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Measurement errors: Here’s how to analyze them right!
Dealing with measurement errors can be especially challenging if you don’t know how to analyze them right. This video will take you through some best ways of analyzing the measurement errors in your research.
And why is this important? Well, a researcher’s objective is to obtain accurate results by minimizing the errors in the research design as much as possible. You can use reliability coefficients, minimally detectable changes, or even the Bland-Altman analysis.
Reliability coefficients
These coefficients are based on reliability scores calculated in diverse ways.
Intraclass correlation coefficient (ICC): ICC is a descriptive statistical measure of the correlation or similarity between individuals in a group. Basically, it tells you the extent to which two individuals are similar. This is often analyzed from a single examiner’s point of view.
Inter-rater reliability: This reliability score indicates to what extent different examiners agree when evaluating the same phenomenon.
Minimally detectable change (MDC)
As the name suggests, this is the smallest change that is unlikely to be a random error. MDC is detected between a control and a treatment group, often calculated using standard error of measurement and confidence intervals.
Bland-Altman analysis
The Bland-Altman analysis provides a visual summary of how two different methods measure the same variable. The difference plot helps determine which method (or instrument) is resulting in lesser measurement error. Typically, this technique is used in biomedicine and analytical chemistry to visually capture the error between two measurement methods.
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This content belongs to the Conducting Research Stage
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