Sampling And Sampling Techniques In Research Pdf

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Sampling Techniques

Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears to be used most commonly in implementation research. However, combining sampling strategies may be more appropriate to the aims of implementation research and more consistent with recent developments in quantitative methods.

This paper reviews the principles and practice of purposeful sampling in implementation research, summarizes types and categories of purposeful sampling strategies and provides a set of recommendations for use of single strategy or multistage strategy designs, particularly for state implementation research.

Recently there have been several calls for the use of mixed method designs in implementation research Proctor et al. This has been precipitated by the realization that the challenges of implementing evidence-based and other innovative practices, treatments, interventions and programs are sufficiently complex that a single methodological approach is often inadequate.

This is particularly true of efforts to implement evidence-based practices EBPs in statewide systems where relationships among key stakeholders extend both vertically from state to local organizations and horizontally between organizations located in different parts of a state.

As in other areas of research, mixed method designs are viewed as preferable in implementation research because they provide a better understanding of research issues than either qualitative or quantitative approaches alone Palinkas et al. Sampling strategies for quantitative methods used in mixed methods designs in implementation research are generally well-established and based on probability theory. In contrast, sampling strategies for qualitative methods in implementation studies are less explicit and often less evident.

Moreover, it is not entirely clear what forms of purposeful sampling are most appropriate for the challenges of using both quantitative and qualitative methods in the mixed methods designs used in implementation research. Such a consideration requires a determination of the objectives of each methodology and the potential impact of selecting one strategy to achieve one objective on the selection of other strategies to achieve additional objectives.

In this paper, we present different approaches to the use of purposeful sampling strategies in implementation research. We begin with a review of the principles and practice of purposeful sampling in implementation research, a summary of the types and categories of purposeful sampling strategies, and a set of recommendations for matching the appropriate single strategy or multistage strategy to study aims and quantitative method designs.

Purposeful sampling is a technique widely used in qualitative research for the identification and selection of information-rich cases for the most effective use of limited resources Patton, In addition to knowledge and experience, Bernard and Spradley note the importance of availability and willingness to participate, and the ability to communicate experiences and opinions in an articulate, expressive, and reflective manner. In contrast, probabilistic or random sampling is used to ensure the generalizability of findings by minimizing the potential for bias in selection and to control for the potential influence of known and unknown confounders.

As Morse and Niehaus observe, whether the methodology employed is quantitative or qualitative, sampling methods are intended to maximize efficiency and validity. Nevertheless, sampling must be consistent with the aims and assumptions inherent in the use of either method.

Qualitative methods are, for the most part, intended to achieve depth of understanding while quantitative methods are intended to achieve breadth of understanding Patton, Qualitative methods place primary emphasis on saturation i. Quantitative methods place primary emphasis on generalizability i.

Each methodology, in turn, has different expectations and standards for determining the number of participants required to achieve its aims.

Quantitative methods rely on established formulae for avoiding Type I and Type II errors, while qualitative methods often rely on precedents for determining number of participants based on type of analysis proposed e.

There exist numerous purposeful sampling designs. Examples include the selection of extreme or deviant outlier cases for the purpose of learning from an unusual manifestations of phenomena of interest; the selection of cases with maximum variation for the purpose of documenting unique or diverse variations that have emerged in adapting to different conditions, and to identify important common patterns that cut across variations; and the selection of homogeneous cases for the purpose of reducing variation, simplifying analysis, and facilitating group interviewing.

A list of some of these strategies and examples of their use in implementation research is provided in Table 1. Embedded in each strategy is the ability to compare and contrast, to identify similarities and differences in the phenomenon of interest. Nevertheless, some of these strategies e. The latter are similar to the use of quantitative central tendency measures e. Moreover, certain strategies, like stratified purposeful sampling or opportunistic or emergent sampling, are designed to achieve both goals.

As Patton , p. Each of the strata would constitute a fairly homogeneous sample. Despite its wide use, there are numerous challenges in identifying and applying the appropriate purposeful sampling strategy in any study.

For instance, the range of variation in a sample from which purposive sample is to be taken is often not really known at the outset of a study. To set as the goal the sampling of information-rich informants that cover the range of variation assumes one knows that range of variation. Second, there are a not insignificant number in the qualitative methods field who resist or refuse systematic sampling of any kind and reject the limiting nature of such realist, systematic, or positivist approaches.

