目的性抽样：复杂还是简单？研究案例

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Introduction

Novice nurse researchers tend to see purposive sampling as either simple or too difficult (Tuckett, 2004) and may therefore default to using a convenience sample for the wrong reasons. Attempting to ensure that nursing research has the right sample is crucial to good processes. This paper came out of the ongoing work of a research group, made up largely of nurses, at the University of Tasmania. The group ranged in experience from PhD students and early career researchers to experienced full professors and the research ranged similarly from PhD studies to funded research. A number of the group were using purposive sampling techniques under different circumstances and with different challenges. The lessons learnt by the individuals and by the group as a whole are interweaved into this paper and the case studies using purposive sampling are used to exemplify the different uses of purposive sampling, and the way in which each context has been handled.

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Purposive sampling

In terms of sampling, the strategy for participant selection should be integrated into the overall logic of any study (Punch, 2004) and the rationale for sample selection needs to be aligned from an ontological, epistemological and axiological perspective with the overarching aims of the study. In a qualitative study, a relatively small and purposively selected sample may be employed (Miles and Huberman, 1994), with the aim of increasing the depth (as opposed to breadth) of understanding (Palinkas et al., 2015). Purposive sampling is ‘used to select respondents that are most likely to yield appropriate and useful information’ (Kelly, 2010: 317) and is a way of identifying and selecting cases that will use limited research resources effectively (Palinkas et al., 2015).

Purposive sampling strategies move away from any random form of sampling and are strategies to make sure that specific kinds of cases of those that could possibly be included are part of the final sample in the research study. The reasons for adopting a purposive strategy are based on the assumption that, given the aims and objectives of the study, specific kinds of people may hold different and important views about the ideas and issues at question and therefore need to be included in the sample (Mason, 2002; Robinson, 2014; Trost, 1986).

With respect to research involving multiple cases, the most popular forms of purposive sampling are stratified, cell, quota and theoretical sampling. The different nature of these approaches is described in brief below.

Stratified sampling selects specific kinds or groups of participants that need to be part of the final sample. The sample is then stratified by the characteristic of the participant or group, with a specific number allocated to each stratification. (The number allocated to each category is also clearly important, particularly when allocation to separate groups is different.) Categories might be age, size of family, IQ, etc. However, and importantly, there needs to be a clear reason linked to the aims and objectives of the study to show why each group is different. Moreover, in terms of interviews, they must have something to add to the study.

Cell sampling is similar to stratified sampling but differs in that the categories for stratification are discrete, and in cell sampling they can overlap like a Venn diagram (Miles and Huberman, 1994). For example, in a study of children with chronic disease, one cell might be obese children and the other might be children with diabetes and the overlap will be obese children with diabetes.

In quota sampling, there is greater flexibility – rather than fixed numbers of cases being required with particular criteria, quota sampling specifies categories and the minimum number needed for each one (Mason, 2002). As the study proceeds, numbers in each area are monitored for fulfilment of the quota. For example, in a study, again of children with chronic illness, there might be quota for kinds of chronic illness and for kinds of family. The research team would specify a minimum for each of the quota. (A minimum of five children each with diabetes, leukaemia, arthritis, etc., and for the kind of family, 10 from a nuclear family, 15 from a reconstituted family, etc.) The use of minimum quota makes sure that key participants are part of the final sample. It is argued that this approach is also more flexible in shaping the final sample and easier, in recruitment terms, compared with stratified and cell sampling (Robinson, 2014).

Theoretical sampling is different by being part of the collection and analysis of the data, following provisional sampling and analysis of some data (Coyne, 1997; Robinson, 2014; Strauss, 1987). Theoretical sampling originally came from Grounded Theory but is applied to other methods as well (Mason, 2002). The process involves either identifying cases from new groups, which might amount to being a comparison or a contrast with other groups, or reshaping the sample into a new set of criteria as a result of the analysis, and in so doing replacing the original sampling strategy chosen a-priori (Draucker et al., 2007; Robinson, 2014).

This paper now introduces three different research studies in which the processes and challenges of purposive sampling are taken up in each instance.

