Kill that Writer’s Block (3 strategies)!
I know that plenty has been written on this topic, but while the first 2 techniques that I propose below may or may not work for you, please do try the 3rd one, as it has really changed my life! Breaking writing tasks into smaller ones. “The secret of getting ahead is getting started. The secret of getting started is breaking your complex overwhelming tasks into small manageable tasks, and then starting on the first one.” — Mark Twain In my other post where I wrote about strategies for managing your thesis/dissertation, I have already discussed breaking larger tasks into smaller ones, so you may read about the benefits of this approach there. These benefits also apply to writing. To both overcome and prevent writer’s block, I like to set myself small and realistic writing tasks which I know I can accomplish – therefore, as I finish, I will experience a sense of success (“I managed to write 500 words today, as planned – tomorrow I’ll do another 500”), rather than a sense of failure which, in my opinion, is the first step towards developing a sense of being stuck (“I didn’t manage to write the 3000 word essay today – Writer’s Block!!”). Getting time off the computer screen. “If you get stuck, get away from your desk. Take a walk, take a bath, go to sleep, make a pie, draw, listen to music, meditate, exercise; whatever you do, don’t just stick there scowling at the problem…” – Hilary Mantel It always helped me to take a walk, although during this walk I wouldn’t really take my mind off work, but rather imagine I’m talking to someone non-academic (e.g. a member of the family), in simple words, about the bit I am stuck at (so, for example, I am describing my research rationale, talking about the literature on the topic, talking about findings that emerge from my analysis, etc.). I would soon find myself amazed at how effortlessly I can formulate my ideas once I am no longer constrained by the academic writing requirements and the blank computer screen in front of me. Sometimes I would write these ideas down in the researcher’s diary on my phone, and then got back to finish the thing as soon as I got home. Free writing Writing about a writer’s block is better than not writing at all. ― Charles Bukowski The most effective way I know to improve your writing is to do freewriting exercises regularly. At least three times a week … simply write for ten minutes (later on, perhaps fifteen or twenty). Don’t stop for anything. Go quickly without rushing. Never stop to look back, to cross something out… If you get stuck it’s fine to write, “I can’t think what to say, I can’t think what to say” as many times as you want… The only requirement is that you never stop.” Elbow (1973: 3) What is free writing? Writing for 5 (I also tried 10 and 15) minutes, with no stopping, no structure, about what you want to write about next… You can do this as a warm-up for writing, to get into the habit of regular writing, or even to look for topics to write about. You will be surprised how much you can produce this way, when you don’t think of your writing as something that will be assessed by your supervisors, but rather as something being a very personal and private connection between you and your study. This will foster the sense of personal “success” that I mentioned in point 1, as well as boost your confidence. As soon as I got into the habit of freewriting, I did it on a regular basis, and also every time when I felt that writer’s block has started to “creep in” again – and it worked Every Time. I wrote about what I want to write next, but also about my previous ideas and reflections, about my observations and opinions about the topic I’m studying, etc. (depending on which stage of writing I was at – in many ways freewriting worked similar to what I described in point 2 – all of a sudden, I could express my ideas freely). You can read a lot more about free writing, as well as about various aspects of writing a thesis in this great book by Rowena Murray. I also discuss freewriting in my online self-study course “Dr Kriukow’s dissertation training – Part 1” and in the following video: What has worked for you in terms of overcoming Writer’s Block? Please share in the comments or on my Facebook page References Elbow, P. (1973). Writing Without Teachers. Oxford: Oxford University Press.
2 dilemmas of Grounded Theory (when to write the literature review and is it good to have “pre-existing knowledge”?)
