Tuesday, March 12, 2019

thematic analysis



Chapter 15
Thematic Analysis

Helene Joffe

In Qualitative Research Methods in Mental Health and Psychotherapy: A Guide for Students and Practitioners. Edited by David Harper and Andrew Thompson,
Chichester: Wiley-Blackwell, 2012, pp. 209-223.


Description of the method
Thematic Analysis (TA) is a method for identifying and analysing patterns of meaning in a dataset (Braun & Clarke, 2006). It illustrates which themes are important in the description of the phenomenon under study (Daly et al., 1997). The end result of a thematic analysis should highlight the most salient constellations of meanings present in the dataset. Such constellations include affective, cognitive and symbolic dimensions. If one were looking at how those who do not take up the services of mental health professionals view them, for example, a thematic analysis of interviews with a carefully chosen sample of such people would reveal how they represent the various mental health professionals. This, in turn, would reveal what keeps them away from the services offered by those such as psychotherapists and psychologists. Thus a thematic analysis can tap the manifest and latent drivers concerning an issue such as uptake of mental health professional services.
Since a TA refers to themes, the notion of a theme must be examined more closely. A theme refers to a specific pattern of meaning found in the data. It can contain manifest content - that is something directly observable such as mentions of stigma across a series of interview transcripts. Alternatively, it can contain more latent content, such as references in the transcripts, which refer to stigma implicitly, via mentions of maintaining social distance from a particular group, such as certain mental health professionals. Specific criteria need to be stipulated concerning what can and cannot be coded within such themes; otherwise this form of content is highly subjective. Themes are thus patterns of explicit and implicit content. Thematic analyses tend to draw on both types of theme. Often one can identify a set of manifest themes, which point to a more latent level of meaning. The deduction of latent meanings underpinning sets of manifest themes requires interpretation (Joffe & Yardley, 2004).
A further important distinction in terms of the demarcation of a theme is whether it is drawn from a theoretical idea that the researcher brings to the research (termed deductive) or from the raw data itself (termed inductive). While theoretically derived themes allow researchers to replicate, extend and refute existing studies (Boyatzis, 1998), there is little point in conducting qualitative work if one does not want to draw on the naturalistically occurring themes evident in the data itself. So one utilises the two together – one goes to the data with certain preconceived categories derived from theories, yet one also remains open to new concepts that emerge. It is important to approach each dataset with knowledge of previous findings in the area under study to avoid ‘reinventing the wheel’. However, in addition, one wants to take seriously findings that do not match with previous frames and have the potential to revolutionise knowledge of the topic under investigation. Thus a dual deductive/inductive and latent/manifest set of themes are used together in high-quality qualitative work.
Thematic analysis has recently been recognised as a method in its own right. Previously it was widely used in psychology and beyond, often without acknowledgement or demarcation (Boyatzis, 1998; Braun & Clarke, 2006). It has also been used in this way in the evaluation of mental health services. Some argue that the ability to thematise meaning is a necessary, generic skill that generalises across qualitative work (Holloway & Todres, 2003). Like other qualitative methods, TA facilitates the gleaning of knowledge of the meaning made of the phenomenon under study by the groups studied and provides the necessary groundwork for establishing valid models of human thinking, feeling and behaviour. However, TA is among the most systematic and transparent forms of such work, partly because it holds the prevalence of themes to be so important, without sacrificing depth of analysis. Thus TA not only forms the implicit basis of much other qualitative work, it strives to provide the more systematic, transparent form of it.

Historical origins and influences
Thematic analysis is rooted in the much older tradition of content analysis (CA). TA shares many of the principles and procedures of CA, a historically quantitative tradition that dates back to the early 20th century within the social sciences, but further back in the humanities (Smith, 2000). CA involves establishing categories and then counting the number of instances in which they are used in a text or image. It determines the frequency of the occurrence of particular categories. Many content analyses rely purely on counting attributes in data (e.g. particular words or images).  CA is appealing because it offers a model for systematic analysis of both elicited and naturally occurring data. It has been widely used for the analysis of mass media material. However, the results it generates have been judged as `trite' (Silverman, 1993) when they rely exclusively on the frequency outcomes it generates. It is also accused of removing codes from their context, thereby stripping data of its meaning.
The concept of ‘thematic analysis’ was developed, in part, to go beyond observable material to more implicit, tacit themes and thematic structures (Merton, 1975). For the founder of thematic analysis, Gerald Horton, such material can be termed ‘themata’ and these tacit preferences or commitments to certain kinds of concepts are shared in groups, without conscious recognition of them. 
Ideally, contemporary TA is able to offer the systematic element characteristic of CA, but also permits the researcher to combine analysis of the frequency of codes with analysis of their more tacit meanings, thus adding the advantages of the subtlety and complexity of phenomenological pursuits.

