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BECOMING ATEACHER:
THE DANCE BETWEEN TACIT
AND EXPLICIT KNOWLEDGE
Masaryk University
Brno 2017
Edition: Education in theory and practice Volume 4
Chapter authors:
Vlastimil Švec (Chapter 6, 10, 11, Conclusion); James Lawley (Chapter 2, 3);
Jan Nehyba (Chapter 4, 5, 7, 9); Petr Svojanovský (Chapter 4, 5, 7); Radim Šíp
(Introduction, Chapter 1); Eva Minaříková (Chapter 6, 8, Summary); Blanka Pravdová
(Chapter 8); Barbora Šimůnková (Chapter 9); Jan Slavík (Introduction)
Reviewers:
doc. PhDr. Vladimír Chrz, Ph.D., Institute of Psychology, Academy of Sciences
of the Czech Republic; prof. PhDr. Bronislava Kasáčová, Ph.D., Faculty of Education,
Matej Bel University Banská Bystrica
© 2017 Masarykova univerzita
Cover © 2017 Eva Šimonová, Jan Nehyba
ISBN 978-80-210-8605-0
ISBN 978-80-210-8428-5 (Czech ed.)
ISBN 978-80-210-8429-2 (Czech ed. online : pdf)
https://doi.org/10.5817/CZ.MUNI.M210-8605-2017
Third Chapter
CLEAN LANGUAGE INTERVIEWING:
MAKING QUALITATIVE RESEARCH INTERVIEWS VERIFIABLE
James Lawley
Download complete eBook from: https://munispace.muni.cz/index.php/munispace/catalog/book/887#downloadTab
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ird Chapter
CLEAN LANGUAGE INTERVIEWING MAKING QUALITATIVE
RESEARCH INTERVIEWS VERIFIABLE
James Lawley
Interview research in social science has been fraught with ataken-for-granted assumption
that interviews straightforwardly provide aresource in relation to participants’ experienc-
es, attitudes, beliefs, identities and orientations toward awide range of social and cultural
phenomena. is, in turn, has proliferated uncritical adoption of the interview in various
empirical studies and researchers have been overly reliant on asimplistic notion, ‘you ask,
they answer, and then you will know’.
(Cho, 2014, pp.42-43)
Particularly relevant to Cho’s critique above is the role played by the wording of ques-
tions in the interview process. Despite considerable evidence from avariety of sources
of the potential for unintended interviewer inuence (discussed below), little has been
written about the potential inuence of the researcher’s own naturally occurring meta-
phors (Tosey, 2015) or the eects of presupposition and framing. is is asurprising
omission given that questions are the sine qua non for conducting interviews, and
interviews are probably the most common technique for collecting data in qualitative
research (King, 2004).
is chapter examines how an interviewer’s use of linguistic structures, such as meta-
phor, presupposition and framing, can unintentionally inuence the content of an
interviewee’s answers, and how that may compromise the authenticity and trustwor-
thiness of the data collected (Lincoln & Guba, 1985). ese concerns are addressed by
adescription of the Clean Language interview method, and amethod for checking the
validity of research interviews. Finally, there is adiscussion of the relevance of Clean
Language interviewing to tacit knowledge research.
3.1 Interviewee biases
Anumber of interviewee biases that can inuence the question-response process
have been documented by Podsako and his colleagues (2003). For example: the
consistency eect is the tendency to answer in ways that are consistent with the ques-
tions; acquiescence bias is the tendency not to challenge an assumption implicit in
aquestion; and the friendliness eect is the tendency to answer how an interviewee
thinks the researcher wants them toanswer. In all three cases the interviewee may
Te x t
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(unconsciously) look for cues from the interviewer about how to answer. In this way
the interviewee and researcher can unintentionally increase the chances of priming,
where the exposure to astimulus inuences alater response. Unconscious priming
eects can aect word choice long aer the words have been consciously forgotten
(Tulving et al., 1982).
