Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education

© 2015 SAGE Publications, Ltd. All Rights Reserved. This PDF has been generated from SAGE Research Methods Datasets. SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education

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Introduction This example introduces coding as an important process for conducting a comprehensive thematic analysis of interview data. Coding helps to achieve all three of the aims of thematic analysis: examining commonality, examining differences and examining relationships.

The interview transcripts used in this exemplar were provided by Jamie Harding, a Senior Lecturer at Northumbria University. The research carried out was inductive and the objectives were to explore lecturers’ motivation in choosing their career, their experiences of teaching students and their views on reflective practice and change in higher education.

Coding Coding helps to achieve all three of the aims of thematic analysis: examining commonalities within a dataset, examining differences and examining relationships. This example shows how to use codes to identify similarities and differences between cases in your data.

Codes are usually notes made in the margin of interview transcripts and can take a number of forms, including: complex system of abbreviations; systems that use both abbreviations and numbers; and full words and short phrases. This example

Page 2 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 uses primarily words and phrases as codes.

It is helpful to make a distinction between two types of code: apriori codes and empirical codes (Gibson & Brown, 2010: 132–133). Apriori codes are created prior to research to reflect categories that are already of interest before the research has begun. They tend to derive at least partly from the researcher’s previous reading and are more appropriately used as part of a deductive approach. Empirical codes are derived after the data evidence has been collected, while reading through the collected data points of importance and commonality are identified. Empirical codes are more likely to be used in inductive pieces of research, where the data is examined and analysed before consideration of the existing theory and literature. It is important to emphasise that these two forms of coding are not entirely separate: even when using empirical codes, it is likely that the researcher’s prior knowledge of the subject will influence decision making to some extent. Similarly, when using apriori codes, it is almost certain that some issues and themes will emerge that were not anticipated from the researcher’s prior reading in the subject area. As Jamie Harding’s research was primarily inductive, we will look at empirical coding in this example.

Data Exemplar The interview data was collected in the Faculty of Social Sciences at a case study university by an interviewer, under the supervision of Jamie Harding, a Senior Lecturer at Northumbria University. This was primarily an inductive piece of research, which meant that there was no theory to test and no research questions to answer. However, there were a number of research objectives. This example focuses on one of these objectives: to identify feelings about reflective practice and methods by which it was put into practice.

The interviewee we focus on in this dataset is Thomas. Thomas is a lecturer with previous work experience in industry. He had been employed at the university

Page 3 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 for a substantial period of time. This dataset will also discuss coding across the excerpts from Thomas and another lecturer named Lewis and the full transcripts with three other lecturers. Full transcripts are available in the Download Dataset section.

Analysis: Coding Jamie Harding describes how he approached the coding of Thomas’s interview transcripts. The process of using empirical codes can be broken into four steps. These steps are:

1. Identifying initial categories based on reading the transcripts. 2. Writing codes alongside the transcripts. 3. Reviewing the list of codes, revising the list of categories and deciding which codes should appear in which category. 4. Looking for themes and findings in each category

Step 1: Identifying Initial Categories Based on Reading the Transcripts Codingshould begin with a thorough reading of the full transcripts to be analysed. This enables you to identify categories that codes can be placed into and so saves time in the analysis that follows. Identifying categories is a major part of separating and sorting your data, however, it is difficult to suggest specific tactics or techniques for creating categories. The researcher can only use their judgment to identify broad subject areas under which the data could be grouped.

The initial list of categories will almost inevitably be modified in the course of the analysis. However, the coding process is likely to take less time and to seem less daunting if the researcher is able to draw up a preliminary category list at the start.

Page 4 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 My list of categories around reflective practice looked like this:

1. Mechanisms for undertaking reflective practice 2. Motivation for reflective practice 3. Aims of reflective practice 4. Limitations to reflective practice

It is important to note that these categories were based not just on Thomas’s interview transcripts, but also on the data collected from other participants in the study at the case study university. The downloadable dataset contains the full transcripts of all the interviews should the interested reader wish to read the data in its entirety.

