Hence, sampling theory also consists of estimation methods. The study sample might have been stronger with a more equal number of NP and PA subjects. All rights reserved. Understanding the differences in sampling techniques may aid nurses in effective appraisal of research literature and provide a reference pointfor nurses who engage in cardiovascular research. Am J Nurs 2021;121(1):647. 03 Obese individuals who choose to enter a program to lose weight may differ from obese individuals who do not enter a program. Importantly, these types of studies do not focus on reasons for the occurrence of the phenomenon. As the sample size becomes larger, overall variation in sample values decreases, with more values being close to the sample mean. (608) 262-2020 Sampling criteria, also referred to as eligibility criteria, include a list of characteristics essential for membership or eligibility in the target population. Bethesda, MD 20894, Web Policies The accessible population might be elements within a country, state, city, hospital, nursing unit, or clinic, such as the adults with diabetes in a primary care clinic in Fort Worth, Texas. The .gov means its official. Fouladbakhsh and Stommel (2010, p. E8) used multistage cluster sampling in their study of the complex relationships among gender, physical and psychological symptoms, and use of specific CAM [complementary and alternative medicine] health practices among individuals living in the United States who have been diagnosed with cancer. These researchers described their sampling method in the following excerpt from their study. The plan is developed to enhance representativeness, reduce systematic bias, and decrease the sampling error. 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%. Patient satisfaction with triage nursing care in Hong Kong. The individual units of the population and sample are called elements. Hainer V, et al. The eligible RNs were those who had a functioning work e-mail account and who worked fulltime, on inpatient units, providing direct patient care. (Djukic et al., 2010, pp. A sampling plan describes the strategies that will be used to obtain a sample for a study. Decisions regarding sampling quotas are made prior to beginning the study. Inferential statistical analyses are based on the assumption that the sample from which data were derived has been obtained randomly. While more cost-effective and often more convenient, nonprobability sampling increases the risk of sampling bias and therefore limits generalizability and creates threats to research validity. 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, One question that arises in relation to stratification is whether each stratum should have equivalent numbers of subjects in the sample (termed, A self-administered questionnaire was mailed to an initial stratified random sample [sampling method] of 3,900 NPs and PAs practicing in the United States. All samples with human subjects must be, 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. You may also needUsing Statistics to Determine DifferencesUsing Statistics to Describe VariablesMeasurement ConceptsUsing Statistics to PredictSelecting a Quantitative Research DesignCollecting and Managing DataFrameworksDisseminating Research Findings 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). For example, suppose a researcher is conducting a study of stress among medicalsurgical nurses. 30 One of the most important surveys that stimulated improvements in sampling techniques was the U.S. census. 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 defines the process of making the sample selections; sample denotes the selected group of people or elements included in a study. Exclusion sampling criteria are characteristics that can cause a person or element to be excluded from the target population. Most of the variation from the mean is in the same direction; it is systematic. There are two main categories of sampling methods: probability and non-probability. All the values in the sample may tend to be higher or lower than the mean of the population (Thompson, 2002). The researcher selects subjects from the sampling frame using a sampling plan. These studies are referred to as population studies (Barhyte, Redman, & Neill, 1990). (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%. High refusal rates to participate in a study have been linked to individuals with serious physical and emotional illnesses, low socioeconomic status, and weak social networks (Neumark, Stommel, Given, & Given, 2001). 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. For example, if 200 potential subjects met the sampling criteria, and 40 refused to participate in the study, the refusal rate would be 20%. FOIA The study was conducted at a large urban hospital in the U.S. northeast region that is a nongovernment, not-for-profit, general medical and surgical major teaching hospital. However, even in a random sample, systematic variation can occur if potential subjects decline participation. Systematic sampling For a sample to be representative, it must be similar to the target population in as many ways as possible. In a study of factors that affect the self-care behaviors of female high school students with dysmenorrhea, researchers randomly sampled five classes to survey within each grade. This study has an excellent acceptance rate (100%) and a very strong sample retention rate of 90% for a 24-month-long study. The opposite of the attrition rate is the retention rate, or the number and percentage of subjects completing the study. 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. 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. All of these factors limit representativeness and limit our understanding of the phenomena important in practice. In some studies, the entire population is the target of the study. Sampling Design in Nursing Research - PubMed Acceptancerateformula=numberpotentialsubjectsagreeingtoparticipatenumberpotentialsubjectsmeetingsamplecriteria100% Because of the importance of generalizing, there are risks to defining the accessible population too narrowly. 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. Degirmen et al. However, sampling criteria should not become so restrictive that the researcher cannot find an adequate number of study participants. Precision in estimating parameters requires well-developed methods of measurement that are used repeatedly in several studies. Values of individual subjects vary from the value of the sample mean. 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. It is better to provide a rate in addition to the number of subjects withdrawing or completing a 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. The number touched is the starting place. In cluster sampling, the researcher develops a sampling frame that includes a list of all the states, cities, 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). 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. MeSH 38 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). The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Sample Representativeness (2009) conducted a quasi-experimental study to examine the effects of strength and weight training (ST) exercises on muscle strength, balance, and falls of breast cancer survivors (BCSs) with bone loss (population). 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. 72 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. 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. The acceptance rate, the sample and group retention rates, and the reasons for subjects attrition indicate limited potential for systematic variation in the study sample. Quota sampling Sampling theory was developed to determine mathematically the most effective way to acquire a sample that would accurately reflect the population under study. Disclaimer. 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. 