Research study 1: Co-led redesign of stroke services in North West Tasmania

This example relates to the redesign of stroke services and is reported at the point when all patient interviews have been collected. Co-led redesign initiatives in healthcare service provision rely on experience-based feedback from patients and their families as well as sourcing information from healthcare staff and data collected specifically for the purpose of a service redesign (Prior and Campbell, 2018). The stroke service co-led redesign project utilised a purposive sampling method developed by Reed et al. (1996) based on stakeholder sampling (Ovretveit, 1998), termed the Matrix sampling method. Matrix sampling empowers the stakeholders, allowing them to select categories of participants who they determine to be representative of the service users, essentially creating a trustworthy sample. For example, the stroke patient interviews consisted of 50% of patients over age 65 and 50% of those aged 65 or under. The stakeholder group identified that these two groups of patients require differing types of acute and rehabilitative stroke care in some instances and placed a high level of importance on being able to achieve the levels of care required for different age groups. The stakeholders included senior medical and nursing management, medical consultants, nursing unit managers, the director of allied health and the research team. The research team is then able to perform the interviews with selected patients on behalf of the stakeholders and report the findings to the group via thematic analysis.

Matrix sampling strengthens qualitative research by providing a structured and purposive method for nominating participants. It creates maximum variability based on stakeholder knowledge of the population and the intended research outcomes. Previously utilised in healthcare redesign research in the United Kingdom (Campbell et al., 2004) as part of a patient journey approach, Matrix sampling is a cost-effective and time-efficient method allowing the stakeholders a level of control over the selected sample. This method of sampling was selected to capture a relevant participant group, representing stroke patients in North West Tasmania. A number of clinical and demographic variables were considered when determining the appropriate stroke patient participants, influenced by the local population and a quantitative data analysis determining the numbers and types of stroke patients admitted. Exclusion criteria were set prior to the sampling process; these included mini strokes (transient ischaemic attacks), patients who were living in a nursing home at the time of their stroke and deceased patients. As with other purposive sampling methods, Matrix sampling utilises the specific characteristic of stroke to provide a potential pool of participants. Other characteristics of importance noted during the participant selection phase for this project included the number of risk factors associated with each stroke patient, mode of arrival to the hospital, whether the patient was transferred into or out of a specific hospital and the type of stroke for which the patient was admitted (haemorrhagic or ischaemic). These specific criteria, determined by the stakeholders, allowed the research team to find candidates for the interviews to represent the patient group who could provide the most appropriate input into stroke service redesign for this particular population area.

Although this sampling method fulfils the needs of the stakeholders by allowing them to make the decisions over the sample population, there are also some weaknesses or disadvantages to the Matrix sampling method. If it is not possible to recruit participants to a selected criterion, gaps appear in the data. In the project it was noted that one particular criterion, patients who were transferred between hospitals, was more difficult to ‘fill’ due to smaller numbers of admitted patients fitting this description, purely due to the population being sampled. The dependability of the data, then, can be difficult to control; however, to overcome this issue, discussions with the stakeholder group suggested other recruitment methods, such as clinicians identifying patients and requesting consent. If these patients were unable to be identified, the group was satisfied that all was done to ensure the stakeholder view was utilised to the best abilities of the research team and the results delivered still reflected a representative population.

The Matrix sampling method is an easily transferable approach for qualitative research, which allows the input of the stakeholder(s) to determine the output of the research through the provision of local information and knowledge. Matrix sampling is a form of stratified sampling, but it is also quota driven. It is a form of stakeholder sampling where the views of the stakeholders are paramount, as they have to be reassured of the adequacy of the sampling so they regard the evidence as adequate and credible.

Research study 2: Child and family health nurses and safety and wellbeing of young children

This example is from a PhD study (Young, 2020 [unpublished thesis]) focusing on the response of child and family health (CFH) nurses to concerns around the safety and wellbeing of young children aged from birth to 5 years within the family, using Interpretive Description (ID) as the methodological approach. The setting in which the study is situated is that of a CFH nursing service provided by an Australian state-wide health department.

