Title

Studying Occupation using Big Data: Methods for Measuring “What’s Behind Door #4”

1

Location

Studio 3

Start Time

21-10-2017 2:30 PM

End Time

21-10-2017 3:30 PM

Session Type

Theoretical Paper

Abstract

Intent – The purpose of this presentation is to provide an overview of current and emerging research methods used in fields such as economics and political science, which combine qualitative inquiry with modeling and statistical analysis to measure and generate explanations for human behaviors on a large scale. This includes the use of fuzzy sets, contrarian case analysis to model complexity theory/multiple realities, and analysis of structural associations to understand multi-layer problems.

Argument – In response to calls to study occupation on a larger scale, and to then apply this knowledge to solve real-world problems (Frank, 2015), we must seek research methods that will lend legitimacy to our work and help us think about complex issues in a systematic manner.

Importance to Occupational Science – Rather than re-inventing the wheel (or in this case, the analytical tools), it behooves us to look to other fields that deal with the complex nature of human behavior, motivation, transactions, and decision-making at organizational and community levels. In this presentation, ways in which such phenomena are studied using big data, in combination with qualitative and theoretical grounding, will be explored and critiqued. Following this review, applications of these methods to current problems in occupational science will be suggested.

Conclusion – While a full tutorial on using big data to answer social questions is beyond the scope of this presentation, it is expected that this discussion will spur occupational scientists to think critically about our current research methods, and about how we might incorporate statistical concepts and ideas from other fields to enhance our work, as we move from individual and small-group analysis toward “looking behind Door #4.”

Key words: Data Analysis, Interdisciplinary, Statistics

Questions to Facilitate Discussion –

  1. What other questions related to occupation came to mind as the analytic methods were discussed?
  2. Are you familiar with any other methods that might be useful to answer questions at population or global levels of analysis?
  3. What is currently being measured/what big data do we have access to that can help us answer questions about occupation?
  4. What are your concerns with using statistics to help explain complex phenomena or generate theory? Are you convinced that the methods described today can help us do this? At what expense?

References

Cohen, W.M., & Fjeld, J. (2016). The Three legs of a stool: Comment on Richard Nelson, “The Sciences are different and the differences matter.” Research Policy, 45, 1708-1712.

Frank, G. (2015). Remarks given October 3, 2015 at the Society for the Study of Occupation: USA Annual Conference, Ft. Lauderdale, FL.

Franzosi, R. (2016). From method and measurement to narrative and number. International Journal of Social Research Methodology, 19(1), 137-141.

Huarng, K-H. (2016). Qualitative analysis with structural associations. Journal of Business Research, 69, 5187-5191.

Woodside, A.G. (2014). Embrace*Perform*Model: Complexity theory, contrarian case analysis, and multiple realities. Journal of Business Research, 67, 2495-2503.

This document is currently not available here.

Share

COinS
 
Oct 21st, 2:30 PM Oct 21st, 3:30 PM

Studying Occupation using Big Data: Methods for Measuring “What’s Behind Door #4”

Studio 3

Intent – The purpose of this presentation is to provide an overview of current and emerging research methods used in fields such as economics and political science, which combine qualitative inquiry with modeling and statistical analysis to measure and generate explanations for human behaviors on a large scale. This includes the use of fuzzy sets, contrarian case analysis to model complexity theory/multiple realities, and analysis of structural associations to understand multi-layer problems.

Argument – In response to calls to study occupation on a larger scale, and to then apply this knowledge to solve real-world problems (Frank, 2015), we must seek research methods that will lend legitimacy to our work and help us think about complex issues in a systematic manner.

Importance to Occupational Science – Rather than re-inventing the wheel (or in this case, the analytical tools), it behooves us to look to other fields that deal with the complex nature of human behavior, motivation, transactions, and decision-making at organizational and community levels. In this presentation, ways in which such phenomena are studied using big data, in combination with qualitative and theoretical grounding, will be explored and critiqued. Following this review, applications of these methods to current problems in occupational science will be suggested.

Conclusion – While a full tutorial on using big data to answer social questions is beyond the scope of this presentation, it is expected that this discussion will spur occupational scientists to think critically about our current research methods, and about how we might incorporate statistical concepts and ideas from other fields to enhance our work, as we move from individual and small-group analysis toward “looking behind Door #4.”

Key words: Data Analysis, Interdisciplinary, Statistics

Questions to Facilitate Discussion –

  1. What other questions related to occupation came to mind as the analytic methods were discussed?
  2. Are you familiar with any other methods that might be useful to answer questions at population or global levels of analysis?
  3. What is currently being measured/what big data do we have access to that can help us answer questions about occupation?
  4. What are your concerns with using statistics to help explain complex phenomena or generate theory? Are you convinced that the methods described today can help us do this? At what expense?