Critiquing Sources of Error in Population Research to Address Gaps in Nursing Practice
As a DNP-educated nurse, part of your role will be to identify the differences, or gaps, between current knowledge and practice and opportunities for improvement leading to an ideal state of practice. Being able to recognize and evaluate sources of error in population research is an important skill that can lead to better implementation of evidence-based practice.
In order to effectively critique and apply population research to practice, you should be familiar with the following types of error:
Selection Bias
Selection bias in epidemiological studies occurs when study participants do not accurately represent the population for whom results will be generalized, and this results in a measure of association that is distorted (i.e., not close to the truth). For example, if persons responding to a survey tend to be different (e.g., younger) than those who do not respond, then the study sample is not representative of the general population, and study results may be misleading if generalized.
Information Bias
Information bias results from errors made in the collection of information obtained in a study. For example, participants’ self-report of their diet may be inaccurate for many reasons. They may not remember what they ate, or they may want to portray themselves as making healthier choices than they typically make. Regardless of the reason, the information collected is not accurate and therefore introduces bias into the analysis.
Confounding
Confounding occurs when a third variable is really responsible for the association you think you see between two other variables. For example, suppose researchers detect a relationship between consumption of alcohol and occurrence of lung cancer. The results of the study seem to indicate that consuming alcohol leads to a higher risk of developing lung cancer. However, when researchers take into account that people who drink alcohol are much more likely to smoke than those who do not, it becomes clear that the real association is between smoking and lung cancer and the reason that those who consume alcohol had a higher risk of lung cancer was because they were also more likely to be smokers. In this example, smoking was a confounder of the alcohol-lung cancer relationship.
Random Error
The previous three types of errors all fall under the category of
systematic errors, which are reproducible errors having to do with flaws in study design, sampling, data collection, analysis, or interpretation.
Random errors, on the other hand, are fluctuations in results that arise from naturally occurring differences in variables or samples. While unavoidable to a small degree even under the most careful research parameters, these types of errors can still affect the validity of studies.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
To Prepare:
· Review this week’s Learning Resources, focusing on how to recognize and distinguish selection bias, information bias, confounding, and random error in research studies.
· Select a health issue and population relevant to your professional practice and a practice gap that may exist related to this issue.
· Consider how each type of measurement error may influence data interpretation in epidemiologic literature and how you might apply the literature to address the identified practice gap.
· Consider strategies you might use to recognize these errors and the implications they may have for addressing gaps in practice relevant to your selected issue.
By Day 3 of Week 6
Post a cohesive scholarly response that addresses the following:
· Describe your selected practice gap.
· Explain how your treatment of this population/issue could be affected by having awareness of bias and confounding in epidemiologic literature.
· Explain two strategies researchers can use to minimize these types of bias in studies, either through study design or analysis considerations.
· Finally, explain the effects these biases could have on the interpretation of study results if not minimized
