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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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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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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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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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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Related Experiment Video

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Biases in multilevel analyses caused by cluster-specific fixed-effects imputation.

Matthias Speidel1, Jörg Drechsler2, Joseph W Sakshaug2,3

  • 1Institute for Employment Research, Regensburger Strasse 104, 90478, Nuremberg, Germany. matthias.speidel@iab.de.

Behavior Research Methods
|August 26, 2017
PubMed
Summary

Imputing missing data with cluster-specific fixed effects can bias multilevel model results, particularly in random coefficient models. This bias impacts variance estimates and confidence intervals, depending on cluster size and data structure.

Keywords:
Cluster-specific fixed-effects imputation approachHierarchical multiple imputationLinear mixed modelMultilevel imputation approach

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Area of Science:

  • Statistics
  • Multilevel Modeling
  • Educational Research

Background:

  • Nonresponse is common in datasets, necessitating imputation for missing values.
  • Multilevel models are widely used in education research but often not supported by standard imputation software.
  • Including cluster-specific fixed effects is a common but analytically unverified strategy for handling hierarchical data in imputation.

Purpose of the Study:

  • To analytically evaluate the impact of cluster-specific fixed-effects imputation on multilevel inference.
  • To compare this common imputation strategy with multilevel imputation in random slopes models.
  • To identify the conditions under which cluster-specific fixed-effects imputation leads to biased results.

Main Methods:

  • Analytical derivation of the bias in multilevel inference due to cluster-specific fixed-effects imputation.
  • Simulation studies to demonstrate the impact on random-effects variances and fixed-effects confidence intervals.
  • Application using data from the National Educational Panel Study (NEPS).

Main Results:

  • Cluster-specific fixed-effects imputation generally biases inferences from random coefficient models.
  • Bias in random-effects variances and fixed-effects confidence intervals is influenced by cluster size, variance components, and missing data mechanisms.
  • Simulation studies and real-data application confirm the negative implications of this imputation strategy.

Conclusions:

  • The common practice of using cluster-specific fixed effects in imputation models can lead to inaccurate multilevel inferences.
  • Researchers should be cautious when using this method, especially with random coefficient models.
  • Alternative, more theoretically sound multilevel imputation methods are recommended for hierarchical data with missing values.