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The use of image elicitation methods has been recognised in qualitative research for some time; however, the use of mood boards to prompt participant discussion is currently an under-researched area. This article explores the use of mood boards as a data collection method in qualitative research. Used in design disciplines mood boards allow designers to interpret and communicate complex or abstract aspects of a design brief. In this study, I utilise mood boards as being part creative visual method and part image elicitation device. The use of mood boards is explained here in the context of a research project exploring masculinity and men's reflexivity. In this article, I consider the benefits of utilising this method in researching reflexivity and gender before offering a critical appraisal of this method and inviting others to explore how mood boards might enhance research projects involving elicitation.

Understanding ethnographic data in a formal way is imperative when faced with multiple responses of humans within their environments. Knowledge Elicitation Tools (KnETs) incorporate techniques for modeling knowledge using methods long used in anthropological fieldwork and formalizing knowledge using knowledge engineering methods from computer science. KnETs enhance our understanding of our data to reveal new avenues for enquiry. KnETs support traditional participatory fieldwork methods and produce input for agent-based models, supporting a formalized link between qualitative and quantitative representations of knowledge and their interaction. The fusion of these techniques has resulted in a four-stage process that incorporates consistent verification and validation on data as it is collected by domain experts and informants. The application of this innovative methodology is successful precisely due to the mutual benefits that each technique provides by addressing current bottlenecks in both processes of ethnographic data collection and knowledge engineering.

Data collection methods for qualitative research are varied and have a rich history. The Life Story Board (LSB) is a game board-like tool that is used to construct a visual representation of a person's narrative and his/her related context. In our study, we comparatively assessed the LSB as a data elicitation tool for social science research. We reviewed eight Canadian research projects that have used the LSB as data elicitation tool for qualitative research and assessed the LSB on the feasibility of its use, on its effectiveness to elicit information, on aspects that facilitate and/or hinder its use, and how it compares with conventional interview approaches. Our findings suggest that the LSB can be used with study participants of different gender, age, ethnicity, and life circumstances; that it is effective as a data elicitation tool, and that it facilitates engagement with interviewees, without presenting any major hindrances.

Elicitation interviews are where participants are either shown items or asked to bring items to the interview in order to shape the direction of the conversation. This approach is often referred to as being part of 'visual methods'. The chapter focuses in particular on when participants are asked to either bring everyday documents, such as photographs, or when they are asked to create a new document, with both sources serving as a 'topic guide' during interviews, which are directed by the participant. The advantage of this method over many documentary analysis methods is the presence of the author and the ability for the researcher to ask the author questions. This allows us to more easily establish meaning than in participant-absent documentary analysis. A detailed case study is presented of the research that aimed to understand health behaviours, such as smoking and drinking alcohol, during pregnancy. Ten women from deprived areas living on low incomes took part in elicitation interviews. Techniques of elicitation included life-history timelining (drawing a timeline of their life), collaging or using a paper template with thought bubbles to describe what it was like being pregnant, and sandboxing (that is, creating an image or scene using sand and a range of everyday items). Data was analysed using a narrative analysis, which is used to consider change over time. Guidance is provided on how to undertake narrative analysis. The findings highlighted a wide range of barriers and facilitators to abstain from alcohol and smoking during pregnancy, which were related to life circumstances. The key challenges of using such a method, including the ethical implications, are discussed. An exercise with additional data is provided to consolidate learning.

Quality requirements (QRs) are a key artifact needed to ensure the quality and success of a software system. Despite their importance, QRs rarely get the same degree of attention as their functional counterpart in agile software development (ASD) projects. Moreover, crucial information that can be obtained from software development repositories (e.g., JIRA, GitHub) is not fully exploited, or is even neglected, in QR elicitation activities. In this work, we present a data-driven tooled approach for the semi-automatic generation and documentation of QRs in the context of ASD. The approach is based on the declaration of thresholds over quality-related issues, whose violation triggers user-defined alerts. These alerts are used to browse a catalog of QR patterns that are presented to the ASD team by means of a dashboard that implements several analysis techniques. Once selected, the patterns generate the QRs, which are documented and stored in the product backlog. The full approach is implemented via a configurable platform. Over the course of 1 year, four companies differing in size and profile followed this approach and deployed the platform in their premises to semi-automatically generate QRs in several projects. We used standardized measurement instruments to elicit the perception of 22 practitioners regarding their use of the tool. The quantitative and qualitative analyses yielded positive results; i.e., the practitioners' perception with regard to the tool's understandability, reliability, usefulness, and relevance was positive. We conclude that the results show potential for future adoption of data-driven elicitation of QRs in agile companies and encourage other practitioners to use the presented tool and adopt it in their companies. ; This work is a result of the Q-Rapids project, which has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 732253. ; Peer Reviewed ; Postprint (author's final draft)

