How Can We Make Sure Our Data is Accurate and Trustworthy in Engineering Design?

When engineers are designing projects, especially in schools, it’s really important to make sure the data they use is both valid and reliable.

• Data validity means that the measurements truly reflect what they are supposed to measure.
• Data reliability means we can trust that the measurements will give us the same results every time we check them.

Here are some easy methods to help ensure the data we collect is good and trustworthy:

1. Use Standard Tools

Using standardized tools like surveys and questionnaires can help make sure our data is valid and reliable. These tools are tested to get accurate and consistent results no matter who is answering.

• Example: The System Usability Scale (SUS) is a popular tool in engineering for checking how users feel about a design. Studies have shown that SUS is very reliable with a score of 0.91, meaning it consistently produces accurate data.

2. Try Triangulation

Triangulation means using different methods or sources to check the data. When we look at things from various angles, we can be sure our findings are more reliable.

• Types of Triangulation:
  • Data triangulation: This involves using different sources like experiments, field studies, and surveys.
  • Methodological triangulation: This uses both surveys (qualitative) and numbers (quantitative).

Studies show that using triangulation can increase the trustworthiness of findings by more than 70% compared to using just one method.

3. Do Pilot Tests

Before we fully launch a project, it helps to do pilot tests. This means trying out our tools and processes on a smaller scale first. It helps us catch any mistakes or biases in how we collect data.

• Fact: Research suggests that pilot testing can reduce errors in measurements by up to 60%.

4. Make Sure the Sample Size is Right

Having the right sample size is very important. If we study too few people, we might not get a true picture of what we’re examining. Using the right sample sizes helps make our results more reliable.

• Rule of Thumb: In engineering studies, it's often recommended to have at least 30 people in a study, and at least 100 for more complicated projects.

5. Clean Up the Data

Cleaning data means we check for errors or things that don’t make sense. This includes removing any strange numbers (outliers) or making sure the data meets certain standards.

• Finding: Research shows that about 25% of raw engineering data can have mistakes, which can lead to wrong conclusions if we don’t fix them.

6. Keep an Eye on Data Collection

Monitoring data collection regularly ensures that everyone is following the rules and reduces mistakes. Regular check-ins help keep our data collection consistent.

• Fact: Studies have shown that constant monitoring can improve the quality of data by more than 50%.

7. Train Those Collecting Data

Training people who help collect data is critical. When they understand the importance of following methods, they make fewer mistakes and help keep the data reliable.

• Impact: Training can cut down data entry mistakes by as much as 40%, making our findings better.

Conclusion

Making sure our data is accurate and trustworthy in engineering not only helps us get stronger results but also helps us create useful and innovative designs. By using standard tools, triangulation, pilot testing, and ensuring we have the right sample size, we can gather top-quality data. Ongoing monitoring, cleaning data, and training people involved further strengthen the research results, which makes a big difference in engineering projects at schools.