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Bias should be accounted for at every step of the data collection process, beginning with clearly defined research parameters and consideration of the team who will be conducting the research. [2] Observer bias may be reduced by implementing a blind or double-blind technique.
Blinded protocols and double-blinded research can act as a corrective lens in terms of reducing observer bias, and thus, to increase the reliability and accuracy of the data collected. [11] Blind trials are often required in order for the attainment of regulatory approval for medical devices and drugs, but are not common practice in empirical ...
[11] [12] Anchoring bias includes or involves the following: Common source bias, the tendency to combine or compare research studies from the same source, or from sources that use the same methodologies or data. [13] Conservatism bias, the tendency to insufficiently revise one's belief when presented with new evidence. [5] [14] [15]
The data should be digitized and prepared for data analysis. Data may be analysed with the use of software to interpret or visualise statistics or data to produce the desired results of the research such as quantitative results including figures and tables. The use of software and automation enhances the reproducibility of research methods. [17]
The "ceiling effect" is one type of scale attenuation effect; [1] the other scale attenuation effect is the "floor effect".The ceiling effect is observed when an independent variable no longer has an effect on a dependent variable, or the level above which variance in an independent variable is no longer measurable. [2]
In statistics, inter-rater reliability (also called by various similar names, such as inter-rater agreement, inter-rater concordance, inter-observer reliability, inter-coder reliability, and so on) is the degree of agreement among independent observers who rate, code, or assess the same phenomenon.
A subsequent meta-analysis published in 2011, based on the original data, found flaws in the 2010 analyses and suggested that the data indicated reboxetine was effective in severe depression (see Reboxetine § Efficacy). Examples of publication bias are given by Ben Goldacre [40] and Peter Wilmshurst. [41]
A variable omitted from the model may have a relationship with both the dependent variable and one or more of the independent variables (causing omitted-variable bias). [3] An irrelevant variable may be included in the model (although this does not create bias, it involves overfitting and so can lead to poor predictive performance).