Part 1 of 3: Preventing the 3 Biggest Types of Survey Bias: Selection Bias
One of the first things that any market research professional needs to understand is the fact that survey bias is an inevitable part of almost all primary data. Different types of survey biases can creep into the data based on the number of people that are questioned, the specific profile of people who are questioned, the manner in which the questions are worded and the flow and structure of the questionnaire. In some cases, survey bias is quantifiable and in others it is not. While the sampling error of a particular sample can be calculated to estimate the range within which the actual responses will fall, the errors caused by other kinds of biases are less easy to resolve.
Selection Bias – Errors and Bias due to Unrepresentative Sample
The selection of a sample plays an important role in survey research. When the method of choosing a sample is faulty, the individual respondents of the sample may not collectively define the population accurately. A bias that results from underrepresentation, overrepresentation or improper selection is called a selection bias.
- Under or over representation bias – This bias is caused when a specific kind of sub profile in the target respondent is over represented or underrepresented. For example, if the study aims at studying women in a developing country, using an Internet survey may result in a biased sample. As the Internet penetration in developing countries is not too high, it might result in a sample that is in favor of high income groups or highly educated women who have adapted to the internet faster than some of the others.
- Non response bias – A non response bias occurs in specific parts of the survey when respondents refuse to answer specific questions. At other times, specific profiles of people may refrain from responding to a question resulting in a biased statistic. A mail survey is typically prone to the non response bias.
- Voluntary response bias – Surveys that adopt the voluntary method of data gathering are prone to voluntary response bias. Typically, those who have strong opinions about the topic are likely to make the effort to respond to a question, thereby causing the data to appear polar in nature.
Selection bias can be avoided by ensuring that the sample that is selected is random. Random sampling can ensure that the sample is chosen purely by chance and every eligible respondent has an equal chance of being selected for the survey.










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