Estimating classification consistency of screening measures and quantifying the impact of measurement bias.

2021 ◽  
Author(s):  
Oscar Gonzalez ◽  
A. R. Georgeson ◽  
William E. Pelham ◽  
Rachel T. Fouladi
Keyword(s):  
Measurement Bias ◽  
Classification Consistency ◽  
The Impact ◽  
Screening Measures

You Can’t Drive a Car With Only Three Wheels

2019 ◽  
Vol 188 (9) ◽  
pp. 1682-1685 ◽  
Author(s):  
Hailey R Banack
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Causal Effects ◽  
Measurement Bias ◽  
Exposure Misclassification ◽  
Unmeasured Confounding ◽  
Obstetrical Care ◽  
The Impact ◽  
Fundamental Requirement

Abstract Authors aiming to estimate causal effects from observational data frequently discuss 3 fundamental identifiability assumptions for causal inference: exchangeability, consistency, and positivity. However, too often, studies fail to acknowledge the importance of measurement bias in causal inference. In the presence of measurement bias, the aforementioned identifiability conditions are not sufficient to estimate a causal effect. The most fundamental requirement for estimating a causal effect is knowing who is truly exposed and unexposed. In this issue of the Journal, Caniglia et al. (Am J Epidemiol. 2019;000(00):000–000) present a thorough discussion of methodological challenges when estimating causal effects in the context of research on distance to obstetrical care. Their article highlights empirical strategies for examining nonexchangeability due to unmeasured confounding and selection bias and potential violations of the consistency assumption. In addition to the important considerations outlined by Caniglia et al., authors interested in estimating causal effects from observational data should also consider implementing quantitative strategies to examine the impact of misclassification. The objective of this commentary is to emphasize that you can’t drive a car with only three wheels, and you also cannot estimate a causal effect in the presence of exposure misclassification bias.

scholarly journals The Impact of Situational Test Anxiety on Retest Effects in Cognitive Ability Testing: A Structural Equation Modeling Approach

2019 ◽  
Vol 7 (4) ◽  
pp. 22 ◽  
Author(s):  
David Jendryczko ◽  
Jana Scharfen ◽  
Heinz Holling
Keyword(s):  
Cognitive Ability ◽  
Test Anxiety ◽  
Structural Equation ◽  
Explanatory Power ◽  
Substantial Reduction ◽  
Equation Modeling ◽  
Measurement Bias ◽  
Model Parameters ◽  
The Impact ◽  
Situational Test

When a cognitive ability is assessed repeatedly, test scores and ability estimates are often observed to increase across test sessions. This phenomenon is known as the retest (or practice) effect. One explanation for retest effects is that situational test anxiety interferes with a testee’s performance during earlier test sessions, thereby creating systematic measurement bias on the test items (interference hypothesis). Yet, the influence of anxiety diminishes with test repetitions. This explanation is controversial, since the presence of measurement bias during earlier measurement occasions cannot always be confirmed. It is argued that people from the lower end of the ability spectrum become aware of their deficits in test situations and therefore report higher anxiety (deficit hypothesis). In 2014, a structural equation model was proposed that specifically allows the comparison of these two hypotheses with regard to explanatory power for the negative anxiety–ability correlation found in cross-sectional assessments. We extended this model for usage in longitudinal studies to investigate the impact of test anxiety on test performance and on retest effects. A latent neighbor-change growth curve was implemented into the model that enables an estimation of retest effects between all pairs of successive test sessions. Systematic restrictions on model parameters allow testing the hypothetical reduction in anxiety interference over the test sessions, which can be compared to retest effect sizes. In an empirical study with seven measurement occasions, we found that a substantial reduction in interference upon the second test session was associated with the largest retest effect in a figural matrices test, which served as a proxy measure for general intelligence. However, smaller retest effects occurred up to the fourth test administration, whereas evidence for anxiety-induced measurement bias was only produced for the first two test sessions. Anxiety and ability were not negatively correlated at any time when the interference effects were controlled for. Implications, limitations, and suggestions for future research are discussed.

scholarly journals Impact of Attending a Healthcare Conference in Toronto During the Severe Acute Respiratory Syndrome Crisis: Survey of Delegates

