Digital Product Sampling vs.
Expert sampling Maximum variation sampling Maximum variation sampling, also known as heterogeneous sampling, is a purposive sampling technique used to capture a wide range of perspectives relating to the thing that you are interested in studying; that is, maximum variation sampling is a search for variation in perspectives, ranging from those conditions that are view to be typical through to those that are more extreme in nature.
By conditions, we mean the units i. These units may exhibit a wide range of attributes, behaviours, experiences, incidents, qualities, situations, and so forth.
The basic principle behind maximum variation sampling is to gain greater insights into a phenomenon by looking at it from all angles.
This can often help the researcher to identify common themes that are evident across the sample. Homogeneous sampling Homogeneous sampling is a purposive sampling technique that aims to achieve a homogeneous sample; that is, a sample whose units e. In this respect, homogeneous sampling is the opposite of maximum variation sampling.
A homogeneous sample is often chosen when the research question that is being address is specific to the characteristics of the particular group of interest, which is subsequently examined in detail.
The word typical does not mean that the sample is representative in the sense of probability sampling i. Rather, the word typical means that the researcher has the ability to compare the findings from a study using typical case sampling with other similar samples i. Therefore, with typical case sampling, you cannot use the sample to make generalisations to a population, but the sample could be illustrative of other similar samples.
Whilst typical case sampling can be used exclusively, it may also follow another type of purposive sampling technique, such as maximum variation sampling, which can help to act as an exploratory sampling strategy to identify the typical cases that are subsequently selected.
Extreme or deviant case sampling Extreme or deviant case sampling is a type of purposive sampling that is used to focus on cases that are special or unusual, typically in the sense that the cases highlight notable outcomes, failures or successes. These extreme or deviant cases are useful because they often provide significant insight into a particular phenomenon, which can act as lessons or cases of best practice that guide future research and practice.
In some cases, extreme or deviant case sampling is thought to reflect the purest form of insight into the phenomenon being studied. Critical case sampling Critical case sampling is a type of purposive sampling technique that is particularly useful in exploratory qualitative research, research with limited resources, as well as research where a single case or small number of cases can be decisive in explaining the phenomenon of interest.
It is this decisive aspect of critical case sampling that is arguably the most important. To know if a case is decisive, think about the following statements: If it happens there, it will happen anywhere?
If that group is having problems, then we can be sure all the groups are having problems? Whilst such critical cases should not be used to make statistical generalisations, it can be argued that they can help in making logical generalisations. However, such logical generalisations should be made carefully.
Total population sampling Total population sampling is a type of purposive sampling technique where you choose to examine the entire population i.7 - 4 When random sampling is used, each element in the population has an equal chance of being selected (simple random sampling) or a known probability of being selected (stratified random sampling).
Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
Two advantages of sampling are lower cost and faster data collection than measuring the. 54 The Pelican grain sampler is used for on-line grain sampling. The probe is a leather pouch, approximately m long, with a band of iron inserted along the edge to hold the pouch open.
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.
Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to .