However, even those who equate purposeful sampling with systematic sampling must offer a rationale for selecting study participants that is linked with the aims of the investigation i. What qualifies them to address the aims of the study? While systematic sampling may be associated with a post-positivist tradition of qualitative data collection and analysis, such sampling is not inherently limited to such analyses and the need for such sampling is not inherently limited to post-positivist qualitative approaches Patton, In implementation research, quantitative and qualitative methods often play important roles, either simultaneously or sequentially, for the purpose of answering the same question through convergence of results from different sources, answering related questions in a complementary fashion, using one set of methods to expand or explain the results obtained from use of the other set of methods, using one set of methods to develop questionnaires or conceptual models that inform the use of the other set, and using one set of methods to identify the sample for analysis using the other set of methods Palinkas et al.

A review of mixed method designs in implementation research conducted by Palinkas and colleagues revealed seven different sequential and simultaneous structural arrangements, five different functions of mixed methods, and three different ways of linking quantitative and qualitative data together. However, this review did not consider the sampling strategies involved in the types of quantitative and qualitative methods common to implementation research, nor did it consider the consequences of the sampling strategy selected for one method or set of methods on the choice of sampling strategy for the other method or set of methods.

For instance, one of the most significant challenges to sampling in sequential mixed method designs lies in the limitations the initial method may place on sampling for the subsequent method.

As Morse and Neihaus observe, when the initial method is qualitative, the sample selected may be too small and lack randomization necessary to fulfill the assumptions for a subsequent quantitative analysis. On the other hand, when the initial method is quantitative, the sample selected may be too large for each individual to be included in qualitative inquiry and lack purposeful selection to reduce the sample size to one more appropriate for qualitative research.

The fact that potential participants were recruited and selected at random does not necessarily make them information rich. An additional three studies Henke et al. The remaining 20 studies provided no description of the sampling strategy used to identify participants for qualitative data collection and analysis; however, a rationale could be inferred based on a description of who were recruited and selected for participation. Of the 28 studies, 3 used more than one sampling strategy.

For instance, in a series of studies based on the National Implementing Evidence-Based Practices Project, participants included semi-structured interviews with consultant trainers and program leaders at each study site Brunette et al. Six studies used some form of maximum variation sampling to ensure representativeness and diversity of organizations and individual practitioners.

Two studies used intensity sampling to make contrasts. Aarons and Palinkas , for example, purposefully selected 15 child welfare case managers representing those having the most positive and those having the most negative views of SafeCare, an evidence-based prevention intervention, based on results of a web-based quantitative survey asking about the perceived value and usefulness of SafeCare. Kramer and Burns recruited and interviewed clinicians providing usual care and clinicians who dropped out of a study prior to consent to contrast with clinicians who provided the intervention under investigation.

One study Hoagwood et al. County mental directors, agency directors, and program managers were recruited to represent the policy interests of implementation while clinicians, administrative support staff and consumers were recruited to represent the direct practice perspectives of EBP implementation. Table 2 below provides a description of the use of different purposeful sampling strategies in mixed methods implementation studies. Criterion-i sampling was most frequently used in mixed methods implementation studies that employed a simultaneous design where the qualitative method was secondary to the quantitative method or studies that employed a simultaneous structure where the qualitative and quantitative methods were assigned equal priority.

Three of the six studies that used maximum variation sampling used a simultaneous structure with quantitative methods taking priority over qualitative methods and a process of embedding the qualitative methods in a larger quantitative study Henke et al.

Two of the six studies used maximum variation sampling in a sequential design Aarons et al. The single typical case study involved a simultaneous design where the qualitative study was embedded in a larger quantitative study for the purpose of complementarity Hoagwood et al.

Although not used in any of the 28 implementation studies examined here, another common sequential sampling strategy is using criteria sampling of the larger quantitative sample to produce a second-stage qualitative sample in a manner similar to maximum variation sampling, except that the former narrows the range of variation while the latter expands the range.

Criterion-i sampling as a purposeful sampling strategy shares many characteristics with random probability sampling, despite having different aims and different procedures for identifying and selecting potential participants.

