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Survey Participation, Nonresponse Bias, Measurement Error Bias, and Total Bias
In: The public opinion quarterly: POQ, Band 70, Heft 5, S. 737-758
ISSN: 1537-5331
Social bias, not time bias
In: Politics, philosophy & economics: ppe, Band 23, Heft 1, S. 100-121
ISSN: 1741-3060
People seem to have pure time preferences about trade-offs concerning their own pleasures and pains, and such preferences contribute to estimates of people's individual time discount rate. Do pure time preferences also matter to interpersonal welfare trade-offs, including those concerning the welfare of future generations? Most importantly, should the intergenerational time discount rate include a pure time preference? Descriptivists claim that the intergenerational discount rate should reflect actual people's revealed preferences, and thus it should include a pure time preference. Prescriptivists claim that the intergenerational discount rate should be based on moral analysis, and thus they (often) claim that the rate of pure time preference should be zero. I argue that regardless of which view is correct, a focus on pure time preference is misplaced. First, the most plausible interpretation of descriptive preferences for intergenerational trade-offs is that people are socially biased and not time biased. Second, social bias is superior to time bias as a prescriptive reason to discount the welfare of future people. Third, recent advances in measuring social bias as a social discount rate make social bias a viable replacement for time bias in economic analyses of intergenerational welfare trade-offs.
Bias Amplification and Bias Unmasking
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Band 24, Heft 3, S. 307-323
ISSN: 1476-4989
In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due toexcludingobserved covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of biasunmaskingand shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.
Bias does not equal bias
In: TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis / Journal for Technology Assessment in Theory and Practice, Band 30, Heft 2, S. 69-70
Bias Amplification and Bias Unmasking
In: Political analysis: official journal of the Society for Political Methodology, the Political Methodology Section of the American Political Science Association, Band 24, Heft 3, S. 307-323
ISSN: 1047-1987
Survey participation, nonresponse bias, measurement error bias, and total bias
In: Peace research abstracts journal, Band 44, Heft 3, S. 737-739
ISSN: 0031-3599
Survey Participation, Nonresponse Bias, Measurement Error Bias, and Total Bias
In: Public opinion quarterly: journal of the American Association for Public Opinion Research, Band 70, Heft 5, S. 737-758
ISSN: 0033-362X
Urban Bias, Rural Bias, or State Bias? Urban-Rural Relations in Post-Revolutionary China
In: The journal of development studies: JDS, Band 20, Heft 3, S. 52-81
ISSN: 0022-0388
An evaluation of the relevance of Ur bias & Ru bias to the postrevolutionary Chinese case. Statistical data are presented on the nature & extent of differentiation in Ru-Ur living standards, the political basis of relevant economic policies is examined, & the relationship between these policies is investigated relative to changes in labor productivity in each sector & to intersectoral savings transfers. Both Ur bias & Ru bias hypotheses illuminate certain dimensions of Chinese development strategy. But the realities of Ru-Ur relations have been complex, & analysis must be supplemented by a focus on the division between state & society, & the question of state bias. 10 Tables, 73 References. HA.
Attentional Bias and Attentional Bias Modification in PTSD
In: HELIYON-D-22-15554
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Confirmation Bias, Ingroup Bias, and Negativity Bias in Selective Exposure to Political Information
In: Communication research, Band 47, Heft 1, S. 104-124
ISSN: 1552-3810
Selective reading of political online information was examined based on cognitive dissonance, social identity, and news values frameworks. Online reports were displayed to 156 Americans while selective exposure was tracked. The news articles that participants chose from were either conservative or liberal and also either positive or negative regarding American political policies. In addition, information processing styles (cognitive reflection and need-for-cognition) were measured. Results revealed confirmation and negativity biases, per cognitive dissonance and news values, but did not corroborate the hypothesis derived from social identity theory. Greater cognitive reflection, greater need-for-cognition, and worse affective state fostered the confirmation bias; stronger social comparison tendency reduced the negativity bias.
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