Participant recruitment is a crucial success factor in research on human subjects, yet its significance for the validity of research outcomes is commonly glossed over, at least in public.
“Recruitment is an issue that tends to be underplayed”, commented Kristrún Gunnarsdóttir when looking back at some of the hurdles faced by the HomeSense field trial.
“… and it is something that should be more openly discussed.”
The reasons, you could say, are understandable. In a world of limited resources of time and budget, recruiting participants will always be a challenge. As with any form of recruitment, it also requires a deep appreciation of the motivations in play to achieve commitment to the cause. Above all, failures in the recruitment stage lead to research outcomes that are either poor (lack of participant commitment, biased sample) or incomplete (not many enough participating). Continue reading “Patience and a balanced approach leads to successful research recruitment”
Dr Hoogendoorn has extensive experience of working with sensor-generated data, with particular expertise in applying machine learning in the domains of eHealth and mHealth.
Jie is working with Mark’s group until mid-June, and will be researching machine learning methods for activity recognition, energy disaggregation and indoor localisation based on data collected from the HomeSense field trial.
With the HomeSense field trial completed, now is an opportunity to share some of the lessons learned on the way to capturing good data.
The technical aspects of this methodological trial were crucial to sort out. The learning process (by doing) highlighted some of the limitations of sensor-based technology in capturing socially relevant information, but other methodological issues in designing the trial, including ethical considerations, were equally important to get right.
As a researcher considering your next project you might well be expected to ask: what are my most important research questions and what methods do I have in my armoury for gathering valid data?
Social researchers, for instance, are interested in questions about social life, social structures, social interactions and elements of societal relations.
They might also be interested in statistical data such as how many people do certain kinds of things by themselves or together, and perhaps consider conducting a survey.
Digital sensors, with increased availability and reduction in size, could add new means and metrologies for collecting data. The question for the research designer though is how and why should I apply this methodological tool ?
… which is exactly the questions our forthcoming short course ‘Using Sensors in Social Research’ on 10/11 September 2018 is designed to provide answers for, and so help jump-start and enrich your future research designs.
Using sensors in social research will mix short presentations, interactive hands-on and exploratory sessions, group work and discussions for participants to obtain a good understanding of the technologies and operating processes required for effective inclusion and management of this method.
It will also enable researchers to ask ‘how’ and ‘if’ sensors could be used in their own research, and how to address the ethical, consent, data security, confidentiality and other issues involved. Participants will receive a certificate of attendance of this NCRM methods course.
The lessons learned and inside knowledge from the HomeSense field trial will also be presented, explaining and demonstrating the analytic tools and techniques required for visualising, interpreting and understanding household activities based largely on sensor-generated data.
Six months into the HomeSense project, Prof. Nigel Gilbert outlined the “interesting ethics issues” that required attention before the field trial could responsibly get underway in volunteer households.
In his presentation, “The Ethics of Sensors” at the 2016 ESRC Research Methods Festival in Bath, Nigel explained the ambition of HomeSense to enable social researchers to use digital sensors alongside self-reported methods and observations. The project is also assessing the extent to which householders might accept sensors in their homes for research, and the final output will be a set of guidelines for use in such studies.
The paper now published in the conference proceedings, covers data analysis from the early stages of the HomeSense field trial. It reports on applying machine learning methods to interpret sensor-generated data, and discusses a method for identifying features of various types of activity and evaluating the agreement between sensor-generated data and self-reported data from time-use diaries.
Since one of the implications of using sensors for social research is that, in due course, activities could be recognised automatically, this study also proposes a method for modelling a range of activities recorded by sensors.
The same approach has been continued by the team in an extended version of the study with data from more households, but the publication of the conference paper in the Proceedings of ICFNDS’17 provided an opportunity for some of its authors – Jie Jiang, Riccardo Pozza, Kristrún Gunnarsdóttir – to sit down and discuss the implications of this work, and where it sits within the project as a whole.
On 7 & 8 September, Jie Jiang will be attending a Joint UK/Japan workshop, titled: “Acceptability and Value of IoT in the Home” at the British Embassy in Tokyo. She’ll present a paper, titled: “Using IoT to study life at home“, sharing the lessons learned from designing, implementing and practising a sensor-based research strategy for the HomeSense field trial.