Step 2: Writing Codes Alongside the Transcripts After deciding on the initial list of categories, and the form that their codes should take (e.g. abbreviations, words and phrases), the researcher should begin to write the codes alongside the interview transcripts. The application of codes involves three elements:

1. Summarising 2. Selecting 3. Interpreting

I will now use a section of an interview with a lecturer, Thomas, to show how codes can be applied. This section is available in the dataset download and is called Coding a Transcript: Thomas. As a reminder, the four categories for codes that had been identified are:

1. Mechanisms for undertaking reflective practice 2. Motivation for reflective practice 3. Aims of reflective practice

Page 5 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 4. Limitations to reflective practice

I followed the three steps of summarising, selecting and interpreting to analyse the data:

Reducing/summarising: This data reduction step helps the researcher to see beyond the detail of the individual case and to identify themes. The interview with Thomas demonstrated that points can often be made more succinctly in written form than when they are made verbally; reducing/summarising information through codes can often be quite a simple task.

Selecting: It is better to err on the side of caution and to limit the amount of selection. It’s preferable to introduce codes that may need to be discarded later, rather than risk failing to code an idea that could become an important feature of the analysis. A key element of the inductive approach is that the development of theory is driven by the research findings, rather than existing theory directing the nature of data collection and analysis. A helpful guiding principle to decide what to code is to search for commonality. The creation of categories in advance assists with the process of selection; on Thomas’s interview transcript I knew to code any comment about mechanisms, motivations, aims or limitations because these had already been identified as areas discussed by a number of respondents.

Interpreting: Interpreting phenomena in their context is a key feature of qualitative research. To correctly interpret the words of respondents, the qualitative researcher needs to consider the context of what has been said and apply a code that reflects the most likely meaning of the speaker. Thomas’s comment that: ‘And I think I do that given time but if you’re only teaching the same things, or doing the same things, with little pressure you can reflect and learn and develop’ was interpreted to mean that time and teaching different subjects were limitations on his ability to reflect on his practice. I understood that Thomas felt that time limited his opportunities for reflection from a number of comments that he made

Page 6 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 elsewhere, e.g. ‘There’s not time now to sit and think about it’. However, there were no additional comments to support my interpretation in the area of teaching different subjects, so I relied on the context. This is the type of occasion where a methodological memo is useful, in order to facilitate later reflection on the judgments that were made during the analysis. I wrote this memo:

Some interpretation was needed for Thomas’ comment: ‘And I think I do that given time but if you’re only teaching the same things, or doing the same things, with little pressure you can reflect and learn and develop.’ It was clear from the context of what was said that ‘do that’ meant ‘reflect on practice’. It also seemed clear from comments that were made elsewhere in the interview that Thomas thought that lack of time was a factor that limited his use of reflective practice. A further interpretation that was made, although one with less supporting evidence, was that Thomas believed that being asked to teach different subjects limited his ability to reflect. The reason for this interpretation was that Thomas seemed to be discussing the conditions under which reflection could most easily take place – having time, teaching the same subjects, not being under pressure – but then implying that, where any of these conditions did not apply, reflection was more difficult. However, as the issue of the subjects taught was not referred to elsewhere in the interview, this was a particularly subjective interpretation.

Step 3: Reviewing the List of Codes, Revising the List of Categories and Deciding Which Codes Should Appear in Which Category A number of practical measures can be taken with an initial list of codes and categories in order to make better sense of the data. These include:

Page 7 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 • Identifying codes which should be placed in pre-set categories. • Creating sub-categories within the initial categories. • Identifying new categories which can bring together a number of codes. • Identifying codes that apply to sufficient numbers of respondents to be part of the findings even though they stand outside any category. • Identifying codes that stand outside any category and should be discarded because they do not apply to sufficient numbers of respondents.