3.2 Interviewer priming
Text books on interview technique refer to the need to minimise interviewer bias,
however, other than the ‘open/closed’ question distinction there is little about the po-
tential eect of linguistic structures on interviewees. More concerning is that several
leading books and papers on qualitative research give examples of interviews replete
with interviewer-introduced metaphors, and questions which could ‘lead the wit-
ness’—without any warning commentary. Below are just two of the dozens of exam-
ples Ihave gathered. e rst extract is from apublished paper on interview technique
(Englander, 2012, pp.31-33 with metaphors underlined):
Interviewer: How has this memory aected your life? What kind of impact has it had
on your life?
Interviewee: My dad’s girlfriend’s apartment or my grandmother? Both?
Interviewer: e rst memory. How has this impacted, what impact has it had on
your life?
Interviewee: … it denitely has avery large impact…
e author of the paper raises some important points about descriptive phenom-
enological research interviews but does not mention that the interviewer’s use of the
metaphor impact three times in quick succession may have apriming eect. If there is
apossibility that the interviewee’s description “it denitely has avery large impact” has
been inuenced by the interviewer’s use of that metaphor three times in the previous
two questions, the veracity of any analysis or conclusions drawn from that statement
could be compromised.
Another paper, published by the UK’s National Institute for Health Research (Lacey
& Lu, 2009, p.45) gives sample transcripts of interviews to be analysed. One transcript
is of an interview with asecondary school teacher about her experience of returning
to work 14 months aer aheart attack. e transcript contains the following three
questions, asked one aer another (metaphors underlined):
Q: Was it hard to go back then?
Q: Doyou think it’s changed your outlook about the future?
Q: So your outlook is dierent?
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In order to understand these questions, the interviewee must make sense of the
metaphors hard to go back and changed outlook, neither of which had appeared in the
transcript previously. Unless the questions are rejected outright, the syntax presup-
poses that going back is hard and that her outlook is changed/dierent. While the paper
explains how to analyse such transcripts, and notes the potential for inquirer biases
during the interpretation phase, at no time is the authenticity of the interviewee data
called into question. Given the tendency for consistency, acquiescence, friendliness
and priming eects to inuence the interviewee to answer within the frames presup-
posed by the questions, concern about the authenticity of the answers would have
been justied. Whether this particular interviewee would have given similar or dif-
ferent answers to questions without such framing will remain forever unknown, but
doubt remains.
Tosey (2011) gives adierent kind of example from apublished paper which examines
the nature of personal transformations experienced by mature students. e metaphor
of an edge (e.g. “edge of knowing”) is mentioned no fewer than one hundred and four
times in the paper, and yet not once does this metaphor appear in the interviewee data
cited. It requires avery detailed read of the paper to notice that edge is likely to be the
author’s metaphor and not the interviewee’s.
3.3 Why are interviewer-introduced metaphors so important?
Research by Lous and Palmer (1974) found that the way in which questions were
worded altered subjects’ memories of events they had witnessed. One experiment
showed that changing asingle word in aquestion could change the speed estima-
tions made by observers of avideo-recorded accident by up to 27%. More recently,
and using very dierent methods, ibodeau and Boroditsky (2011) changed asingle
metaphor in acrime report, crime as a virus or crime as a beast, and discovered that
this was enough to systemically inuence the way people reasoned about crime. ey
concluded, ‘even the subtlest instantiation of ametaphor (via asingle word) can have
apowerful inuence [and furthermore] the inuence of the metaphorical framing ef-
fect is covert: people donot recognize metaphors as inuential’. ‘People’ in the context
of this chapter includes both interviewer and interviewee.
Lous (1975) also showed that questions which falsely presupposed that an object or
event existed in alm, e.g. ‘Did the woman who was pushing the carriage cross into
the road?’ doubled the likelihood that the subject would later report having seen that
event, compared to if they had been asked ‘Did you see awoman pushing acarriage?’
and more than tripled the likelihood compared to the control group who were not
subject to either of the presupposition-laden questions.
