Whilst some researchers may view non-probabilit y sampling techniques as inferior to probability sampling techniques, there are strong theoretical and practical reasons for their use. Non-probability sampling techniques refer on the subjective judgement of the researcher when selecting units from the population to be included in the sample. Quota sampling Convenience sampling Purposive sampling Self-selection sampling Snowball sampling. Non-probability sampling to learn more about non-probability sampling, and Sampling: To select a sample of students, we need to identify all 10, students at the university. In order to select a sample n of students from this population of 10, students, we could choose to use a simple random sample. Therefore, we choose to study just a sample of these Facebook users.

For example, imagine that we are interested in studying some of the psychological aspects of people living with a rare disease that affects just 1 person in every 1 million people e. Alternately, click on the articles below: STEP FIVE Calculate the sampling fraction Assuming we have chosen a sample size of students, we now need to work out the sampling fraction , which is simply the sample size selected expressed as n divided by the population size N. Whilst total population sampling is a purposive sampling technique i. 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. Advantages of simple random sampling The aim of the simple random sample is to reduce the potential for human bias in the selection of cases to be included in the sample. The population is expressed as N.

These 10, students are our population N. To minimise sampling bias, probabilistic methods are used so that units from the population are selected at random; the objective is that each unit has an equal chance of being selected. You can read about this later in the article under Disadvantages limitations of systematic random sampling. The sample is expressed as n. This may have been intentional or unintentional. If we were only interested in female university students, for example, we would exclude all males in creating our sampling frame, which would be much less than 10, To select a sample of students, we need to identify all 10, students at the University of Bath.

As a result, this will lead to a serious biased sample that is not ideal for sample creating. With these, we can obtain subsequent observation by using a constant interval between the population in order to choose the sample members of the population.

Disadvantages limitations of stratified random sampling A stratified random sample can only be carried out if a complete list of the population is available. This may sqmpling be an unconscious action. Unlike the simple random sample and the systematic random sample, sometimes we are interested in particular strata meaning groups within the population e.

In our example, the population is the 10, students at the University of Bath. A critical component of probability sampling is the need to create a sample that is representative of the population.

# Total population sampling | Lærd Dissertation

The basicssome of these populations will be expensive and time consuming to contact, even where a list is available. As a result, the systemic random sample provides us with a sample that is highly representative of the population being studied, assuming that there is limited missing data. We discuss this next.

If we wanted to look at the differences in male and female students, this would mean choosing gender as the stratificationbut it could similarly involve choosing students from different subjects e. For example, when Student Records put together the list of the 10, students our examplethe list may have been ordered so that each record moved from a male to female student i.

## Cluster Sampling

When following a qualitative research designnon-probability sampling techniques, such as purposive samplingcan provide researchers with strong theoretical reasons for their choice of units or cases to be included in their sample. Dissertatiob number was chosen because it reflects the limit of our budget and the time we have to distribute our questionnaire to students.

To create a systemic random sample, there are seven steps: These 10, students are our population N. With the stratified random sample, there is an equal chance probability of selecting each unit from within a particular stratum group of the population when creating the sample. In our case, this would mean assigning a consecutive number from 1 to 10, i.

To select a sample of students, we need to identify all 10, students at the university. Systematic random sampling is a type of probability sampling technique [see our article Probability sampling if you do not know what probability sampling is]. Imagine that a researcher wants to understand more about the career goals of students at a single university.

You can read about this later in the article under Disadvantages of simple random sampling. Systematic random sample larrd a variation on the simple random sample.

However, first we need to select the first unit i. This number was chosen because it reflects the limit of our budget and the time we have to distribute dissertatiln questionnaire to students. A systematic random sample can only be carried out if a complete list of the population is available. The basicsif you are unsure about the terms unitsamplestrata and population ].

For students doing dissertations at the undergraduate and master’s level, such practicalities often lead to the use of non-probability sampling techniques.