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SAMPLE - Coggle Diagram
SAMPLE
Sampling is the statistical process of selecting a subset of interest for purposes of making observations and statistical inferences about that population.
The sampling process comprises of several stage. The first stage is defining the target population. A population can be defined as all people or items (unit of analysis) with the characteristics that one wishes to study. The unit of analysis may be a person, group organization, country, object, or any other entity that you wish to draw scientific inferences about.
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The second step in the sampling process is to choose a sampling frame. This is an accessible section of the target population (usually a list with contact information) from where a sample can be drawn.
The last step in sampling is choosing a sample from the sampling frame using a well- defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if
generalizability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.
Probability Sampling
Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined
Simple random sampling. In this technique, all possible subsets of a population (more accurately, of a sampling frame) are given an equal probability of being selected. The probability of selecting any set of n units out of a total of N units in a sampling frame is NCn. Hence, sample statistics are unbiased estimates of population parameters, without any weighting.
Systematic sampling. In this technique, the sampling frame is ordered according to some criteria and elements are selected at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every kth element from that point onwards, where k = N/n, where k is the ratio of sampling frame size N and the desired sample size n, and is formally called the sampling ratio.
Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping and a simple random sample is drawn within each subgroup
Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into clusters boundaries), randomly sample a few clusters, and measure all units within that cluster.
Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion.
Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling.
NON PROBABILITY SMAPLING
Nonprobability samplingis a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined.
Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient.
Quota sampling. In this technique, the population is segmented into mutually- exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota.
Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied
Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria.
STATISTICS OF SAMPLING
response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences.
For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution, which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample).
Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters ). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error.