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3.4 Clean Language
If the inclusion of metaphors and presupposed ways of thinking are unintended and
mostly unconscious (Lako & Johnson, 1999), is it possible to mitigate the potential
to produce data ‘contaminated’ by the interviewer’s linguistic structures? Counselling
psychologist David Grove (1989) found away to keep his metaphors and constructs
out of his therapeutic interviews with severely traumatised clients. Grove called his
approach Clean Language. Over the last 20 years Clean Language has migrated out
of therapy and into the world of business (Doyle, Tosey & Walker, 2010; Martin 1999;
Martin & Sullivan, 2007), education (Gröppel-Wegener, 2015; McCracken, 2016;
Nixon & Walker 2009; Nixon, 2013), and qualitative research (detailed below).
By paying careful attention to the language they use, researchers can minimise unde-
sired inuence and unintended bias during all stages of research—design, data gath-
ering, analysis and reporting (Van Helsdingen & Lawley, 2012). In particular, Clean
Language can rene interviewing by minimising the introduction of researchers’ met-
aphors and constructs (Tosey et al., 2014). is is not to suggest that the interviewer
who uses Clean Language is not inuential. Clean Language aims to minimise the
co-construction of the content while at the same time recognising that the interviewer
plays asignicant role in the co-construction of the process through directing the
interviewee’s attention to certain aspects of his or her experience (Tosey, 2015).
Owen (1996) was the rst to see the value of phenomenological interviewers adopt-
ing Grove’s questions and since then the technique has been employed in research as
varied as:
• Iranian students’ metaphors for their teachers (Akbari, 2013)
• Narratives of people who are living with the diagnosis of dementia (Calderwood,
2011)
• ADutch case study on the role of knowledge in ood protection (Janssen et al., 2014)
• How older workers in the re and rescue service deal with work-life balance issues as
they plan for, approach and transition through retirement (Pickerden, 2013)
• Experiences of members of the Ulster Defence Regiment in Belfast, Northern Ireland
(Snoddon, 2005)
3.5 Need for a‘cleanness’ rating
Researchers must demonstrate the quality of their work in ways that are commensurate
with their assumptions about their use of interviews.
(Roulston, 2010, p.199)
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Even if an interviewer plans to closely follow Clean Language protocol, this addresses
only half the problem. Given the tendency for interviewer metaphors and constructs
to enter the interview unplanned, how dowe know what actually happens? Current
quantitative research papers describe measures that ensure the analysis of interview
data is robust, but very little is written about applying validity criteria to the interview
process itself.
To verify whether interviewers using Clean Language remain faithful to their method,
the author has devised a‘cleanness rating’ (Lawley, 2010; Lawley & Linder-Pelz, 2016).
Every question or statement by the interviewer is allocated to one of ve categories:
• Classically clean—drawn from the standard Clean Language question set (Lawley
& Tompkins, 2000) or repeating only the interviewee’s words.89
• Contextually clean—only introduces ‘neutral’ words based on the context of the re-
search or logic inherent in the interviewee’s information.
• Mildly leading—introduces words with the potential to lead but with no discernible
eect on the interviewee’s answers.
• Strongly leading—introduces words, especially metaphors, presuppositions, frames
or opinions that could cast doubt on the authorship of interviewee answers.
• Other—comments outside of the interview content, e.g., about the process of the
interview or answering apractical question from the interviewee.
e results of the line-by-line analysis are tabulated and used to arrive at asummary
assessment of the ‘cleanness’ of each interview. While the goal may be to remain 100%
‘clean’ in an interview, there are other factors which can make this almost impossible.
However, to see what happens in practice the author combined the results from the
ratings of 15 interviews (875 interviewer questions/statements) conducted by three in-
terviewers experienced in the use of Clean Language during three separate published
research projects. Table 1 shows that on average, ve (out of 58) of the interviewers’
questions or comments were assessed as ‘mildly leading’ and just one was classied as
‘strongly leading’.
89 85% of questions in six Work-Life Balance interviews made use of the following classic Clean
Language questions, (Tosey, Lawley & Meese, 2014):
And what kind of ...? And where/whereabouts is …
And is there anything else about … And how doyou know …
And that’s … like what? And when … what happens to …?
And is there arelationship between … and …
And is … the same or dierent as …
And then what happens? And what happens next?
And where did … come from? And what happens just before …
Note: ‘…’ indicates the interviewee‘s words.
