Random Sampling in Healthcare Essay
HLT 362 Topic 1 Questions to Be Graded: Exercises 6, 8 and 9
Details:Random Sampling in Healthcare Essay
Complete Exercises 6, 8, and 9 in Statistics for Nursing Research: A Workbook for Evidence-Based Practice, and submit as directed by the instructor.
HLT 362 Topic 2 DQ 1
Explain the importance of random sampling. What problems/limitations could prevent a truly random sampling and how can they be prevented?
HLT 362 Topic 2 DQ 2
Explain each sampling technique discussed in the “Visual Learner: Statistics” in your own words, and give examples of when each technique would be appropriate.Random Sampling in Healthcare Essay
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In the field of research different sampling technique are used for different fields. It is very essential to choose the adequate technique of sampling. In this paper first we clarify the proper meaning of sampling. Further we discus about the different techniques and types of sampling. We mainly concentrate on two types of probability and non- probability and their sub categories. Further we discus about the pros and cons of these techniques. Pros are the primary positive aspect of an idea process or thing. Cones are the primary negative aspects. It is very necessary to choose the write sampling technique for a specific research work. Before we choose the sampling technique it is necessary to know about the ‘Pros’ and ‘Cons’ of sampling technique. If the researcher know about the ‘Pros’ and ‘Cons’ he/she will select the adequate technique of sampling for his research work. Keywords: Sampling, Pros, Cons. Introduction Pros and Cons “Pros” are the primary positive aspects of an idea, process or thing; “Cons” are the primary negative aspects. The term Pros and Cons means both the primary positive and negative aspects of an idea, process or thing and is often used to clarify or decide whether that idea, process or thing is mainly positive or mainly negative.Random Sampling in Healthcare Essay Sampling Sampling is a technique (procedure or device) employed by a researcher to systematically select a relatively smaller number of representative items or individuals (a subset) from a per-defined population to serve as subjects (data source) for observation or experimentation as per objectives of his or her study. For example, if, by using some systematic device, you pick up a group of 100 undergraduates from out of a total of 1500 on the rolls of a college for testing their physical fitness, you have selected a desired sample from a particular population. Researchers usually use sampling for it is impossible to be testing every single individual in the population. Although it is a subset, it is representative of the population and suitable for research in terms of cost, convenience and time. Still, every researcher must keep in mind that the ideal scenario is to test all the individuals to obtain reliable, valid and accurate results. If testing all the individuals is impossible, that is the only time we rely on sampling techniques. True to the science of research and statistics, the sampling procedures must be carried out in consideration of several important factors such as (a) population variance, (b) size of the universe or population, (c) objectives of the study, (d) precision in results desired, (e) nature of the universe i.e. homogeneity or heterogeneity in the constituent units, (f) financial implications of the study, (g) nature and objectives of the investigation, (h)techniques of the sampling employed, (i) accuracy needed in making inference about the population being studied, and so on. Types of Sampling Techniques1.Probability Sampling: – Pro
This paper presents a stratified random sampling plan for estimating accuracy of bill processing performance for the health care bills submitted to third party payers in health care systems. Bill processing accuracy is estimated with two measures: percent accuracy and total dollar accuracy. Difficulties in constructing a sampling plan arise when the population strata structure is unknown, and when the two measures require different sampling schemes. To efficiently utilize sample resource, the sampling plan is designed to effectively estimate both measures from the same sample. The sampling plan features a simple but efficient strata construction method, called rectangular method, and two accuracy estimation methods, one for each measure. The sampling plan is tested on actual populations from an insurance company. Accuracy estimates obtained are then used to compare the rectangular method to other potential clustering methods for strata construction, and compare the accuracy estimation methods to other eligible methods. Computational study results show effectiveness of the proposed sampling plan.Random Sampling in Healthcare Essay
In quantitative research, the sample is presumed to be representative of the target population when it shares the attributes of that population. If the sample is not representative, varying degrees of sampling errors may compromise the generalization of research findings. Probability sampling is a common approach used by researchers to ensure that samples are indeed representative. It involves randomly selecting participants from a sampling frame (the portion of the target population that is accessible to the researchers), so that each individual in that sampling frame has an equal probability of being selected.
Probability sampling is a three-step procedure. The first step is to identify the target population — school-aged children, for example. The second step is to identify the sampling frame — perhaps all school-aged children in Community X. The third step is to randomly select the required sample of children from the sampling frame, which is often too large to constitute the study sample.
Probability sampling techniques include simple, systematic, stratified and cluster random sampling. The selection of a technique is often governed by the geographical distribution of the target population and/or by the characteristics of the population that may particularly interest the researchers. For instance, for a study at a single site on a uniform population, researchers may use simple random sampling. Excel or other statistics programs are used to randomly shuffle an electronic list of all available individuals in the sampling frame to select the required sample.Random Sampling in Healthcare Essay
Systematic random sampling is a form of simple random sampling that is especially attractive for large sampling frames. The researchers divide the accessible population (e.g., 1,000 people) by the required sample size (e.g., 100) to determine the sampling interval (i.e., 10). They then randomly select a number between 1 and the sampling interval number (e.g., 5). Starting with participant 5, they will select every tenth participant until they have recruited 100 people for their sample.
Researchers often use stratified random sampling when they believe that specific attributes must be proportionately represented in the sample. For example, if they believe that treatment response may vary depending on the severity of the disease, they may elect to recruit patients for two proportionate simple random sub samples: one for the segment of the population with the mild form of the disease and another for those with the severe form. The two sub samples are then merged to formulate the final sample.Random Sampling in Healthcare Essay
Cluster random sampling is especially useful when the research extends over a vast geographical area that may be costly and time consuming to cover through simple random sampling. For example, if researchers are studying primary care services in Nova Scotia, they may divide primary care providers into clusters across county lines and randomly select four or five clusters from which to sample.
Remember, probability sampling is meant to minimize sampling error associated with poorly representative samples. Readers should be concerned about research generalization if researchers fail to properly outline their probability sampling technique.Random Sampling in Healthcare Essay