Laerd Dissertation: How to write a great Sampling Strategy section

The Sampling Strategy section of your Research Strategy chapter (usually Chapter Three: Research Strategy) tells the reader: (a) what units you included in your sample; (b) the ways you could have selected these units; (c) how you actually selected the units; and (d) the reasons that you selected them in this way.

If you are just starting to think about the sampling strategy you will use in your dissertation, this article acts as a guide to help you get started.

Core ARTICLES

Sampling: The basics

If you are new to sampling, there are a number of key terms (e.g. population, unit, sample, sampling frame) and basic principles that you will need to know before starting to collect data. This article discuss these key terms and basic principles.

How to structure the Sampling Strategy section of your dissertation

A well-structured Sampling Strategy section within your Research Strategy chapter involves four steps: (1) describing; (2) explaining; (3) stating; and (4) justifying. We explain each of these steps, together with associated examples.

Probability SAMPLING Probability sampling explained Probability sampling represents a group of sampling techniques that use probabilistic methods to help the researcher to select units from a population to be included in your sample. This article explains the principles of probability sampling, as well as a brief overview of the different types of probability sampling technique. Simple random sampling: An overview With simple random sampling, there is an equal chance that each unit from the population could be selected for inclusion in the sample. This article explains what simple random sampling is, how to create a simple random sample, as well as its advantages and disadvantages. Systematic random sampling: An overview Systematic random sampling is a variation of the simple random sample. There is also an equal chance that each unit from the population could be selected for inclusion in the sample, but a different method is used to select units compared with simple random sampling. This article explains what systematic random sampling is, how to create a systematic random sample, as well as its advantages and disadvantages. Stratified random sampling: An overview Unlike the simple random sample and the systematic random sample, sometimes we are interested in particular strata (meaning groups) within the population (e.g. males vs. females; houses vs. apartments, etc.). This article explains what stratified random sampling is, how to create a stratified random sample, as well as its advantages and disadvantages.	Non-Probability SAMPLING Non-probability sampling explained Non-probability sampling represents a group of sampling techniques that rely on the subjective judgement of the researcher when selecting units to be included in your sample. This article explains the principles of non-probability sampling, as well as a brief overview of the different types of non-probability sampling technique. Convenience sampling: An overview A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest (and often cheapest) to access. This article explains what convenience sampling is, as well as its advantages and disadvantages. Quota sampling: An overview With proportional quota sampling, the aim is to end up with a sample where the strata (groups) being studied (e.g. males vs. females students) are proportional to the population being studied. This article explains what quota sampling is, how to create a quota sample, as well as its advantages and disadvantages. Self-selection sampling: An overview Self-selection sampling is appropriate when we want to allow units or cases, whether individuals or organisations, to choose to take part in research on their own accord. This article explains what self-selection sampling is, how to create a self-selection sample, as well as its advantages and disadvantages. Snowball sampling: An overview Snowball sampling is particularly appropriate when the population you are interested in is hidden and/or hard-to-reach. This article explains what snowball sampling is, how to create a snowball sample, as well as its advantages and disadvantages.