Djukic, Kovner, Budin, and Norman (2010) studied the effect of nurses perceived physical work environment on their job satisfaction and described their sampling frame in the following excerpt. For example, if in conducting your research you selected a stratified random sample of 100 adult subjects using age as the variable for stratification, the sample might include 25 subjects in the age range 18 to 39 years, 25 subjects in the age range 40 to 59 years, 25 subjects in the age range 60 to 79 years, and 25 subjects 80 years or older. Table 15-2 shows a section from a random numbers table. Twiss et al. Because of the importance of generalizing, there are risks to defining the accessible population too narrowly. If potential subjects have diminished autonomy or are unable to give informed consent, consent must be obtained from their legal representatives. Before Sampling error decreases, power increases, data collection time is reduced, and the cost of the study is lower if stratification is used (Fawcett & Garity, 2009; institutions, or organizations with which elements of the identified population would be linked. It is especially important that the sample be representative in relation to the variables you are studying and to other factors that may influence the study variables. Sampling theory in medical science is a study of gathering information about a population by investigating only a part of it. Network or snowball sampling Freedman D, et al. When elements are persons, they are usually referred to as subjects or research participants or informants (see Figure 15-1). 2021 Oct 1;121(10):59-62. doi: 10.1097/01.NAJ.0000794280.73744.fe. It is used to measure a concept of interest. Probability samples reduce sampling error. To accomplish this goal, the researcher must acquire a list of every member of the population through the use of the sampling criteria to define membership. An extreme example of this problem is the highly restrictive sampling criteria used in some experimental studies that result in a large sampling error and greatly diminished representativeness. If you know the refusal rate, you can also subtract the refusal rate from 100% to obtain the acceptance rate. Non-probability sampling does not involve random selection and so cannot rely on probability theory to ensure that it is representative of the population of interest. Some populations are elusive and constantly changing. The subjects were evenly divided with 25 in each group, but the researchers do not indicate if the assignment to groups was random or based on the convenience of the subjects or researchers. Systematic variation or bias is most likely to occur when the sampling process is not random. Hu Li Za Zhi. If the mean is used to describe the sample, the values of individuals in that sample will not all be exactly the same as the sample mean. Stratification ensures that all levels of the identified variable, in this example age, are adequately represented in the sample. An element can be a person, event, behavior, or any other single unit of study. (2009) identified that 249 participants or subjects met the sampling criteria and 249 were enrolled in the study indicating that the acceptance rate for the study was 100%. Random sampling leaves the selection to chance and decreases sampling error and increases the validity of the study (Thompson, 2002). There are five main types of probability sampling including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Sampling error is the approximate difference between the results from a sample of people from a larger group, and the likely results of studying every single person in that group. Data from subjects associated with the same institution are likely to be correlated and not completely independent. Burlington, MA: Jones and Bartlett Learning; 2020. This study included clearly identified inclusion and exclusion sampling or eligibility criteria that are presented in the following excerpt. Knowledge of sampling methods is essential to design quality research. The retention rates for both groups were very strong and comparable (treatment group 89% and comparison group 90%). 2021 Jul 8;13(7):e16260. Probability sampling methods are most commonly applied in quantitative, outcomes, and intervention research. Some error has occurred while processing your request. Your sampling decisions heavily influence the validity of the findings. However, the sample was a great strength of this study and appeared to represent the target population of NPs and PAs currently practicing in primary care in the United States. For each person in the target or accessible population to have an opportunity to be selected for the sample, each person in the population must be identified. There are many ways to achieve random selection, such as with the use of a computer, a random numbers table, drawing names out of a hat, or a roulette wheel. A sampling plan describes the strategies that will be used to obtain a sample for a study. 19 [A comparison of convenience sampling and purposive sampling]. Probability sampling methods are most commonly applied in quantitative, outcomes, and intervention research. In studies involving a treatment, subjects in the control group who do not receive the treatment may be more likely to withdraw from the study. 16. The second situation is in cases in which the individual elements making up the population are unknown, preventing the development of a sampling frame. The most common method of random selection is the computer, which can be programmed to select a sample randomly from the sampling frame with replacement. The method you use to pick individuals out of the group to study. The sample is most like the target population if the attrition rate is low (<10% to 20%) and the subjects withdrawing from the study are similar to the subjects completing the study. It is applicable only to random samples. Capili B. These samples are more likely to represent the population than samples obtained with nonprobability sampling methods. Researchers also need to provide a rationale for subjects withdrawing from the study and to determine if they are different from the subjects who complete the study. Variables commonly used for stratification are age, gender, ethnicity, socioeconomic status, diagnosis, geographical region, type of institution, type of care, care provider, and site of care. (2009) also provided a rationale for the subjects attrition, and the reasons were varied and seemed appropriate and typical for a study lasting 24 months. Finally, within each secondary sampling unit, all African American and Hispanic households were selected for interviews, whereas other households were sampled at differing