These other methods frequently require you to have a greater understanding of your population than SRS requires. Alternative methods can reduce the need for a complete list and reduce the logistical headaches of a geographically extensive study.įor example, a national opinion poll company might consider an alternative method to assess differences between subpopulations, such as gender, race, and age. However, other sampling methods can be more efficient when creating the population list is difficult, your population is large and dispersed, or you need to guarantee sufficient data for specific subpopulations. Simple random sampling is a great option when you don’t know much about your population other than its membership. Large populations can require extensive amounts of time and resources just to create the complete list. For example, if you’re surveying a company and can easily obtain a list of employees from Human Resources, SRS isn’t too difficult. Other Methodsīecause you need a list of the entire population, simple random sampling is most feasible when working with a relatively small population that is already defined. Other sampling methods can ensure sufficient numbers from small subgroups that produce a clear picture and increase the ability to compare subgroups. Simple random sampling can fail to provide precise data about particular subgroups and differences between subgroups. Your sample might not include anyone in this vital group! If it does, it’ll be a tiny number that doesn’t provide a clear picture of this subgroup. However, there are only 50 people in town who are older than 90. You plan to obtain a sample size of 1000, which is 1 out of 100 residents. For example, in our town with 100,000 residents, imagine that we’re particularly interested in surveying those who are at least 90 years old. Insufficient Representation of Subpopulationsĭespite being entirely random, simple random sampling can miss important subpopulations and features in the population. Depending on the nature of your study, that process can be pretty expensive and time-consuming if your participants span a wide geographic range, particularly when you need a large sample size. Then you’ll need to contact and interact with everyone you randomly select. After simple random sampling, you can use statistical hypothesis tests to use the sample to draw conclusions about the population. In a study, having a representative sample improves both its internal and external validity. With SRS, you just randomly draw from the list until you have enough subjects.īecause simple random sampling tends to produce unbiased samples that mirror the population, it’s excellent for analysts who need to use a sample to infer the properties of a population (i.e., inferential statistics). Then, using that knowledge and a lot of preplanning, they divide the population into strata or clusters and perform other procedures before sampling. While the researchers need a list of the entire population, they don’t need other information about that population, its subpopulations, and its features.Ĭonversely, other more complex forms of sampling require researchers to understand the population’s characteristics. reason of using stratified and cluster sampling in which data is categorized into multiple category and then the item is picked in each category.Procedurally, SRS is the simplest method for obtaining an unbiased sample. Using simple random sampling consider that data is same, but it is not true in all the cases i.e.Limitations of Using Simple Random Sampling Data Collection from different area is not filtered in a later stage after initial input collection.It uses the concept of fair and equitable basis that chances are almost equal for every item in the total population.When the cost of using data is not so high then it is cost effective method.Simple random sampling is used when the research department is totally unknown about the facts of the population.It is totally unbiased so people consider it as it is transparent and simple to use.It consider that the population data is not skewed and not dependent on selection of any particular sample item.When the data is too large then it is good to use simple random sampling because chances of getting selection of each sample is almost same.Simple random sampling assumes that the population has no anomaly i.e.It is as simple sampling that can be applied in general in our day to day life.It is used when there is a population data which is homogeneous.If taken the random sample then they are categorized as:. Horizontal Integration vs Vertical Integration.Simple Interest Rate vs Compound Interest Rate.
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