444-445). Each of these sampling strategies has trade-offs. A sampling plan defines the process of making the sample selections; sample denotes the selected group of people or elements included in a study. International Journal for Quality in Health Care, 15 (3): 261-266. 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). However, in quasi-experimental or experimental studies, the primary purpose of sampling criteria is to limit the effect of extraneous variables on the particular interaction between the independent and dependent variables. The sampling method implemented in a study varies with the type of research being conducted. sharing sensitive information, make sure youre on a federal The retention rates for both groups were very strong and comparable (treatment group 89% and comparison group 90%). The method of achieving this opportunity is referred to as random sampling. government site. The articles will be accompanied by a podcast offering more insight and context from the author. From a sampling theory point of view, randomization means that each individual in the population should have a greater than zero opportunity to be selected for the sample. FOIA Women were excluded if they (a) had a recurrence of breast cancer; (b) were currently taking hormone therapy, bisphosphonates, glucocorticosteroids, or other drugs affecting bone; (c) were currently engaging in ST exercises; (d) had a body mass index (BMI) of 35 or greater; (e) had serum calcium, creatinine, or thyroid stimulating hormone (if on thyroid therapy) outside normal limits; or (f) had active gastrointestinal problems or other conditions that prohibited ST exercises, risedronate, calcium, or vitamin D intake [exclusion sampling criteria]. (Twiss et al., 2009, p. 72). For example, if 200 potential subjects met the sampling criteria, and 40 refused to participate in the study, the refusal rate would be 20%. Cluster sampling provides a means for obtaining a larger sample at a lower cost. 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%. Exclusion sampling criteria are characteristics that can cause a person or element to be excluded from the target population. It is applicable only to random samples. Copyright 2021 Wolters Kluwer Health, Inc. All rights reserved. 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. Random sampling can be: simple random sampling stratified random sampling, and Am J Nurs. Each column will present the concepts that underpin evidence-based practice-from research design to You may hold opinions about the adequacy of these techniques, but there is not enough information to make a judgment. There are two main categories of sampling methods: probability and non-probability. Random variation is the expected difference in values that occurs when one examines different subjects from the same sample. Many of these studies use data available in large databases, such as the census data or other government-maintained databases. In experimental studies that use a control group, subjects are randomly selected and randomly assigned to either the control group or the experimental group. Similar to stratified random sampling, cluster random sampling uses natural geographic and organizational clusters of potential research participants to create a sampling frame.2 For example, in exploring work satisfaction among clinical nurses using a cluster sampling methodology, the researcher would randomly select cities from which to draw the sample, then randomly select hospitals from within each city, and finally randomly assign nurses from within each hospital to participate in the study. 11. Age limitations are often specified, such as adults 18 years and older. This theory was developed so as to establish which of the ways utilized in acquiring . St. Louis: Elsevier; 2017. p. 32962. Probability sampling is the random selection of elements from the population, where each element of the population has an equal and independent chance of being included in the sample. The theoretical, mathematical rationale for decisions related to sampling emerged from survey research, although the techniques were first applied to experimental research by agricultural scientists. All subsets of the population, which may differ from one another but contribute to the parameters of the population, have a chance to be represented in the sample. In the past, some groups, such as women, ethnic minorities, elderly adults, and poor people, were unnecessarily excluded from studies (Larson, 1994). Capili B. Perspectives on Emotional Care: A Qualitative Study with Cancer Patients, Carers, and Health Professionals. The sample selection strategy (the sampling plan) is constructed with the intent to minimize selection bias in the selection of participants from within the sampling frame. Most textbooks on sampling describe this procedure (Levy & Lemsbow, 1980; Thompson, 2002; Yates, 1981). Sampling error reduces the power of a study, or the ability of the statistical analyses conducted to detect differences between groups or to describe the relationships among variables (Aberson, 2010; Cohen, 1988). A study might have inclusion or exclusion sampling criteria (or both). MeSH As the sample size becomes larger, overall variation in sample values decreases, with more values being close to the sample mean. These sampling criteria probably were narrowly defined by the researchers to promote the selection of a homogeneous sample of postmenopausal BCSs with bone loss. 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. Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data. 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 refusal rate is calculated by dividing the number of potential subjects refusing to participate by the number of potential subjects meeting sampling criteria and multiplying the results by 100%. Quantitative, outcomes, and intervention research The difference is random because the value of each subject is likely to vary in a different direction. Cluster Sampling 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. The criteria are developed from the research problem, the purpose, a review of literature, the conceptual and operational definitions of the study variables, and the design. In the example mentioned earlier, 200 potential subjects met the sampling criteria; 160 agreed to participate in the study, and 40 refused. 30 The sampling methods to be included in this text are identified in Table 15-1 and are linked to the types of research that most commonly incorporate them. In some cases, a hypothetical population is defined for a study. One of the most important surveys that stimulated improvements in sampling techniques was the U.S. census. The variable or variables chosen for stratification need to be correlated with the dependent variables being examined in the study. Essentials of epidemiology in public health. 