What factors do you consider in choosing your sample for market research?

1 Sampling methods

There are two main types of sampling methods: probability and non-probability. Probability sampling means that every member of the population has an equal chance of being selected, and the results can be generalized to the whole population. Non-probability sampling means that the selection is based on some criteria or convenience, and the results may not be representative of the whole population. Some examples of probability sampling are simple random sampling, stratified sampling, and cluster sampling. Some examples of non-probability sampling are convenience sampling, quota sampling, and snowball sampling.

2 Sampling size

The sampling size is the number of units or individuals that are included in the sample. The larger the sample size, the more accurate and reliable the results will be, but also the more time and resources it will require. The smaller the sample size, the less accurate and reliable the results will be, but also the less time and resources it will require. To determine the optimal sample size, you need to consider the level of confidence, the margin of error, the population size, and the variability of the population.

3 Sampling error

Sampling error is the difference between the results obtained from the sample and the results that would be obtained from the whole population. Sampling error can occur due to random or systematic factors. Random factors are those that affect the selection of the sample, such as human error, sampling bias, or sampling frame. Systematic factors are those that affect the measurement of the data, such as measurement error, response bias, or non-response. To reduce sampling error, you need to use appropriate sampling methods, sample size, and data collection techniques.

5 Non sampling error

Non sampling error is the error that arises from the data collection, processing, and analysis stages, regardless of whether sampling or non sampling techniques are used. Non sampling error can occur due to human or technical factors. Human factors are those that affect the quality of the data, such as interviewer error, respondent error, or coding error. Technical factors are those that affect the accuracy of the data, such as data entry error, data cleaning error, or data analysis error. To reduce non sampling error, you need to use proper data quality control, validation, and verification methods.

6 Sampling vs non sampling

Sampling and non sampling techniques have their advantages and disadvantages, depending on the research objectives, resources, and constraints. Sampling techniques can provide more accurate and generalizable results, but also more complex and costly. Non sampling techniques can provide more comprehensive and timely results, but also more prone to error and bias. Therefore, you need to weigh the pros and cons of each technique and choose the one that best suits your market research needs.