You are conducting your first research and want to understand how to detect errors in your work. Then, you may hear the word attrition bias and be curious about it. In this post, we’ll cover its meaning, examples, prevention, and significance. But first, let’s talk about why bias is so important in research.
Researchers typically use the term bias to indicate a systematic error in the research. In simple words, it’s an error that skews outcomes from their actual value, leading to incorrect findings.
Bias can be categorized into two types:
Attrition bias happens when some subjects leave the study midway, especially if they share similar traits. It’s also known as subject mortality. This can lead to skewed findings as the remaining sample may differ significantly from the initial group.
Attrition bias commonly occurs in longitudinal studies, where participants are observed over a long period. It's especially problematic in randomized controlled trials in the healthcare field.
You can modify the independent variable in your research or combine experimental and longitudinal designs to detect within-subject variation.
Suppose you are examining if a social media detox helps reduce phone addiction among youth. You give detox plans to one group and allow the other to use social media freely for 3 months. Naturally, not all participants remain through every stage.
You conduct four waves of data collection: a preliminary interview, two mid-tests, and a post-experiment survey. With each wave, some participants drop out, reducing your sample and creating attrition bias.
Some level of attrition is expected, but if it’s systematic, it can affect both internal and external validity.
If more people leave one group (e.g., treatment vs. control), it distorts the link between independent and dependent variables.
If 35 people drop out of your treatment group and only 5 from the control group, comparing the outcomes becomes misleading because you're unsure how those who left would have responded.
When those who drop out are systematically different (e.g., heavy phone users), your results may not apply to the entire population.
If mostly heavy phone users drop out, your final sample is biased toward moderate users. Thus, your findings can’t be generalized to all youth.
Here are ways to reduce attrition in your study:
Even with prevention, some attrition is inevitable. You can detect it by comparing baseline data of those who stayed vs. those who dropped out.
Use logistic regression analysis to assess which variables are significantly related to dropout. If hours of phone use and types of apps differ between stayers and leavers, attrition bias is present.
Since eliminating attrition bias entirely isn’t possible, consider statistical methods to reduce its impact.
This involves filling missing data using simulated values. Each missing value is replaced with multiple plausible values to create several full datasets. You then analyze all datasets and combine results.
Adjust your sample to better reflect the population. Assign more weight to participants similar to those who dropped out.
You assign a weight of 1.5 to heavy phone users and 1 to low/moderate users to balance the final dataset.
Attrition bias is common and must be accounted for—especially in long-term or randomized medical research. It affects both internal and external validity.
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