Confirmatory bias and the persistent influence of discredited data in interpreting the stress-cancer link: Commentary on Michael et al. (2009).

Purpose The purpose of this paper is to present a theory of how corporations and brands can address the prevalence of fake news. A matrix is proposed to examine how the transparency of the motivation of the communicator disseminating fake news interacts with how well the content of the fake news coincides with a consumer’s previously held bias. Design/methodology/approach A dichotomy is presented examining the role of “Schemer’s Schema” transparency by confirmatory bias. Findings Consumers will react differently to fake news depending on their “schemer schema” and the source of the information, as well as the believability of the story based on already existing beliefs. Research implications/limitations This paper provides readers with a strategy to address the prevalence and reality of fake news. The purpose of this paper is theoretical in nature. While this manuscript lays the foundation for future empirical studies, said studies have not been conducted. Further, given the ever-changing nature of fake news dissemination this manuscript provides a picture at a specific time and place. Practical implications This manuscript provides insights for brand managers who are forced to address fake news. Originality/value This manuscript provides marketers with a strategy to better address fake news for organizations and brand.

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Identifying Confirmatory Bias in the Field

Journal of Sports Economics ◽

10.1177/1527002515617511 ◽

2015 ◽

Vol 19 (1) ◽

pp. 50-81 ◽

Cited By ~ 3

Author(s):

Rodney J. Andrews ◽

Trevon D. Logan ◽

Michael J. Sinkey

Keyword(s):

Real World ◽

Laboratory Experiments ◽

Cognitive Biases ◽

Explanatory Power ◽

Confirmatory Bias ◽

Data Set ◽

Betting Market ◽

World Economic ◽

Market Expectations ◽

World Environment

Laboratory experiments have established the existence of cognitive biases, but their explanatory power in real-world economic settings has been difficult to quantify. We evaluate the extent to which a cognitive bias, confirmatory bias, affects the opinions of experts in a real-world environment. In the Associated Press Top 25 College Football Poll, expert pollsters are tasked with assessing team quality, and their beliefs are treated week to week with game results that serve as signals about an individual team’s quality. We exploit the variation provided by actual game results relative to market expectations to develop a novel regression-discontinuity approach to identify confirmatory bias. We construct a data set that matches more than 20 years of individual game characteristics to poll results and betting market information and show that teams that slightly exceed and barely miss market expectations are exchangeable. The likelihood of winning the game, the average number of points scored by teams and their opponents, and even the average week of the season are no different between teams that slightly exceed and barely miss market expectations. Pollsters, however, significantly upgrade their beliefs about a team’s quality when a team slightly exceeds market expectations. The effects are sizable—one fifth of the standard deviation in poll points in a given week can be attributed to confirmatory bias, which is equivalent to nearly half of the voters in the poll ranking a team one slot higher when they slightly exceed market expectations. This type of updating suggests that even when informed agents make repeated decisions, they may act in a manner consistent with confirmatory bias.

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Confirmatory bias in hypothesis testing for client-identified and counselor self-generated hypotheses.

Journal of Counseling Psychology ◽

10.1037/0022-0167.40.3.303 ◽

1993 ◽

Vol 40 (3) ◽

pp. 303-315 ◽

Cited By ~ 35

Author(s):

Beth E. Haverkamp

Keyword(s):

Hypothesis Testing ◽

Confirmatory Bias

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Is It Biased? Empirical Analysis of Various Phenomena That Affect Survey Results

Revija za sociologiju ◽

10.5613/rzs.51.2.3 ◽

2021 ◽

Vol 51 (2) ◽

pp. 231-256

Author(s):

Luka Mandić ◽

Ksenija Klasnić

Keyword(s):

Measurement Invariance ◽

Survey Design ◽

Measuring Instruments ◽

Confirmatory Bias ◽

Mean Values ◽

Careless Responding ◽

Item Wording ◽

Method Effects ◽

Survey Results ◽

And Gender

It is often assumed that survey results reflect only the quality of the sample and the underlying measuring instruments used in the survey. However, various phenomena can affect the results, but these influences are often neglected when conducting surveys. This study aimed to test the influences of various method effects on survey results. We tested the influences of the following method effects: item wording, confirmatory bias, careless responding, and acquiescence bias. Using a split-ballot survey design with online questionnaires, we collected data from 791 participants. We tested if these method effects had an influence on mean values, item correlations, construct correlations, model fits, and construct measurement invariance. The instruments used to test these influences were from the domain of personality and gender inequality, and their items were adapted based on the method effect tested. All tested method effects, except careless responding, had a statistically significant effect on at least one component of the analysis. Item wording and confirmatory bias affected mean values, model fit, and measurement invariance. Controlling for acquiescence bias improved the fit of the model. This paper confirms that the tested method effects should be carefully considered when using surveys in research, and suggests some guidelines on how to do so.

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