rates within the substrata. With a stratified random sample, you could use a smaller sample size to achieve the same degree of representativeness as a large sample acquired through simple random sampling. Generalizability refers to the inferences that can be made about the target population based on results from the study sample. It is from the sample that data is collected in the course of a study, and because that sample is representative of the population, the results can be generalized to the population in a scientific study. The treatment group retention was 110 women with a retention rate of 89% (110 124 100% = 88.7% = 89%). Probability and Nonprobability Sampling Methods Commonly Applied in Nursing Research different from the subjects who complete the study. These values do not vary randomly around the population mean. The comparison group retention was 113 women with a retention rate of 90% (113 125 = 90.4% = 90%). Cluster sampling is a probability sampling method applied when the population is heterogeneous; it is similar to stratified random sampling but takes advantage of the natural clusters or groups of population units that have similar characteristics (Fawcett & Garity, 2009). In some cases, a hypothetical population is defined for a study. Finally, within each secondary sampling unit, all African American and Hispanic households were selected for interviews, whereas other households were sampled at differing rates within the substrata. Careers, The group of people you want to study and understand. Even if the original sample is random, persons who volunteer or consent to participate in a study may differ in important ways from persons who are unwilling to participate. An accessible population is the portion of the target population to which the researchers have reasonable access. Again, these units could be people, events, or other subjects of interest. 54 8. Wolters Kluwer Health These inclusion and exclusion sampling criteria were appropriate for the study to reduce the effect of possible extraneous variables that might have an impact on the treatment (ST exercises) and the measurement of the dependent variables (muscle strength, balance, and falls). The site is secure. Good Practice in the Conduct and Reporting of Survey Research. The series is designed to give nurses the knowledge and skills they need to participate in research, step by step. In: Burns and Grove's the practice of nursing research: appraisal, synthesis, and generation of evidence. (2006) used a stratified random sampling method to obtain their sample of nurse practitioners (NPs) and physician assistants (PAs) for the purpose of studying the ethical conflict of these healthcare providers associated with managed care. In quantitative, intervention, and outcomes research, the findings from a study are generalized first to the accessible population and then, if appropriate, more abstractly to the target population. In a third step, clusters of dwelling units form the secondary sampling units selected from each substratum [3rd stage cluster sampling]. Am J Nurs. Random sampling increases the extent to which the sample is representative of the target population. 39 444-445) Sampling theory is the study of the relationship between a given population and portion picked randomly as a representation of the whole population (McNiff & Petrik, 2018). Thus, persons who are legally or mentally incompetent, terminally ill, or confined to an institution are more difficult to access as subjects (see Chapter 9). Common methods of probability sampling include the following6: In simple random sampling, study participants are randomly selected from the sampling frame using a randomization mechanism, as previously discussed. However, the sample was a great strength of this study and appeared to represent the target population of NPs and PAs currently practicing in primary care in the United States. Search for Similar Articles The difference is random because the value of each subject is likely to vary in a different direction. Sampling strategies have been devised to accomplish these three tasks and to optimize sample selection. For instance, perhaps a nurse researcher is interested in examining work satisfaction among clinical nurses within a large health care system. 444-445). The researcher selects subjects from the sampling frame using a sampling plan. The .gov means its official. Clipboard, Search History, and several other advanced features are temporarily unavailable. Probability samples reduce sampling error. If your sample is very similar to the population you have a strong case to say that the same things you found in the sample also apply in the population. The first situation is when a simple random sample would be prohibitive in terms of travel time and cost. Sampling criteria, also referred to as eligibility criteria, include a list of characteristics essential for membership or eligibility in the target population. Ultimately, researchers hope to make generalizations about the target population (for example, persons in the United States with lung cancer) based on data collected from the study sample (lung cancer patients at a regional oncology center). In any case, it is rarely possible to obtain a purely random sample for nursing studies because of informed consent requirements. While recruitment and retention of research subjects can be one of the most challenging aspects of sampling, strategic messaging, relationship building, and financial incentives may facilitate the process. Exclusion criteria limit true randomness. Probability samples contain some type of randomization and consist of simple, stratified, systematic, cluster, and sequential ty Selecting the sample for a research study MeSH Statistical sampling theory provides a powerful theoretical framework for generalizing from samples to corresponding populations and is most relevant when generalizing to populations of units and settings (external validity question 1) that can be enumerated and are under the control of the researchers. What Adult Electrocardiogram (ECG) Diagnoses or Findings are Most Important for Advanced Care Paramedics to Know? Bethesda, MD 20894, Web Policies Figure 15-1 shows the relationships among the population, target population, and accessible populations. Systematic random sampling is the selection of participants in a preordained, orderly sequence. In purposeful sampling, the researcher intentionally recruits participants based on population, exposure, experience, or outcome to obtain information-rich data relating to a phenomenon of interest.2, 11 For example, a nurse researcher may want to purposefully select young adults who began using opioids during