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A sampling error is a data collection error that occurs as a result of the researcher using a sample from a population instead of the entire population. This type of error is one of the two reasons why there is a difference between a parameter’s estimated value and the true but unknown value. Theoretical methods can always be used to calculate the variation extent caused by this type of error when the sampling is randomly done.
Non-sampling error also arises during data collection because of factors not related to taking a sample. These types of errors can lead to biases in samples, surveys, and even polls. While there are a plethora of non-sampling errors, their names and descriptions are not consistent. Some of the reasons that can lead to this include behavioral effects, ineffective sampling technique, errors in measurements, etc.
Weighting is a method used in surveys to manipulate datasets. Calculations are done on the survey results to make sure that they conform to the target population. Weights are usually implemented after the survey. They allow the researchers to correct errors that happened when data was being collected. This process is a post-stratification method because it is carried out after the sample has been selected.
Designing Survey Questions
An excellent survey questionnaire guarantees accurate and correct data. This task may seem fairly simple on the surface but there are several pitfalls that you should avoid. When designing your questionnaire, you should ensure that it is short but captures all the details that you need. For this reason, we recommend that you outline a set of objectives for your research. These objectives will help you curate the questions that you want to ask in the survey.