The following questions were sent to me recently by one of you: I really like these questions, as not so long ago I was facing the same problem. Interestingly, the dilemmas described above have, in fact, been dividing Grounded Theory (GT) scholars for years… It is a common belief that since in GT studies the theory, or explanation, should emerge directly from the collected data, and using disciplinary ideas and established frameworks to guide the study may bias data collection and analysis, we should have no previous knowledge of the topic to conduct a GT study. In practice, this would mean that we should neither conduct nor write literature reviews until we collect and analyse the data, which would prove extremely problematic for students (including the above student) and researchers who wish to employ this methodology in their research – after all, research proposals and grant applications are expected to contain evidence of extensive knowledge of the field, and one of the aims of going through progression board meetings is to demonstrate such knowledge. Moreover, aren’t our research ideas the product of our being familiar with at least some key concepts in the field, of our critical engagement with the literature, of our view on whether there exist certain gaps in knowledge that need addressing… So, what is the role of previous knowledge and ideas in GT studies…. (Dilemma 1) While “classic” scholars of early versions of GT (e.g. Glaser, 1998) did, in fact warn about the negative influence of having pre-existing knowledge, nowadays GT scholars increasingly recognise that not having such previous knowledge is neither desirable nor possible (see, for example, Charmaz, 2014; Clarke, 2005). They see value that being familiar with the field has for us, researchers, in terms of sparkling our interest in the topic and fostering our “curiosity”, enabling us to develop ideas and raise questions and, ultimately, leading us to explore the topic further. They do stress, however, that this knowledge should guide but not command our research, and that it should constitute just a starting point to our research. We should also be aware of the possible influence of this knowledge on our analytical thinking, and rather than trying to dismiss it, we should recognise, reflect on, and be explicit about this influence. In my doctoral study, I investigated Polish migrants’ “English Language Identity” which I defined (based on the literature I had read, by the way) as a “relationship between self-concept and the English language”. Although at a later stage, as I was collecting and analysing the data, for some time I did consciously refrain from reading more on the topic in order not to start analysing my data from the perspective of some specific concepts described in the literature, the very concept of this English Language Identity was already a product of the knowledge I had gained from reading on the topics related to language and identity, self-concept, self-esteem and self-confidence, as well as a number of other topics within diverse fields, including psychology, social psychology, sociolinguistics and ELT. Had I not explored these topics, I wouldn’t have been able to think critically about them, to link the findings of the various studies I read about to my own ideas stemming from my knowledge about the context and people I was to investigate, to hypothesise (another process which is wrongly assumed to be absolutely inexcusable in GT) about the patterns I would find, etc. Having some previous knowledge is essential in GT just as in any other methodology of approach to research. We should familiarise ourselves with the literature, at least to the extent that would enable us to be critical and develop ideas about the topics we would like to investigate. When I went back to reading the literature after my theory emerged and was fully develop, I learned that some of the ideas I “found” had, in fact, been already discussed by other scholars. This enriched my discussion and, to some extent, contributed to the validity of my findings, as I could state with certainty that these (specific) findings emerged from my data and had not been influenced by the literature I had read – the true essence of GT 🙂 … and what does this mean for the process of writing the literature review? (Dilemma 2) The question of when to write the literature review has also been dividing the GT scholars for a long time. And again, while traditionally it was proposed that we should delay writing a literature review until the process of data analysis is complete, this view has been challenged by recent GT proponents (see, for example, Dunne, 2011; Tummers and Karsten, 2012). Thus, rather than delaying writing the literature review, we should come back to it, just like I did, after the data analysis is complete and make adjustments, a process that is not much different (apart, maybe, from the extent of the required adjustments) from any other approach to research. As Charmaz (2014) notes, constant comparison method, which typically relates to investigating, and comparing, a range of individual cases in the process of data analysis, “does not end with completion of your data analysis” (p.305). In the final stages of your GT research, you will start to compare your “theory” to the existing literature, and this will require changes to your Literature Review chapter, as you are not supposed to add new themes and literature to the Discussion chapter. When I first found out that I was to re-structure my literature review as if all these concepts had been there from the start (as opposed to them being the result of all the effort I put into making the theory “emerge” from my own data!), I felt frustrated and discouraged. For me, the most amazing part of the study was the fact that at least 50% of the described concepts that were now in the literature review had actually emerged directly from my own findings, with me having no idea at
Who Owns English and why is this important?