Each of these steps is demonstrated below for the data relating to reflective practice, where the initial list of codes was long and unwieldy. The list is shown below with the name(s) of the respondent(s) who each code applied to. Despite its length, it is included in full, in the hope that you will not be disheartened if your initial list looks equally unmanageable:

Complete List of Codes Used in Relation to Reflective Practice

RP important: Fern, Susan, Rachel, Lewis, Thomas Mechanism – student feedback: Fern, Susan Mechanism – personal reflection: Fern, Susan, Rachel, Lewis, Thomas RP should be constant: Fern, Susan, Rachel, Lewis, Motivation – pride: Fern RP for both teaching and research: Fern Lecturers who do not reflect become outdated and stale: Susan Motivation – for students and lecturer to enjoy teaching: Susan Internal and external motivation for RP: Susan Mechanism – personal teaching reviews: Susan Mechanism – comparing with practice elsewhere in the faculty: Susan Danger of dreading teaching: Susan Aim of RP – to excite and engage students: Susan Accepts responsibility for students’ reaction: Lewis,

Page 8 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 Motivation – to teach well: Rachel Motivation – to stay updated: Rachel Failure to reflect leads to outdated practice: Rachel Change of job helpful: Rachel, Motivation internal: Rachel, Lewis, Thomas Need for internal pressure: Rachel Surprised by lack of external pressure for RP: Rachel, Thomas Should be collective reflection: Lewis Limitation to RP – time: Lewis, Thomas Motivation – to be good at job: Lewis Motivation – wants to communicate effectively: Lewis RP should be informal: Lewis Limitation to RP – teaching new subjects (implied): Thomas RP has led to improved practice: Thomas Mechanism – working with colleagues: Thomas Personal reflection and working with colleagues more effective than teaching course: Thomas Used to be greater opportunities for RP: Thomas Mechanism – watching the teaching of colleagues: Thomas No opportunity to reflect on bad lecture until next year: Thomas Delay means reflection will be less effective: Thomas Motivation – to do the best possible job: Thomas Motivation internal: Thomas No oversight of quality of teaching: Thomas Mechanism – peer review: Thomas Peer review limited by time: Thomas

Identifying Codes Placed in Pre-Set Categories The categories identified when first reading through the transcripts were the

Page 9 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 obvious starting point in seeking to identify themes. Including the name of the category in the code makes it easy to bring the relevant codes together. In the case of mechanisms for reflective practice, the codes that were easy to place in this category were:

Mechanism – student feedback: Fern, Susan Mechanism – personal reflection: Fern, Susan, Rachel, Lewis, Thomas Mechanism – personal teaching reviews: Susan Mechanism – comparing with practice elsewhere in the faculty: Susan Mechanism – working with colleagues: Thomas Mechanism – watching the teaching of colleagues: Thomas Mechanism – peer review: Thomas

Creating Sub-Categories A further stage of analysis may be helpful after the codes that should be placed in a category have been identified. One method of sub-dividing commonalities is the creation of sub-categories. It may be possible to identify common characteristics of some codes beyond membership of the main category, meaning that a sub- category can be created. The grouping together of codes into sub-categories can contribute substantially to the identification of themes. For example, in the case of the list of mechanisms for reflective practice above, all except ‘personal reflection’ and ‘personal teaching reviews’ could be placed into a sub-category of ‘Mechanisms involving working with others’.

Creating New Categories This is often more difficult than creating sub-categories and may require some more conceptual thinking. While it may be obvious from the researcher’s list of codes that some should go together, in other cases they may need to think a little further about a common factor that could justify the creation of a new category. It

Page 10 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 may be reassuring to know that this is a skill, like many others in qualitative data analysis, which develops with practice.

