Search results

The aim of this chapter is to explore clean language interviewing (CLI) for incidents where a serious injury or fatality (SIF) has occurred and to identify what difference this type of interviewing can make where high risk and high efficacy must co-exist. The primary focus is non-criminal SIF investigative interviews in North American utilities and the use of CLI in root cause (RC) accident investigations.

Nearly 900,000 serious injury or fatality accidents occur annually in the US, which are quite literally a matter of life and death for individuals, distressing for loved ones with grave consequences for organisations in which they occur. Despite the gravity of these accidents, training for interviewers is woefully lacking. This chapter describes how 11 experienced root cause analysts conducted investigative interviews and reports on their experience before and after learning skills in clean language interviewing.

Findings show that when investigators learn how to ask cleaner or non-content leading questions, there is a higher level of confidence in the data elicited. The analysts noted several advantages of conducting interviews with clean language including: appreciably easing interviewee's response to questions; creating an environment of trust and non-blame for injured individuals and witnesses; and a non-interrogative approach that provided psychological and emotional safety. Transcripts of an interview prior to and post training in clean language interviewing methods illustrate the difference that questions make. The chapter concludes by highlighting some benefits and challenges of using clean language interviewing in serious injury or fatality interviews.

This chapter illustrates the elicitation of metaphor using clean language interviewing (CLI) from a study with a population of 30 business leaders to find out what they could learn through articulating and exploring their metaphors about leadership. This responded to claims in the literature on leadership that mental models are the key to leadership and yet leaders are largely unaware of their models. The aim of the study was to encourage leaders to pay attention to their metaphors and to understand if this could help develop their self-awareness. The entire phenomenological study was based on clean language principles to guide the research process. The chapter includes an extract from one interview to illustrate the use of clean questions to elicit verbal and visual metaphors. It shows the application of the cleanness rating to the interview as a method to understand the relative extent of clean or leading questions and facilitate researcher reflexivity. Moreover, the chapter illustrates the use of drawings as integral to clean language practice. The chapter concludes with findings from the study about facilitating self-awareness through CLI and shares some lessons learned about using CLI with business populations.

This chapter explores clean language interviewing for qualitative market research. It focuses primarily on how clean language interviewing was used within a process devised by the authors called ‘value-strings’. The chapter describes this process and how it was used in a client project for a food company that wanted to understand their customers' motivations and decision-making in relation to buying and using tinned sweetcorn.

Value-strings provided insights that went beyond the practical, functional attributes of the product to those that related to the benefits to the respondents of consuming sweetcorn and through to more tacit, often difficult-to-reach information about their values and beliefs. The client used the outcomes to develop a new TV advertising campaign.

The steps of the value-strings process, along with the optional metaphor elicitation steps, are described and illustrated using examples from the project. We show how we integrated opportunities for metaphor elicitation into value-strings, adding to the flexibility of the process and accommodating the client's requirement for the project to provide visual metaphors as well as verbal information.

Time being always in short supply in market research projects, we also describe the quick approach we used to enable respondents to access and generate metaphors. Some findings from the project, along with challenges and learnings are offered.

The purpose of this chapter is to enable interviewers to understand how they can elicit interviewee-generated data that are not ‘muddied’ by the researcher. The chapter has three main components. First, we discuss the authorship of data and illustrate how questions may unwittingly affect this authorship. Second, we outline the problem with ‘leading’ questions and introduce three features of leading questions that are relevant to researchers from different research epistemologies. Third, we introduce the ‘cleanness rating’, which is a way to categorise how questions are used in an interview according to the extent to which they are leading or ‘clean’. We conclude with the difference this can make for researchers, including enhancing the capacity for interviewers to reflect on their practice and making their role in the generation of interview data more transparent.