2004 ◽  
Vol 9 (3) ◽  
pp. 137-143 ◽  
Author(s):  
Jennifer Stinson ◽  
Colin JL McCartney ◽  
Andrea Leung ◽  
Joel Katz
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Health Care Professionals ◽  
Survey Design ◽  
Minimal Risk ◽  
Prospective Survey ◽  
Risks And Benefits ◽  
Potential Risks ◽  
Level Of Concern ◽  
The Impact ◽  
Screening Measures

OBJECTIVE:To describe the impact on delegates of attending the Canadian Pain Society's annual meeting in Toronto during the severe acute respiratory syndrome (SARS) crisis in May 2003.METHODS:A prospective survey design was used. Surveys were sent to all delegates (n=432) who attended the conference, and 294 delegates responded (68% response rate). The survey was developed to determine the level of concern about travelling to Toronto; the adequacy of screening measures; the level of stress about attending; and the occupational consequences of attending.RESULTS:Fifty per cent of the participants were not concerned about travelling to Toronto, while the other 50% expressed some concern ranging from mild to serious. Concerns included being exposed to SARS and fear of transmitting it to others upon return. Reasons for attending the conference despite concern included a desire for continuing education, decrease in the number of reported SARS cases, and perceived minimal risk. Almost one-half (n=140) felt the screening measures at the conference were adequate, while 4% felt they were inadequate and 9% somewhat adequate. Delegates (n=99) suggested that temperature-taking (32.2%), improved screening vigilance (14.4%), SARS screening forms checked daily (9.1%), strictly controlled entry (8.1%) and adopting hospital screening procedures (7.1%) should have been instituted.CONCLUSION:Health care professionals planning conferences in this era of new respiratory diseases can benefit from understanding the responses of delegates who attended conferences during outbreaks. Clear communication about the potential risks and benefits, as well as instituting full screening precautions, will help to allay concerns.

scholarly journals Measurement Bias Estimation in the Problem of Target Tracking

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Honglin Ren ◽  
Zhihua Lu
Keyword(s):  
Target Tracking ◽  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Estimation Algorithm ◽  
Measurement Bias ◽  
Bias Estimation ◽  
Extended Kalman Filtering ◽  
Iterative Estimation ◽  
Different Types ◽  
The Impact

In the problem of target tracking, different types of biases can enter into the measurement collected by sensors due to various reasons. In order to accurately track the target, it is essential to estimate and correct the measurement bias. Considering practical backgrounds, the bias is assumed to be locally stationary Gaussian distributed and an iterative estimation algorithm is proposed. Firstly, a mechanism is established to detect whether the bias switches between different Gaussian distributions. Secondly, the expectation maximization algorithm with the assistance of extended Kalman filtering and smoothing is proposed to iteratively estimate the bias and target state in an offline manner. Simulations show the proposed algorithm can suppress the impact of the measurement bias on target tracking.

The Effect of Measures Taken by Craigslist to Screen Online Ads for Commercial Sex

2017 ◽  
Vol 36 (3) ◽  
pp. 296-310 ◽  
Author(s):  
Shana M. Judge
Keyword(s):  
North Carolina ◽  
Research Design ◽  
Online Marketing ◽  
Commercial Sex ◽  
Unique Data ◽  
The Impact ◽  
Manual Review ◽  
Screening Measures

A 2017 U.S. Senate subcommittee report charging employees at backpage.com with editing the website’s online ads for prostitution has revived debate over the willingness and ability of such websites to screen ads for unlawful commercial sex activity. This study sheds light on the controversy by revisiting the dispute surrounding similar advertising on craigslist.com . Using an observational pre–post research design, I examined unique data collected from commercial sex ads on a North Carolina Craigslist site to assess the impact of enhanced ad screening measures that Craigslist implemented to address misuse of its ad hosting services. Results indicate that Craigslist’s switch to a manual review of ads led to significant decreases in illicit ad content, temporarily inhibiting online marketing of commercial sex by regional advertisers.

scholarly journals Constraining the masses of high-redshift clusters with weak lensing: Revised shape calibration testing for the impact of stronger shears and increased blending

2020 ◽  
Vol 640 ◽  
pp. A117 ◽  
Author(s):  
B. Hernández-Martín ◽  
T. Schrabback ◽  
H. Hoekstra ◽  
N. Martinet ◽  
J. Hlavacek-Larrondo ◽  
...  
Keyword(s):  
Selection Bias ◽  
High Redshift ◽  
Real Data ◽  
Weak Lensing ◽  
Measurement Bias ◽  
The Real ◽  
Refined Estimate ◽  
The Masses ◽  
The Impact ◽  
Simulated Images

