ODGAR: Framework for Online Declarative Data Gathering, Analyzing, and Reporting.


Off-line research paradigms are still dominant even in on-line survey techniques. Most online research techniques convert old-fashioned off-line questionnaires to HTML forms, so not only they do not exploit the full potential of Data Science & Internet Technologies, but they are outdated already by design. Thus, contemporary on-line techniques are not natively on-line—they are mainly off-line questionnaires converted into more or less advanced on-line HTML forms, but with inherited off-line characteristics (high burden and low value for respondents, non-equivalent information exchange). We want to change that!

Social researchers usually fail to establish long-term relationships with participants of their studies. On-line surveys are boring for respondents, and nothing compensates this boredom—the respondents devote their time and share their knowledge but receive nothing valuable in return. Hence, that their intrinsic motivation is low, resulting in long-term trend of decreasing response rates in social studies. We want to change that!

Proposed solution

The most important principle for establishing long-term relations with intrinsically motivated respondents is that they should be able to immediately access the feedback relevant to their answers, and thus, they should be enclosed within an instant feedback loop (see Figure 1). To increase its value, the feedback should be dynamically customized to each respondent. This can be achieved by pre-programmed templates of feedback scenarios, which can be adaptively customized by the respondent’s answers to this or previous questions. Most traditional research techniques do not provide instant feedback to respondents. We want to utilize the possibilities of instant feedback to the maximum, apply it to different topics, and combine different feedback sources: a respondent’s answers to a given question and to other questions; other users’ answers; external open data; aggregated or summarized outcomes from reference studies.

Figure 1: Declarative Data Collection with Instant Feedback Loop

Research Plan

  1. Desk Research and designing on-line experiment.
  2. Development & testing of prototype of new research software fully supporting instant feedback.
  3. Conducting large-scale on-line experiment in full factorial design.
  4. Empirical data analysis.

Call for Collaboration

If you are interested in of any form of collaboration within this project, please let me know! Especially, we are looking for research partners that could implement our approach in real-world case studies.

Looking forward to hearing from You!
Kamil Wais Ph.D.
Data Scientist / R & Shiny Developer