Definition: Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population. If for some reasons, the sample does not represent the population, the variation is called a sampling error. Show Description: Random sampling is one of the simplest forms of collecting data from the total population. Under random sampling, each member of the subset carries an equal opportunity of being chosen as a part of the sampling process. For example, the total workforce in organisations is 300 and to conduct a survey, a sample group of 30 employees is selected to do the survey. In this case, the population is the total number of employees in the company and the sample group of 30 employees is the sample. Each member of the workforce has an equal opportunity of being chosen because all the employees which were chosen to be part of the survey were selected randomly. But, there is always a possibility that the group or the sample does not represent the population as a whole, in that case, any random variation is termed as a sampling error. An unbiased random sample is important for drawing conclusions. For example when we took out the sample of 30 employees from the total population of 300 employees, there is always a possibility that a researcher might end up picking over 25 men even if the population consists of 200 men and 100 women. Hence, some variations when drawing results can come up, which is known as a sampling error. One of the disadvantages of random sampling is the fact that it requires a complete list of population. For example, if a company wants to carry out a survey and intends to deploy random sampling, in that case, there should be total number of employees and there is a possibility that all the employees are spread across different regions which make the process of survey little difficult. The entire group of people or objects to which the researcher wishes to generalize the study findings Meet set of criteria of interest to researcher Examples All institutionalized elderly with Alzheimer's All people with AIDS All low birth weight infants All school-age children with asthma All pregnant teens Accessible population the portion of the population to which the researcher has reasonable access; may be a subset of the target population May be limited to region, state, city, county, or institution Examples All institutionalized elderly with Alzheimer's in St. Louis county nursing homes All people with AIDS in the metropolitan St. Louis area All low birth weight infants admitted to the neonatal ICUs in St. Louis city & county All school-age children with asthma treated in pediatric asthma clinics in university-affiliated medical centers in the Midwest All pregnant teens in the state of Missouri Samples Terminology used to describe samples and sampling methods Sample = the selected elements (people or objects) chosen for participation in a study; people are referred to as subjects or participants Sampling = the process of selecting a group of people, events, behaviors, or other elements with which to conduct a study Sampling frame = a list of all the elements in the population from which the sample is drawn Could be extremely large if population is national or international in nature Frame is needed so that everyone in the population is identified so they will have an equal opportunity for selection as a subject (element) Examples A list of all institutionalized elderly with Alzheimer's in St. Louis county nursing homes affiliated with BJC A list of all people with AIDS in the metropolitan St. Louis area who are members of the St. Louis Effort for AIDS A list of all low birth weight infants admitted to the neonatal ICUs in St. Louis city & county in 1998 A list of all school-age children with asthma treated in pediatric asthma clinics in university-affiliated medical centers in the Midwest A list of all pregnant teens in the Henderson school district Randomization = each individual in the population has an equal opportunity to be selected for the sample Representativeness = sample must be as much like the population in as many ways as possible Sample reflects the characteristics of the population, so those sample findings can be generalized to the population Most effective way to achieve representativeness is through randomization; random selection or random assignment Parameter = a numerical value or measure of a characteristic of the population; remember P for parameter & population Statistic = numerical value or measure of a characteristic of the sample; remember S for sample & statistic Precision = the accuracy with which the population parameters have been estimated; remember that population parameters often are based on the sample statistics Probability Sampling Methods Also called random sampling
Types of probability sampling - see table in course materials for details Simple random
A table displaying hundreds of digits from 0 to 9 set up in such a way that each number is equally likely to follow any other See text for random sampling details & table of random numbers Stratified random Population is divided into subgroups, called strata, according to some variable or variables in importance to the study Variables often used include: age, gender, ethnic origin, SES, diagnosis, geographic region, institution, or type of care Two approaches to stratification - proportional & disproportional Proportional Subgroup sample sizes equal the proportions of the subgroup in the population Example: A high school population has 15% seniors 25% juniors 25% sophomores 35% freshmen With proportional sample the sample has the same proportions as the population Disproportional Subgroup sample sizes are not equal to the proportion of the subgroup in the population Example Class Population Sample Seniors 15% 25% Juniors 25% 25% Sophomores 25% 25% Freshmen 35% 25% With disproportional sample the sample does not have the same proportions as the population Cluster random sampling A random sampling process that involves stages of sampling The population is first listed by clusters or categories Procedure Randomly select 1 or more clusters and take all of their elements (single stage cluster sampling); e.g. Midwest region of the US Or, in a second stage randomly select clusters from the first stage of clusters; eg 3 states within the Midwest region In a third stage, randomly select elements from the second stage of clusters; e.g. 30 county health dept. nursing administrators from each state Systematic A random sampling process in which every kth (e.g. every 5th element) or member of the population is selected for the sample after a random start is determined Example Population (N) = 2000, sample size (n) = 50, k=N/n, so k = 2000 ) 50 = 40 Use a table of random numbers to determine the starting point for selecting every 40th subject With list of the 2000 subjects in the sampling frame, go to the starting point, and select every 40th name on the list until the sample size is reached. Probably will have to return to the beginning of the list to complete the selection of the sample. Is a part selected from population?Sampling. It is the process of selecting a sample from the population. For this purpose, the population is divided into a number of parts called sampling units. Most of the educational phenomena consist of a large number of units.
What is a process of selecting unit from a population of interest?Sampling is defined as a technique of selecting individual members or a subset from a population in order to derive statistical inferences, which will help in determining the characteristics of the whole population.
What is population sampling method?Population sampling is the process of taking a subset of subjects that is representative of the entire population. The sample must have sufficient size to warrant statistical analysis.
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