In both instances, study participants are drawn from agencies, organizations or systems involved in the implementation process. Individuals are selected based on the assumption that they possess knowledge and experience with the phenomenon of interest i. Participants for a qualitative study, usually service providers, consumers, agency directors, or state policy-makers, are drawn from the larger sample of participants in the quantitative study. From the perspective of qualitative methodology, participants who meet or exceed a specific criterion or criteria possess intimate or, at the very least, greater knowledge of the phenomenon of interest by virtue of their experience, making them information-rich cases.

However, criterion sampling may not be the most appropriate strategy for implementation research because by attempting to capture both breadth and depth of understanding, it may actually be inadequate to the task of accomplishing either.

Although qualitative methods are often contrasted with quantitative methods on the basis of depth versus breadth, they actually require elements of both in order to provide a comprehensive understanding of the phenomenon of interest. Ideally, the goal of achieving theoretical saturation by providing as much detail as possible involves selection of individuals or cases that can ensure all aspects of that phenomenon are included in the examination and that any one aspect is thoroughly examined.

This goal, therefore, requires an approach that sequentially or simultaneously expands and narrows the field of view, respectively. By selecting only individuals who meet a specific criterion defined on the basis of their role in the implementation process or who have a specific experience e.

For instance, a focus only on practitioners may fail to capture the insights, experiences, and activities of consumers, family members, agency directors, administrative staff, or state policy leaders in the implementation process, thus limiting the breadth of understanding of that process.

To address the potential limitations of criterion sampling, other purposeful sampling strategies should be considered and possibly adopted in implementation research Figure 1. For instance, strategies placing greater emphasis on breadth and variation such as maximum variation, extreme case, confirming and disconfirming case sampling are better suited for an examination of differences, while strategies placing greater emphasis on depth and similarity such as homogeneous, snowball, and typical case sampling are better suited for an examination of commonalities or similarities, even though both types of sampling strategies include a focus on both differences and similarities.

Alternatives to criterion sampling may be more appropriate to the specific functions of mixed methods, however. For instance, using qualitative methods for the purpose of complementarity may require that a sampling strategy emphasize similarity if it is to achieve depth of understanding or explore and develop hypotheses that complement a quantitative probability sampling strategy achieving breadth of understanding and testing hypotheses Kemper et al.

Similarly, mixed methods that address related questions for the purpose of expanding or explaining results or developing new measures or conceptual models may require a purposeful sampling strategy aiming for similarity that complements probability sampling aiming for variation or dispersion.

A single method that focuses only on a broad view may decrease internal validity at the expense of external validity Kemper et al. On the other hand, the aim of convergence answering the same question with either method may suggest use of a purposeful sampling strategy that aims for breadth that parallels the quantitative probability sampling strategy.

Refers to sequential structure; refers to simultaneous structure. Furthermore, the specific nature of implementation research suggests that a multistage purposeful sampling strategy be used.

Three different multistage sampling strategies are illustrated in Figure 1 below. Several qualitative methodologists recommend sampling for variation breadth before sampling for commonalities depth Glaser, ; Bernard, Multistage I. This approach begins with a broad view of the topic and then proceeds to narrow down the conversation to very specific components of the topic. However, as noted earlier, the lack of a clear understanding of the nature of the range may require an iterative approach where each stage of data analysis helps to determine subsequent means of data collection and analysis Denzen, ; Patton, Multistage II.

Similarly, multistage purposeful sampling designs like opportunistic or emergent sampling, allow the option of adding to a sample to take advantage of unforeseen opportunities after data collection has been initiated Patton, , p. Multistage I models generally involve two stages, while a Multistage II model requires a minimum of 3 stages, alternating from sampling for variation to sampling for similarity.

A Multistage III model begins with sampling for variation and ends with sampling for similarity, but may involve one or more intervening stages of sampling for variation or similarity as the need or opportunity arises.

Multistage purposeful sampling is also consistent with the use of hybrid designs to simultaneously examine intervention effectiveness and implementation. Such designs may give equal priority to the testing of clinical treatments and implementation strategies Hybrid Type 2 or give priority to the testing of treatment effectiveness Hybrid Type 1 or implementation strategy Hybrid Type 3. When conducting a Hybrid Type 1 design conducting a process evaluation of implementation in the context of a clinical effectiveness trial , the qualitative data could be used to inform the findings of the effectiveness trial.