In September 2017 I was invited by the Polish Psychologists’ Association to facilitate a workshop for Polish migrants on Communication in English. I called this workshop “Expressing yourself and expressing Your Self in English” and I spoke about English as a Lingua Franca, links between language and identity, and why how we, Non-native English Speakers (NNESs), perceive ourselves as users of the language has an influence on our daily lives in migration contexts. The following is a loose translation from an article that I prepared for a Polish news website (and aimed at Poles) prior to the workshop (you can find the original article Here). English as a Global Language English has become a “global language” and has a status of an official, co-official, national or dominant language in more than a hundred countries. The estimated combined number of Native English Speakers (NESs), for whom English is their first language (L1), and Non-native English Speakers (NNESs), or those who have an L1, or L1s, other than English, is around two billion. The majority of users of English worldwide, however, are NNESs and the largest part of communication in English takes place between those for whom English is not their mother tongue. English is a dominant language in the world’s advertising, tourism, education, law, banking and scientific publications. Native English Speaker Superiority Notwithstanding the above facts that point to a rapidly changing “landscape” of English worldwide, the myth about “NES superiority” persists among many scholars, teachers and students, both native and non-native English speakers. People who come from a handful of countries where English has been “traditionally” spoken (e.g. the US and UK) are perceived as the “legitimate” users, or even “owners” of the language. They are treated as language experts and research suggests that even the linguistic errors they make are often mistaken for being sings of linguistic innovation or creativity. The majority of English learners prefer Native English (NE) accents and would like to sound “native”, even though this goal is both vague (“Native English Speakers” are not only American, British or Australian users of the language) virtually impossible to achieve. NES Superiority and English language teaching The traditional English instruction has been criticised for promoting an irrelevant and unrealistic goal of achieving ‘native-like’ English competence and reinforcing the promotion of “standard language ideology”, or the assumption that there exists one inherently ‘correct’ variety of English. In their arguments, scholars have pointed to the dominance of the NES model English language examinations and to teacher hiring practices that discriminate Non-native English Speaking Teachers (NNESTs), and to policies that largely influence both of these issues by dictating the goals of learning English and the methods of teaching the language and assessing the learners’ competence. Native-like competence is still treated as the main benchmark of achievement, and English learners are seen as a kind of “imitations” of NESs. English learning does not reflect, or prepare for, the reality that the students face when they leave the classroom. The learners are not aware of the multitude of different English accents and varieties that they are likely to encounter if they go abroad. The dominance of NESs is also promoted in English language textbooks, where the members of this group are usually presented as being authoritative and “omniscient” figures who assist the lost and confused foreigners or students. I also observed that the assumption that speaking with a native-like accent equals being competent in English has been underlying many English learning resources online. Why is all the above important? A number of studies conducted in educational contexts suggest that such unrealistic and irrelevant goals in English learning may have negative effects on the learners’ self-perceptions. By continuously comparing their pronunciation with native models, NNESs are likely to perceive themselves as a kind of “imitations” of native English speakers, instead of thinking of themselves as successful, multilingual speakers. They choose to pursue the unattainable goal of achieving a ‘perfect’, ‘native-like’ accent, being unaware that their efforts are likely to result in failure. The feeling that they cannot achieve their goal of speaking with the “proper” accent may lead to self-perceptions of being less competent users of the language that belongs to the ‘superior’ NESs and may result in low “Second Language Confidence”, and this has, in turn, been linked to self-perceived and, ultimately, actual language competence. These findings are also crucial for the contexts of migration. A number of studies have drawn strong links between language and the overall “sense of self”, including self-esteem, self-confidence, or even self-perception of one’s skills outside the language. My own study of “English Language Identity” of Polish migrants living in Scotland revealed that some of the participants’ low self-confidence and self-esteem indeed resulted from their overall beliefs about the language, as well as from their perceptions of themselves, and other speakers, as users of the language. The beliefs about NESs as experts in, as well as owners and the only legitimate speakers of, English, which often resulted from the English language instruction they had received back in Poland, in the long-run had a very negative influence on a number of aspects of the migrants’ experiences, including their socialisation practices, professional situation or, ultimately, the decision of whether to remain in Scotland or go back to Poland. Of course, to counter this myth of native English speaker superiority would require a lot of time and effort at the level of national education systems (fortunately, such changes have in fact been gradually taking place in some countries). At the individual level, however, sometimes the mere awareness of some facts about the language may help (Please Share this post to help spread this awareness!). It is important to remember that language belongs to everyone who uses it and as I already noted – English is a global language spoken all over the world. So, next time you are anxious to speak to a Native English Speaker (if English is not your mother tongue), remember that regardless of your