Key epistemological assumptions

What kind of research questions is thematic analysis most suited to addressing?
Thematic analysis is best suited to elucidating the specific nature of a given group’s conceptualisation of the phenomenon under study. In my own work this has ranged from publics’ conceptualisations of emerging infectious diseases (EID) such as AIDS (Joffe, 1999), the Ebola virus (Joffe & Haarhoff, 2002), and MRSA (Washer et al., 2008; Joffe et al., 2010), to mass media conceptualisations of these entities (e.g. Washer & Joffe, 2006). It has been used in the mental health arena in a similar way, for example Morant’s (2006) exploration of the social representations of mental illness from the perspective of French and British mental health practitioners. I use TA to discern possible identity issues associated with the representations of each disease and their impact on lay people’s sense of personal and societal concern. More specifically, a key thread running through the EID findings is that there is a tendency to distance self and in-group from vulnerability to such diseases via a set of symbolic associations to marginalised, non-dominant groups and foreigners. The nuances of such associations are well tapped by TA, a method that can capture latent meaning while remaining systematic.

What kinds of data are most appropriate and from whom should they be collected?
Verbal interview (or focus group) data or textual newspaper data tend to be at the root of thematic research. However, open-ended responses to questionnaire items, diaries, video material, images and essays can also be thematically analysed. Interview data are usually collected via semi-structured interviews: an interview with 5-7 topics that the respondent is prompted to talk about (see Wilkinson et al., 2004). This imposes topic areas on people’s thinking, where it may be preferable to gain a more naturalistic inroad into people’s meaning systems concerning the phenomenon under study.
Instead of using topics introduced by the researchers as the basis for the interview, I have developed a more naturalistic method to elicit material. It produces data that follow the pathways of the respondent’s thoughts and feelings rather than imposing questions and topic areas. To obtain this data the meeting with each respondent begins with a task that elicits first thoughts: Respondents are presented with a grid containing four empty boxes. They are prompted to write or draw in each box any word, image or feeling that comes to mind concerning the research issue. Prior to this they are only given a very general sense of the field of study, for example, being invited to an interview on `a public health issue’ in the example that follows.

Instruction given to elicit free associations:

The following is an example of the instructions given for this grid method. The grid presented to respondents in a study of public engagement with MRSA in Britain was preceded by the following instruction ‘We are interested in what you associate with MRSA. Please list the different images and words you associate with MRSA using these boxes. Include everything you associate with one image and/or word into one box.’ (Joffe et al., 2010; see also Solberg, Rosetto, & Joffe, 2010).

