official website and that any information you provide is encrypted Probability sampling. Addressing Bias in Electronic Health Record-Based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise. Attritionrate=40(numberwithdrawing)160(samplesize)=0.25100%=25%. Recruitment of hard-to-reach population subgroups via adaptations of the snowball sampling strategy. Often researchers identify either the attrition rate or the retention rate but not both. The accessible population must be representative of the target population. Some populations are elusive and constantly changing. A sampling method is the process of selecting a group of people, events, behaviors, or other elements that represent the population being studied. One of the most important surveys that stimulated improvements in sampling techniques was the U.S. census. For a study examining the relationship between patient satisfaction and triage nursing care in the ED, researchers randomly sampled every fifth person who presented to the ED. To enhance representation, this number could be proportionally weighted based on the size of each hospital's clinical nursing employee pool. 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%. In some studies, the entire population is the target of the study. For example, individuals who successfully lose weight would be a hypothetical population. The selection included all of the most populous primary sampling units in the United States and stratified probability samples (by state, area poverty level, and population size) of the less populous ones. Sampling theory was developed to determine mathematically the most effective way to acquire a sample that would accurately reflect the population under study. Your message has been successfully sent to your colleague. 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 population is a particular group of people, such as people who have had a myocardial infarction, or type of element, such as nasogastric tubes, that is the focus of the research. representative in relation to the variables you are studying and to other factors that may influence the study variables. Studies conducted in private hospitals usually exclude poor patients, and other settings could exclude elderly or undereducated patients. The treatment group retention was 110 women with a retention rate of 89% (110 124 100% = 88.7% = 89%). For example, if 200 potential subjects met the sampling criteria, and 40 refused to participate in the study, the refusal rate would be 20%. The site is secure. Patient satisfaction with triage nursing care in Hong Kong. Common Applications To use a table of random numbers, the researcher places a pencil or a finger on the table with the eyes closed. Fundamentals of mathematical statistics. To achieve simple random sampling, elements are selected at random from the sampling frame. The theoretical and mathematical rationale for sampling-related decisions evolved from survey research. 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. Theoretically, to obtain a probability sample, the researcher must develop a sampling frame that includes every element in the population. 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%. Table 15-2 is useful only if the population number is less than 100. If five subjects are to be selected from a population of 100 and the researcher decides to go across the column to the right, the subject numbers chosen are 58, 25, 15, 55, and 38. The sampling plan must be described in detail for purposes of critical appraisal, replication, and future meta-analyses. The researcher selects subjects from the sampling frame using a sampling plan. To achieve these goals, researchers need to understand the techniques of sampling and the reasoning behind them. The opposite of the attrition rate is the retention rate, or the number and percentage of subjects completing the study. You may hold opinions about the adequacy of these techniques, but there is not enough information to make a judgment. Each approach offers distinct advantages and disadvantages and must be considered critically. To enhance representativeness of the sample, researchers attempt to align the study sample with the target population on as many characteristics as possible. Exclusion criteria limit true randomness. New York, NY: W.W. Norton and Company; 2007. p. 33353. In some studies, the entire population is the target of the study. However, some researchers still use a table of random numbers to select a random sample. Good arguments exist for both approaches. I think this all sounds like a pretty standard way to . 66 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. The target population is the entire set of individuals or elements who meet the sampling criteria, such as women who have experienced a myocardial infarction in the past year. (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. 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. Exclusion criteria limit the generalization of the study findings and should be carefully considered before being used in a study. 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. 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. Potential subjects cannot be excluded just because they are too sick, not sick enough, coping too well, or not coping adequately. Find information about graduate programs? MeSH In the aforementioned situation, if proportions are used and the sample size is 100, the study would include only five Asians, hardly enough to be representative. 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). FOIA HHS Vulnerability Disclosure, NLM Support Center Degirmen et al. 94 For example, if stratification is being achieved by ethnicity and the population is 45% white non-Hispanic, 25% Hispanic nonwhite, 25% African American, and 5% Asian, your research team would have to decide whether to select equal numbers of each ethnic group or to calculate a proportion of the sample. With a comparison group, there is an increase in the possibility of preexisting differences between that group and the experimental group receiving the treatment. A modified grounded theory study of how psychiatric nurses work with suicidal people. Sampling involves selecting a group of people, events, behaviors, or other elements with which to conduct a study. Sampling criteria may include characteristics such as the ability to read, to write responses on the data collection instruments or forms, and to comprehend and communicate using the English language. For example, if 200 potential subjects met the sampling criteria, and 40 refused to participate in the study, the refusal rate would be 20%. 