Method Stacking

Thiel, Katharina ORCID: https://orcid.org/0009-0007-5274-8271 and Eimontaite, Iveta (2024) Method Stacking. In: Methods for Change Volume 2: Impactful social science methodologies for 21st century problems. Project Report. Aspect and The University of Manchester, Manchester.

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Abstract

Method Stacking is a new approach to data gathering that is based on a non-hierarchical, interdisciplinary collaboration between design researchers and the social sciences. It brings insider and outsider perspectives to the study of human-machine interactions in a design context via the iterative evaluation of data with participants. Method Stacking focuses on these human-machine interactions and investigates workflows by assessing and reflecting on the output of data collection methods to co-construct knowledge with participants. In short, this approach shifts the focus from capturing the most ‘truthful’ data. Instead, it uses visual output generated by physiological methods as a jumping-off point to generate discussions with the participants who become active agents in the study. This slowly builds up nuanced evidence of participants’ cognitive processes and task handling. As such, Method Stacking is useful as a vehicle for discussion, learning and generating feedback, as well as carrying out a research project in a creative environment. It was developed to combine principles of Fashion Practice Research and Human Factors Research, namely the research of fashion fabrication processes while considering improvements to the physical, cognitive, and socio-cultural aspects of human-machine interactions. It is used most effectively in a Futuring context which involves analysing emerging technologies as well as social, economic, and environmental factors to forecast and envision possible technology solutions. In Method Stacking, objective and subjective measures are stacked, which means that once completed, a method’s output is probed by the same participants, forming the first layer of an iterative process. This allows the findings to gradually stack up by challenging and critiquing the outputs of the different methods, rather than seeing individual methods as conclusive collection points. A task, such as sewing an element of a garment, is set to be carried out by a sample of experts in a work environment that is only loosely controlled; for example, in their home studio or workplace. The research team first conducts the physiological data collection (i.e., biometric data, eye tracking). Then, they actively engage the same participants in the data analysis using qualitative methods (i.e., reflective interviews, hierarchical and cognitive task analysis) to capture subjective experiences, collect evidence of individual decision-making and receive feedback on the effectiveness of the capture devices used in the first step. This leads to a verification or reevaluation of the first visual data set, which is then followed by another iteration of testing and evaluation.