Once first associations have been written or drawn respondents are asked to talk about the content of each box in the order that the boxes have been completed. The aim is to elicit subjectively relevant material with a minimum of interference, to tap ‘stored’, naturalistic ways of thinking about a given topic and to then pursue the chains of association or pathways of thought that the respondents go down. Each interview is then transcribed and entered into a qualitative software package such ATLAS.ti, NUD*IST or NVivo.
In terms of who such data should be collected from, the decision concerning how many participants are required has vexed researchers who use thematic analysis. There is no notion of ‘power’ for the choice of the sample. A power analysis, for those working quantitatively, can be used to calculate the minimum sample size the researcher requires to accept the outcome of a statistical test with a particular level of confidence. The choice of sample size for a TA rests upon certain guiding principles: since the researcher is generally looking at group-based variation and/or similarity across groups, sufficient numbers of participants in each group are needed to make valid comparisons that are likely to reveal group-based threads in the data rather than idiosyncratic tangents of meaning. Furthermore, the sample size generally needs be divisible - for equal cell sizes to be used – so a primary number is not desirable. Since the idea is to look at patterning, sufficient numbers are required to discern patterns within the dataset as a whole and across sub-groups thereof. According to such criteria numbers such as 32, 48, 60 and 80 are appropriate and when work is cross-cultural one multiplies these sample sizes by the number of cultures one is studying. These are large sample sizes in comparison to most qualitative approaches. However, with the aid of computer packages large datasets can be handled. Such packages also allow for systematic examination across the data at co-occurring themes, the sequence of themes and other more complex relations between themes, in a way that would be very difficult manually.
A more fundamental issue is what the accounts provided by a given sample represent. How do they relate to what a representative sample might have revealed about the topic? Each individual’s account contains threads of the social thinking in which the individual is embedded. So in individuals one picks up the thinking that surrounds them in their social environments, as well as the more idiosyncratic ways in which they position themselves in relation to this context. Using qualitative datasets to full advantage involves comparing the views and experiences of respondents who have been selected precisely—indeed, purposively—to illuminate potentially important differences and similarities. In other words, samples must be selected purposively in accordance with the research questions, to enhance how potential group differences and similarities, as well as intra-group variation, can be illuminated.

What approach is taken to the involvement of research participants, including mental health service users? 
There has been a major expansion in service user involvement / user-led research in mental health over recent years.  Qualitative research by service users often draws on generic thematic methods, rather than on TA per se. However, there is a growing body of research that uses TA. Gilburt et al.’s (2008) TA of service users' experiences of psychiatric hospital admission in the UK not only prioritises the voice of users, but is led by two service user researchers. 
A further cluster of studies using TA concern themselves with subjective experiences of different therapies, such as Allen’s (2009) exploration of participants’ subjective experience of Mindfulness-Based Cognitive Therapy for the treatment of their depression. TA analysis is also a powerful tool for casting light on non-use of mental health services. Its potential utility in this regard can be seen via a TA pertaining to the mental health and psychotherapy sphere (Johnston, 2000), which casts light on barriers to service use.

Some of the findings from the TA on non-use of mental health services:
The study explored the meanings people with no direct experience of psychological services assigned to the concept ‘the psychologist’ using aspects of the social representations framework and a TA. It showed that a sample of lower socio-economic status Londoners, who had no contact with mental health services, represented the psychologist as a medical expert, in particular, of the mind. Furthermore, there was considerable consensus in linking the psychologist to strong emotional responses based on threat. The two were connected in that when confounded with the psychiatrist, the psychologist was seen to have the power to section people. Fear was also associated with other symbolisations of the psychologist: as akin to a mind-reader, parasite and archaeologist (as in excavating and ‘digging up dirt’). Furthermore, fear sprang from the confounding of the ‘sickness’ of clients and that of the psychologist, as in ‘one has to be a psycho to want to work with psychos’. Although a range of fears and stigma were pervasive, a model of the psychologist as helpful was also evident, particularly among females. The findings complement those from the existing survey-based help-seeking literature concerning treatment fearfulness, particularly among older people and men, but add insight into the symbolisations that form the barriers to help-seeking. A depth understanding of social representations of the psychologist can sensitise clinicians to the preconceptions that clients bring to the encounter. It can also aid efforts to promote psychological services by highlighting widely circulating representations that block the desire to seek help (Johnstone, unpublished D.Clin).

How to use this method
There are surprisingly few published guides on to how to carry out TA, and it is often used in published studies without clear specification of the techniques employed. However, there are a few useful guides including: Boyatzis (1998), Braun and Clarke (2006), and, Joffe and Yardley (2004). This chapter moves to laying out the set of key steps involved in a TA.