Identifying Codes That Apply to Sufficient Numbers of Respondents to be Part of the Findings Although They Stand Outside Any Category Despite their best efforts to fit as many codes as possible into categories, qualitative researchers tend to find that they have some codes that simply do not have much in common with any others. They then have to make an important decision. Should the codes be retained, because they can contribute to the findings on their own, or should they be discarded? There is no easy answer to this question but the simplest method of deciding is by looking at the number of respondents that the code applies to: I might choose a ‘threshold’ of one quarter of the respondents.

Identifying Codes That Stand Outside Any Category and Do Not Apply to Sufficient Numbers of Respondents to Be Considered to Constitute a Theme This action is closely related to the previous one. Using the threshold of one quarter of respondents in this case meant that any code which stood outside a category and which applied to only one respondent should be eliminated from the analysis. The codes that were eliminated because they did not fit into any category and only applied to one respondent were:

RP for both teaching and research: Fern Used to be greater opportunities for RP: Thomas No opportunity to reflect on bad lecture until next year: Thomas

Step 4: Looking for Themes and Findings in Each Category

Page 11 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 Three pieces of advice are offered when identifying findings:

1. Remember the purpose of thematic analysis

Identifying findings, like every part of thematic analysis, should be guided by the aims identified by Gibson and Brown (2009: 128–129), i.e. examining commonality, examining differences and examining relationships. However, not every dataset, or issue within a dataset, allows for the examination of relationships, so it may be that only the first two of these aims can be achieved. Examining relationships is associated more with conceptual findings and building theory. For the new researcher, identifying similarities and differences within the data is a very worthwhile first goal of analysis.

2. Be content with simple findings

If the process of creating and modifying categories and codes has been effective, then identifying findings becomes quite straightforward. Indeed, it is a common experience for the qualitative researcher to feel disappointed that their findings are simple and do not seem to be saying anything particularly profound. The skills that you develop through the analysis will be invaluable when examining more complex data, which may have a more complicated story to tell.

3. Find ways of expressing trends that avoid the use of numbers

It is rare for qualitative findings to be expressed in terms of specific numbers. Instead, other words are found to provide indications of trends within the data. The qualitative researcher must find their own language with which to identify trends – findings are often expressed in terms such as ‘some’, ‘the majority’ and ‘a number’.

The above three pieces of advice were taken into account when identifying some of the findings in relation to reflective practice. There were some obvious

Page 12 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 commonalities between respondents, which could be simply stated. However, the findings in relation to some of the other codes – including those in the sub- category of ‘Mechanisms involving working with others’ – needed a little more detail when they were noted:

• Several respondents discussed methods of involving colleagues in seeking to identify best practice. The methods discussed were both formal (e.g. peer review) and informal (e.g. watching the teaching of colleagues). • A small number of respondents discussed incorporating student feedback into their reflective practice.

Reflective Questions

1. Using the template in the downloadable data, code the section of Jamie’s interview with the lecturer, Lewis, relating to reflective practice. 2. Identify which codes from the list provided should be placed in the category of motivation for reflective practice. Then sort them into sub- categories and identify findings in relation to the theme of motivation. 3. This data example has focused on Jamie’s aim to identify feelings about reflective practice. Another of Jamie’s objectives was to get lecturers to discuss different types of students and the experience of teaching them. With this second objective in mind and using the full interview transcripts available in the download, follow Jamie’s steps to categorise and code the data.

Further Reading Charmaz, K. (2006). Constructing Grounded Theory. London: Sage.

Dey, I. (1993). Qualitative Data Analysis. Abingdon: Routledge. The notes made are considerably longer than the ones that are used in this chapter but they are

Page 13 of 14 Identifying Themes and Coding Interview Data: Reflective Practice in Higher Education SAGE SAGE Research Methods Datasets Part 2015 SAGE Publications, Ltd. All Rights Reserved. 1 helpful as an illustration.

Gibson, W. J, & Brown, A. (2009). Working with Qualitative Data. London: Sage.

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