Week 6: Sources of Error in Population-Based Research This week, you will review different sources of error in population-based research, focusing on bias and confounding. Bias refers to deviations of results, or inferences, from the truth (Friis & Sellers, 2021). There are two overarching types of bias: information bias and selection bias. Both types can […]
To start, you canWeek 6: Sources of Error in Population-Based
Research
This week, you will review different sources of error in population-based research, focusing on bias and confounding. Bias refers to deviations of results, or inferences, from the truth (Friis & Sellers, 2021). There are two overarching types of bias: information bias and selection bias. Both types can be detrimental to the validity and reliability of results. Several strategies exist to help prevent bias, but it is virtually impossible to eliminate bias altogether.
In addition to bias, confounding variables can pose challenges for epidemiologists. Confounding is the masking of an
association between an exposure and an outcome because of the influence of a third variable that was not considered in the study design or analysis. For example, if weight loss is the topic of study and exercise is the only variable considered, diet could mask the results of the study.
Learning Objectives
Students will:
·
Analyze nursing practice implications of bias, confounding, and random error in epidemiologic and population
health research
Propose strategies to minimize sources of error in population research
Differentiate epidemiologic measures and measurement errors
Learning Resources
Required Readings (click to expand/reduce)
Required Readings (click to expand/reduce)
Curley, A. L. C. (Ed.). (2020). Population-based nursing: Concepts and competencies
for advanced practice (3rd ed.). Springer.
● Chapter 4, “Epidemiological Methods and Measurements in Population-Based
Nursing Practice: Part II”
Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.).
Jones & Bartlett.
● Chapter 10, “Data Interpretation Issues”
Discussion Resources
Enzenbach, C., Wicklein, B., Wirkner, K., & Loeffler, M. (2019). Evaluating selection
bias in a population-based cohort study with low baseline participation: The LIFE-Adult-
Study. BMC Medical Research Methodology, 19 (1), Article 135.
https://doi.org/10.1186/s12874-019-0779-8
Khalili, P., Nadimi, A. E., Baradaran, H. R., Janani, L., Rahimi-Movaghar, A., Rajabi, Z.,
Rahmani, A., Hojati, Z., Khalagi, K., & Motevalian, S. A. (2021). Validity of self-reported
substance use: Research setting versus primary health care setting. Substance abuse
Treatment, Prevention, and Policy, 16 (1), Article 66. https://doi.org/10.1186/s13011-
021-00398-3
Karr, J. E., Iverson, G. L., Isokuortti, H., Kataja, A., Brander, A., Öhman, J., & Luoto, T.
M. (2021). Preexisting conditions in older adults with mild traumatic brain injuries. Brain
Injury , 1–9. Advance online publication.
https://doi.org/10.1080/02699052.2021.1976419
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