ID methodology, developed by Thorne et al. (1997), is a way of generating increased understanding of clinical phenomena that are complex and experiential. ID studies generate an ID of the themes and patterns captured within subjective perceptions around a phenomena of clinical interest (Thorne et al., 2004) and produce practice-relevant knowledge that can be immediately applied in the clinical context (Thorne, 2016; Hunt, 2009). When using ID methodology, researchers identify who should be included in the study, so the eventual findings allow better understanding of the phenomenon of interest (Hunt, 2009; Thorne, 2016). Purposive sampling is an accepted and often used initial sampling strategy in ID methodology as it allows settings and people to be recruited based on their expected contribution to the study (Schensul, 2011) and by virtue of some angle of the phenomenon that they might help us better understand (Hunt, 2009; Thorne, 2016). Participants are those who are most likely to have in-depth knowledge and experience of the phenomenon being studied. With this in mind, the inclusion criteria developed for this study were that participants must be nurses currently employed as CFH nurses with a minimum of 2 years recent (within the last 5 years) experience working in this specialist area of nursing. This was to help to ensure the opinions obtained were those of experienced CFH nurses with exposure to relevant practice experiences in a range of situations. Excluded from the study were those nurses who did not have at least 2 years recent post-graduate experience as a CFH nurse.

In developing the sample subset, an awareness was maintained of how this might either privilege or silence particular angles or perspectives and thus impact the eventual findings of the study and its credibility (Thorne, 2016). To enhance credibility, care was taken to clearly, transparently and explicitly describe the logic used in selecting the sample subset (Robinson, 2014; Thorne, 2016). Furthermore, a critical awareness of the nature of the selected sample and how this might impact on any findings generated was maintained throughout the study to help ensure claims beyond the sample subset were not made (Robinson, 2014; Thorne, 2016).

Transferability was enhanced by the way in which study participants were clearly identified in terms of inclusion and exclusion criteria and demographic information. This helps others to determine whether the findings are applicable to other situations and population groups (Shenton, 2004; Amankwaa, 2016). A sample that is fully contextualised helps prevent unwarranted generalisation (Robinson, 2014). Dependability was enhanced by the description of participants using clear inclusion and exclusion criteria (Shenton, 2014). In addition, a well-accepted sampling strategy appropriate to an ID study was used (Thorne, 2016). Confirmability was enhanced by the provision of a rationale for the choice of inclusion and exclusion criteria, so that the integrity of the process could be determined by others (Shenton, 2014).

Research study 3: How can mental wellbeing for new mothers be achieved?

This example is from a PhD study (Young, 2020 [unpublished thesis]) about women's experiences after childbirth, where recruitment is about to commence. This research aims to determine what influences mothers’ mental wellbeing in the year after the birth of a first baby and asks, ‘how can mental wellbeing for new mothers be achieved?’ Narrative inquiry involving three or four in-depth interviews with ∼10 women will be used to answer this question. The interviews will be conducted longitudinally over a period of 9–12 months and will aim to capture a rich, deep picture of the first year after childbirth. It is hoped that the major influences impacting mental wellbeing will be identified.

To determine which women to include in this study, purposive sampling will be employed. Specific inclusion and exclusion criteria will be indicated, making the inclusion of participants in this study non-probabilistic, and indeed purposive, in nature. Women will be recruited for involvement from the antenatal clinic at the local public hospital by way of response to a posted flyer. Although there is an element of convenience sampling involved in this process, the very specific nature of the criteria for involvement make this design purposive. Inclusion criteria will include considerations such as first-time mothers only, singleton pregnancy, maternal age over 18 years and gestational due date within a specified timeframe to facilitate the longitudinal interview schedule. Exclusion criteria will include anyone who has had a previous mental health issue or a pregnancy-related health complication (e.g., gestational diabetes, placenta praevia, known foetal issues, etc.).

The trustworthiness and rigour of the data will be enhanced by the purposive sampling design. In terms of credibility, this method of sampling supports the likelihood that ‘member checking’ may occur, which will increase the credibility of the findings (Guba, 1981). Because women will self-select for participation in the study, this degree of interest and investment increases the likelihood of their willingness to remain involved for the duration of the research.