Clean language is the foundation of coaching work delivered by Genius Within, a non-profit company that provides assessment, coaching, training and HR consultancy for neurodifferent adults and their employers. Genius Within works with thousands of employed and unemployed clients each year as well as those who are incarcerated. Evaluation of clean language in coaching for neurodifferent clients has formed one doctoral thesis (Doyle, 2018) with a further study in progress. The method's utility in drawing out experiences of mastery is in line with self-efficacy theory (Bandura, 1997) and has been demonstrated to be particularly successful with this minority population (Doyle, 2021; Doyle & McDowall, 2015). The staff of Genius Within apply clean language fluently in a range of contexts, across multiple research activities, training and awareness initiatives, as well as workplace intervention programmes. In this chapter, two Genius Within employees who are also involved in academic research describe the use of a clean language interviewing (CLI) approach to evaluate a psychometric tool used in recruitment. The authors were commissioned by the test designer to identify the prevalence of implicit biases within the tool, which might constitute hidden barriers for neurodifferent applicants. The chapter is introduced with a brief history of neurodifferences and a contextual frame for the study, followed by outlining our process and results. We will conclude with proposals for the utility of CLI more broadly within neurodiversity as a method of facilitating innovation and dismantling socially constructed norms.

This chapter describes the work-life balance project, which was the first to investigate the potential of clean language as an academic research interview methodology (Lawley, Meyer, Meese, Sullivan, & Tosey, 2010). It resulted in the publication of an article in the British Journal of Management (Tosey, Lawley, & Meese, 2014) that has since been cited in several academic papers, including Langley and Meziani's (2020) review of interview methodologies in the field of organisational change. This chapter describes the project's methodology and findings and highlights six lessons learnt that have helped to inform the further development of clean language interviewing.

Modelling is a research methodology that has received little academic attention since it began to be formulated in the 1970s. On the spectrum of clean language interviewing (CLI) applications described in Chapter 1, the most sophisticated is modelling, and especially modelling that takes place in real time during the interview.

This chapter defines what we mean by ‘a model’ and ‘modelling’ and explains how they are related to CLI. We situate the chapter by recounting how modelling became linked to CLI. To conclude we consider some of the methodological challenges faced by both the interviewee and interviewer involved in a modelling research project.

We also explain how interviewee metaphors discussed in Chapter 3 can support the modelling process. Much of the modelling that takes place during an interview resides in the background of the interaction. To illustrate modelling we provide an annotated transcript of a symbolic modelling interview that uses clean language to model the skill of ‘knowing what is essential’.

To conclude this book, we take stock of the state of the field of clean language interviewing (CLI). The field has matured considerably in 20 years and yet is still young and emergent. Through articulating the principles of CLI and exploring its application in many fields of practice, we hope this book might come to be seen as a milestone on its path. From its informal beginnings and earliest applications, we believe we can claim with justification that clean language interviewing has developed into a well-specified, well-tested and well-appreciated method that can be used to access both explicitly- and tacitly-held knowledge in a wide range of research projects.

As editors of this volume, we have been gratified and humbled by the ways in which CLI has been used by the contributors. Part II has demonstrated the value of clean language interviewing in both academic and applied research. The applications presented illustrate that CLI has breadth – given the diverse fields in which it has been applied – as well as depth, due to the various levels at which it can be used.

Our aim in this chapter is to reflect on themes that have emerged from the contributions in Part II and the experience of compiling the book as a whole. We begin by reviewing the frameworks that we regard as essential to CLI, then discuss three issues of practice and theory that have emerged from Part II. We sum up the key benefits and limitations of CLI for interviewers and interviewees before indicating some possible directions for future research.

Underpinning clean language interviewing is a set of skills that allow the interviewer great facility in tracking what has been presented. These skills include minimising personal inference and making an informed choice of what question to ask. They are grounded in the logic of the interviewee's data and the purpose of the interview.

This chapter makes visible four hidden skills I identified through reflection on a doctoral study I conducted using clean language interviewing. These are, how I: ‘parcel out’ sentences in order to build visual-spatial schema; apply content-free codes during the interview; decide what is salient in the interviewee's words and gestures; and use adjacency to navigate my way around the data. Since these skills are applied moment-by-moment during the interview, I refer to them as ‘coding in-the-moment’. I conclude with a comparison between grounded theory methodology and clean language interviewing.