Sammenlign metoder

	Bogus Pipeline	Unmatched Count Technique
Fagområde	Socialpsykologi	Socialpsykologi
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1971	2010
Ophavsperson	Edward Jones & Harold Sigall	Survey-methodology tradition; Holbrook & Krosnick (validation)
Type	Methodological technique to reduce social-desirability bias	Indirect survey technique for sensitive questions
Oprindelig kilde	Jones, E. E., & Sigall, H. (1971). The bogus pipeline: A new paradigm for measuring affect and attitude. Psychological Bulletin, 76(5), 349-364.	Holbrook, A. L., & Krosnick, J. A. (2010). Social desirability bias in voter turnout reports: Tests using the item count technique. Public Opinion Quarterly, 74(1), 37-67.
Aliasser	Bogus Pipeline Procedure, Fake Lie Detector Method, Pipeline-to-the-Truth Technique	Item Count Technique, List Experiment, Unmatched Block Design
Relaterede	3	3
Resumé	The bogus pipeline, devised by Jones and Sigall in 1971, is a methodological technique for reducing social-desirability bias in the measurement of attitudes, especially sensitive ones such as prejudice. Participants are connected to an impressive-looking apparatus and convinced that it functions as an accurate lie detector capable of revealing their true feelings. Believing that dishonesty will be exposed, participants are motivated to report their attitudes truthfully rather than giving socially acceptable answers. In the classic procedure participants are asked to predict what the machine will say about them, which encourages them to consult and disclose their genuine attitudes. By comparing reports given under the bogus pipeline with ordinary self-reports, researchers can estimate the extent of social-desirability distortion and obtain more candid measures of socially sensitive attitudes. The technique was an early and influential solution to a fundamental problem in attitude measurement.	The unmatched count technique (also called the item count technique or list experiment) is an indirect survey method for estimating the prevalence of sensitive attitudes or behaviors while protecting respondents' privacy. Respondents are randomly assigned to one of two versions of a question. The control group sees a list of several non-sensitive items and reports only how many of them apply to them; the treatment group sees the same list plus one additional sensitive item and likewise reports only the count. Because respondents report a number rather than which items apply, no one's answer reveals their stance on the sensitive item. The estimated prevalence of the sensitive attribute is simply the difference in mean counts between the treatment and control groups. By breaking the link between an individual and the sensitive item, the technique reduces social-desirability bias for topics like prejudice, illegal behavior, or stigmatized attitudes, as documented in validation work by Holbrook and Krosnick.