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. If 20% of the nursing target population is male, ideally, 20% of the study sample would be male as well. When the study is complete, the findings are generalized from the sample to the accessible population and then to the target population if the study has a representative sample (see the next section). 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. It is a selection process that ensures each participant the same probability of being selected. Random sampling is the best method for ensuring that a sample is representative of the larger population. Random sampling can be: It is the selection process in which the probability that any one individual or subject selected is not equal to the probability that another individual or subject may be chosen. The probability of inclusion and the degree to which the sample represents the population are unknown. The major problem with nonprobability sampling is that sampling bias can occur. Nonprobability sampling can be: Suresh KP and Chandrashekara, S. Sample size estimation and power analysis for clinical research studies.Journal of Human Reprouductive Sciences. Theoretical sampling is a qualitative sampling technique that evolves over the course of a study as the researcher begins to understand more from the emerging data. 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). A numerical value of a population is called a parameter. Thus, a study that uses random sampling techniques may have such restrictive sampling criteria that the sample is not truly random. According to sampling theory, it is impossible to select a sample randomly from a population that cannot be clearly defined. 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. Sampling, data collection, and data analysis. The selection included all of the most populous primary sampling units in the United States and stratified probability samples (by state, area poverty level, and population size) of the less populous ones. The target population is the entire set of individuals or elements who meet the sampling criteria, such as women who have experienced a myocardial infarction in the past year. 14-15) sampling text. 19 MeSH National Library of Medicine 444-445). The study would have been strengthened if the researchers would have included not only the numbers but also the sample and group retention rates. National Library of Medicine In experimental studies that use a control group, subjects are randomly selected and randomly assigned to either the control group or the experimental group. Network sampling helps recruit study participants who might otherwise be difficult to reach. The series is designed to give nurses the knowledge and skills they need to participate in research, step by step. Acceptancerateformula=numberpotentialsubjectsagreeingtoparticipatenumberpotentialsubjectsmeetingsamplecriteria100% To achieve simple random sampling, elements are selected at random from the sampling frame. Chang SF, Chuang MH. PMC Representativeness is usually evaluated by comparing the numerical values of the sample (a, The difference between a sample statistic and a population parameter is called the, Systematic variation or bias is most likely to occur when the sampling process is not random. Capili B. The term used by researchers depends of the philosophical paradigm that is reflected in the study and the design. Some values are higher and others are lower than the sample mean. government site. Accessibility These criteria ensure a large target population of heterogeneous or diverse potential subjects. In the past, some groups, such as women, ethnic minorities, elderly adults, and poor people, were unnecessarily excluded from studies (Larson, 1994). In these cases, it is often possible to obtain lists of institutions or organizations with which the elements of interest are associated. A study might have inclusion or exclusion sampling criteria (or both). Acceptancerate=160(numberaccepting)200(numbermeetingsamplingcriteria)=0.8100%=80%, Acceptancerate=100%refusalrateor100%20%=80%. The subgroup that you are going to study, intended to be representative of the population at large for the characteristics of interest. For example, if the researcher draws names out of a hat to obtain a sample, each name must be replaced before the next name is drawn to ensure equal opportunity for each subject. The difference between a sample statistic and a population parameter is called the sampling error (Figure 15-2). 93 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). Refusalrate=40(numberrefusing)200(numbermeetingsamplingcriteria)=0.2100%=20%. 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. 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. Keywords: nurses, male, turnover, adaptation, grounded theory, qualitative research Introduction Because of rapidly changing medical policies and changes in awareness about nursing . 2003 Nov;44(3):278-88. doi: 10.1046/j.1365-2648.2003.02803.x. TABLE 15-1 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. The likelihood is increased that the sample is representative of the target population and the results are an accurate reflection of reality. For example, the researcher places a pencil on 58 in Table 15-2, which is in the fourth column from the left and fourth row down. Most textbooks on sampling describe this procedure (Levy & Lemsbow, 1980; Thompson, 2002; Yates, 1981). An official website of the United States government. Types of probability sampling. Probability and Nonprobability Sampling Methods Commonly Applied in Nursing Research Sampling error limits generalizability and research accuracy (validity) by introducing bias into the study. Application of simple random sampling and the attrition of only three (4%) subjects from the study seem to provide a sample representative of the target population. Sampling criteria may include characteristics such as the ability to read, to write responses on the data collection instruments or forms, and to comprehend and communicate using the English language. You can define parameters by conducting a series of descriptive and correlational studies, each of which examines a different segment of the target population; then perform a meta-analysis to estimate the population parameter (Thompson, 2002). These sampling criteria probably were narrowly defined by the researchers to promote the selection of a homogeneous sample of postmenopausal BCSs with bone loss. 