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With the application of probability sampling in the 1930s, surveys became a standard tool for empirical research in social sciences, marketing, and official statistics. [1] The methods involved in survey data collection are any of a number of ways in which data can be collected for a statistical survey. These are methods that are used to ...
Survey methodology is "the study of survey methods". [1] As a field of applied statistics concentrating on human-research surveys, survey methodology studies the sampling of individual units from a population and associated techniques of survey data collection, such as questionnaire construction and methods for improving the number and accuracy of responses to surveys.
Data collection is a research component in all study fields, including physical and social sciences, humanities, [2] and business. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same.
The nature of the focus group contrasts with the more typically extractive nature of traditional social science research which seeks to "mine" participants for data (with few benefits for participants); this difference is especially important for indigenous researchers who employ focus groups to conduct research on their own ethnic group.
Bias in surveys is undesirable, but often unavoidable. The major types of bias that may occur in the sampling process are: Non-response bias: When individuals or households selected in the survey sample cannot or will not complete the survey there is the potential for bias to result from this non-response.
An online focus group is one type of focus group, and is a sub-set of online research methods. [1] They are typically an appropriate research method for consumer research, business-to-business research and political research.
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4]
A variety of data re-sampling techniques are implemented in the imbalanced-learn package [1] compatible with the scikit-learn Python library. The re-sampling techniques are implemented in four different categories: undersampling the majority class, oversampling the minority class, combining over and under sampling, and ensembling sampling.