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In this book the terms ‘clean language’ and ‘clean language interviewing’ are written using lower case, according to the convention of the American Psychological Association (sixth edition). ‘Clean language interviewing’ is sometimes abbreviated to CLI.

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’.

This chapter describes an application of clean language interviewing to organisational change work through a process we call modelling shared reality (MSR). This process was developed in 2006 by Stefan Outober, James Lawley, Annemiek van Helsdingen, Wendy Nieuwland and Maaike Nooitgedagt (Van Helsdingen & Lawley, 2012). It is based on the clean language & symbolic modelling process (Lawley & Tompkins, 2000). MSR has been applied in a multitude of settings over the last 15 years in the context of organisational change and development, as well as policy decisions and evaluations and other participative processes.

We believe that how people in organisations behave is steered mostly by how they perceive ‘reality’ (Nieuwland & Nooitgedagt, 2018). MSR aims to facilitate movement and change in organisations by enabling participants to explore their individual and collective perceptions of reality.

In this chapter, we describe the principles and steps of the MSR process, highlighting the role of clean language interviewing (CLI). MSR represents a form of action research, the aim of which is to facilitate organisational change rather than to produce abstract knowledge. Illustrated by a case study of a finance department in a social housing corporation, the chapter shows how CLI can be used to work with shared constructs of reality.

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.

Metaphor is an important concept for clean language interviewers. This chapter describes what metaphor is and overviews the experimental research showing the potential for metaphor to influence interviewees. It expands on the brief introduction to metaphor in Chapter 1 and describes the role clean language can play by enabling interviewees' metaphors to be elicited, explored and modelled without the influence of the interviewer's metaphors. It justifies the value of a heightened awareness of the ubiquity and variety of metaphors and their involvement in the different phases of qualitative research; and builds an argument for how a clean language interviewing approach to metaphor can enrich the research process.

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.

When an individual achieves exceptional outcomes in a particular endeavour - sporting success, academic achievement, business results, creative outputs and more – various influences, such as physiology, education and IQ – play a part. Yet these influences often fail to explain the essential difference between one individual's outstanding performance and another's.

This chapter describes the use of clean language interviewing to research the patterns of beliefs, values, thinking and behaviours of a person who gains consistently excellent outcomes in a specific pursuit. The analysis of the data gathered can be used to create a model of the individual's process which describes ‘the elements, patterns and relationships that are characteristic of a particular ability’ (Gordon & Dawes, 2005, p. 8). Modelling is ‘the process of identifying and describing in a useful way those patterns that make up an ability. Once we know the patterns, we can make them our own and begin to manifest the ability’ (Gordon & Dawes, 2005, p. 5).

The case study presented here focuses on a case manager, Tanya, who worked with neurodiverse (mostly autistic) offenders, supporting them to move successfully into education, training or employment. Tanya, who had attained outstanding results, dramatically outperforming her peers, was the exemplar who became the subject of the research. The methodology involved is described, and the ways in which an interviewee's conscious and unconscious processes were elicited through clean language questions are outlined.

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.