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. These sampling criteria probably were narrowly defined by the researchers to promote the selection of a homogeneous sample of postmenopausal BCSs with bone loss. Stratification is not as useful if one stratum contains only a small number of subjects. Knowledge of sampling methods is essential to design quality research. Quantitative, outcomes, and intervention research apply a variety of probability and nonprobability sampling methods. This goal can be accomplished in various ways, limited only by the imagination of the researcher. The treatment group retention was 110 women with a retention rate of 89% (110 124 100% = 88.7% = 89%). In statistical theory based on probability, this means that the sample is more likely to resemble the larger population, and thus more accurate inferences can be made about the larger population. Random sampling leaves the selection to chance and decreases sampling error and increases the validity of the study (Thompson, 2002). Qualitative and sometimes quantitative research One of the most important surveys that stimulated improvements in sampling techniques was the U.S. census. For example, if your study examines attitudes toward acquired immunodeficiency syndrome (AIDS), the sample should represent the distribution of attitudes toward AIDS that exists in the specified population. Purpose or purposeful sampling National Center for Biotechnology Information, Lister Hill National Center for Biomedical Communications, Agency for Healthcare Research and Quality, Centers for Disease Control and Prevention, Robert Wood Johnson Foundation County Health Rankings & Roadmaps, Centers for Medicare and Medicaid Services. The outcomes of the study were that foot and hand massage interventions significantly reduced postoperative pain experienced by the women and that foot and hand massage was significantly more effective than foot massage only. Sibley A, MacLeod MH, Patocka C, Yu J, Stryhn H, Jain T. Cureus. Burlington, MA: Jones and Bartlett Learning; 2020. A podcast with the authors is available at www.ajnonline.com. Cluster sampling In addition, a researcher cannot exclude a subset of people from selection as subjects because he or she does not agree with them, does not like them, or finds them hard to deal with. Four sampling designs have been developed to achieve probability sampling: simple random sampling, stratified random sampling, cluster sampling, and systematic sampling. While probability sampling minimizes selection bias and enhances generalizability of a study, it is often associated with sizable time and financial costs, particularly if the study sample is large. The researcher selects subjects from the sampling frame using a sampling plan. Research ethics, informed consent, and participant recruitment. Unauthorized use of these marks is strictly prohibited. The first situation is when a simple random sample would be prohibitive in terms of travel time and cost. If the first name is not replaced, the remaining 49 names have a 9 in 49 chance, or a 0.18 probability, of being selected. Sampling theory can be considered biased since the researcher is picking the population group they want to study. In a second step, primary sampling units were partitioned into substrata (up to 21) based on concentrations of African American and Hispanic populations [2nd stage cluster sampling]. The sample is the set of data collected from the population of interest or target population. Sample size estimation and power analysis for clinical research studies. 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). In some cases, this random selection continues through several stages and is referred to as multistage cluster sampling. Twiss et al. Twiss et al. In selecting the study sample, the primary goal is to minimize sampling error (the discrepancy between the study sample and the target population). However, it has some disadvantages. The sampling method implemented in a study varies with the type of research being conducted. different from the subjects who complete the study. Biases may be introduced that make generalization to the broader target population difficult to defend. 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 sample is collected from a sampling frame, or the set of information about the accessible units in a sample. It is often impossible to study every person in a large population of interest. All samples with human subjects must be volunteer samples, which includes individuals willing to participate in the study, to protect the rights of the individuals (Fawcett & Garity, 2009). Steinke EE. The next column will discuss measurement in quantitative research, including the concepts of reliability and validity. Figure 15-2 Sampling error. Cluster sampling provides a means for obtaining a larger sample at a lower cost. Sampling error occurs as a result of random variation and systematic variation. The term subject, and sometimes research participant, is used within the context of the postpositivist paradigm of quantitative research (see Chapter 2). Stratified random sampling 444-445) Degirmen et al. Ample research demonstrates the effectiveness of simulation-based experiences for improving learner performance. A sampling plan defines the process of making the sample selections; sample denotes the selected group of people or elements included in a study. Systematic variation, or systematic bias, is a consequence of selecting subjects whose measurement values are different, or vary, in some specific way from the population. People who do not have access to care are usually excluded from health-focused studies. The method of achieving this opportunity is referred to as random sampling. The sampling theory was established to help find the most suitable method of acquiring a sample that exactly represents the study population using mathematical formulae. In: 7. For example, suppose a researcher is conducting a study of stress among medicalsurgical nurses. Generalizing means that the findings can be applied to more than just the sample under study because the sample is representative of the target population. Again, these units could be people, events, or other subjects of interest. If the sampling frame is small, the researcher can write names on slips of paper, place the names in a container, mix well, and draw out one at a time until the desired sample size has been reached. As the sample size becomes larger, overall variation in sample values decreases, with more values being close to the sample mean. Age limitations are often specified, such as adults 18 years and older. Sometimes researchers provide an acceptance rate, or the number and percentage of the subjects who agree to participate in a study, rather than a refusal rate. This chapter examines sampling theory and concepts; sampling plans; probability and nonprobability sampling methods for quantitative, qualitative, outcomes, and intervention research; sample size; and settings for conducting studies. 