The mental models approach, a leading strategy to develop risk communications, involves a time‐ and labor‐intensive interview process and a lengthy questionnaire to elicit group‐level risk perceptions. We propose that a similarity ratings approach for structural knowledge elicitation can be adopted to assist the risk mental models approach. The LinkIT game, inspired by games with a purpose (GWAP) technology, is a ludic elicitation tool designed to elicit group understanding of the relations between risk factors in a more enjoyable and productive manner when compared to traditional approaches. That is, consistent with the idea of ludic elicitation, LinkIT was designed to make the elicitation process fun and enjoyable in the hopes of increasing participation and data quality in risk studies. Like the mental models approach, the group mental model obtained via the LinkIT game can hence be generated and represented in a form of influence diagrams. In order to examine the external validity of LinkIT, we conducted a study to compare its performance with respect to a more conventional questionnaire‐driven approach. Data analysis results conclude that the two group mental models elicited from the two approaches are similar to an extent. Yet, LinkIT was more productive and enjoyable than the questionnaire. However, participants commented that the current game has some usability concerns. This presentation summarizes the design and evaluation of the LinkIT game and suggests areas for future work.

Pedestrian modelling and simulation play a fundamental role in reducing risks and implementation costs. Such models require a accurate representation of pedestrian behavior. However, to elicit human behaviour is not a trivial task. Virtual environments have been gaining notoriety as a behaviour elicitation tool, but it is still necessary to research the validity of this technique in the context of pedestrian studies, as well as to create guidelines for its use. This work proposes a methodology for pedestrian behavior elicitation using virtual environments, in conjunction with surveys or questionnaires. The methodology focuses on gathering data about the subject, the context and the action taken; analyzing the collected data, to finally output a behavioral model. The resulting model can be used to improve the original virtual environment, providing better conditions for future experiments. A concrete implementation was built based on this methodology, serving as an example for future studies. A virtual environment with several data collection mechanism was developed. The environment was used in conjunction with two surveys in a experiment in order to collected behavioral data about crossing. In the experiment the subjects controlled a virtual avatar through an HTC Vive and were tasked with traversing between two points of a city. The data collected during the experiment was analyzed and used to create a model for the pedestrians speed taking into account its actions and perceptions. The proposed methodology was successful in gathering the required data and make use of it to create behavioral models. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N. 723386 (SIMUSAFE).

The use of diagrams to stimulate dialogue in research interviews, a technique known as graphic elicitation, has burgeoned since the year 2000. Reviews of the graphic elicitation literature have relied on the inconsistent terminology currently used to index visual methods, and have so far drawn only a partial picture of their use. Individual diagrams are seen as stand-alone tools, often linked to particular disciplines, rather than as images created from a toolbox of common elements which can be customized to suit a research study. There is a need to examine participant-led diagramming with a view to matching the common elements of diagrams with the objectives of a research project. This article aims to provide an overview of diagramming techniques used in qualitative data collection with individual participants, to relate the features of diagrams to the aspects of the social world they represent, and to suggest how to choose a technique to suit a research question.

This article discusses the photo-elicitation interview (PEI), a qualitative methodology, by addressing its fundamentals, providing examples of how to use it, and arguing its benefits and potential challenges. In PEIs, researchers introduce photographs into the interview context. The photographs used in PEIs can originate from the interviewee or the researcher. Researchers can use photographs as a tool to expand on questions and simultaneously, participants can use photographs to provide a unique way to communicate dimensions of their lives. Featured, in detail, are school ethnography and inner-city childhood studies that used PEIs.

Residential choice behaviour is a complex process underpinned by both housing market restrictions and individual preferences, which are partly conscious and partly tacit knowledge. Due to several limitations, common survey methods cannot sufficiently tap into such tacit knowledge. Thus, this paper introduces an advanced knowledge elicitation process called SilverKnETs and combines it with data mining using random forests to elicit and operationalize this type of knowledge. For the application case of the city of Leipzig, Germany, our findings indicate that rent, location and type of housing form the three predictors strongly influencing the decision making in residential choices. Other explanatory variables appear to have a much lower influence. Random forests have proven to be a promising tool for the prediction of residential choices, although the design and scope of the study govern the explanatory power of these models.