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Table 1
Average cleanness ratings for 15 interviews using Clean Language
Classication of Interviewer
questions/statements
Average number of questions/statements
per interview
Classically clean 35 60 %
Contextually clean 15 25 %
Mildly leading 5 9 %
Strongly leading 1 2 %
Other 2 4 %
TOTALS 58 100 %
When there are only one or two strongly leading questions in an interview, it is possi-
ble to exclude aportion of the interviewee’s answers from the analysis while retaining
the majority of the data. As the number of leading questions and statements increases,
the delity and value of the interview data becomes more and more debatable.
Asystematic study of the cleanness of Clean Language interviews compared to tradi-
tional interviews is forthcoming. In the meantime, even acursory review of ‘model’
interview samples (in text books and academic papers on qualitative research tech-
niques) provides evidence for the hypothesis that traditional interviews are more
likely to introduce content and lead by presupposition. Given that, ‘the goal of any
qualitative research interview is … to see the research topic from the perspective of
the interviewee, and to understand how and why they come to have this particular
perspective’ (King, 2004), the widespread use of unintended leading questions and
the imposition of content casts doubts on the validity of results obtained from such
interviews.
3.6 Other features of Clean Language interviewing
In addition to the mechanics of minimising the introduction of interviewer meta-
phors and presupposition, proponents of the Clean Language method maintain that
it has two additional features. It is ideal for researching interviewees’ metaphors and
mental models; and it has the potential to gather in-depth data more eectively than
traditional methods.
3.6.1 Researching metaphors and mental models
e claim that Clean Language is particularly useful for researching autogenic meta-
phors and mental models is supported by anumber of studies (Cairns-Lee, 2015;
Linder-Pelz & Lawley, 2015; Tosey, et. al., 2014).
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Tosey (2015, p.203) was impressed by Clean Language’s ‘potential for research due to
its systematic and rigorous way of exploring, and maintaining delity to, aperson’s
own inner world’. Since the adherence to Clean Language prevents interviewers from
introducing their metaphors into the conversation, the data analyst and end user can
be assured that all quoted metaphors are generated by the interviewee.
3.6.2 Gather in-depth data
Because aClean Language interview is centred entirely on the interviewee’s descrip-
tions, using only his or her lexicon, the interviewee is more likely to become self-
reective and enquiring of the workings of his or her subjective experience. Astudy
of the metaphors of managers on their perceptions of their ‘work-life balance’ showed
amuch richer description of their experience than the conventional ‘balance’ meta-
phor might suggest (Tosey et al., 2014):
• Two halves of acircle.
• Going up amountain dodging boulders.
• Asplit and aswitch.
• Juggling and aspinning top.
• Riding on the crest of awave.
• Physical and mental separation.
• Adeal with abit of ex on both sides.
ese metaphors were accompanied by rich and detailed descriptions, both verbal
and visual (drawn). Interestingly, four of the six managers could recall their personal
metaphors in an informal follow-up three years later. (W. Sullivan, personal commu-
nication, 2 July 2013).
In another study, Lloyd (2011) compared the number of ‘meaning units’ provided by
the interviewee in interviews using Clean Language and in interviews using atradi-
tional interview technique. Lloyd found the average number of meaning units from
aClean Language question was close to ve, while an equivalent traditional-style
interview produced fewer than two meaning units per question. While the author
acknowledges anumber of possible aws in the data analysis, it does suggest auseful
direction for future research.
3.7 Tacit knowledge research
Tacit knowledge is by denition hard to access and dicult to articulate. is poses
real challenges for an interviewer. Clean Language can assist in this endeavour in sev-
eral ways. First, it is dicult for an interviewee to access tacit knowledge even under
the best circumstances, and the addition of an interviewer’s unintended constructs
is liable to complicate the situation. Second, almost any attempt by an interviewee to
express tacit knowledge will require the use of metaphor (see Chapter 2 on Embodied
Metaphor). ird, in struggling to access and articulate their experience, interviewees
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may unconsciously look to the interviewer for suggestions and hints, which, if pro-
vided, would compromise the authenticity of the account. For these reasons, the use
of Clean Language interviewing has the capacity to provide high-quality and veriable
data for qualitative research projects—such as those involving tacit knowledge.
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