Weak lensing measurements suffer from well-known shear estimation biases, which can be partially corrected for with the use of image simulations. In this work we present an analysis of simulated images that mimic Hubble Space Telescope/Advance Camera for Surveys observations of high-redshift galaxy clusters, including cluster specific issues such as non-weak shear and increased blending. Our synthetic galaxies have been generated to have similar observed properties as the background-selected source samples studied in the real images. First, we used simulations with galaxies placed on a grid to determine a revised signal-to-noise-dependent (S/NKSB) correction for multiplicative shear measurement bias, and to quantify the sensitivity of our KSB+ bias calibration to mismatches of galaxy or PSF properties between the real data and the simulations. Next, we studied the impact of increased blending and light contamination from cluster and foreground galaxies, finding it to be negligible for high-redshift (z >  0.7) clusters, whereas shear measurements can be affected at the ∼1% level for lower redshift clusters given their brighter member galaxies. Finally, we studied the impact of fainter neighbours and selection bias using a set of simulated images that mimic the positions and magnitudes of galaxies in Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) data, thereby including realistic clustering. While the initial SExtractor object detection causes a multiplicative shear selection bias of −0.028 ± 0.002, this is reduced to −0.016 ± 0.002 by further cuts applied in our pipeline. Given the limited depth of the CANDELS data, we compared our CANDELS-based estimate for the impact of faint neighbours on the multiplicative shear measurement bias to a grid-based analysis, to which we added clustered galaxies to even fainter magnitudes based on Hubble Ultra Deep Field data, yielding a refined estimate of ∼ − 0.013. Our sensitivity analysis suggests that our pipeline is calibrated to an accuracy of ∼0.015 once all corrections are applied, which is fully sufficient for current and near-future weak lensing studies of high-redshift clusters. As an application, we used it for a refined analysis of three highly relaxed clusters from the South Pole Telescope Sunyaev-Zeldovich survey, where we now included measurements down to the cluster core (r >  200 kpc) as enabled by our work. Compared to previously employed scales (r >  500 kpc), this tightens the cluster mass constraints by a factor 1.38 on average.

scholarly journals On the measurement bias of low-$\ell$ solar $\vec{p}$-mode excitation parameters: The impact of a ground-based window function

2003 ◽  
Vol 398 (1) ◽  
pp. 305-314 ◽  
Author(s):  
W. J. Chaplin ◽  
Y. Elsworth ◽  
G. R. Isaak ◽  
B. A. Miller ◽  
R. New ◽  
...  
Keyword(s):  
Window Function ◽  
Measurement Bias ◽  
Mode Excitation ◽  
The Impact ◽  
Excitation Parameters

scholarly journals Survey Mode Effects on Objective and Subjective Questions: Evidence from the Labour Force Survey

2021 ◽  
Vol 37 (1) ◽  
pp. 213-237
Author(s):  
Joachim Schork ◽  
Cesare A.F. Riillo ◽  
Johann Neumayr
Keyword(s):  
Labour Force ◽  
Measurement Bias ◽  
Labour Force Survey ◽  
Randomized Experiments ◽  
Exact Matching ◽  
Survey Mode ◽  
Mode Effects ◽  
Specific Measurement ◽  
Sample Composition ◽  
The Impact

Abstract Web questionnaires are increasingly used to complement traditional data collection in mixed mode surveys. However, the utilization of web data raises concerns whether web questionnaires lead to mode-specific measurement bias. We argue that the magnitude of measurement bias strongly depends on the content of a variable. Based on the Luxembourgish Labour Force Survey, we investigate differences between web and telephone data in terms of objective (i.e., Employment Status) and subjective (i.e., Wage Adequacy and Job Satisfaction) variables. To assess whether differences in outcome variables are caused by sample composition or mode-specific measurement bias, we apply a coarsened exact matching that approximates randomized experiments by reducing dissimilarities between web and telephone samples. We select matching variables with a combination of automatic variable selection via random forest and a literature-driven selection. The results show that objective variables are not affected by mode-specific measurement bias, but web participants report lower satisfaction-levels on subjective variables than telephone participants. Extensive supplementary analyses confirm our results. The present study supports the view that the impact of survey mode depends on the content of a survey and its variables.

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