Quali-Quantitative Evaluation: An experiment in heterogeneous engineering

Abstract

This project is based on an opportunity to bridge an increased desire for architectural user evaluation in an industry context that currently does not allow it, and the somewhat precarious employment situation a techno-anthropologist might find themselves in. Based on this, this thesis is an attempt at experimenting with quali-quantitative analysis as a means to add a level of scalability to qualitative architectural evaluation. As such, an experiment has been conducted which seeks to fulfil said aim on the Lyngby campus of The Technical University of Denmark with the employment of a mobile app directed at students. As a least-likely case, to test out the boundaries of delegating data collection to students and dedicated digital tools—with the least in-situ involvement of a researcher. This way, the experiment contains the double aim of both producing insights about campus, but more importantly shedding light on the challenges encountered along the way. The result of which has been many challenges, but little data. Despite this, doing quali-quantitative analysis in a ‘complementarity’-sense, has proved to still be a viable option. As such, our project demonstrates a core challenge of data projects: aligning network affordances with the matters of concerns of all parties involved. Where our efforts of translation fell short, we encountered challenges with the following: our perceived legitimacy, owing to our role as students; the trade-off, of offering recruitment incentives external to the goal of the evaluation itself; and by involuntarily relating ourselves to the existing data practices of apps on smartphones. From this, we recommend a focus on achieving transparency, when using dedicated digital tools for architectural evaluation—aiming at concrete matters of concern of the participants you wish to engage with and putting special effort into communicating the outcomes of the evaluation. Based on this, as well as a wider discussion, we argue that the role of the researcher in data projects is much more than just attending to their own research interest: It is a matter of translating between tools, methods, participants and conflicting data conceptions—a marathon of interdisciplinarity.

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A master thesis from Aalborg University

Quali-Quantitative Evaluation: An experiment in heterogeneous engineering