Bias is a familiar occurrence in research and quality improvement studies

Please respond to this discussion post, at least 200 words 2 references APA 7 format

Bias is a familiar occurrence in research and quality improvement studies. It arises when an error is noted in testing or sampling, which can occur during sampling, testing, data collection, or data analysis (Aspers & Corte, 2019). For my project which is focused on implementing text messaging to impact diabetes outcomes, bias can occur when the text messaging is not implemented as arranged or when the people responsible for sending the messages haven’t received adequate training on efficiently carrying out the task. To reduce the risk of bias, I will have to adequately train the staff at the project site on the study and the tools for implementation. Also, everyone involved will be educated on the importance of accurate data entry and the implications that false data entry can cause.

Another area of bias can be found in the sampling process which is a methodology bias. Convenience sampling will be used in this project. It is impossible or impractical to select a random sample when carrying out a quality improvement project. In such instances, a convenience sample may be utilized. Occasionally, it is feasible that a convenience sample could be deemed a random sample, although convenience samples are frequently biased (Jager et al., 2017). While convenience sampling is easy and convenient, it is subject to several potential biases. This sampling is prone to biases because it does not produce a statistically balanced selection of the population due to the fact that the participants are selected based on convenience rather than equal probability (Jager et al., 2017). To mitigate this bias, the project manager can use stratified sampling techniques to ensure that the sample is representative of the overall population (Jager et al., 2017).

A small sample size can also potentiate bias in a study. The potential sample size for this project is 34, which is small. Bias due to small sample size occurs when the sample size is too small to detect meaningful differences in the outcome being measured (Gyawali et al., 2021). To mitigate bias due to small sample size, the project manager can calculate the sample size needed to detect meaningful differences in the outcome and recruit a larger sample if necessary.

Information bias is another type of bias that can impact this project. This bias occurs when there are errors or inaccuracies in the measurement or recording of data (Gyawali et al., 2021). For example, if data is collected from patient charts or electronic health records, there may be inconsistencies or errors in the way the data is recorded. To lessen information bias, the project manager should use standardized data collection procedures and ensure that data is collected consistently across all participants. In addition, staff should be well trained on how to collect data accurately and consistently.

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