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. Random variation is the expected difference in values that occurs when one examines different subjects from the same sample. Random Variation The term probability sampling method refers to the fact that every member (element) of the population has a probability higher than zero of being selected for the sample. Sampling Theory Subjects are selected to maximize the effects of the independent variable and minimize the effects of variation in other extraneous variables so that they have a limited impact on the dependent variable scores. The researcher can use a computer to select these numbers randomly to obtain a sample. Subjects within each stratum are expected to be more similar (homogeneous) in relation to the study variables than they are to be similar to subjects in other strata or the total sample. 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. Steinke EE. National Library of Medicine 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. The higher the refusal rate, the less the sample is representative of the target population. Each column will present the concepts that underpin evidence-based practice-from research design to data interpretation. 2021 Jul 24;13(8):2529. doi: 10.3390/nu13082529. 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. Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data. In the first stage, 339 primary sampling units were selected from about 1,900 area sampling units representing counties, groups of adjacent counties, or metropolitan areas covering the 50 states and the District of Columbia [1st stage cluster sampling]. 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. 70 Sampling Theory. Subjects and the care they receive in research centers are different from patients and the care they receive in community clinics, public hospitals, veterans hospitals, and rural health clinics. Clipboard, Search History, and several other advanced features are temporarily unavailable. In experimental studies that use a control group, subjects are randomly selected and randomly assigned to either the control group or the experimental group. PDF If you could just provide me with a sample: examining sampling in A heterogeneous sample increases your ability to generalize the findings to a larger target population. According to sampling theory, it is impossible to select a sample randomly from a population that cannot be clearly defined. Twiss et al. 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%. doi: 10.7759/cureus.16260. The treatment group retention was 110 women with a retention rate of 89% (110 124 100% = 88.7% = 89%). 18. Imagine trying to arrange personal meetings with 100 people, each in a different part of the United States. In large population sets, elements may already have assigned numbers. 33 However, some researchers still use a table of random numbers to select a random sample. (2009) identified specific inclusion and exclusion sampling criteria to designate the subjects in the target population precisely. J Adv Nurs. Only gold members can continue reading. The following sections explain these concepts; later in the chapter, these concepts are used to explain various sampling methods. A sampling plan describes the strategies that will be used to obtain a sample for a study. There can be some differences in the probability for the selection of each element, depending on whether the name or number of the selected element is replaced before the next name or number is selected. For example, if a study had a sample size of 160, and 40 people withdrew from the study, the attrition rate would be 25%. Sampling strategies have been devised to accomplish these three tasks and to optimize sample selection. For example, a narrow definition of the accessible population reduces the ability to generalize from the study sample to the target population and diminishes the meaningfulness of the findings. Table 15-2 is useful only if the population number is less than 100. Twiss et al. Because of space restrictions, this editorial focuses on the randomised controlled trial (RCT) as an example of quantitative research,and grounded theory as an exampleofqualitativeresearch . Again, these units could be people, events, or other subjects of interest. With a comparison group, there is an increase in the possibility of preexisting differences between that group and the experimental group receiving the treatment. With this knowledge, you can make intelligent judgments about sampling when you are critically appraising studies or developing a sampling plan for your own study. This ensures that each nurse employed by the health care system has an equal and independent chance for selection into the study sample. Log In or Register to continue Twiss et al. The sample retention was 223 women for a retention rate of 90% (223 249 100% = 89.6% = 90%), and the sample attrition rate was 26 women for an attrition rate of 10% (100% 90% = 10%). Reasons for withdrawal included the desire for a different exercise program (. You might identify broad sampling criteria for a study, such as all adults older than 18 years of age able to read and write English. HHS Vulnerability Disclosure, Help Probability sampling. The term used by researchers depends of the philosophical paradigm that is reflected in the study and the design. Misrepresenting random sampling? Thus, a study that uses random sampling techniques may have such restrictive sampling criteria that the sample is not truly random. Simple random sampling Selection of the study participants. evolve.elsevier.com/Grove/practice/ It is an effective method to get information that can be used to develop hypotheses and propose associations. 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. Sample attrition is the withdrawal or loss of subjects from a study. 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. Wooldridge JM. In the first stage, 339 primary sampling units were selected from about 1,900 area sampling units representing counties, groups of adjacent counties, or metropolitan areas covering the 50 states and the District of Columbia [1st stage cluster sampling]. 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. However, such disadvantages can be offset to some extent by the use of a larger sample. 2012 Jan;5(1):7-13. doi: 10.4103/0974-1208.97779. Since researchers generally do not have access to the full population of interest for a research project (the target population), they must rely on studying a subset of that population (the study sample or sample population). That said, the researcher must be alert to a number of methodological and ethical pitfalls associated with recruitment and retention.18 Recruitment techniques such as monetary incentives for participation may bias the study outcome; and the extent that subjects systematically discontinue participation in the study may disrupt the balance of the study sample, creating threats to generalizability and study validity. Cluster sampling is used in two situations. In Nyquist-Shannon sampling theory, a given polychromatic (i.e., multiple frequency) temporal (or spatial) continuous function, f(x), with known maximum spatial frequency Xmax, is determined by its sampled ordinates at a series of points spaced less than or equal to a distance of 1/(2Xmax) apart (Shannon, 1949) The threshold 2Xmax is called the Nyquist rate and is an . The difference between a sample statistic and a population parameter is called the sampling error (Figure 15-2). 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. For example, if study participants who choose to leave the study tend to be poorer with less education, the nurse researcher will have difficulty generalizing the study findings to these populations. FOIA The likelihood is increased that the sample is representative of the target population and the results are an accurate reflection of reality. The retention rates for both groups were very strong and comparable (treatment group 89% and comparison group 90%). New York, NY: W.W. Norton and Company; 2007. p. 33353. Cluster sampling provides a means for obtaining a larger sample at a lower cost. representative in relation to the variables you are studying and to other factors that may influence the study variables. 56 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. These researchers obtained their sample using a simple random sampling method that is described in the following excerpt from their study. 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. A sample is collected from a sampling frame, or the set of information about the accessible units in a sample.