Measurement bias is defined as the bias that occurs when biases or errors occur in the process of measurement and calculations of the data based on which the results are obtained. Such biases can result from erroneous tools of measurement, errors in calculations, errors while measurements are recorded, participant bias, recall bias, and so on. For example, if dietary recalls are considered for a 24 hours time period, then measurement biases can occur with the methods of the interview, the assumptions of coding, or through errors in the consumption of databases based on the selection of nutrition, and the number of days for which the interviews should be conducted.
Biases in such cases can affect the national diet intake process of several individuals (Monsen and Horn 2008). Thus when research involves measurement biases, the study cannot be considered as reliable and valid as the true results would not be obtained with wrong data collections or measurements. Another example is in the calculations of BMI (Body Mass Index) of individuals. The calculation requires the measurement of exact height and weight of the body.
In case of slight biases or errors in any of the two measurements, the overall calculation of the index would be affected. It is possible in many cases that patients are unable to stand, thus likely to give a wrong measurement of their heights, affecting the calculation of BMI. For such patients, only estimates of their heights are used that can lead to errors in the outcomes of the results (Howlett, Rogo and Shelton 2013). Analysis bias is defined as the bias that occurs at the stage of analysis of collected data when the researcher presents positive outcomes to avoid negative reflections (Pannucci and Wilkins 2011).
For example, research involves performing surveys and interviews and hence collecting data and all necessary information for the study. Risks are still involved in the study since all the collected information will have to be analyzed by the researcher.
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