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# 3.5 Simple Random Sampling and Other Sampling Methods

## How a Simple Random Sample Is Generated

❶We would select the 11 th , 9, nd and 2, st students from our list to be part of the sample.

## What is 'Simple Random Sample'   The entire process of sampling is done in a single step with each subject selected independently of the other members of the population. There are many methods to proceed with simple random sampling.

The most primitive and mechanical would be the lottery method. Each member of the population is assigned a unique number. Each number is placed in a bowl or a hat and mixed thoroughly. The blind-folded researcher then picks numbered tags from the hat. All the individuals bearing the numbers picked by the researcher are the subjects for the study.

Another way would be to let a computer do a random selection from your population. For populations with a small number of members, it is advisable to use the first method but if the population has many members, a computer-aided random selection is preferred. One of the best things about simple random sampling is the ease of assembling the sample. It is also considered as a fair way of selecting a sample from a given population since every member is given equal opportunities of being selected.

Another key feature of simple random sampling is its representativeness of the population. Theoretically, the only thing that can compromise its representativeness is luck. If the sample is not representative of the population, the random variation is called sampling error. An unbiased random selection of individuals is important so that if a large number of samples were drawn, the average sample would accurately represent the population.

However, this does not guarantee that a particular sample is a perfect representation of the population. Simple random sampling merely allows one to draw externally valid conclusions about the entire population based on the sample. Conceptually, simple random sampling is the simplest of the probability sampling techniques. It requires a complete sampling frame , which may not be available or feasible to construct for large populations.

Even if a complete frame is available, more efficient approaches may be possible if other useful information is available about the units in the population. Advantages are that it is free of classification error, and it requires minimum advance knowledge of the population other than the frame. Its simplicity also makes it relatively easy to interpret data collected in this manner.

For these reasons, simple random sampling best suits situations where not much information is available about the population and data collection can be efficiently conducted on randomly distributed items, or where the cost of sampling is small enough to make efficiency less important than simplicity.

If these conditions do not hold, stratified sampling or cluster sampling may be a better choice. Several efficient algorithms for simple random sampling have been developed. We continue until we have sample of desired size k.

The drawback of this method is that it requires random access in the set. The selection-rejection algorithm developed by Fan et al. A very simple random sort algorithm was proved by Sunter in  which simply assigns a random number drawn from uniform distribution 0, 1 as key to each item, sorts all items using the key and selects the smallest k items.

Vitter in  proposed reservoir sampling algorithm which is often widely used. This algorithm does not require advance knowledge of n and uses constant space. Random sampling can also be accelerated by sampling from the distribution of gaps between samples,  and skipping over the gaps.

Consider a school with students, and suppose that a researcher wants to select of them for further study. The difference between the two types is whether or not the sampling selection involves randomization. Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study. Following is a discussion of probability and non-probability sampling and the different types of each.

Probability Sampling — Uses randomization and takes steps to ensure all members of a population have a chance of being selected. There are several variations on this type of sampling and following is a list of ways probability sampling may occur:. Non-probability Sampling — Does not rely on the use of randomization techniques to select members. This is typically done in studies where randomization is not possible in order to obtain a representative sample.

Bias is more of a concern with this type of sampling. The different types of non-probability sampling are as follows:. The following Slideshare presentation, Sampling in Quantitative and Qualitative Research — A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding.

Examples of Data Collection Methods — Following is a link to a chart of data collection methods that examines types of data collection, advantages and challenges. Qualitative and Quantitative Data Collection Methods - The link below provides specific example of instruments and methods used to collect quantitative data. Sampling and Measurement - The link below defines sampling and discusses types of probability and nonprobability sampling.

Principles of Sociological Inquiry — Qualitative and Quantitative Methods — The following resources provides a discussion of sampling methods and provides examples. This pin will expire , on Change. ## Main Topics

Simple random sampling (also referred to as random sampling) is the purest and the most straightforward probability sampling strategy. It is also the most popular method for choosing a sample among population for a wide range of purposes.

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Advantages of Simple Random Sampling One of the best things about simple random sampling is the ease of assembling the sample. It is also considered as a fair way of selecting a sample from a given population since every member is given equal opportunities of being selected.