scholarly journals Do Astrological Beliefs Reflects Systematic Bias in Personality Measurement?

2021 ◽  
Author(s):  
Gözde BOZKURT ◽  
Ahmet ÇİLİNGİRTÜRK
Keyword(s):  
Systematic Bias ◽  
Personality Measurement

On Scale Correction in Personality Measurement

1972 ◽  
Vol 4 (4) ◽  
pp. 238-241 ◽  
Author(s):  
Robert M. Thorndike
Keyword(s):  
Personality Measurement

Response distortion in frequency-based versus traditional personality measurement

2008 ◽  
Author(s):  
Matthew Fleisher ◽  
Kristin Cullen ◽  
David Woehr ◽  
Bryan Edwards
Keyword(s):  
Response Distortion ◽  
Personality Measurement

Common method variance in NEO-FFI and IPIP personality measurement

2009 ◽  
Author(s):  
Michael Biderman ◽  
Nhung T. Nguyen ◽  
Christopher J. L. Cunningham
Keyword(s):  
Common Method ◽  
Personality Measurement ◽  
Common Method Variance ◽  
Method Variance

The role of "perceptions of others" in personality measurement

2011 ◽  
Author(s):  
Fabian Elizondo ◽  
Patrick Wadlington
Keyword(s):  
Personality Measurement

Personality measurement.

1967 ◽  
Vol 22 (8) ◽  
pp. 675-675
Author(s):  
Henry A. Alker
Keyword(s):  
Personality Measurement

Counter culture: Causes, extent and solutions of systematic bias in the analysis of behavioural counts

2019 ◽  
Author(s):  
Joel L Pick ◽  
Nyil Khwaja ◽  
Michael A. Spence ◽  
Malika Ihle ◽  
Shinichi Nakagawa
Keyword(s):  
Recent Literature ◽  
Null Model ◽  
Mechanistic Explanation ◽  
Effect Sizes ◽  
Systematic Bias ◽  
Intra Class Correlation ◽  
Counter Culture ◽  
Provisioning Rate ◽  
Parental Provisioning ◽  
Observation Period

We often quantify a behaviour by counting the number of times it occurs within a specific, short observation period. Measuring behaviour in such a way is typically unavoidable but induces error. This error acts to systematically reduce effect sizes, including metrics of particular interest to behavioural and evolutionary ecologists such as R2, repeatability (intra-class correlation, ICC) and heritability. Through introducing a null model, the Poisson process, for modelling the frequency of behaviour, we give a mechanistic explanation of how this problem arises and demonstrate how it makes comparisons between studies and species problematic, because the magnitude of the error depends on how frequently the behaviour has been observed (e.g. as a function of the observation period) as well as how biologically variable the behaviour is. Importantly, the degree of error is predictable and so can be corrected for. Using the example of parental provisioning rate in birds, we assess the applicability of our null model for modelling the frequency of behaviour. We then review recent literature and demonstrate that the error is rarely accounted for in current analyses. We highlight the problems that arise from this and provide solutions. We further discuss the biological implications of deviations from our null model, and highlight the new avenues of research that they may provide. Adopting our recommendations into analyses of behavioural counts will improve the accuracy of estimated effect sizes and allow meaningful comparisons to be made between studies.

Are perceived prevalences of infection also biased and how? Lessons from large epidemics of mosquito-borne diseases in tropical regions

2017 ◽  
Author(s):  
Jocelyn Raude
Keyword(s):  
Infectious Diseases ◽  
Cognitive Biases ◽  
Adult Population ◽  
Systematic Bias ◽  
Tropical Regions ◽  
Telephone Surveys ◽  
Behavioral Decision ◽  
Risk Estimates ◽  
Representative Samples ◽  
Actual Prevalence

Objectives: Although people have been repeatedly found to underestimate the frequency of risks to health from common diseases, we still do not know much about reasons for this systematic bias, which is also referred to as “primary bias” in the literature. In this study, we take advantage of a series of large epidemics of mosquito-borne diseases to examine the accuracy of judgments of risk frequencies. In this aim, we assessed the perceived versus the observed prevalence of infection by zika, chikungunya or dengue fever during these outbreaks, as well as their variations among different subpopulations and epidemiological settings.Design: We used data drawn from 4 telephone surveys, conducted between 2006 and 2016, among representative samples of the adult population in tropical regions (Reunion, Martinique, and French Guiana). The participants were asked to estimate the prevalence of these infections by using a natural frequency scale.Results: The surveys showed that (1) most people greatly overestimated the prevalence of infection by arbovirus, (2) these risk overestimations fell considerably as the actual prevalence of these diseases increased, (3) the better-educated and male participants consistently yielded less inaccurate risk estimates across epidemics, and (4) that these biases in the perception of prevalence of these infectious diseases are relatively well predicted by probability weighting function.Conclusions: These findings suggest that the cognitive biases that affect perception of prevalence of acute infectious diseases are not fundamentally different from those that characterize other types of probabilistic judgments observed in the field of behavioral decision-making. They also indicate that numeracy may play a considerable role in people’s ability to transform epidemiological observations from their social environment to more accurate risk estimates.

Test Theory and Personality Measurement

2009 ◽  
Author(s):  
Fritz Drasgow ◽  
Oleksandr S. Chernyshenko ◽  
Stephen Stark
Keyword(s):  
Test Theory ◽  
Personality Measurement
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