Examining the full dataset as a precursor to developing a coding frame
Having read and reread the entire corpus of data (or if images constitute one’s data, had a careful look through all), one needs to create a conceptual tool with which to classify, understand and examine the data. Thus one begins to devise a coding frame (also termed a ‘coding manual’ or ‘coding book’) to guide the thematic analysis. It contains the full set of codes that one chooses to apply to the dataset. It is developed on the basis of both inductive codes grounded in the content of the data, and more theoretically driven codes inspired by past research in the area. Thus in devising a coding frame for the TA of social representations of MRSA, Joffe et al. (2010) drew on social representational work on responses to other emerging infectious diseases, the themes regarding MRSA found in national newspapers (Washer & Joffe, 2006) and an inductive reading of the full set of interviews. The following (see Table 1) is a small section of that coding frame:

Code-name
Definition
Example

META: CAUSES
Explicit statement about the causes of MRSA

Cause - Body products
Cause of MRSA is transmission of body products, such as sputum
“Not nice at all.  I mean at the end of the day it’s like when someone’s lying in front of you who’s sick as anything, he’s coughing, he’s puking, he’s sneezing and you’re sitting in the same environment, aren’t you.  You’re sitting in the same room even though there’s another 8 people with you, but this person’s so bad he should, you know, common-sense, he should be on his own.”
Cause - Cleaners – Foreign
Cause of MRSA is foreign cleaners
“It [cleaning] is sub-contracted, out-sourced, and the guy that’s, that’s looking after him is looking after 150 hospitals and this guy’s being paid £1.50 an hour, illegal immigrant, so it’s supervision again.  Nobody cares.”
Table 1: Section of the MRSA coding frame

For each code, its name appears in the first column, a definition of what should be classified with this code appears in the second, and an example of material that should be coded with this code appears in the third column. In both examples that appear in Table 1 one can see that the context is important. Such statements are made in the context of discussing the causes of MRSA in the interview. Furthermore, what one sees in these excerpts is that they contain other meanings too. So, for example, the ‘cause-cleaners-foreign’ is also coded ‘cause-sub-contracting of cleaning’. Multiple codes can be assigned to the same excerpt in a TA. Devising this frame is taxing and time-consuming as there are no standardised categories to draw on; one devises a coding frame that will enable one to answer one’s research question/s in a balanced manner.

Checking the reliability of the coding frame
Once the codes have been developed, refined, and clearly described in the coding frame, the researcher should determine its reliability. A rigorous way to ascertain reliability is to calculate the correspondence between the applications of the codes to the data by two independent coders. This should be applied to a substantial proportion of the data, usually between 10 - 20%. In the study of public engagement with MRSA, having defined and operationalised what content was to be coded under each code, two researchers coded the same 20% of the dataset independently. Rather than report the percentage of corresponding codes, in this case, where there was inconsistency, the relevant code was more carefully described and operationalised via a discussion between the two researchers. A new coding frame was then produced with more clearly and explicitly defined codes. The aim was to increase the transparency of the coding frame such that those using it would consistently apply the same codes to the same excerpts. In a more rigorous version of inter-rater reliability one reports the degree of concordance between coders. If it is high (e.g. above 75%) this coding frame is regarded as relatively transparent and reliable.

Coding the data using a computer assisted data analysis package
Once a coding frame has been devised and reliability checked, the entire dataset must be coded anew. Coding is the widely accepted term for categorising data: taking chunks of text and labelling them as falling into certain categories, in a way that allows for later retrieval and analysis. Coding tells the researcher in how many interviews the category occurs and, if relevant, how many times it occurs within an interview. It also allows for analysis of the relationship of this code to other codes, in terms of co-occurrence and sequencing.
Since a rigorous TA must draw on a substantial number of interviews, computer assisted data coding and analysis is most appropriate. Packages used for thematic analyses range from ATLAS.ti to NUD*IST to NVivo, among others. Computers cannot analyse textual data in the way that they can numerical data. Yet, as a mechanical aid, the computer is able to enhance research for the following reasons: It allows researchers to deal with many more interviews than manual analyses can; Since it can handle large datasets useful comparisons between groups can be made; The researcher is assisted in looking at patterns of codes, links between codes, sequencing and co-occurrence in a highly systematic fashion, since retrieval of data is made far easier.

