Value Judgment
Value Judgment - Related Videos
09:20
Using fMRI to Dissect Moral Judgment
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2023
Source: William Brady & Jay Van Bavel—New York University In examining the roles of reason and emotion in moral judgments, psychologists and philosophers alike point to the trolley dilemma and the footbridge dilemma. With the trolley dilemma, most people say that it is appropriate to pull a switch to stop a train from hitting five people by diverting it to kill one person. However, with the footbridge dilemma, most people say it is inappropriate to push a large man off of a bridge in order...
08:04
Evaluating the Accuracy of Snap Judgments
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2023
Source: Diego Reinero & Jay Van Bavel—New York University Social psychologists have long been interested in the way people form impressions of others. Much of this work has focused on the errors people make in judging others, such as the exaggerated influence of central traits (such as "warm" and "cold"), the insufficient weight given to the context in which others' behavior takes place, and the tendency for people to make judgments that conform to their initial expectations about another.
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Cited by 3 •
2022
The protocol presents an experimental psychophysics paradigm to obtain large quantities of similarity judgments, and an accompanying analysis workflow. The paradigm probes context effects and enables modeling of similarity data in terms of Euclidean spaces of at least five dimensions.
12:10
Assessment of Mouse Judgment Bias through an Olfactory Digging Task
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2022
This article provides a detailed description of a novel mouse judgment bias protocol. Evidence of this olfactory digging task's sensitivity to affective state is also demonstrated and its utility across diverse research fields is discussed.
13:00
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Cited by 20 •
2017
Temporal-order judgments can be used to estimate processing speed parameters and attentional weights and thereby to infer the mechanisms of attentional processing. This methodology can be applied to a wide range of visual stimuli and works with many attention manipulations.
FAQs
It can shape the research question and determine which outcomes count as important. Researchers also make evaluative choices when translating broad ideas into operational definitions, such as deciding how to measure fairness, health, achievement, or acceptable risk. These choices influence the evidence collected, so conclusions may reflect both observed patterns and the priorities built into the study design.
Sampling and exclusion rules determine whose experiences become visible in the analysis and whose do not. A criterion may be statistically convenient yet affect groups differently, especially when the research concerns access, risk, or resource allocation. Examining these decisions as value judgments helps clarify whether the resulting evidence supports broad conclusions or only applies to the included population.
Choosing a model determines which relationships, assumptions, and sources of variation receive attention, while selecting a significance threshold affects how readily results are treated as noteworthy. Neither choice is simply a mechanical reading of the data. Reporting these decisions and their effects helps readers distinguish statistical evidence from the practical or normative importance assigned to a finding.
A descriptive claim reports what the data show, such as a pattern, difference, or level of uncertainty. A value judgment goes further by evaluating whether that pattern is desirable, harmful, fair, or sufficient to justify action. Keeping the two statements separate improves interpretation because a statistical result alone does not determine which policy or behavior should be preferred.
Preregistration can document planned questions, measures, analysis choices, and thresholds before results are known. Sensitivity analysis then examines whether conclusions change under reasonable alternative assumptions or specifications. Transparent reporting connects these choices to the findings, allowing readers to identify which conclusions are robust and which depend strongly on evaluative or methodological decisions.
In public health, economics, education, and social research, statistical findings can influence how resources are distributed and how groups are treated. Decision makers must therefore consider not only estimated effects and uncertainty but also whose interests, risks, and definitions of fairness are reflected. Making those priorities explicit supports more accountable interpretation without presenting policy preferences as purely statistical conclusions.