Gathering those information is not always easy as the researcher has to go through huge pile of information be it primary or secondary.
Thus, in such situation it would be practically very difficult for the researcher to perform the task. Hence, methods like sampling is used when the universe is very broad and among many of its type purposive sampling method is one of the widely used method where the researcher has a very important role to play.
The citation rule in the study has been applied as per Kathmandu School of Law Style Guide to academic writing. Ongoing Kathmandu School of Law. Loyola University, Chicago School of Law. The method of selecting for study the portion of universe with a view to draw conclusions about the universe is called sampling. Sampling is used when,5 i. The researcher has to collect information from a wider area. The researcher does not require cent percent accuracy. The population is homogenous iv.
It is not possible to adopt census method. Assumptions underlying in sampling6 a. Homogeneity amidst complexity: Although there is complexity in socio-legal phenomena, there appears dominantal unity in diversity. The assumption is that there is possibility of representative types in the whole population that makes sampling possible. If no two units were alike in any respect the sampling would have been impossible. Possibility of representative selection: 2 S.
If a certain number of units are selected from a mass on purely random basis, every unit will have a chance of being included and the sample so selected will contain all types of units, so that it may be representative.
Absolute accuracy not essential: The assumption is that absolute accuracy is not essential. Independency or interchangeability: All the items in a sample should be independent of each other. Characteristics of a Good Sample Design:7 i. Sample design must result in a truly representative sample. It must be such which results in a small sampling error. It must be viable in the context of funds available for the research study. It must be such so that systematic bias can be controlled in a better way.
Sample should be such that the results of the sample study can be applied, in general, for the universe with a reasonable level of confidence. Process used in Sampling: 1. Identify the population of interest8 A population is the group of people that you want to make assumptions about.
For example, if I want to know how much stress college students experience during finals. My population is every college student in the world because that's what I am interested in. Of course, there's no way that I can feasibly study every college student in the world, so I move on to the next step. Specify a sampling frame9 7 C.
For example, I might decide that her sampling frame is every student at the university where I study. Administration and Policy In Mental prevention of youth suicide. Clinical Trials, 3, — Health, 35, — Brunette, M. Qualitative data analysis: Jones, A. Implementation of integrated dual An expanded sourcebook 2nd ed.
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Download PDF. Fourth, quota sampling is a method for selecting numbers of subjects to represent the conditions to be studied rather than to represent the proportion of people in the universe.
The goal of quota sampling is to assure inclusion of people who may be underrepresented by convenience or purposeful sampling techniques. Fifth, case study Ragin and Becker ; Patton samples select a single individual, institution, or event as the total universe. A variant is the key-informant approach Spradley , or intensity sampling Patton where a subject who is expert in the topic of study serves to provide expert information on the specialized topic. When qualitative perspectives are sought as part of clinical or survey studies, the purposive, quota, or case study sampling techniques are generally the most useful.
How many subjects is the perennial question. There is seldom a simple answer to the question of sample or cell size in qualitative research. There is no single formula or criterion to use. Morse The question of sample size cannot be determined by prior knowledge of effect sizes, numbers of variables, or numbers of analyses—these will be reported as findings.
Sample sizes in qualitative studies can only be set by reference to the specific aims and the methods of study, not in the abstract. The answer only emerges within a framework of clearly stated aims, methods, and goals and is conditioned by the availability of staff and economic resources. In practice, from 12 to 26 people in each study cell seems just about right to most authors.
In general, it should be noted that Americans have a propensity to define bigger as better and smaller as inferior. Quantitative researchers, in common with the general population, question such small sample sizes because they are habituated to opinion polls or epidemiology surveys based on hundreds or thousands of subjects.
However, sample sizes of less than 10 are common in many quantitative clinical and medical studies where statistical power analyses are provided based on the existence of very large effect sizes for the experimental versus control conditions. Other considerations in evaluating sample sizes are the resources, times, and reporting requirements.
In anthropological field research, a customary formula is that of the one to seven: for every 1 year of fieldwork by one researcher, 7 years are required to conduct the analysis.
Thus, in studies that use more than one interviewer, the ability to collect data also increases the burden for analyses. An outstanding volume exploring the logic, contributions, and dilemmas of case study research Ragin and Becker reports that survey researchers resort to case examples to explain ambiguities in their data, whereas qualitative researchers reach for descriptive statistics when they do not have a clear explanation for their observations.