Both the transferability and dependability of the data will be enhanced by the specific nature of the inclusion and exclusion criteria laid out for this research. Transferability will be affected because these detailed criteria will allow readers to develop a clear picture of participants involved. Guba notes the importance of ‘full description of all the contextual factors impinging on the inquiry’ (1981: 70) and the participants themselves can be considered a ‘contextual factor’ in the research. In a similar vein, the detailed nature of the criteria will form part of the audit trail that contributes to dependability in a study (Baillie, 2015; Guba, 1981). A risk to trustworthiness in interview-based research is the role of the interviewer themselves and the influence of their own beliefs and perspectives (Haga et al., 2012; Shenton, 2004).

When determining the sample size for a study of this nature, several factors are considered. Morse notes that the scope of the study, the nature of the topic, the quality of the data, the study design and the use of shadowed data all require consideration (2000). Relatedly, Morse (2000: 4) emphasises that ‘the quality of data and the number of interviews per participant determine the amount of useable data obtained. There is an inverse relationship between the amount of useable data obtained from each participant and the number of participants’. This is an important consideration with a longitudinal study where, for example, four interviews with 10 participants would amass data very quickly. With these considerations in mind, a sample size of 10 participants will be the aim.

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Implications for research in nursing and health

The sample, particularly for qualitative research, is often not analysed by the nursing reader of practice papers (Gelling et al., 2014). The sample itself, the context and the process are all important issues to consider when reading a paper and considering its impact, particularly when making potential policy changes. Therefore, novice nursing researchers need to ensure the sampling process fits the needs of the study and be clear about the actual process that ensued. For instance: does the sample in the nursing research strategy match the patients who are being considered? The context of sampling in nursing research, as in all research, is a key issue.

Each of these research studies has considered purposive sampling in very different contexts. However, all of them, although purposive, have a convenience element to them given the voluntary nature of all consent processes, where the researcher is at the mercy of the pool of potential participants. However, the voluntary nature of the participation means the researchers can characterise them as fitting not only the inclusion criteria of the study, but also being interested in the topic and motivated to take part out of this interest and their potential to contribute to development of knowledge in this arena.

The Co-Led Stroke Redesign sampling process was about interviewing a representative sample that was persuasive enough to inform change of practice in the stakeholders. The CFH nurse study is the simplest of the designs cited in this paper and has power in this simplicity. However, the analysis of the data is already showing important differences in the nature of the sample. The identification of the right mothers to gain their views of motherhood shows the lengths researchers can go to when considering complex forms of purposive sampling, only to discard them for a simpler process. However, this process of considering options is important in developing high-quality research designs rather than settling for standard approaches.

A continued narrative for all of the research studies that have been exemplified in this paper was whether being purposive in some more complex manner was actually necessary. The only clarity was that all studies were purposive with the intent of recruiting participants who could inform the researchers' aims and objectives. The argument was that the reader of the research would be able to make the judgements about the relevance of the research, if the nature of the sample was transparent. This is another example of the context of research being all important in qualitative research. In combination, the case studies highlight important elements researchers should consider when using purposive sampling techniques to address the four elements of trustworthiness for the research design.

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Key points for policy, practice and/or research

• Novice nurse researchers need to ensure purposive sampling is used where appropriate and not default to a convenience sample.

• The context of the data collection is an important consideration in purposive sampling for trustworthiness of data in nursing research.

• Nurse researchers adopt theoretical positions that are reflected in purposive sampling techniques and assist policy makers to understand the relevance of the research.

• The voluntary nature of nursing research supports the purposive sampling approach, it does not mitigate against it.

