Concept Grants from Sage

This year, the winner of Sage’s £15,000 Concept Grant is Empirica, a customizable virtual lab platform for conducting human-participant experiments. In addition, five software tools were awarded £2,000 each for concept testing and software development.

We spoke to each of the winners about their tools, and how they will benefit social science researchers:

£15,000 Concept Grant winner

Empirica

A customizable virtual lab platform for conducting human-participant experiments.

Abdullah Almaatouq and Nicolas Paton

Virtual labs present an exciting opportunity for asking fundamentally different types of questions and running larger, cheaper and more complex experiments than is possible in physical labs. Unfortunately, the potential of virtual lab experiments has been limited by the often-substantial programming and administrative effort required to launch them – effort that is often not transferable from one experiment to the next.

Empirica is built to support two different “revolutions” in experimental social and behavioral science: 

1. High-throughput experiments in which researchers can run, in effect, thousands of experimental conditions that systematically cover the parameter space of a given experimental design. Researchers can define a set of dimensions along which the experiment can vary, then generate and sample the set of conditions to be used in the experiment.

2. Macro-level experiments in which the unit of analysis is a collective entity such as a group, market, or an organization comprising dozens or even hundreds of interacting individuals. Empirica is designed with multi-participant recruitment, assignment, and interaction as a core principle.

We hope Empirica will decouple flexibility from ease-of-use and open the door to an economy of software built around conducting new kinds of virtual labs experiments.

We will use the SAGE Concept Grant to hire a developer that can integrate more "components" into Empirica. The next on the list is integrating tools that allow for join typing and coding like Firepad. In addition, association with SAGE will provide legitimacy and increase visibility of Empirica – amongst users but also potential funders.

£2,000 Concept Grant winners

Multytude

Hatice Ugurel, Yalin Solmaz and Hande Enes

As opposed to existing social media platforms where echo-chambers, low quality content and ongoing, unstructured ‘talk’ dominate, making them useless for research, Multytude is a conversation platform that allows its users to make sense of the conversation for authentic insights.

We achieve this goal via our unique UX/UI that layers the conversation, putting the user in control of how they’d like to explore and engage, and our natural language processing that provides automated summaries of conversations. We think this creates a new method for social science researchers to collect data about people’s opinions in a more robust, authentic and collaborative manner

The SAGE Concept Grant will help us validate the following use cases for the research community so we can understand and deploy additional product features geared towards social scientists.

1. A new method of qualitative research instead of online surveys, focus groups, etc. allowing researchers to gain unrestricted, collaborative feedback.

2. A portal for academic events where participants can gather their discussion topics and opinions on Multytude before, during and after the official sessions with insights at the end. This would also allow participants to network based on opinions, not just names and titles of the participants.

AcademicTwitteR Studio

An R package that makes the new Academic Research Product Track more accessible to researchers. 

Christopher Barrie and Justin Chun-ting Ho

Social scientists use Twitter for a wide variety of applications in the applied social sciences. Many access Twitter data using the official API, which was recently updated to provide academics with much expanded access. While some have the capabilities to use different libraries in popular programming languages (e.g., R and Python) to query the API, others do not.

Our proposal to provide a user interface (academictwitteR Studio) to query the Twitter v2 Academic Research Product Track will allow practitioners who lack programming knowledge to query the Twitter API according to user-specified parameters and download the resulting data.

The SAGE Concept Grant will be enormously beneficial in providing us with the necessary means to pay for the server space and research assistance required to develop this software.

Intelliplanner

A software application to guide students and researchers in planning, mapping, and making decisions about the methods used in their social research projects.

Willem Jan Horninge Roestenburg, Janus Roestenburg and Emmerentie Oliphant

Formulating workable research proposals based on the most appropriate research design remains a significant challenge for most social science students. Although extensive research knowledge is available in research methods textbooks, crafting the proposal remains a painful exercise, often requiring intense consultation with the study supervisor to get it right.

With Intelliplanner, we want to facilitate the learning process by introducing a novel Expert Software Application that supports researchers' methods decisions more efficiently. Intelliplanner is like a study supervisor looking over the student's shoulder, suggesting different options and designs and reminding them of the implications and limitations of their choices. The application intelligently suggests different design options as the user answers specific questions. In this way, Intelliplanner progressively reveals designs narrowed down to the point of making an optimal decision.

The SAGE Concept Grant will be used to research and refine the initial progressive reveal algorithm within the X-O-P logic and develop a working model of the Intelliplanner Software Application. The software will be web-based, and we would like to test it on two samples of postgraduate students as part of the project.

REFI-QDA Project Exchange Standard

An open access and free standard to enable the transfer and accessibility of qualitatively analysed data across CAQDAS tools.

Christina Silver, Kristi Jackson, Fred van Blommestein and Graham Gibbs

The REFI-QDA Exchange Standard supports researchers undertaking qualitative data analysis by enabling data to be transferred from one CAQDAS-package to another, therefore providing access to a greater range of analysis tools. The free and open-source standard has four key benefits:

• Collaboration on one set of qualitative data among researchers using different software

• Continued use of data when researchers move to institutions with a site license for a different CAQDAS-package 

• Archiving of research results and analysis process in a neutral format

• More pragmatic and agile discussions of similarities and differences among CAQDAS-packages by instructors of qualitative data analysis (QDA)

The SAGE Concept Grant will help us in three ways: First, to test the exchange of data between CAQDAS-packages in order to facilitate the development of the REFI-QDA Exchange Standard. Secondly, the current version of the Standard results in some inconsistencies/data-loss with respect to audio-visual data. The use of these data are increasing (especially during COVID-19) and this is therefore a key focus moving forward. Thirdly, to develop testing datasets relevant across disciplinary/methodological contexts.

SMIDgen

A scalable, mixed-initiative dataset generation tool for online social science research.

Matthew Louis Mauriello

Online social networks provide a naturalistic view into the way users interact with technologies, communities, and each other. Recent work has leveraged Reddit to investigate suicidality, Twitter to study activism, and YouTube for the usage of touchscreen devices. Time and again researchers replicate the same infrastructure for this work, spending valuable time “rebuilding the wheel.”

We will use funding from the SAGE Concept Grant to develop our SMIDGen platform to support human-in-the-loop machine learning that reduces the time it takes to conduct data collection and annotation such that researchers can spend less time wrangling data and more time synthesizing it into meaningful results.