take a look at several tools and startups that are already supporting many of you in teaching quantitative research methods; and some cool new tools you could use to enhance your classroom.

As technology becomes more integral to everything we do, the time we spend in front of screens such as smartphones and computers continues to increase. The pervasiveness of screen time has raised concerns among researchers, policymakers, educators, and health care professionals about the effects of digital technology on well-being. Despite growing concerns about digital well-being, it has been a challenge for scientists to measure how we actually navigate the digital landscape through our screens. For example, it is well documented that self-reports of one’s media use are often inaccurate despite survey respondents’ best efforts. Just knowing screen time spent on individual applications does not fully capture a person’s usage of the digital device either. Some could spend an hour on YouTube watching people play video games whereas others might spend the same amount of time watching late night television talk shows to keep up to date with the news. Even though the screen time is the same for the same application, the intentions and values of consumption of certain types of content can be vastly different among users.

We catch up with Ken Benoit, who developed Quanteda, a large R package originally designed for the quantitative analysis of textual data, from which the name is derived. In 2018, Quanteda received $35,000 of seed funding as inaugural winners of the SAGE Concept Grants program. We find out what challenges Ken faced and how the funding helped in the development of the package.

Human social behavior has rapidly shifted to digital media services, whether Gmail for email, Skype for phone calls, Twitter and Facebook for micro-blogging, or WhatsApp and SMS for private messaging.  This digitalization of social life offers researchers an unprecedented world of data with which to study human life and social systems. However, accessing this data has become increasingly difficult.

I would argue that computational social science necessitates collaboration, and indeed is tamed by it. A collaborative approach provides the necessary structure, goals, and a critical approach to research methods. In response to the question of what computational social science has helped me achieve, it may seem obvious to mention the concrete projects, the outputs, the measurable outcomes. However, for me computational social science has achieved something more substantial and enduring—a new way of working, a new way of thinking, and a new kind of enthusiasm for research.