全文翻译（仅供参考）

介绍

       新手护士研究人员倾向于认为有目的的抽样要么简单，要么太难（Tuckett，2004)，因此可能出于错误原因默认使用便利示例。努力确保护理研究有正确的样本对于良好的流程至关重要。这篇论文来自塔斯马尼亚大学一个主要由护士组成的研究小组正在进行的工作。该小组的经验范围从博士生和早期职业研究人员到经验丰富的正教授，研究范围从博士研究到资助研究。许多小组在不同情况下和面临不同挑战时使用有目的的抽样技术。个人和整个群体的经验教训交织在本文中，使用目的抽样的案例研究用于举例说明目的抽样的不同用途，以及处理每种情况的方式。

有目的的抽样

       在抽样方面，参与者选择的策略应整合到任何研究的整体逻辑中（Punch，2004），样本选择的基本原理需要从本体论、认识论和价值论的角度与研究的总体目标保持一致. 在定性研究中，可以使用相对较小且有目的地选择的样本（Miles 和 Huberman，1994 年），目的是增加理解的深度（而不是广度）（Palinkas 等人，2015 年）。目的抽样“用于选择最有可能产生适当和有用信息的受访者”（Kelly，2010: 317) 是一种识别和选择案例的方法，可以有效地利用有限的研究资源（Palinkas 等人，2015 年）。

       有目的的抽样策略远离任何随机抽样形式，并且是确保可能包括的特定类型的案例成为研究中最终样本的一部分的策略。采用有目的性策略的原因是基于这样的假设，即鉴于研究的目的和目标，特定类型的人可能对所讨论的想法和问题持有不同且重要的观点，因此需要将其包括在样本中（梅森，2002 年；罗宾逊，2014 年；特罗斯特，1986 年）。

       对于涉及多个案例的研究，最流行的目的抽样形式是分层抽样、单元抽样、定额抽样和理论抽样。下面简要描述这些方法的不同性质。

       分层抽样选择需要成为最终样本一部分的特定类型或群体的参与者。然后根据参与者或组的特征对样本进行分层，并为每个分层分配一个特定数量。（分配给每个类别的数量显然也很重要，特别是当分配给不同的组时。）类别可能是年龄、家庭规模、智商等。然而，重要的是，需要有一个明确的原因与研究的目的和目的，以说明为什么每个组都不同。此外，在采访方面，他们必须对研究有所补充。

       细胞抽样类似于分层抽样，但不同之处在于分层的类别是离散的，在细胞抽样中，它们可以像维恩图一样重叠（Miles 和 Huberman，1994 年）。例如，在一项针对患有慢性病的儿童的研究中，一个细胞可能是肥胖儿童，另一个细胞可能是糖尿病儿童，重叠的部分将是患有糖尿病的肥胖儿童。

       在配额抽样中，有更大的灵活性——而不是根据特定标准要求固定数量的案例，配额抽样指定类别和每个类别所需的最小数量（梅森，2002）。随着研究的进行，每个地区的人数都会受到监测，以确保完成配额。例如，在一项针对慢性病儿童的研究中，可能存在慢性病种类和家庭种类的配额。研究小组将指定每个配额的最小值。（至少有 5 名患有糖尿病、白血病、关节炎等的儿童，对于这种家庭，10 名来自核心家庭，15 名来自重组家庭等）使用最低配额确保关键参与者是最终样本的一部分。有人认为，与分层抽样和细胞抽样相比，这种方法在塑造最终样本方面也更灵活，在招聘方面也更容易（Robinson，2014 年）。

       在对某些数据进行临时抽样和分析之后，作为数据收集和分析的一部分，理论抽样有所不同（Coyne，1997；Robinson，2014；Strauss，1987）。理论抽样最初来自扎根理论，但也适用于其他方法（梅森，2002 年）。该过程涉及从新组中识别案例，这可能相当于与其他组进行比较或对比，或者作为分析的结果将样本重塑为一组新的标准，并以此替换原始抽样策略先验选择（Draucker 等人，2007 年；Robinson，2014 年）。

