National EMSC Data Analysis Resource Center
You can eliminate or at least reduce sources of bias and confounding by carefully designing your data project or study.
The following outlines some of the major sources of bias and confounding and how to overcome these in your project design.
Your sample should be representative of your target population. See random selection and introduction to sampling for more guidance in this area. If you are collecting data using a survey or questionnaire, you need to evaluate those who did not respond to the survey and make sure that they are not systematically different from those that completed the survey.
You can reduce bias and confounding by making sure your outcome(s) and other variables are specific, objective, and clearly defined.
For example, suppose you want to describe what percentage of motor vehicle crash fatalities were preventable if immediate medical attention were possible. This could vary depending on who is reviewing the crash circumstances and injuries and how they evaluate the available evidence. Your outcome should be standardized so it can be evaluated consistently across everyone in your sample.
If your data are inaccurate or incomplete, you may introduce bias into your evaluation. Plan your data collection carefully and understand the data definitions and values you are using. Provide education and motivation for accurate and complete data to those who are collecting and entering the data. Institute checks to make sure that the data provided or collected are valid. For example, is 0 meaningful for reasonable diastolic blood pressure?
In a comparative study, you want to evaluate how a specific factor affects your outcome. For example, suppose you want to see if sex (male/female) influences whether or not you get in a car crash. Your study finds that more men drivers are in car crashes than women. You may conclude from this that men are poorer drivers than women. However, you must take into account that men typically drive more than women. In this case, the total miles driven are a confounding variable that can affect your results and conclusions.
When comparing two groups, the two groups should be as similar as possible in every way except the intervention or the factor of interest. When comparing two groups (intervention vs. control or factor A vs. factor B), the two groups should be as similar as possible in every way except the intervention or the factor of interest. In a comparative study with an intervention, you can make sure that the intervention and control groups are comparable by randomly assigning participants to either the intervention or control group. If there is no intervention, you can either match cases so that they are similar to controls or you can control for important variables that could also be affecting your outcome in your analysis.
If you are implementing a program, policy, or other intervention, there are several key considerations in your project design.
Before and after measures
First, you must take a measurement before you institute the new program or intervention. This is called a baseline measurement and describes your outcome. You then measure the same outcome after the intervention to see how your outcome has changed over time.
A control group that is as similar as possible to the intervention group strengthens the conclusion that changes in your evaluation measures are due to your program.
Loss to follow up
You should also consider what participants are available at follow-up (post intervention) versus who originally received the intervention. Is your final sample still representative of your population?