Analysing the data using a data analysis package
When all of the data have been categorised, the analysis can begin. The analysis facilitates examination of the themes and their interconnections, and the prevalence of the themes in the sample and sub-samples. In a TA, especially one underpinned by a theory such as social representations, the nuances of the high frequency themes are explored in depth, as are group-based differences (such as those pertaining to gender, class or other groupings that emerge as relevant to answering the research question). The question arises concerning what to ‘do with’ idiosyncratic mentions of a particular theme. While idiosyncratic or occasional responses cannot, of course, be categorised as prevalent themes, they may nevertheless be important. They may, for example, express what many in the sample take for granted, or articulate something that most members of the sample find difficult to voice.
Packages such as ATLAS.ti allow researchers to examine the patterning of themes across the range of interviews, and the common pathways or chains of association within interviews. More specifically, the filtering functions of such packages allow researchers to retrieve the patterns of codes prevalent in particular groups (e.g. different demographics), and such patterns can be retrieved as frequency charts, lists of textual excerpts, or visually, as visual networks. The following is an excerpt from the results section in the MRSA paper mentioned, showing how the most prevalent theme was presented. The paper began the reporting of the theme by presenting a typical excerpt that demonstrated the theme. Following this, the meanings and connections that constituted the theme were conveyed and also depicted in a chart that indicated the prevalence and links between the components of the theme visually. When the chart indicates ‘Better hygiene (n=53)’ as a way of countering MRSA, for example, this means that 53 of the 60 people in the sample specifically said that better hygiene would help to counter MRSA in some way:

Presentation of themes in a Results section

Theme 1: Causal links made between dirt and MRSA


I just had this image of every hospital being disgustingly dirty and you’re more likely to get ill, more ill than you were when you went in.  So that’s why [the first association in my grid is ‘dirt’].  So it was a bit of a worry.  And I’m, I’m associating it with infection and germs and, you know, places that are not very clean.
[Female, 38, white British, broadsheet reader, hospitalised in the past year]

Almost all respondents represented MRSA via a framework (see Figure 1) that linked dirt to its cause. In particular, mention was made of MRSA being caused by the lack of hygiene within National Health Service (NHS) hospitals, explained by deficits in hand washing practices and shortfalls in resources. In particular, staff supervision was regarded as deficient. Consequently staff hygiene procedures were not enforced (also see structural theme 2).
Figure 1. Causal links made between dirt and MRSA.

Also highly prevalent in the data concerning ‘lack of hygiene’ were mentions of a wide range of ‘contamination sources’….” (Joffe et al., 2010)

What makes for a better quality TA?
In place of seeking accurate measurement of hypothetically related variables, and assessing their relationship statistically, good qualitative work seeks detailed, complex interpretations of socially and historically located phenomena. It involves a shift from measurement to understanding; from causation to meaning; from statistical analysis to interpretation (Smith, Harre & Van Langenhove, 1995; Joffe, 2003). A particular aspiration of TA is to balance being faithful to the data and being systematic in one’s approach. What criteria can ensure that one does this?
A good thematic analysis must describe the bulk of the data – it must not simply select examples of text segments that support the arguments it wants to make. However, the prevalence of a given theme does not tell the whole story. The aspiration of TA is to reflect a balanced view of the data, and its meaning within a particular context of thoughts, rather than attaching too much importance to the frequency of codes abstracted from their context.
Science is concerned with how knowledge is produced. It is a systematic way of finding answers to research questions. An increasingly accepted view is that work becomes scientific by adopting methods of study appropriate to its subject matter (Silverman, 1993). Yet it also needs to produce knowledge systematically so that claims can be made concerning its reliability and validity (Silverman, 1993).
Beyond the aforementioned criteria that are conventionally associated with quantitative work, those using a TA need to create a transparent trail as to how they selected and collected their data, from whom, and how it was analysed. This should involve providing access to the coding frame and if possible where the data is housed. In practice this is often prohibited by ethical constraints (e.g. which state that one must destroy all of the interviews 6 months after the end of the study) and space constraints in journals (where the coding frame would occupy the valuable words of the word limit). Also in the name of transparency, researchers need to present systematically a sufficient portion of the original evidence in the written account to satisfy the sceptical reader of the relation between the interpretation and the evidence (Greenhalgh & Taylor, 1997). In practice, this too is often limited by space constraints in journals.
In addition to transparency, the following questions regarding the findings of a TA must be answered in the affirmative if the work is to be regarded as being of high quality: Are they robust, when compared to studies of similar topics using different methods and theoretical orientations? Do they incorporate the possibility of revision? Do they expand current thinking? Are they useful in advancing either theoretical knowledge or knowledge of the substantive issue under investigation? (see Silverman, 1993; Yardley, 2000).

