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This is one example of a type of survey that can be highly vulnerable to the effects of response bias. Response bias is a general term for a wide range of tendencies for participants to respond inaccurately or falsely to questions. These biases are prevalent in research involving participant self-report, such as structured interviews or surveys ...
In social science research social-desirability bias is a type of response bias that is the tendency of survey respondents to answer questions in a manner that will be viewed favorably by others. [1] It can take the form of over-reporting "good behavior" or under-reporting "bad" or undesirable behavior.
Recall bias is of particular concern in retrospective studies that use a case-control design to investigate the etiology of a disease or psychiatric condition. [ 3 ] [ 4 ] [ 5 ] For example, in studies of risk factors for breast cancer , women who have had the disease may search their memories more thoroughly than members of the unaffected ...
Questions affected by acquiescence bias take the following format: a stimulus in the form of a statement is presented, followed by 'agree/disagree,' 'yes/no' or 'true/false' response options. [2] [6] For example, a respondent might be presented with the statement "gardening makes me feel happy," and would then be expected to select either ...
Subjects may also forget pertinent details. Self-report studies are inherently biased by the person's feelings at the time they filled out the questionnaire. If a person feels bad at the time they fill out the questionnaire, for example, their answers will be more negative. If the person feels good at the time, then the answers will be more ...
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The interviewer effect (also called interviewer variance or interviewer error) is the distortion of response to an interviewer-administered data collection effort which results from differential reactions to the social style and personality of interviewers or to their presentation of particular questions. The use of fixed-wording questions is ...
Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test. For example, a high prevalence of disease in a study population increases positive predictive values, which will cause a bias between the prediction values and the real ones. [4]