       本文现在介绍三种不同的研究，其中分别讨论了有目的抽样的过程和挑战。

研究 1：共同领导重新设计塔斯马尼亚西北部的中风服务

       此示例涉及中风服务的重新设计，并在收集所有患者访谈时报告。共同领导的医疗服务提供重新设计计划依赖于患者及其家人的基于经验的反馈，以及从医疗保健人员那里获取信息以及专门为重新设计服务而收集的数据（Prior 和 Campbell，2018 年）。中风服务共同领导的重新设计项目利用了Reed 等人开发的有目的的抽样方法。(1996)基于利益相关者抽样 ( Ovretveit, 1998)，称为矩阵抽样方法。矩阵抽样赋予利益相关者权力，允许他们选择他们确定代表服务用户的参与者类别，本质上是创建一个值得信赖的样本。例如，中风患者访谈包括 50% 的 65 岁以上患者和 50% 的 65 岁或以下患者。利益相关者小组确定，这两组患者在某些情况下需要不同类型的急性和康复性卒中护理，并高度重视能够达到不同年龄组所需的护理水平。利益相关者包括高级医疗和护理管理人员、医疗顾问、护理单位经理、专职医疗主任和研究团队。

       矩阵抽样通过为提名参与者提供结构化和有目的的方法来加强定性研究。它基于利益相关者对人群的了解和预期的研究结果创造了最大的可变性。以前用于英国的医疗保健重新设计研究（Campbell 等人，2004 年）) 作为患者旅程方法的一部分，矩阵抽样是一种经济高效且省时的方法，允许利益相关者对所选样本进行一定程度的控制。选择这种抽样方法是为了捕获一个相关的参与者组，代表塔斯马尼亚西北部的中风患者。在确定合适的卒中患者参与者时，考虑了许多临床和人口统计学变量，这些变量受到当地人口的影响，以及确定入院卒中患者数量和类型的定量数据分析。排除标准是在抽样过程之前设定的；其中包括小型中风（短暂性脑缺血发作）、中风时住在疗养院的患者和已故患者。与其他目的性抽样方法一样，矩阵抽样利用中风的特定特征来提供潜在的参与者池。在该项目的参与者选择阶段注意到的其他重要特征包括与每个中风患者相关的风险因素的数量、到达医院的方式、患者是否转入或转出特定医院以及中风的类型。患者入院（出血性或缺血性）。这些由利益相关者确定的具体标准使研究团队能够找到代表患者群体的面试候选人，他们可以为这一特定人群区域的卒中服务重新设计提供最合适的投入。在该项目的参与者选择阶段注意到的其他重要特征包括与每个中风患者相关的风险因素的数量、到达医院的方式、患者是否转入或转出特定医院以及中风的类型。患者入院（出血性或缺血性）。这些由利益相关者确定的具体标准使研究团队能够找到代表患者群体的面试候选人，他们可以为这一特定人群区域的卒中服务重新设计提供最合适的投入。在该项目的参与者选择阶段注意到的其他重要特征包括与每个中风患者相关的风险因素的数量、到达医院的方式、患者是否转入或转出特定医院以及中风的类型。患者入院（出血性或缺血性）。这些由利益相关者确定的具体标准使研究团队能够找到代表患者群体的面试候选人，他们可以为这一特定人群区域的卒中服务重新设计提供最合适的投入。患者是否转入或转出特定医院以及患者入院的中风类型（出血性或缺血性）。这些由利益相关者确定的具体标准使研究团队能够找到代表患者群体的面试候选人，他们可以为这一特定人群区域的卒中服务重新设计提供最合适的投入。患者是否转入或转出特定医院以及患者入院的中风类型（出血性或缺血性）。这些由利益相关者确定的具体标准使研究团队能够找到代表患者群体的面试候选人，他们可以为这一特定人群区域的卒中服务重新设计提供最合适的投入。

       虽然这种抽样方法通过允许利益相关者对样本总体做出决定来满足利益相关者的需求，但矩阵抽样方法也存在一些弱点或缺点。如果无法根据选定的标准招募参与者，数据中就会出现空白。在该项目中注意到，一个特定的标准，即在医院之间转移的患者，由于符合这一描述的入院患者数量较少，这纯粹是因为被抽样的人口，因此更难以“填补”。因此，数据的可靠性可能难以控制；然而，为了克服这个问题，与利益相关者团体的讨论提出了其他招募方法，例如临床医生识别患者和请求同意。如果无法确定这些患者，

       矩阵抽样方法是一种易于转移的定性研究方法，它允许利益相关者的输入通过提供本地信息和知识来确定研究的输出。矩阵抽样是分层抽样的一种形式，但也是配额驱动的。这是利益相关者抽样的一种形式，利益相关者的意见是最重要的，因为他们必须确信抽样的充分性，以便他们认为证据是充分和可信的。