What are the recent developments and innovations concerning this method?
The most salient development regarding this method is the recent exponential growth in the use of TA as a method across a broad range of empirical papers. This includes a wide range of studies concerning mental and physical health, among many other areas. The citation of papers such as Braun and Clarke’s (2006) step-by-step guide to conducting a thematic analysis, as well as growth in citation of Boyatzis’ (1998) older book, reflects TAs increased use for empirical work both within psychology and beyond.
While TA is not intrinsically linked to a particular theory, it has been usefully paired with two in particular. A tradition is developing of pairing it with social representations theory (Moscovici & Duveen, 2000) to study how the public engage with a range of social issues (e.g. see Devine-Wright & Devine-Wright, 2009, Joffe, 1999, Joffe et al., 2010). There are also a stream of studies using thematic analysis and social representations theory in relation to discerning media representations of a range of issues (e.g. see Joffe & Haarhoff, 2002, Washer, 2004, 2006, Washer & Joffe, 2006, Washer, Joffe & Solberg, 2008, Smith & Joffe, 2009). 
Furthermore, TA’s combination with phenomenology (e.g. Fereday & Muir-Cochrane, 2006), in its various forms, offers rich pickings for future work. Here the emphasis is on subjective experience and the ‘taken-for-granted’ of research participants. There is an emphasis on safeguarding the social reality of participants in a given study, rather than replacing it with a fictional reality that is the researcher’s construct (Fereday & Muir-Cochrane, 2006). Thematic analysis is well suited to this endeavour.
Concluding remarks
TA is an empirically-driven approach for detecting the most salient patterns of content in interview, media and imagery content. It examines observable content as a first step in a more probing approach. A TA often does the following simultaneously: it looks at manifest themes as a route to understanding more latent, tacit content; its uses existing theoretical constructs to look at data while also allowing emerging themes to ‘speak’ by becoming the categories for analysis. Thus pressing issues concerning the uptake of mental health services, or evaluation of the impacts of such services, can be explored systematically via TA.
Unlike many other qualitative methods, studies utilising TA tend not to reflect on the impact of the researcher’s preconceived ideas, and presence, on the data that emerge. This may, in part, be a residue from the aspects of the method that emerged from CA, with its more quantitative, apparently ‘objective’ epistemological positioning. While this may be seen as problematic, the emphasis on being systematic and transparent regarding the analysis (e.g. with a clearly laid out coding frame and reporting the outcome of reliability checks) allows other researchers to trace the process whereby the results were reached, and, if necessary, challenge them. In addition, as in all qualitative work, it is taken for granted that the interpretative aspects of TA are by definition influenced by the researchers’ perspectives.
In terms of further developing this method, a fruitful future direction would be to return to a key aspect of the history of the field - Holton’s development of the notion of ‘themata’ – to understand the tacit content that underpins the ‘themes’ in a thematic analysis. Interestingly, a key figure within social representations theory, Ivana Marková, links themata to the genesis of social representations (Marková, 2007) and thus the link between thematic analysis, social representations and themata may provide fertile ground for further developing and deepening this burgeoning method.
Acknowledgements
I would like to thank Nicola Morant and the editors for useful improvement suggestions for this chapter. I would also like to acknowledge ERSC grant RES–000–22–1694 for development of the MRSA TA and EPSRC grant EP/F012179/1 for development of the Earthquake TA.
Further reading:
Boyatzis, R. E. (1998). Transforming qualitative information: thematic analysis and code development. Sage.

Braun, V., & Clarke, V. (2006), Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77-101.

Fereday, J., & Muir-Cochrane, E. (2006) Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods, 5, 1-11.

References:
Allen, M., Bromley, A., Kuyken, W., & Sonnenberg, S. J. (2009). Participants’ experiences of mindfulness-based cognitive therapy: “It Changed Me in Just about Every Way Possible”. Behavioural & Cognitive Psychotherapy, 37, 413-430.

Boyatzis, R. E. (1998). Transforming qualitative information: thematic analysis and code development. Thousand Oaks: Sage.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77-101.

