Non probability sampling methods pdf

Non probability sampling methods pdf visual representation of the sampling process. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population. In business and medical research, sampling is widely used for gathering information about a population. Successful statistical practice is based on focused problem definition.

A population can be defined as including all people or items with the characteristic one wishes to understand. Sometimes what defines a population is obvious. In this case, the batch is the population. Although the population of interest often consists of physical objects, sometimes we need to sample over time, space, or some combination of these dimensions. For instance, an investigation of supermarket staffing could examine checkout line length at various times, or a study on endangered penguins might aim to understand their usage of various hunting grounds over time. For the time dimension, the focus may be on periods or discrete occasions.

In other cases, our ‘population’ may be even less tangible. In such cases, sampling theory may treat the observed population as a sample from a larger ‘superpopulation’. For example, a researcher might study the success rate of a new ‘quit smoking’ program on a test group of 100 patients, in order to predict the effects of the program if it were made available nationwide. Note also that the population from which the sample is drawn may not be the same as the population about which we actually want information. Often there is large but not complete overlap between these two groups due to frame issues etc. 2008 in order to make predictions about people born in 2009. Time spent in making the sampled population and population of concern precise is often well spent, because it raises many issues, ambiguities and questions that would otherwise have been overlooked at this stage.

However, in the more general case this is not usually possible or practical. There is no way to identify all rats in the set of all rats. These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory. The combination of these traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units according to their probability of selection. Example: We want to estimate the total income of adults living in a given street. We visit each household in that street, identify all adults living there, and randomly select one adult from each household.

We then interview the selected person and find their income. People living on their own are certain to be selected, so we simply add their income to our estimate of the total. But a person living in a household of two adults has only a one-in-two chance of selection. To reflect this, when we come to such a household, we would count the selected person’s income twice towards the total. Such designs are also referred to as ‘self-weighting’ because all sampled units are given the same weight.

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