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Related Concept Videos

Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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Bias Correction for Replacement Samples in Longitudinal Research.

Jessica A M Mazen1, Xin Tong1

  • 1Department of Psychology, University of Virginia.

Multivariate Behavioral Research
|August 27, 2020
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Summary
This summary is machine-generated.

Researchers can correct biased estimates from supplemental replacement samples in longitudinal studies. This study evaluates four novel bias correction methods for improved data accuracy.

Keywords:
Supplemental samplebias correctionlongitudinal designmissing datareplacement sample

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Area of Science:

  • Statistics
  • Longitudinal Data Analysis
  • Biostatistics

Background:

  • Missing data are a frequent challenge in longitudinal research.
  • Supplemental samples are used to address missing data, with refreshment and replacement being common approaches.
  • Replacement samples, while used, can introduce bias in parameter estimates due to unrepresentativeness.

Purpose of the Study:

  • To propose and evaluate methods for correcting bias introduced by supplemental replacement samples in longitudinal studies.
  • To address the uncorrected estimation bias prevalent in studies utilizing replacement samples.
  • To compare the performance of four novel bias correction techniques.

Main Methods:

  • Parametric bootstrapping replacement sample correction
  • Non-parametric bootstrapping replacement sample correction
  • Primary inverse probability reweighting correction
  • Likelihood-based inverse probability reweighting correction
  • Evaluation through simulation and empirical studies.

Main Results:

  • The study evaluates the performance of four proposed bias correction methods.
  • Simulation and empirical analyses are conducted to assess the effectiveness of each correction technique.
  • Results will indicate which methods best mitigate bias from replacement samples.

Conclusions:

  • Bias introduced by supplemental replacement samples in longitudinal data can be corrected.
  • The proposed parametric bootstrapping, non-parametric bootstrapping, and inverse probability reweighting methods offer potential solutions.
  • These corrections are crucial for obtaining accurate parameter estimates in longitudinal research with missing data.