Validity and Reliability in Qualitative research
What is Validity and Reliability in Qualitative research? In Quantitative research, reliability refers to consistency of certain measurements, and validity – to whether these measurements “measure what they are supposed to measure”. Things are slightly different, however, in Qualitative research. Reliability in qualitative studies is mostly a matter of “being thorough, careful and honest in carrying out the research” (Robson, 2002: 176). In qualitative interviews, this issue relates to a number of practical aspects of the process of interviewing, including the wording of interview questions, establishing rapport with the interviewees and considering ‘power relationship’ between the interviewer and the participant (e.g. Breakwell, 2000; Cohen et al., 2007; Silverman, 1993). What seems more relevant when discussing qualitative studies is their validity, which very often is being addressed with regard to three common threats to validity in qualitative studies, namely researcher bias, reactivity and respondent bias (Lincoln and Guba, 1985). Researcher bias refers to any kind of negative influence of the researcher’s knowledge, or assumptions, of the study, including the influence of his or her assumptions of the design, analysis or, even, sampling strategy. Reactivity, in turn, refers to a possible influence of the researcher himself/herself on the studied situation and people. Respondent bias refers to a situation where respondents do not provide honest responses for any reason, which may include them perceiving a given topic as a threat, or them being willing to ‘please’ the researcher with responses they believe are desirable. Robson (2002) suggested a number of strategies aimed at addressing these threats to validity, being prolonged involvement, triangulation, peer debriefing, member checking, negative case analysis and keeping an audit trail. So, what exactly are these strategies and how can you apply them in your research? Prolonged involvement refers to the length of time of the researcher’s involvement in the study, including involvement with the environment and the studied participants. It may be granted, for example, by the duration of the study, or by the researcher belonging to the studied community (e.g. a student investigating other students’ experiences). Being a member of this community, or even being a friend to your participants (see my blog post on the ethics of researching friends), may be a great advantage and a factor that both increases the level of trust between you, the researcher, and the participants and the possible threats of reactivity and respondent bias. It may, however, pose a threat in the form of researcher bias that stems from your, and the participants’, possible assumptions of similarity and presuppositions about some shared experiences (thus, for example, they will not say something in the interview because they will assume that both of you know it anyway – this way, you may miss some valuable data for your study). Triangulation may refer to triangulation of data through utilising different instruments of data collection, methodological triangulation through employing mixed methods approach and theory triangulation through comparing different theories and perspectives with your own developing “theory” or through drawing from a number of different fields of study. Peer debriefing and support is really an element of your student experience at the university throughout the process of the study. Various opportunities to present and discuss your research at its different stages, either at internally organised events at your university (e.g. student presentations, workshops, etc.) or at external conferences (which I strongly suggest that you start attending) will provide you with valuable feedback, criticism and suggestions for improvement. These events are invaluable in helping you to asses the study from a more objective, and critical, perspective and to recognise and address its limitations. This input, thus, from other people helps to reduce the researcher bias. Member checking, or testing the emerging findings with the research participants, in order to increase the validity of the findings, may take various forms in your study. It may involve, for example, regular contact with the participants throughout the period of the data collection and analysis and verifying certain interpretations and themes resulting from the analysis of the data (Curtin and Fossey, 2007). As a way of controlling the influence of your knowledge and assumptions on the emerging interpretations, if you are not clear about something a participant had said, or written, you may send him/her a request to verify either what he/she meant or the interpretation you made based on that. Secondly, it is common to have a follow-up, “validation interview” that is, in itself, a tool for validating your findings and verifying whether they could be applied to individual participants (Buchbinder, 2011), in order to determine outlying, or negative, cases and to re-evaluate your understanding of a given concept (see further below). Finally, member checking, in its most commonly adopted form, may be carried out by sending the interview transcripts to the participants and asking them to read them and provide any necessary comments or corrections (Carlson, 2010). Negative case analysis is a process of analysing ‘cases’, or sets of data collected from a single participant, that do not match the patterns emerging from the rest of the data. Whenever an emerging explanation of a given phenomenon you are investigating does nto seem applicable to one, or a small number, of the participants, you should try to carry out a new line of analysis aimed at understanding the source of this discrepancy. Although you may be tempted to ignore these “cases” in fear of having to do extra work, it should become your habit to explore them in detail, as the strategy of negative case analysis, especially when combined with member checking, is a valuable way of reducing researcher bias. Finally, the notion of keeping an audit trail refers to monitoring and keeping a record of all the research-related activities and data, including the raw interview and journal data, the audio-recordings, the researcher’s diary (see this post about recommended software for researcher’s diary) and the coding book. If you adopt the above strategies skilfully, you are likely to minimize threats to validity of your study. Don’t forget to look at
