When conducting market research or survey to understand better your consumers, customers, or the performance of your products, it is rare or at least impractical to collect data from all people or items researchers are interested in. Gathering data from everyone would be costly, so using samples are crucial for researchers, since it allows them to gauge data and factors better.
Sampling in a statistical survey is a method of extracting a group of people from a larger population for measurement. The sample should be representative of the population to ensure that the findings from the group are generalized to the population as a whole.
Sample surveys are done when a part of the population is extracted and surveyed. It has two main types of methods and will depend on the goals of the study or the researcher:
In this method, researchers set a selection of a few criteria and choose members of a population randomly. It guarantees that the entire population is represented and all members have an equal opportunity to be part of the sample with the selected parameter.
So why use probability-based sampling? It is the right method to use for researchers who would want to reduce sample bias. The method mainly depicts understanding and the inference of the researcher leading to a higher quality of data collection as the sample appropriately represents the population.
Another reason for researchers to use this method is when the population is vast and diverse. It's essential to have an adequate representation so that the data is not biased towards one demographic.
Lastly, using probability-based sampling helps researchers plan and create accurate samples which help to obtain well-defined data.
In this method, the researchers choose members at random. There is no fixed or predefined selection process and does not ensure that each member of the population has equal opportunities to be included in the sample.
The output of a survey conducted with non-probability sampling leads to a high chance of skewed results which may not represent the desired target population. While it may not narrow down the group to a more accurate representation, there are situations in research that inhibits researchers to do a probability sampling. This may be due to cost constraints for conducting the research or the research may still be in the preliminary stage. This is where non-probability sampling will be much more useful.
The use of non-probability sampling can help researchers to create a hypothesis or assumption when limited to no prior information is available. This method has an immediate return of data and builds a base for further research. It can also be used for exploratory research when conducting qualitative research, or pilot studies. Importantly, it suits research that is within budget and time constraints, especially when some preliminary data is needed to be collected already, making it easier to pick random respondents and have them take the survey.
A sample survey is the only practical way to gather data on a large population and it is usually the preferred method even when other data collection strategies work. It's an effective tool for collecting data from a wide variety of people on a wide variety of topics while also providing a deeper understanding in a time and cost-efficient manner.
Stay tuned for our next post for more information on sample surveys where we discuss the different subtypes or techniques under probability and non-probability sampling. You can also check the Market Research Survey Essentials and discover how you can properly execute a good survey.