研究 2：儿童和家庭保健护士与幼儿的安全和福祉

       这个例子来自一项博士研究（Young，2020 [未发表论文]），重点关注儿童和家庭健康 (CFH) 护士对家庭中从出生到 5 岁幼儿的安全和福祉的担忧，使用解释性描述（ID）作为方法论方法。研究所在的环境是澳大利亚全州卫生部门提供的 CFH 护理服务。

       ID 方法，由Thorne 等人开发。（1997 年），是一种加深对复杂和经验性临床现象的理解的方法。ID 研究生成主题和模式的 ID，这些主题和模式在围绕临床感兴趣的现象的主观感知中捕获（Thorne 等，2004），并产生可以立即应用于临床环境的实践相关知识（Thorne，2016；Hunt， 2009 年）。在使用 ID 方法时，研究人员确定谁应该被包括在研究中，因此最终的发现可以更好地理解感兴趣的现象（亨特，2009 年；索恩，2016 年））。有目的抽样是 ID 方法中一种公认且经常使用的初始抽样策略，因为它允许根据他们对研究的预期贡献（Schensul，2011）以及他们可能对我们有帮助的某种现象的角度来招募环境和人员更好地理解（亨特，2009 年；索恩，2016 年））。参与者是最有可能对所研究的现象有深入了解和经验的人。考虑到这一点，为本研究制定的纳入标准是，参与者必须是目前担任 CFH 护士的护士，并且最近（过去 5 年内）在该专业护理领域工作至少 2 年。这是为了帮助确保获得的意见是经验丰富的 CFH 护士在各种情况下接触相关实践经验的意见。研究排除了那些最近没有至少 2 年作为 CFH 护士的研究生经验的护士。

       在开发样本子集的过程中，人们一直意识到这可能如何优先或沉默特定角度或观点，从而影响研究的最终结果及其可信度（Thorne，2016 年）。为了提高可信度，我们注意清晰、透明和明确地描述选择样本子集时使用的逻辑（Robinson，2014；Thorne，2016）。此外，在整个研究过程中保持对所选样本的性质以及这可能如何影响所产生的任何结果的批判意识，以帮助确保不提出超出样本子集的声明（Robinson，2014 年；Thorne，2016 年）。

       通过在纳入和排除标准以及人口统计信息方面明确识别研究参与者的方式，提高了可转移性。这有助于其他人确定这些发现是否适用于其他情况和人群（Shenton，2004 年；Amankwaa，2016 年）。完全情境化的样本有助于防止无根据的概括（Robinson，2014 年）。使用明确的纳入和排除标准（ Shenton，2014 ）对参与者的描述增强了可靠性。此外，还使用了适合 ID 研究的广为接受的抽样策略（Thorne，2016）。通过提供选择纳入和排除标准的理由来增强可确认性，以便其他人可以确定过程的完整性（Shenton，2014 年）。

研究 3：如何实现新妈妈的心理健康？

       这个例子来自一项关于女性分娩后经历的博士研究（Young，2020 [未发表论文]），招聘即将开始。这项研究旨在确定在第一个婴儿出生后一年内影响母亲心理健康的因素，并询问“如何实现新妈妈的心理健康？” 将使用涉及对 10 名女性进行三到四次深入访谈的叙事调查来回答这个问题。访谈将在 9-12 个月期间纵向进行，旨在捕捉分娩后第一年的丰富、深刻的画面。希望能够确定影响心理健康的主要影响因素。

       为了确定将哪些女性纳入本研究，将采用目的性抽样。将指出具体的纳入和排除标准，使本研究中的参与者纳入本质上是非概率性的，实际上是有目的的。将通过回应张贴的传单，从当地公立医院的产前诊所招募妇女参与。尽管此过程涉及方便抽样的元素，但参与标准的非常具体的性质使该设计具有目的性。纳入标准将包括诸如仅限第一次母亲、单胎妊娠、母亲年龄超过 18 岁以及在指定时间范围内的妊娠预产期等考虑因素，以促进纵向访谈时间表。