Again, the choice of sample size and group design is guided by the qualitative goal of describing the nature and contents of cultural, social, and personal values and experiences within specific conditions or circumstances, rather than of determining incidence and prevalence.
In the tradition of informant-based and of participatory research, it is assumed that all members of a community can provide useful information about the values, beliefs, or practices in question. Experts provide detailed, specialized information, whereas nonexperts do so about daily life.
In some cases, the choice is obvious, dictated by the topic of study, for example, childless elderly, retirees, people with chronic diseases or new disabilities. In other cases, it is less obvious, as in studies of disease, for example, that require insights from sufferers but also from people not suffering to gain an understanding for comparison with the experiences and personal meanings of similar people without the condition.
Comparisons can be either on a group basis or matched more closely on a one-to-one basis for many traits e. However, given the labor-intensive nature of qualitative work, sometimes the rationale for including control groups of people who do not have the experiences is not justifiable. Currently, when constructing samples for single study groups, qualitative research appears to be about equally split in terms of seeking homogeneity or diversity.
There is little debate or attention to these contrasting approaches. For example, some argue that it is more important to represent a wide range of different types of people and experiences in order to represent the similarities and diversity in human experience, beliefs, and conditions e. In contrast, others select informants to be relatively homogeneous on several characteristics to strengthen comparability within the sample as an aid to identifying similarities and diversity. 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.
This article has discussed the guiding principles, features, and practices of sampling in qualitative research. The guiding rationale is that of the discovery of the insider's view of cultural and personal meanings and experience. Major features of sampling in qualitative research concern the issues of identifying the scope of the universe for sampling and the discovery of valid units for analyses.
The practices of sampling, in comparison to quantitative research, are rooted in the application of multiple conceptual perspectives and interpretive stances to data collection and analyses that allow the development and evaluation of a multitude of meanings and experiences. This article noted that sampling concerns are widespread in American culture rather than in the esoteric specialized concern of scientific endeavors Luborsky and Sankar Core scientific research principles are also basic cultural ideals Luborsky Knowledge about the rudimentary principles of research sampling is widespread outside of the research laboratory, particularly with the relatively new popularity of economic, political, and community polls as a staple of news reporting and political process in democratic governance.
Core questions about the size, sources, and features of participants are applied to construct research populations, courtroom juries, and districts to serve as electoral universes for politicians.
The cultural contexts and popular notions about sampling and sample size have an impact on scientific judgments. It is important to acknowledge the presence and influence of generalized social sensibilities or awareness about sampling issues.
Such notions may have less direct impact on research in fields with long-established and formalized criteria and procedures for determining sample size and composition. The generalized social notions may come to exert a greater influence as one moves across the spectrum of knowledge-building strategies to more qualitative and humanistic approaches.
Even though such studies also have a long history of clearly articulated traditions of formal critiques e. The authors suggested that some of the rancor between qualitative and quantitative approaches is rooted in deeper cultural tensions. Prototypic questions posed to qualitative research in interdisciplinary settings derive from both the application of frameworks derived from other disciplines' approaches to sampling as well as those of the reviewers as persons socialized into the community where the study is conceived and conducted.
Such concerns may be irrelevant or even counterproductive. 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. 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: patterns constructed by the individual's life history; generation; cohort; psychological, developmental, and social structure; and health. 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.
Mark R. Luborsky, Ph. 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. Robert L. Rubinstein, Ph. 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. Res Aging. Author manuscript; available in PMC Nov 3. MARK R. Author information Copyright and License information Disclaimer. Philadelphia Geriatric Center. Copyright notice. 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.
Appropriate analytic units: 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? Populations are used when your research question requires, or when you have access to, data from every member of the population. Qualitative researchers typically make sampling choices that enable them to deepen understanding of whatever phenomenon it is that they are studying. That said, the fact that nonprobability samples do not represent a larger population does not mean that they are drawn arbitrarily or without any specific purpose in mind once again, that would mean committing one of the errors of informal inquiry discussed in Chapter 1 "Introduction".
So when are nonprobability samples ideal? This can be a quick way to gather some initial data and help us get some idea of the lay of the land before conducting a more extensive study. From these examples, we can see that nonprobability samples can be useful for setting up, framing, or beginning research. Researchers also use nonprobability samples in full-blown research projects.
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