How to deal with situations if there is missing data in the questionnaire?

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Dealing with missing data in a questionnaire is a common challenge in research and data analysis. It's important to address missing data appropriately to ensure the validity and reliability of your findings. Here are some strategies to handle missing data in a questionnaire:
1. Understand the types of missing dat.a: Missing data can be categorized into different types, such as completely missing (when an entire questionnaire or section is missing), item missing (when specific questions are missing), or missing at random (when the absence of data is unrelated to the variables being measured). Understanding the nature of missing data can guide your approach to handling it.
2. Assess the pattern of missing data.: Examine the pattern of missing data to identify any systematic trends. Determine if the missing data is random or if there are specific factors associated with its occurrence. This analysis can help you understand potential biases and inform your decisions on how to handle missing data.
3. Consider the missing data mechanism: Different missing data mechanisms require different handling approaches. If the missing data is missing completely at random (MCAR), meaning the missingness is unrelated to the variables being measured, you can generally ignore the missing data and proceed with the available data. However, if the missing data is missing at random (MAR) or missing not at random (MNAR), more sophisticated techniques may be required to handle the missing data appropriately.
4. Complete case analysis: One simple approach is to perform a complete case analysis, where only cases with complete data are included in the analysis. This approach is straightforward but may lead to reduced sample size and potentially biased results if the missing data is not MCAR.
5. Imputation techniques: Imputation involves estimating missing values based on observed data. Various imputation methods exist, including mean imputation, median imputation, regression imputation, and multiple imputation. Imputation can help retain the sample size and reduce bias, but the choice of method should be based on the missing data mechanism and the characteristics of the data.