       有目的的抽样设计将增强数据的可信度和严谨性。在可信度方面，这种抽样方法支持可能发生“成员检查”的可能性，这将增加调查结果的可信度（Guba，1981）。因为女性会自行选择参与研究，这种程度的兴趣和投资增加了她们愿意在研究期间继续参与的可能性。

       为本研究制定的纳入和排除标准的具体性质将增强数据的可转移性和可靠性。可转移性将受到影响，因为这些详细的标准将使读者能够清楚地了解所涉及的参与者。Guba 指出“完整描述影响调查的所有背景因素”（1981：70）的重要性，参与者本身可以被视为研究中的“背景因素”。同样，标准的详细性质将构成审计跟踪的一部分，有助于提高研究的可靠性（Baillie，2015；Guba，1981）。基于访谈的研究中的可信度风险是访谈者自己的角色以及他们自己的信念和观点的影响（Haga 等人，2012 年；Shenton，2004 年）。

       在确定这种性质的研究的样本量时，需要考虑几个因素。Morse 指出，研究的范围、主题的性质、数据的质量、研究设计和阴影数据的使用都需要考虑（2000 年）。与此相关的是，Morse (2000 : 4) 强调“数据的质量和每位参与者的访谈次数决定了获得的可用数据的数量。从每个参与者获得的可用数据量与参与者的数量之间存在反比关系。这是纵向研究的一个重要考虑因素，例如，对 10 名参与者进行四次访谈会很快收集数据。考虑到这些因素，目标是 10 名参与者的样本量。

对护理和健康研究的启示

       样本，尤其是定性研究的样本，通常不会被实践论文的护理读者分析（Gelling 等人，2014 年）。样本本身、背景和过程都是阅读论文并考虑其影响时需要考虑的重要问题，尤其是在进行潜在的政策变更时。因此，新手护理研究人员需要确保采样过程符合研究需要，并清楚随后的实际过程。例如：护理研究策略中的样本是否与正在考虑的患者相匹配？与所有研究一样，护理研究中的抽样背景是一个关键问题。

       这些研究中的每一项都考虑了在非常不同的背景下进行的有目的的抽样。然而，考虑到所有同意过程的自愿性质，研究人员在这些过程中都受到潜在参与者的支配，尽管它们都是有目的的，但它们都具有便利因素。然而，参与的自愿性质意味着研究人员可以将他们描述为不仅符合研究的纳入标准，而且对主题感兴趣并有动力参与这种兴趣和他们为知识发展做出贡献的潜力在这个舞台上。

       Co-Led Stroke Redesign 抽样过程是关于采访具有足够说服力的代表性样本，以告知利益相关者的实践变化。CFH 护士研究是本文引用的设计中最简单的，并且在这种简单性方面具有强大的力量。然而，数据分析已经显示出样本性质的重要差异。确定合适的母亲以获得他们对母性的看法表明研究人员在考虑复杂形式的有目的抽样时可以采取的措施，只是为了更简单的过程而放弃它们。然而，这个考虑选项的过程对于开发高质量的研究设计而不是满足于标准方法很重要。

       本文举例说明的所有研究的持续叙述是，以某种更复杂的方式进行有目的的研究是否实际上是必要的。唯一明确的是，所有研究都是有目的的，目的是招募能够告知研究人员目的和目标的参与者。论点是，如果样本的性质是透明的，研究的读者将能够对研究的相关性做出判断。这是研究背景在定性研究中非常重要的另一个例子。结合起来，案例研究突出了研究人员在使用有目的的抽样技术来解决研究设计的四个可信度要素时应考虑的重要因素。

政策、实践和/或研究的要点

• 新手护士研究人员需要确保在适当的情况下使用有目的的抽样，而不是默认为方便样本。

• 数据收集的背景是护理研究中数据可信度的目的抽样的重要考虑因素。

• 护士研究人员采用反映在有目的抽样技术中的理论立场，并帮助政策制定者了解研究的相关性。

• 护理研究的自愿性质支持有目的的抽样方法，但它并没有减轻它的影响。

THE

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