Several methods to deal with missing data were reviewed, issues raised, and advice offered for those that remain unresolved.
Since retrieval of missing data cannot create fewer cases, these differences raise concern about bias or incorrect summations.
Making queries on the presented model raises the problem of managing missing data, precisely missing role fillers.
The validation procedure raised a large amount of missing data due to low applicability, which were assessed with two methods.
However, low applicability raises the issue of dealing with missing data, increasingly discussed in the literature [ 29].
In particular, problems with compliance with the numerous repeated assessments common to many EMA protocols raises concerns about the preponderance of missing data that these methods sometimes produce.
An immediate question that is raised when considering the problem of filling in missing data, particularly in the context of large sample surveys such as PISA and TALIS, is the validity of the method used to fuse the data sets.
This exclusion of some sample inevitably raises the possibility of selection bias due to missing data.
Recognizing concerns raised about potential bias due to the use of missing data categories in regression models (Jones 1996), we repeated our main model as a complete case analysis, omitting participants with missing values for any of the covariates.
The database software provides a number of features to help maintain data quality, including; maintaining an audit trail, allowing custom validations on all data, allowing users to raise data query requests and search facilities to identify validation failure/ missing data.
