There are number of ways that a study can be ruined before you even start collecting data. The first we have already explored – sampling or selection bias, which is when the sample is not representative of the population. Another example of this is voluntary response bias, which is bias introduced by only collecting data from those who volunteer to participate. But these are not the only potential sources of bias.
sources of bias
Sampling bias – when the sample is not representative of the population
Voluntary response bias – the sampling bias that often occurs when the sample is volunteers
Self-interest study – bias that can occur when the researchers have an interest in the outcome
Response bias – when the responder gives inaccurate responses for any reason
Perceived lack of anonymity – when the responder fears giving an honest answer might negatively affect them
Loaded questions – when the question wording influences the responses
Non-response bias – when people refusing to participate in the study can influence the validity of the outcome
Undercoverage occurs when some groups of the population are left out of the sampling process.
Consider a recent study which found that chewing gum may raise math grades in teenagers.[1] This study was funded by the Wrigley Science Institute, a branch of the Wrigley chewing gum company. Identify the type of sampling bias found in this example.
This is an example of a self-interest study; one in which the researchers have a vested interest in the outcome of the study. While this does not necessarily ensure that the study was biased, it certainly suggests that we should subject the study to extra scrutiny.
A survey asks people “when was the last time you visited your doctor?” What type of sampling bias might this lead to?
This might suffer from response bias, since many people might not remember exactly when they last saw a doctor and give inaccurate responses.
Sources of response bias may be innocent, such as bad memory, or as intentional as pressuring by the pollster. Consider, for example, how many voting initiative petitions people sign without even reading them.
A survey asks participants a question about their interactions with members of other races. Which sampling bias might occur for this survey strategy?
Here, a perceived lack of anonymity could influence the outcome. The respondent might not want to be perceived as racist even if they are, and give an untruthful answer.
An employer puts out a survey asking their employees if they have a drug abuse problem and need treatment help. Which sampling bias may occur in this scenario?
Here, answering truthfully might have consequences; responses might not be accurate if the employees do not feel their responses are anonymous or fear retribution from their employer. This survey has the potential for perceived lack of anonymity.
A survey asks “do you support funding research of alternative energy sources to reduce our reliance on high-polluting fossil fuels?” Which sampling bias may result from this survey?
This is an example of a loaded or leading question – questions whose wording leads the respondent towards an answer.
Loaded questions can be used intentionally by pollsters with an agenda, or accidentally through poor question wording. Another concern is question order, where the order of questions changes the results. A psychology researcher provides an example:[2]
“My favorite finding is this: we did a study where we asked students, ‘How satisfied are you with your life? How often do you have a date?’ The two answers were not statistically related – you would conclude that there is no relationship between dating frequency and life satisfaction. But when we reversed the order and asked, ‘How often do you have a date? How satisfied are you with your life?’ the statistical relationship was a strong one. You would now conclude that there is nothing as important in a student’s life as dating frequency.”
A telephone poll asks the question “Do you often have time to relax and read a book?”, and [latex]50\%[/latex] of the people called refused to answer the survey. Which sampling bias is represented by this survey?
It is unlikely that the results will be representative of the entire population. This is an example of non-response bias, introduced by people refusing to participate in a study or dropping out of an experiment. When people refuse to participate, we can no longer be so certain that our sample is representative of the population.