A note on sample size - Once a sampling method has been determined, the researcher must consider the sample size. In qualitative studies, sampling typically continues until information redundancy or saturation occurs. This is the point at which no new information is emerging in the data.
Therefore, in qualitative studies is it critical that data collection and analysis are occurring simultaneously so that the researcher will know when the saturation point is reached. It is important to understand that the saturation point may occur prematurely if the researcher has a narrow sampling frame, a skewed analysis of the data, or poor methodology.
Because of this, the researcher must carefully create the research question, select an appropriate target group, eliminate his or her own biases and analyze data continuously and thoroughly throughout the process to bring validity to the data collected. The following slideshare presentation, Collecting Qualitative Data , and the Resource Links on this page provide additional insight into qualitative sampling.
Qualitative Research Methods - A Data Collectors Field Guide - This comprehensive, detailed guide describes various types of sampling techniques and provides examples of each, as well as pros and cons. Qualitative Research Overview - The following link provides a full overview of qualitative research, but also contains sections discussing types of sampling methods and methods of participant recruitment. Sampling - This resource provides a brief overview of sampling and sample size with links to descriptions of purposeful sampling strategies.
A Guide to Using Qualitative Research Methodology - The file linked below contains a full description of how to conduct qualitative sampling, including a chart that lists the types of sampling techniques and includes examples.
Sampling Designs in Qualitative Research - The following article discusses sampling designs and ways to make the sampling process more public.
This pin will expire , on Change. This pin never expires. Select an expiration date. About Us Contact Us. Search Community Search Community. The discovery-oriented goals, use of meanings as units of analyses, and interpretive methods of qualitative research dictate that the exact factors, dimensions, and distribution of phenomena identified as important for analyses may not always be specified prior to data analyses activities.
These emerge from the data analyses and are one of the major contributions of qualitative study. No standardized scales or tests exist yet to identify and describe new arenas of cultural, social, or personal meanings. Meaning does not conform to normative distributions by known factors. No probability models exist that would enable prediction of distributions of meanings needed to perform statistical power analyses.
Qualitative studies however can, and should, be judged in terms of how well they meet the explicit goals and purposes relevant to such research. The authors have suggested that the concept of qualitative clarity be developed to guide evaluations of sampling as an analog to the concept of statistical power. Qualitative clarity refers to principles that are relevant to the concerns of this type of research. That is, the adequacy of the strength and flexibility of the analytic tools used to develop knowledge during discovery procedures and interpretation can be evaluated even if the factors to be measured cannot be specified.
The term clarity conveys the aim of making explicit, for open discussion, the details of how the sample was assembled, the theoretical assumptions and the pragmatic constraints that influenced the sampling process.
These are briefly described next. In the absence of standardized measures for assessing meaning, the analogous qualitative research tools are theory and discovery processes. Strong and well-developed theoretical preparation is necessary to provide multiple and alternative interpretations of the data. The relative degree of theoretical development in a research proposal or manuscript is readily apparent in the text, for example, in terms of extended descriptions of different schools of thought and possible multiple contrasting of interpretive explanations for phenomena at hand.
In brief, the authors argue that given the stated goal of sampling for meaning, qualitative research can be evaluated to assess if it has adequate numbers of conceptual perspectives that will enable the study to identify a variety of meanings and to critique multiple rich interpretations of the meanings.
Sampling within the data is another important design feature. The discovery of meaning should also include sampling within the data collected. The entire set of qualitative materials should be examined rather than selectively read after identifying certain parts of the text to describe and confirm a finding without reading for sections that may provide alternative or contradictory interpretations. As a second component of qualitative clarity, sensitivity to context refers to the contextual dimensions shaping the meanings studied.
It also refers to the historical settings of the scientific concepts used to frame the research questions and the methods. Researchers need to be continually attentive to examining the meanings and categories discovered for elements from the researchers' own cultural and personal backgrounds. The first of these contexts is familiar to gerontologists: Another more implicit contextual aspect to examine as part of the qualitative clarity analysis is evidence of a critical view of the methods and theories introduced by the investigators.
Because discovery of the insiders' perspective on cultural and personal meanings is a goal of qualitative study, it is important to keep an eye to biases derived from the intrusion of the researcher's own scientific categories.
Qualitative research requires a critical stance as to both the kinds of information and the meanings discovered, and to the analytic categories guiding the interpretations. One example is recent work that illustrates how traditional gerontological constructs for data collection and analyses do not correspond to the ways individuals themselves interpret their own activities, conditions, or label their identities e. A second example is the growing awareness of the extent to which past research tended to define problems of disability or depression narrowly in terms of the individual's ability, or failure, to adjust, without giving adequate attention to the societal level sources of the individual's distress Cohen and Sokolovsky Thus researchers need to demonstrate an awareness of how the particular questions guiding qualitative research, the methods and styles of analyses, are influenced by cultural and historical settings of the research Luborsky and Sankar in order to keep clear whose meanings are being reported.
To conclude, our outline for the concept of qualitative clarity, which is intended to serve as the qualitatively appropriate analog to statistical power, is offered to gerontologists as a summary of the main points that need to be considered when evaluating samples for qualitative research. The descriptions of qualitative sampling in this article are meant to extend the discussion and to encourage the continued development of more explicit methods for qualitative research. Ongoing support for the second author from the National Institute of Aging is also gratefully acknowledged.
Federal and foundation grants support his studies of sociocultural values and personal meanings in early and late adulthood, and how these relate to mental and physical health, and to disability and rehabilitation processes. He also consults and teaches on these topics. His gerontological research interests include social relations of the elderly, childlessness in later life, and the home environments of old people. National Center for Biotechnology Information , U. Author manuscript; available in PMC Nov 3.