2012 Jan;5(1):7-13. doi: 10.4103/0974-1208.97779. From a sampling theory point of view, randomization means that each individual in the population should have a greater than zero opportunity to be selected for the sample. Quantitative, outcomes, and intervention research 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. In a study by the UK Neonatal Staffing Study Group of the association between patient volume, staffing levels, and workload and risk-adjusted outcomes of neonatal mortality and hospital-acquired infections, the sample was stratified into units by patient volume (high, medium, low), pediatrician availability (high and low), and nurse-to-patient ratios (high and low). The following sections explain these concepts; later in the chapter, these concepts are used to explain various sampling methods. Keyword Highlighting Thesample is the set of data collected from the population of interest or target population. For example, suppose a researcher is conducting a study of stress among medicalsurgical nurses. In stratified random sampling, the subjects are randomly selected on the basis of their classification into the selected strata. Quantitative, outcomes, and intervention research apply a variety of probability and nonprobability sampling methods. Editor's note: This is the third article in a series on clinical research by nurses. Sample selection is a very important but sometimes underestimated part of a research study. Hu Li Za Zhi. When a systematic bias occurs in an experimental study, it can lead the researcher to believe that a treatment has made a difference when, in actuality, the values would be different even without the treatment. However, the study would have been strengthened by a discussion of the process for random sampling and a clarification of how the subjects were assigned to groups. Cutcliffe JR, et al. This pointthat studying an entire population is, in most cases, unnecessaryis the key to the theory of sampling. Sampling theory was developed to determine mathematically the most effective way to acquire a sample that would accurately reflect the population under study. The theoretical, mathematical rationale for decisions related to sampling emerged from survey research, although the techniques were first applied to experimental research by agricultural scientists. Yang MF, et al. 63 Accessible potential research participants who meet the research subject selection criteria create the sampling frame from which the study sample is drawn. Often researchers identify either the attrition rate or the retention rate but not both. A study might have inclusion or exclusion sampling criteria (or both). That said, nonprobability sampling has important drawbacks. Nutrients. The NHIS [National Health Interview Survey] methodology employs a multistage probability cluster sampling design [sampling method] that is representative of the NHIS target universe, defined as the civilian noninstitutionalized population (Botman, Moore, Moriarty, & Parsons, 2000, p. 14; National Center for Health Statistics). Good arguments exist for both approaches. In general, the larger the sample size, the smaller the sampling error. These values do not vary randomly around the population mean. The remaining 26 women (exercise = 14; comparison = 12) withdrew from the study before 24 months. Research ethics, informed consent, and participant recruitment. PMC 2020 Oct;120(10):41-44. doi: 10.1097/01.NAJ.0000718644.96765.b3. Simple random sampling Some populations are elusive and constantly changing. 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. These criteria ensure a large target population of. According to sampling theory, it is impossible to select a sample randomly from a population that cannot be clearly defined. Patients who answered the call and agreed to participate were included in the study. We can estimate the population parameter by identifying the values obtained in previous studies examining the same variables. 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. These studies are referred to as population studies (Barhyte, Redman, & Neill, 1990). In other studies, the entire population of interest in the study is small and well defined. Obese individuals who choose to enter a program to lose weight may differ from obese individuals who do not enter a program. States, cities, institutions, or organizations are selected randomly as units from which to obtain elements for the sample. Representativeness is usually evaluated by comparing the numerical values of the sample (a statistic such as the mean) with the same values from the target population. 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. Methods of achieving random sampling are described later in the chapter. Selection of the study participants. 2014 Jun;61(3):105-11. doi: 10.6224/JN.61.3.105. Values of individual subjects vary from the value of the sample mean. Continuing with the opioid example above, the nurse researcher may ask study participants who started opioid use during adolescence to recruit other participants with a similar substance use history. The accessible population must be representative of the target population. (2009) of the effects of ST exercises on muscle strength, balance, and falls of BCSs with bone loss was introduced earlier in this chapter with the discussion of sampling criteria; the following excerpt presents the acceptance rate and sample attrition for this study. Probability Sampling Methods Sampling theory in nursing research is the process of grouping a set of individuals, events, behaviors, or other items to investigate. The higher the retention rate, the more representative the sample is of the target population, and the more likely the study results are an accurate reflection of reality. These biases are similar to biases that may be encountered in a nonrandom sample (Thompson, 2002). Patient volume, staffing, and workload in relation to risk-adjusted outcomes in a random stratified sample of UK neonatal intensive care units: a prospective evaluation. Key concepts of sampling theory include populations, target population, sampling or eligibility criteria, accessible population, elements, representativeness, sampling frames, and sampling methods or plans. Because of the importance of generalizing, there are risks to defining the accessible population too narrowly. evolve.elsevier.com/Grove/practice/ 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).
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