Constructivist Grounded Theory
Constructivist grounded theory is Charmaz’s (2014) take on a methodology developed by Glaser and Strauss (1967) and characterised particularly by its inductive approach to research (with an ongoing process of simultaneous data collection and analysis), aimed at developing a theory, or explanation, ‘grounded in’, and emerging from, the data, rather than pre-existing categories and theoretical frameworks. Charmaz (2014) summarised the logic of grounded theory as follows: Stated simply, grounded theory methods consist of systematic, yet flexible, guidelines for collecting and analysing qualitative data to construct theories from the data themselves (…) Grounded theory begins with inductive data, invokes iterative strategies of going back and forth between data and analysis, uses comparative methods and keeps you interacting and involved with your data and emerging analysis. Charmaz (2014:1) Grounded theory was developed as a response to the research methodologies of the early 20th century. These predominantly deductive methodologies were mainly oriented towards testing pre-formulated hypotheses. The research would normally start with a ‘general’ concept or theory and progress towards studying specific instances, or cases, in order to investigate its validity. In contrast to these approaches, and despite certain differences between various versions of the methodology stemming from differing epistemological views, grounded theory, in general, is an inductive approach aimed at developing a theory, or an explanation, through a thorough investigation of a range of individual cases through a process known as constant comparison (Glaser and Strauss, 1967). However, despite some understandable similarities between the various versions of grounded theory, they are distinguished by contrasting epistemological views, which results in significant differences in how the studied reality is perceived and approached (Oliver, 2012). Thus, as the early 20th century grounded theory was deeply rooted in positivism, it subscribed to the idea of objective reality, which may be assessed by a neutral observer who would approach it without pre-existing knowledge of the topic and through rigorous and systematic application of a set of procedures. This, in turn, resulted in grounded theory being a strict and narrow set of specific scientific guidelines for conducting rigorous research. This strictly inductive approach was aimed at developing a theory based on a systematic enquiry into various individuals. More recently, however, constructivist grounded theory is being based on the assumption that “knowledge rests on social constructions” (Charmaz, 2009: 130) and, thus, the studied reality is not an objective, but a social construct, and the very notion of a neutral observer is inherently invalid. As Charmaz (2014) argued: Unlike [Glaser and Strauss’] position, I assume that neither data nor theories are discovered either as given in the data or the analysis. Rather, we are part of the world we study, the data we collect and the analyses we produce. Charmaz (2014: 17) The implications of this epistemological position for research practice are that, no matter how rigorous the procedures are, the studied reality cannot be assessed objectively, as the researchers and the research participants alike “construct research processes and products” (Charmaz, 2009, p. 130). This significant shift in the perception of the studied reality required a different approach to studying it. Constructivist grounded theory is much less ‘prescribed’ in its design and places more importance on “diverse local worlds [and] multiple realities” (Creswell, 2013: 65), by putting the emphasis on the participants’ views, assumptions and beliefs and by emphasising the subjectivity of the researchers’ interpretations. At the same time, however, the existence of social structures independent of interpretation is not denied. The strictly inductive approaches to research promoted by the earlier versions of grounded theory do not provide an opportunity to effectively investigate this complex reality. Hence, constructivist grounded theorists advocate investigating the research topic either through “reverberating induction fostering deduction” (Glaser, 1998: 43) or through abduction, or A type of reasoning that begins with the researcher examining inductive data and observing a surprising or puzzling finding that cannot be explained with conventional theoretical accounts. After scrutinizing these data, the researcher entertains all possible theoretical explanations for the observed data, and then forms hypotheses and tests them to confirm or disconfirm each explanation until he or she arrives at the most plausible theoretical interpretation of the observed data. Charmaz (2014: 341) Research based on abductive reasoning begins with induction, but does not end with it. Rather, the inductive stage leads to formulating hypotheses that may be based on both the researcher’s pre-existing theoretical knowledge and the emerging research findings and testing them through empirical investigation. Abduction also involves pursuing personal hunches, making highly theoretical interpretations and making assumptions about any unspoken knowledge (Oliver, 2012). References: Charmaz, K. (2009). Shifting the grounds: Constructivist grounded theory methods. In J. M. Morse, P. N. Stern, J. M. Corbin, B. Bowers & A. E. Clarke (eds.) Developing Grounded Theory: The Second Generation.Walnut Creek, CA: University of Arizona Press. Charmaz, K. (2014). Constructing Grounded Theory. London: SAGE. Glaser, B. (1998). Doing Grounded Theory: Issues and Discussions. Mill Valley, CA: Sociology Press. Oliver, C. (2012). Critical Realist Grounded Theory: A New Approach for Social Work Research. British Journal of Social Work, 42, 371-387.