Daly, J., Kellehear, A., & Gliksman, M. (1997). The public health researcher: A methodological approach. Melbourne, Australia: Oxford University Press.

Devine-Wright, H. & Devine-Wright, P. (2009). Social representations of electricity network technologies: Exploring processes of anchoring and objectification through the use of visual research methods. British Journal of Social Psychology, 48, 357–373.

Farr, R. M., & Moscovici, S. (2004). Social Representations. Cambridge: Cambridge University Press.

Fereday, J., & Muir-Cochrane, E. (2006) Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods, 5, 1-11.

Gilburt, H., Rose, D., & Slade, M. (2008). The importance of relationships in mental health care: A qualitative study of service users' experiences of psychiatric hospital admission in the UK. BMC Health Services Research, 8, 92.

Greenhalgh, T., & Taylor, R. (1997). How to read a paper: Papers that go beyond numbers (qualitative research). British Medical Journal, 315, 740-743.

Holloway, I., & Todres, L. (2003). The status of method: Flexibility, consistency and coherence. Qualitative Research, 3, 345-357.

Joffe, H. (1999) Risk and 'the other'. Cambridge: Cambridge University Press.

Joffe, H. & Haarhoff, G. (2002) Representations of far-flung illnesses: the case of Ebola in Britain. Social Science & Medicine, 54, 955-969.

Joffe, H., Washer, P. & Solberg, C. (in press). Public engagement with emerging infectious disease: The case of MRSA in Britain. Psychology & Health

Joffe, H., & Yardley, L (2004). Content and thematic analysis. In D. Marks & L. Yardley (Eds.). Research methods for clinical and health psychology. London: Sage.

Johnston, F. (2000).  Social representation of psychology and the psychologist.  Unpublished clinical psychology doctoral thesis, University College London, London.

Lupton, D. (1999). Risk. London and New York: Routledge.
Marková, I. (2007). Social identities and social representations: How are they related? In Moloney, G., & Walker, I. (Eds.) Social representations and Identity. Hampshire: Palgrave Macmillan
Merton, R. K. (1975). Thematic analysis in science: Notes on Holton’s concept. Science, 188, 335 – 338.

Morant, N. (2006). Social representations and professional knowledge: The representation of mental illness among mental health practitioners. British Journal of Social Psychology, 45, 817–838.

Moscovici, S., & Duveen, G. (2000). Social representations: Studies in social psychology. Cambridge: Polity Press.

Silverman, D. (1993). Doing qualitative research. Sage.
Smith, C. P. (2000). Content analysis and narrative analysis. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 313-335). New York: Cambridge University Press.
Smith, J. A., Harre, R., & Van Langenhove, L. (1995). Rethinking methods in psychology. Sage.
Smith, N., & Joffe, H. (2009). Climate change in the British Press: the role played by visual images. Journal of Risk Research, 12, 647-663
Solberg, C., Rossetto, T., & Joffe, H. (2010). The social psychology of seismic hazard adjustment: re-evaluating the international literature. Natural Hazards & Earth Systems Sciences, 10, 1663-1677.
Washer, P. (2006). Representations of mad cow disease. Social Science & Medicine, 62, 457-466.

Washer, P. & Joffe, H. (2006). The hospital ‘superbug’: Social representations of MRSA. Social Science & Medicine, 63, 2142-2152.

Washer, P., Joffe, H., & Solberg C. (2008). Audience readings of media messages about MRSA. Journal of Hospital Infection, 70, 42-47.
Wilkinson, S., Joffe, H., & Yardley, L. (2004). Qualitative data collection: interviews and focus groups.  In D. Marks, & L. Yardley (Eds.). Research Methods for Clinical and Health Psychology (pp. 39-55). London: SAGE Publications.
Willig , C. (in press).  Perspectives on the Epistemological Bases for Qualitative Research.  In H. Cooper (Ed.).   The Handbook of Research Methods in Psychology.  Washington, DC:  American Psychological Association.

Yardley, L. (2000). Dilemmas in qualitative health research. Psychology and Health, 15, 215-228.


Posted by at No comments:
Subscribe to: Posts (Atom)