Online phenomena like echo chambers and polarization are believed to be driven by humans' penchant to selectively expose themselves to attitudinally congenial content. However, if like-minded content were the only predictor of online behavior, heated debate and flaming on the Internet would hardly occur. Research has overlooked how online behavior changes when people are given an opportunity to reply to dissenters. Three experiments (total = 320; convenience student samples from Germany) and an internal meta-analysis show that in a discussion-forum setting where participants can reply to earlier comments larger cognitive conflict between participant attitude and comment attitude predicts higher likelihood to respond (). When the discussion climate was friendly (vs. oppositional) to the views of participants, the uncongeniality bias was more pronounced and was also associated with attitude polarization. These results suggest that belief polarization on social media may not only be driven by congeniality but also by conflict.
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http://dx.doi.org/10.1177/09567976231194590 | DOI Listing |
Psychol Sci
October 2023
Perception and Action Lab, Leibniz-Institut für Wissensmedien, Tübingen.
Online phenomena like echo chambers and polarization are believed to be driven by humans' penchant to selectively expose themselves to attitudinally congenial content. However, if like-minded content were the only predictor of online behavior, heated debate and flaming on the Internet would hardly occur. Research has overlooked how online behavior changes when people are given an opportunity to reply to dissenters.
View Article and Find Full Text PDFBiometrics
December 2017
Shire, 300 Shire way, Lexington, Massachusetts, U.S.A.
Control-based pattern mixture models (PMM) and delta-adjusted PMMs are commonly used as sensitivity analyses in clinical trials with non-ignorable dropout. These PMMs assume that the statistical behavior of outcomes varies by pattern in the experimental arm in the imputation procedure, but the imputed data are typically analyzed by a standard method such as the primary analysis model. In the multiple imputation (MI) inference, Rubin's variance estimator is generally biased when the imputation and analysis models are uncongenial.
View Article and Find Full Text PDFCancer Epidemiol
June 2017
London School of Hygiene and Tropical Medicine, UK; MRC Clinical Trials Unit at UCL, London, UK.
Background: Population-based net survival by tumour stage at diagnosis is a key measure in cancer surveillance. Unfortunately, data on tumour stage are often missing for a non-negligible proportion of patients and the mechanism giving rise to the missingness is usually anything but completely at random. In this setting, restricting analysis to the subset of complete records gives typically biased results.
View Article and Find Full Text PDFBiostatistics
July 2016
Departments of Pharmaceutical Outcomes & Policy, and Epidemiology, University of Florida, Gainesville, FL 32601, USA.
To conduct comparative effectiveness research using electronic health records (EHR), many covariates are typically needed to adjust for selection and confounding biases. Unfortunately, it is typical to have missingness in these covariates. Just using cases with complete covariates will result in considerable efficiency losses and likely bias.
View Article and Find Full Text PDFPsychol Bull
July 2009
Department of Psychology, University of Florida, Gainesville, FL 32611, USA.
A meta-analysis assessed whether exposure to information is guided by defense or accuracy motives. The studies examined information preferences in relation to attitudes, beliefs, and behaviors in situations that provided choices between congenial information, which supported participants' pre-existing attitudes, beliefs, or behaviors, and uncongenial information, which challenged these tendencies. Analyses indicated a moderate preference for congenial over uncongenial information (d=0.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!