How to choose the right sample size for a qualitative study… and convince your supervisor that you know what you’re doing
The question of how many participants are enough for a qualitative interview is, in my opinion, one of the most difficult questions to find an answer to in the literature. In fact, many authors who set out to find specific guidelines on the ideal sample size in qualitative research in the literature have also concluded that these are “virtually non-existent” (Guest, Bunce and Johnson, 2005: 59). This is particularly unfortunate, given that as a student planning to undertake your research, one of the things that will be most likely to be asked of you is to indicate, and justify, the number of participants in your planned study (this also includes your PhD proposal in which you are expected to give as much detail of the study as possible). If you, then, turn to the literature, hoping to find advice from some of the great minds in research methodology, you are likely to find them evading the question and often hiding behind the term “saturation” which refers to the point at which gathering new data does not provide any new theoretical insights into the studied phenomenon. Although the concept of saturation may also be controversial, not least because the longer you explore, analyse and reflect on your data, you are always likely to find something “new” in it, it has come to be the guiding concept in establishing sample size in many qualitative studies. As Guest, Bunce and Johnson (2005) rightly point out, however “although the idea of saturation is helpful at the conceptual level, it provides little practical guidance for estimating sample sizes for robust research prior to data collection” (Guest, Bunce and Johnson, 2005: 59) In other words – how in the world are we supposed to know when we will reach saturation PRIOR TO THE STUDY??? My advice is to use the available literature on the point of saturation and use it to justify your decision regarding the sample size. I did it for my PhD study, as I was growing frustrated that I really have to justify my decision to include 20 participants for an interview, even though I had read dozens of reports in which this number, or smaller, was common (“are you going to interview 20 participants just because others did?”). I just felt that this would be enough, and my common sense, which as I learnt throughout my PhD was the last thing that anyone would care about, was telling me the same thing. In order to support my decision with the literature, however, and considering that there are hardly any guidelines for establishing sample size, I decided to try to reach some sort of conclusion as to how many participants are enough to reach saturation and use it as my main argument for establishing the size of the sample. So what does the literature tell us about this? Just as there is not single answer as to what sample size is sufficient, there is no single answer to the question of what sample size is sufficient to reach theoretical saturation. Such factors as heterogeneity of the studied population, the scope of the study and the adopted methods and their application (e.g. the length of the interviews) are believed, however, to have a central role in achieving this (cf. Baker and Edwards, 2012; Guest, Bunce and Johnson, 2005; Mason, 2010). Mason’s (2010) analysis of 560 PhD studies that adopted a qualitative interview as their main method revealed that the most common sample size in qualitative research is between 15 and 50 participants, with 20 being the average sample size in grounded theory studies (which was also the type of study I was undertaking). Guest, Bunce and Johnson (2005) used data from their own study to conclude that 88% of the codes they developed when analysing the data from 60 qualitative interviews were created by the time 12 interviews had been conducted. These findings helped me in arguing that my initial sample size was going to be 20. “Given the detailed design of the study, which includes triangulation of the data and methods”, I argued, “I believe that this number will enable me to make valid judgements about the general trends emerging in the data”. I also stated that I am planning to recruit more participants, should the saturation not occur. I hope that this article will help you in your quest to determine the sample size for your study and give you an idea of how you can go about arguing that it is a well thought-through decision. Do remember, however, that 20 participants may be enough for one study and not enough, or too many, for another. The point of this article was not to argue that 20 participants is a universally right number for a qualitative study, but rather to point to the fact that there is no such universally right number and that you are not the only one struggling to find guidelines regarding the interview sample size, as well as to put forward the concept of saturation as one of possible principles that may guide you in deciding how many participants to recruit for your study. If you have any questions regarding this topic, comment below or send me a message through my Facebook page. UPDATE – see my Facebook page for my response to the question about the relevance of “saturation” for Phenomenological research References: Baker, S. & Edwards, R. (eds., 2012). How many qualitative interviews is enough? Expert voices and early career reflections on sampling and cases in qualitative research. National Centre for Research Methods, 1-42. Guest, G., Bunce, A. & Johnson, L. (2005). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18 (1), 59-82. Mason, M. (2010). Sample Size and Saturation in PhD Studies Using Qualitative Interviews. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 11 (3).