In computer science research, data collection methods such as surveys, interviews, and observational studies can introduce biases and limitations that researchers need to be aware of. One common bias is selection bias, where the sample population may not be representative of the broader population, leading to skewed results. Researchers can mitigate this issue by using random sampling techniques and ensuring a diverse and inclusive sample.
Another limitation is response bias, where participants may provide inaccurate or socially desirable responses, affecting the validity of the data. To address this, researchers can use anonymized surveys, establish rapport with participants to encourage honest feedback, and employ validation techniques to cross-check responses.
Additionally, data collection methods in computer science research may be prone to measurement errors, especially when dealing with complex technical concepts or subjective interpretations. Researchers can mitigate this by using standardized measurement tools, conducting pilot studies to refine data collection instruments, and employing multiple data sources for triangulation.