adolescence within a rural community for a contextual examination of opioid use initiation. If nonrandom sampling methods are used for sample selection, the group not receiving a treatment receives usual or standard care and is generally referred to as a comparison group. The number of individuals in the population, who they are, how much weight they have lost, how long they have kept the weight off, and how they achieved the weight loss are unknown. Sampling Plan For example, the researcher might first randomly select states and next randomly select cities within the sampled states. The acceptance rate is calculated by dividing the number of potential subjects who agree to participate in a study by the number of potential subjects who meet sampling criteria and multiplying the result by 100%. Table 15-2 shows a section from a random numbers table. These samples are more likely to represent the population than samples obtained with nonprobability sampling methods. The first situation is when a simple random sample would be prohibitive in terms of travel time and cost. 2003. States, cities, institutions, or organizations are selected randomly as units from which to obtain elements for the sample. Fundamentals of mathematical statistics. Selection bias is the systematic preferential inclusion or exclusion of subjects such that the sample population systematically differs from the target population.3, 4 For instance, suppose a nurse researcher recruited adult participants for a study by calling patients on a personal cell phone or landline between 1 PM and 3 PM, Monday through Friday, for two weeks. One question that arises in relation to stratification is whether each stratum should have equivalent numbers of subjects in the sample (termed disproportionate sampling) or whether the numbers of subjects should be selected in proportion to their occurrence in the population (termed proportionate sampling). For example, there is no list of all the heart surgery patients who complete rehabilitation programs in the United States. As the sample size increases, the sample mean is also more likely to have a value similar to that of the population mean. In the example just presented with a sample size of 160, if 40 subjects withdrew from the study, then 120 subjects were retained or completed the study. 70 simple . Attritionrate=40(numberwithdrawing)160(samplesize)=0.25100%=25% Biases may be introduced that make generalization to the broader target population difficult to defend. The retention rates for both groups were very strong and comparable (treatment group 89% and comparison group 90%). Would you like email updates of new search results? Djukic, Kovner, Budin, and Norman (2010) studied the effect of nurses perceived physical work environment on their job satisfaction and described their sampling frame in the following excerpt. However, many researchers use nonprobability sampling because in many cases, probability sampling is not practical, feasible, or ethical. This situation usually occurs because of an interaction of the systematic bias with the treatment. Often researchers identify either the attrition rate or the retention rate but not both. J Adv Nurs. Cutcliffe JR, et al. Copyright 2021 Wolters Kluwer Health, Inc. All rights reserved. You may hold opinions about the adequacy of these techniques, but there is not enough information to make a judgment. Figure 15-2 Sampling error. In stratified random sampling, the subjects are randomly selected on the basis of their classification into the selected strata. This ensures that each nurse employed by the health care system has an equal and independent chance for selection into the study sample. Sample attrition rate is calculated by dividing the number of subjects withdrawing from a study by the sample size and multiplying the results by 100%. Because the subjects have something in common, their values tend to be similar to the values of others in the sample but different in some way from the values of the population as a whole. Before According to sampling theory, it is impossible to select a sample randomly from a population that cannot be clearly defined. Qualitative and sometimes quantitative research The term subject, and sometimes research participant, is used within the context of the postpositivist paradigm of quantitative research (see Chapter 2). The higher the refusal rate, the less the sample is representative of the target population. For example, one could conduct a study in which the defined population was all living recipients of heart and lung transplants. Twiss et al. 2023 Feb 4;11(4):452. doi: 10.3390/healthcare11040452. Because this is a quasi-experimental study that examined the impact of the treatment on the dependent or outcome variables, the increased controls imposed by the sampling criteria strengthened the likelihood that the study outcomes were caused by the treatment and not by extraneous variables. In large population sets, elements may already have assigned numbers. "Sampling theory was developed to determine the most effective was of acquiring a sample that accurately reflects the population under study. Therefore, the sampling design of the NHIS includes oversampling of minorities. (, Using Statistics to Determine Differences, The Practice of Nursing Research Appraisal Synthesis and Gener, Quantitative, outcomes, and intervention research, Quantitative, qualitative, outcomes, and intervention research, Qualitative and sometimes quantitative research. In sampling methods, parameters of the population are estimated from the sample drawn from the population. Because of the importance of generalizing, there are risks to defining the accessible population too narrowly. Exclusion criteria tend to increase the systematic bias in the sample and increase the sampling error. Nurs Health Sci. In this case, mathematically weighting the findings from each stratum can equalize the representation to ensure proportional contributions of each stratum to the total score of the sample. Cluster sampling The researcher, who has a vested interest in the study, could (consciously or unconsciously) select subjects whose conditions or behaviors are consistent with the study hypothesis. Degirmen, Ozerdogan, Sayiner, Kosgeroglu, and Ayranci (2010, p. 153) conducted a pretest-posttest randomized controlled experimental study to determine the effect of hand and foot massage and foot massage only interventions on the postoperative pain of women who had a cesarean operation. Sample surveys. This study included clearly identified inclusion and exclusion sampling or eligibility criteria that are presented in the following excerpt.
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