Related Experiment Video
Updated: Nov 22, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
Published on: September 27, 2019
Misclassification and measurement error - planning a study and interpreting results
Steven Alfred Frost1, Evan Alexandrou2
1South Western Sydney Local Health District, Centre for Applied Nursing Research, Liverpool BC, New South Wales, Australia.
Background:
Measurement error must always be considered when planning a research project and interpreting its results. The accuracy of some data collected during a study can often be confidently assured, but more than one measurement or observer is needed to assess exposure and outcomes status in cases where clinical measurement is prone to measurement error. Little attention is paid in nursing research to misclassification and measurement error. Bias is often discussed in nursing research education, but not its potential consequences or measures that can be taken to improve the study's quality.
Aim:
To present examples of random measurement error - misclassification of a binary outcome - in a continuous exposure and outcomes variable, to address this gap in nurses' research training.
Discussion:
The article discusses the relationship between exposure and outcome in the absence and presence of measurement error using risk (relative risk) and association using correlation. It provides methods to estimate the true value of these measures of risk and association, when only given the clinical measurements with errors.
Conclusion:
If the assumption of random error holds, attenuation of risk or association towards the null will occur.
Implications For Practice:
Understanding the effect of measurement error including misclassification will enable researchers to interpret the results of their studies, and to take into consideration this potential error when planning and conducting a study.
Related Concept Videos
Strategies for Assessing and Addressing Confounding
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Bias in Epidemiological Studies
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Study Design in Statistics
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...

