## Sampling Techniques

In statistics and survey methodology, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population . The three main advantages of sampling are that the cost is lower, data collection is faster, and the accuracy and quality of the data can be easily improved.

## Normal Distribution Curve

The normal distribution curve can help indicate if the results of a survey are significant and what the margin of error may be.

## Simple Random Sampling

In a simple random sample (SRS) of a given size, all such subsets of the frame are given an equal probability. Each element has an equal probability of selection. Furthermore, any given *pair* of elements has the same chance of selection as any other pair. This minimizes bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results.

However, SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population.

## Systematic Sampling

Systematic sampling relies on arranging the target population according to some ordering scheme, a random start, and then selecting elements at regular intervals through that ordered list. As long as the starting point is randomized, systematic sampling is a type of probability sampling. It is easy to implement and the stratification can make it efficient, if the variable by which the list is ordered is correlated with the variable of interest.

However, if periodicity is present and the period is a multiple or factor of the interval used, the sample is especially likely to be *un*representative of the overall population, decreasing its accuracy. Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to *quantify* that accuracy. As described above, systematic sampling is an EPS method, because all elements have the same probability of selection.

## Stratified Sampling

Where the population embraces many distinct categories, the frame can be organized by these categories into separate "strata. " Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. In this way, researchers can draw inferences about specific subgroups that may be lost in a more generalized random sample. Additionally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata, potentially enabling researchers to use the approach best suited for each identified subgroup. Stratified sampling can increase the cost and complicate the research design.

## Probability-Proportional-to-Size Sampling

Probability-proportional-to-size (PPS) is sampling in which the selection probability for each element is set to be proportional to its size measure, up to a maximum of 1.The PPS approach can improve accuracy for a given sample size by concentrating the sample on large elements that have the greatest impact on population estimates. PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available.

## Cluster Sampling

Sometimes it is more cost-effective to select respondents in groups ("clusters"). Sampling is often clustered by geography or by time periods. Clustering can reduce travel and administrative costs. It also means that one does not need a sampling frame listing all elements in the target population. Instead, clusters can be chosen from a cluster-level frame, with an element-level frame created only for the selected clusters.

Cluster sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between themselves, as compared with the within-cluster variation.

## Quota Sampling

In quota sampling, the population is first segmented into mutually exclusive subgroups, just as in stratified sampling. Then judgment is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60. In quota sampling the selection of the sample is non-random. The problem is that these samples may be biased because not everyone gets a chance of selection.

## Accidental Sampling

Accidental sampling (or grab, convenience, or opportunity sampling) is a type of non-probability sampling which involves the sample being drawn from that part of the population which is close to hand. The researcher cannot scientifically make generalizations about the total population from this sample because it would not be representative enough.

## Panel Sampling

Panel sampling is the method of first selecting a group of participants through a random sampling method and then asking that group for the same information again several times over a period of time. This longitudinal sampling-method allows estimates of changes in the population.