Simple Random Sampling - A Research Paper.
The basic principle of simple random sampling is like drawing names out of a hat and is based on the mathematical property that a truly random sample (if big enough) will be representative of the target population. The simple random sample has two key properties: Unbiased: Each unit has the same chance of being selected. Independent: The.
Under simple random sampling without replacement (SRSWOR) scheme, the expressions of bias and mean-squared error (MSE) up to the first order of approximation are derived. The results obtained have.
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.
Disadvantages of Simple random sampling. Simple random sampling suffers from the following demerits: 1. This method carries larger errors from the same sample size than that are found in stratified sampling. 2. In simple random sampling, the selection of sample becomes impossible if the units or items are widely dispersed. 3.
M. H. Alvi (2016): A Manual for Selecting Sampling Techniques in Research 4 PREFACE The Manual for Sampling Techniques used in Social Sciences is an effort to describe various types of sampling methodologies that are used in researches of social sciences in an easy and understandable way.
One way of obtaining a random sample is to give each individual in a population a number, and then use a table of random numbers to decide which individuals to include. 1 For example, if you have a sampling frame of 1000 individuals, labelled 0 to 999, use groups of three digits from the random number table to pick your sample. So, if the first three numbers from the random number table were.
Because simple random sampling is a fair way to select a sample, it is reasonable to generalize the results from the sample back to the population. Simple random sampling is not the most statistically efficient method of sampling and you may, just because of the luck of the draw, not get good representation of subgroups in a population.