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Related Concept Videos

Bias01:22

Bias

4.7K
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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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

94
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

488
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Croon's Bias-Corrected Estimation for Multilevel Structural Equation Models with Non-Normal Indicators and Model

Kyle Cox1, Benjamin Kelcey2

  • 1University of North Carolina at Charlotte, USA.

Educational and Psychological Measurement
|January 5, 2023
PubMed
Summary

Croon's bias-corrected factor score (BCFS) estimation shows promise for multilevel structural equation models (MSEMs) in educational research. It performs well with limited sample sizes and model misspecifications, offering a dependable alternative.

Keywords:
Croon’s estimationmodel misspecificationmultilevel structural equation modelnon-normality

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Area of Science:

  • Educational research
  • Quantitative psychology
  • Statistical modeling

Background:

  • Multilevel structural equation models (MSEMs) are crucial for analyzing complex educational data with latent variables.
  • Croon's bias-corrected factor score (BCFS) path estimation offers a promising approach for MSEMs, especially with limited sample sizes common in educational research.
  • The performance of BCFS in MSEMs under challenging conditions like non-normal indicators and model misspecification requires thorough investigation.

Purpose of the Study:

  • To evaluate the accuracy and efficiency of BCFS estimation for MSEMs.
  • To assess BCFS performance under conditions of non-normal indicators and model misspecifications.
  • To compare BCFS with other estimation methods for MSEMs in educational research contexts.

Main Methods:

  • Conducted two simulation studies to assess BCFS estimation in MSEMs.
  • Varied conditions included limited sample sizes, non-normal indicators, and model misspecifications.
  • Compared the accuracy and efficiency of BCFS against other estimation techniques.

Main Results:

  • BCFS estimation for MSEMs demonstrated greater dependability, efficiency, and reduced bias compared to other methods under limited sample sizes and model misspecifications.
  • BCFS estimation was found to be more susceptible to non-normality in indicators.
  • The findings support the utility of BCFS as an alternative or supplemental estimator for MSEMs.

Conclusions:

  • BCFS estimation is a valuable tool for multilevel structural equation modeling in educational research, particularly when dealing with constrained sample sizes or model misspecifications.
  • Researchers should consider potential impacts of non-normal indicators when employing BCFS.
  • The study encourages the use of BCFS as a robust estimation strategy for complex educational research designs.