I am unsure if this question has been answered, but I will quickly provide some feedback that may help.

First, concerning the 11 participants who are missing all their data - I would exclude them from your analyses. However, if you have partial data (e.g., demographics) from these 11 participants, it may be worthwhile to investigate why these individuals did not complete the survey (e.g., is it a specific subset of your sample who withdrew?).

Next, regarding the missing values outside of the missing 11 respondents. You would first need to determine the mechanism of missing data. Is this data missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR).

Once you have this determined, it will inform which approach you can take to address your missing data. For example, listwise deletion requires data to be MCAR to reduce/prevent bias in results. If your data is not MCAR it may be worthwhile to use multiple imputation (MI). Using MI or other techniques (e.g., maximum likelihood) instead of listwise deletion may allow you to retain more of your data. Depending on your planned analyses you may also be able to use pairwise deletion; however, note that like listwise deletion this requires your data to be MCAR.

I am unsure if this question has been answered, but I will quickly provide some feedback that may help.

First, concerning the 11 participants who are missing all their data - I would exclude them from your analyses. However, if you have partial data (e.g., demographics) from these 11 participants, it may be worthwhile (and even recommended) to investigate why these individuals did not complete the survey (i.e., is it a specific subset of your sample who withdrew?).

Next, regarding the missing values outside of the missing 11 respondents. You would first need to determine the mechanism of missing data. Is this data missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). See this previous post I made on missing data mechanisms: Panel data analysis with a lot of missing values and a lot of variables

Once you have this mechanism determined, it will inform which approach you can take to address your missing data. For example, listwise deletion requires data to be MCAR to reduce/prevent bias in results. It may be worthwhile (and even recommended) to use multiple imputation (MI) if your data is at least MAR rather than listwise deletion. Using MI or other techniques (e.g., maximum likelihood) instead of listwise deletion may allow you to retain more of your data. Depending on your planned analyses, you may also be able to use pairwise deletion; however, like listwise deletion, this requires your data to be MCAR. This resource by Woods et al. (in press) is also fantastic for missing data and best practices/recommendations: https://psyarxiv.com/uaezh