Thus, an effectiveness trial that finds substantial variation might purposefully select participants using a broader strategy like sampling for disconfirming cases to account for the variation.

Alternatively, a narrow strategy may be used to account for the lack of variation. In either instance, the choice of a purposeful sampling strategy is determined by the outcomes of the quantitative analysis that is based on a probability sampling strategy. In Hybrid Type 2 and Type 3 designs where the implementation process is given equal or greater priority than the effectiveness trial, the purposeful sampling strategy must be first and foremost consistent with the aims of the implementation study, which may be to understand variation, central tendencies, or both.

In all three instances, the sampling strategy employed for the implementation study may vary based on the priority assigned to that study relative to the effectiveness trial. For instance, purposeful sampling for a Hybrid Type 1 design may give higher priority to variation and comparison to understand the parameters of implementation processes or context as a contribution to an understanding of effectiveness outcomes i.

Sampling Methods

Social science research is generally about inferring patterns of behaviors within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalized back to the population of interest. Presidential elections. The sampling process comprises of several stage. The first stage is defining the target population. A population can be defined as all people or items unit of analysis with the characteristics that one wishes to study.

By Saul McLeod , updated In psychological research we are interested in learning about large groups of people who all have something in common. We call the group that we are interested in studying our 'target population'. In some types of research the target population might be as broad as all humans, but in other types of research the target population might be a smaller group such as teenagers, pre-school children or people who misuse drugs. It is more or less impossible to study every single person in a target population so psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

An introduction to sampling methods

Posted on 18th November by Mohamed Khalifa. This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research. It is important to understand why we sample the population; for example, studies are built to investigate the relationships between risk factors and disease.

Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest. Although there are several different purposeful sampling strategies, criterion sampling appears to be used most commonly in implementation research. However, combining sampling strategies may be more appropriate to the aims of implementation research and more consistent with recent developments in quantitative methods. This paper reviews the principles and practice of purposeful sampling in implementation research, summarizes types and categories of purposeful sampling strategies and provides a set of recommendations for use of single strategy or multistage strategy designs, particularly for state implementation research.

It would normally be impractical to study a whole population, for example when doing a questionnaire survey. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association.

Published on September 19, by Shona McCombes. Revised on February 15, Instead, you select a sample.

What are sampling methods and how do you choose the best one?

Home QuestionPro Products Audience. Sampling definition: Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights. It is also a time-convenient and a cost-effective method and hence forms the basis of any research design. Sampling techniques can be used in a research survey software for optimum derivation. Select your respondents. Sampling in market research is of two types — probability sampling and non-probability sampling.

Quantitative researchers are often interested in being able to make generalizations about groups larger than their study samples. While there are certainly instances when quantitative researchers rely on nonprobability samples e. The goals and techniques associated with probability samples differ from those of nonprobability samples. The reason is that, in most cases, researchers who use probability sampling techniques are aiming to identify a representative sample A sample that resembles the population from which it was drawn in all the ways that are important for the research being conducted.

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Types of Probability Samples

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 Боже всемилостивый, - прошептал Джабба. Камера вдруг повернулась к укрытию Халохота. Убийцы там уже не. Подъехал полицейский на мотоцикле. Женщина, наклонившаяся над умирающим, очевидно, услышала полицейскую сирену: она нервно оглянулась и потянула тучного господина за рукав, как бы торопя. Оба поспешили уйти.

 - Это должно быть что-то фундаментальное. Соши пожирала глазами текст. - Подождите… сейчас посмотрю… отлично… - Сорок пять секунд! - раздался крик.

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Types of Sampling: Sampling Methods with Examples

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 Это может быть не вашим делом! - раздался зычный голос у них за спиной. Мидж от неожиданности стукнулась головой о стекло. Бринкерхофф опрокинул директорский стул и бросился к двери. Он сразу же узнал этот голос.

Позвоните Танкадо.

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Methods of sampling from a population
1 Response
  1. Fortun D.

    Furthermore, as there are different types of sampling techniques/methods, researcher needs to understand the differences to select the proper.

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