The publisher's final edited version of this article is available at Res Aging. See other articles in PMC that cite the published article. Abstract In gerontology the most recognized and elaborate discourse about sampling is generally thought to be in quantitative research associated with survey research and medical research.
Contributions, Logic and Issues in Qualitative Sampling Major contributions Attention to sampling issues has usually been at the heart of anthropology and of qualitative research since their inception.
Ideals and Techniques of Qualitative Sampling The preceding discussion highlighted the need to first identify the ideal or goal for sampling and second to examine the techniques and dilemmas for achieving the ideal. Core ideals include the determination of the scope of the universe for study and the identification of appropriate analytic units when sampling for meaning Defining the universe This is simultaneously one of qualitative research's greatest contributions and greatest stumbling blocks to wider acceptance in the scientific community.
Sampling for meaning The logic or premises for qualitative sampling for meaning is incompletely understood in gerontology. Techniques for selecting a sample As discussed earlier, probability sampling techniques cannot be used for qualitative research by definition, because the members of the universe to be sampled are not known a priori, so it is not possible to draw elements for study in proportion to an as yet unknown distribution in the universe sampled.
Who and who not? Homogeneity or diversity Currently, when constructing samples for single study groups, qualitative research appears to be about equally split in terms of seeking homogeneity or diversity. Summary and Reformulation for Practice To review, the authors suggest that explicit objective criteria to use for evaluating qualitative research designs do exist, but many of these focus on different issues and aspects of the research process, in comparison to issues for quantitative studies.
Qualitative Clarity as an Analog to Statistical Power The guiding logic of qualitative research, by design, generally prevents it from being able to fulfill the assumptions underlying statistical power analyses of research designs.
Rich and diverse theoretical grounding In the absence of standardized measures for assessing meaning, the analogous qualitative research tools are theory and discovery processes. Sensitivity to contexts As a second component of qualitative clarity, sensitivity to context refers to the contextual dimensions shaping the meanings studied.
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The Genesis of Chronic Illness: Sociology of Health and Illness. Chronicle of Living With a Disability. Temple University Press; Philadephia: So in this type of sampling, we select samples that have a particular process, examples, categories and even types that are relevant to the ideal or wider universe.
One of the most commonly given example is of discourse analysis of gender. The sample relevant units in qualitative research are very often viewed as theoretically defined.
This is due to the fact that most often attributions are themselves the topic of the research. In theoretical sampling the belief is that researchers need to overcome the tendency to select cases and instances that support their side of the argument. Instead it states that it would be more beneficial to look out for negative instances and cases which are defined by the theory that we are working with. This feature basically states that a researcher should not exclude any fact from the process of research just because it seems impossible.
The first two features of a theoretical sample deals with issues right at the beginning of the research project. The third feature however deals with concerns or application during the process of the research.
One of the advantages the qualitative research as a whole has over quantitative research is its flexibility. In theoretical sampling the researcher manipulates or changes the theory, sampling activities as well as the analysis during the course of the research.
Flexibility occurs in this style of sampling when the researchers want to increase the sample size due to new factors that arise during the research. Finally flexibility is also allowed when the researcher finds unexpected generalization and wants to look into deviant cases. In theoretical sampling, there are two main criteria for initial data collection, general sociological perspective and problem area.
Collection criteria for the future cannot be planned in advance as the criterion emerges as the theory evolves. Which groups are included? To study this often multiple comparison groups are used. The groups are chosen based on the theoretical criteria or relevance. Sociologists or researchers often evade the problem by studying only one group and trying to describe the subgroups. Often the differences among the groups or sub groups are just stated but a theoretical analysis is not conducted.
One of the advantages here is that the analyst has the liberty to adjust his control of the data collection, to ensure that the data is relevant to the emerging theory. Also it should be noted that usually groups are chosen only for a single comparison, therefore there is usually no pre-planned or definite set of groups for all the categories.
Another interesting fact is that it is almost impossible to cite the number of groups and the type of groups until the research is completed. One of the major differences with comparative analysis is that comparative analysis focus on the verification and description using accurate evidence.
Why are groups selected? Comparing groups gives the researcher the advantage of development of variety of categories. The main criterion is that the data collected should apply to a particular category or property, irrespective of the differences or similarities. As the researcher compares groups, he gains control over two scales of generality. Population scope Also differences and similarities can either be maximised or minimised, depending on the type of groups being compared.
Theoretical sampling helps in exploring various hibernating research questions that are eventually evident in the data collection as a theory. According to Glaser and Holton (), Grounded theory that has a data collecting inclination towards theoretical sampling was first derived from qualitative sampling.
Theoretical sampling is associated with grounded theory approach based on analytic induction. Theoretical sampling is different from many other sampling methods in a way that rather than being representative of population or testing hypotheses, theoretical sampling is aimed at generating and developing theoretical data. Theoretical sampling may not be necessary for bachelor level or even .
Theoretical sampling is an important component in the development of grounded theories. Glaser and Strauss () describe an iterative sampling process that is based on emerging theoretical concepts. This sampling approach has the goal of developing a rich understanding of the dimensions of a concept across a range of settings and conditions. First of all, both theoretical sampling and snowball sampling correspond to what is usually called "sampling strategies" in qualitative research. 1) Theoretical sampling makes up the classical sampling strategy of grounded theory approach.
Qualitative Research Overview - The following link provides a full overview of qualitative research, but also contains sections discussing types of sampling methods and methods of participant recruitment. Sampling is a very complex issue in qualitative research as there are many variations of qualitative sampling described in the literature and much confusion and overlapping of types of sampling, particularly in the case of purposeful.