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. network sampling (otherwise known as snowball sampling). The chapter concludes with a discussion of the process for recruiting and retaining subjects or participants for study samples in various settings. The eating inventory, body adiposity and prevalence of diseases in a quota sample of Czech adults. Another technique is to assign a number to each name in the sampling frame. Network sampling clearly violates both assumptions of probability samplingrandom and independent selectionand therefore is a nonprobability sampling method intended to develop a deeper theoretical understanding and does not allow for generalizability. Sampling error limits generalizability and research accuracy (validity) by introducing bias into the study. These samples are more likely to represent the population than samples obtained with nonprobability sampling methods. 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. Nurse researchers used a convenience sample of 36 toddlers from two developmental clinics to examine the relationship between postnatal weight gain, cortisol, and blood pressure in those who were born extremely preterm. In any case, it is rarely possible to obtain a purely random sample for nursing studies because of informed consent requirements. The articles will be accompanied by a podcast offering more insight and context from the author. Good arguments exist for both approaches. (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%. Yang MF, et al. These studies are referred to as population studies (Barhyte, Redman, & Neill, 1990). States, cities, institutions, or organizations are selected randomly as units from which to obtain elements for the sample. Researchers have adopted the assumptions of sampling theory identified for the census surveys and incorporated them within the research process (Thompson, 2002). 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%. This study included clearly identified inclusion and exclusion sampling or eligibility criteria that are presented in the following excerpt. 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. 2021 Jan 1;121(1):64-67. doi: 10.1097/01.NAJ.0000731688.58731.05. 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. Refusal and Acceptance Rates in Studies The extent of the difference is the sampling error (see Figure 15-2). 8600 Rockville Pike The use of the term control groupthe group not receiving the treatmentis usually limited to studies using random sampling and random assignment to the treatment and control groups. to maintaining your privacy and will not share your personal information without 07 To use a table of random numbers, the researcher places a pencil or a finger on the table with the eyes closed. HHS Vulnerability Disclosure, Help Systematic variation is greatest when a high number of subjects withdraw from the study before the data have been collected or when a large number of subjects withdraw from one group but not the other in the study (Kerlinger & Lee, 2000; Thompson, 2002). Random sampling is the best method for ensuring that a sample is representative of the larger population. In sampling methods, parameters of the population are estimated from the sample drawn from the population. The sampling criteria determine the target population, and the sample is selected from the accessible population within the target population (see Figure 15-1). To avoid disparities in the representation of any one hospital in a random sample of clinical nurses within the health care system, the researcher can use stratified random sampling to randomly select a designated number of nurses within each hospital. Measures which are Physical and physiological have higher chance of success in attaining these goals than measures that are psychological and behavioral. The sampling component is an important part of the research process that needs to be carefully thought out and clearly described. However, even in a random sample, systematic variation can occur if potential subjects decline participation. Sampling theory in nursing research is the process of grouping a set of individuals, events, behaviors, or other items to investigate. Hu Li Za Zhi. See Table 213-17 for examples of nonprobability sampling from the literature. The series is designed to give nurses the knowledge and skills they need to participate in research, step by step. Selection with replacement, the most conservative random sampling approach, provides exactly equal opportunities for each element to be selected (Thompson, 2002). 4. In general, the larger the sample size, the smaller the sampling error. 30 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; Thompson, 2002). Stigmatization and mental health in a diverse sample of transgender women. Probability sampling assumes both random selection of participants and sampling independence.6Sampling independence requires two conditions: the selection of one participant must not impact or affect the equal chance of selection of other participants, and selection probability should not be influenced by shared characteristics among prospective participants.6Random selection of participants from the sampling frame can be performed using a number of mechanisms, including a random digit-dialing telephone survey, a computerized randomization tool, a spreadsheet randomization function, a table of random numbers, or by manually drawing from a hat or flipping a coin.
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