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For example, if an outdoor experiment were to be conducted to compare how different wing designs of a paper airplane (the independent variable) affect how far it can fly (the dependent variable), one would want to ensure that the experiment is conducted at times when the weather is the same, because one would not want weather to affect the ...
The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one (or more) non-treated unit(s) with similar observable characteristics against which the covariates are balanced out (similar to the K-nearest neighbors algorithm).
The "propensity" describes how likely a unit is to have been treated, given its covariate values. The stronger the confounding of treatment and covariates, and hence the stronger the bias in the analysis of the naive treatment effect, the better the covariates predict whether a unit is treated or not.
In the first example provided above, the sex of the patient would be a nuisance variable. For example, consider if the drug was a diet pill and the researchers wanted to test the effect of the diet pills on weight loss. The explanatory variable is the diet pill and the response variable is the amount of weight loss.
An operational confounding can occur in both experimental and non-experimental research designs. This type of confounding occurs when a measure designed to assess a particular construct inadvertently measures something else as well. [20] A procedural confounding can occur in a laboratory experiment or a quasi-experiment. This type of confound ...
Ignoring confounding factors can lead to a problem of omitted variable bias. In the special case of selection bias, the endogeneity of the selection variables can cause simultaneity bias. Spillover (referred to as contagion in the case of experimental evaluations) occurs when members of the comparison (control) group are affected by the ...
Statistical bias exists in numerous stages of the data collection and analysis process, including: the source of the data, the methods used to collect the data, the estimator chosen, and the methods used to analyze the data. Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their ...
Good blinding can reduce or eliminate experimental biases that arise from a participants' expectations, observer's effect on the participants, observer bias, confirmation bias, and other sources. A blind can be imposed on any participant of an experiment, including subjects, researchers, technicians, data analysts, and evaluators.
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