Rafael is my soon-to-graduate PhD student. I felt an extreme amount of pride, while also realizing what an enormous amount of work this doctoral dissertation has entailed. Rafa did ethnographic fieldwork for two years analyzing three cases of water conflict, plus a quantitative analysis of a global dataset.
Introduction Samples for qualitative studies are generally much smaller than those used in quantitative studies. There is a point of diminishing return to a qualitative sample—as the study goes on more data does not necessarily lead to more information.
This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework.
Frequencies are rarely important in qualitative research, as one occurrence of the data is potentially as useful as many in understanding the process behind a topic. Finally, because qualitative research is very labour intensive, analysing a large sample can be time consuming and often simply impractical.
Qualitative samples must be large enough to assure that most or all of the perceptions that might be important are uncovered, but at the same time if the sample is too large data becomes repetitive and, eventually, superfluous.
If a researcher remains faithful to the principles of qualitative research, sample size in the majority of qualitative studies should generally follow the concept of saturation e. This paper examines the size of the samples from PhD studies that have used interviews as their source of data collection.
It does not Qualitative research for thesis at the data found in those studies, just the numbers of the respondents in each case.
CHARMAZ suggests that the aims of the study are the ultimate driver of the project design, and therefore the sample size. She suggests that a small study with "modest claims" p. MORSE feels that researchers often claim to have achieved saturation but are not necessarily able to prove it.
This is also suggested by BOWEN who feels that saturation is claimed in any number of qualitative research reports without any overt description of what it means or how it was achieved. It is entirely possible that a researcher will claim that the category "experiencing stigma" is saturated very quickly.
However, while an inexperienced researcher might claim saturation, a more experienced researcher would explore the context of stigma in more detail and what it means to each of these women p. He suggests that researchers often close categories early as the data are only partially coded, and cite others to support this practice, such as and STRAUSS and CORBIN  who suggest that saturation is a "matter of degree" p.
They suggest that the longer researchers examine, familiarise themselves and analyse their data there will always be the potential for "the new to emerge". Instead, they conclude that saturation should be more concerned with reaching the point where it becomes "counter-productive" and that "the new" is discovered does not necessarily add anything to the overall story, model, theory or framework p.
They admit that sometimes the problem of developing a conclusion to their work is not necessarily a lack of data but an excess of it. As the analysis begins to take shape it is important for the researcher to become more disciplined and cut data where necessary.
However, some clearly find this frustrating. During the literature search for the background to their study they found "only seven sources that provided guidelines for actual sample sizes" p.
Also the issue of why some authors feel that certain methodological approaches call for more participants compared to others, is also not explored in any detail. THOMSON for example carried out a review of fifty research articles accessed using Proquest ABI Inform 1with the search parameter "grounded theory" in citation and abstract, and found sample sizes ranging from five to Some researchers have gone further than providing guidelines and have tried to operationalise the concept of saturation, based on their own empirical analysis.
This sought to identify common characteristics between communities and cultural groups.
The model suggests that each culture has a shared view of the world, which results in a "cultural consensus"—the level of consensus of different topics does vary but there are considered to be a finite set of characteristics or views.
Their work was undertaken from a market research perspective to assist in the development of robust bids and campaigns. Because of their analysis, they hypothesized that twenty to thirty in-depth interviews would be needed to uncover ninety to nine-five per cent of all customer needs.
They examined the codes developed from their sixty interviews, in an attempt to assess at which point their data were returning no new codes, and were therefore saturated. Their findings suggested that data saturation had occurred at a very early stage. Of the thirty six codes developed for their study, thirty four were developed from their first six interviews, and thirty five were developed after twelve.
Their conclusion was that for studies with a high level of homogeneity among the population "a sample of six interviews may [be] sufficient to enable development of meaningful themes and useful interpretations" p.
This is particularly apparent in what they call "funded work" or that limited by time. They suggest that researchers do not have the luxury of continuing the sort of open-ended research that saturation requires.
This is also true when the point of saturation particularly in relation to an approach like grounded theory methodology, which requires that all of the properties and the dimensions are saturated they consider to be "potentially limitless" p.
They go on to add that sponsors of research often require a thorough proposal that includes a description of who, and how many people, will be interviewed at the outset of the research see also SIBLEY, They further suggest that this also applies to ethics committees, who will want to know who will be interviewed, where, and when, with a clearly detailed rationale and strategy.
This is no less relevant to PhD researchers. The University of Toronto for example ranked 29th inrequires prospective students of PhD research programmes to "[j]ustify the anticipated sample size and its representativeness.
When do I stop gathering data? Throughout the supervisory process the study is scrutinised by national, and often international, experts, and once completed, the methodology and findings scrutinised further.
If a high level of rigour is to be found in the types of methods used in research studies then it should be in PhDs.Qualitative Research: A Guide to Design and Implementation, 4th Edition [Sharan B. Merriam] on initiativeblog.com *FREE* shipping on qualifying offers. The bestselling guide to qualitative research, updated and expanded Qualitative Research is the essential guide to understanding.
In this article, a newly minted Ph.D.
shares seven lessons learned during the process of preparing a dissertation based on qualitative research methods. While most of the lessons may be applicable to any kind of research, the writer focuses on the special challenges of employing a qualitative. Abstract: A number of issues can affect sample size in qualitative research; however, the guiding principle should be the concept of saturation.
This has been explored in detail by a number of authors but is still hotly debated, and some say little understood.
A sample of PhD studies using. All research reports (including dissertations) begin with an introduction describing the problem under investigation in quantitative studies and need for the study in qualitative studies and its background, its relevance to the field, and the assumptions and the.
Qualitative Exam Part 1 (5%): Compare and contrast two qualitative research studies in your field and interest.
Include brief summaries of the studies, with relevant details about the research question and the qualitative methods. initiativeblog.com is a powerful workbench for the qualitative analysis of large bodies of textual, graphical, audio and video data. It offers a variety of sophisticated tools for accomplishing the tasks associated with any systematic approach to "soft" data.