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Combining offline and online data collection is normally straightforward provided that the collection mechanisms are compatible. Data is often collected in a CSV or other standard formatting procedure that can be exported to Excel or another software packaging system that can be cleaned and analyzed.

Certain patterns in responses warrant cases to be removed to protect the integrity of data. Cases should be flagged where respondents answer too quickly or slowly. Additionally, responses may need to be removed from participants who drop off after certain points, answer with limited to no variance or answer antithetical questions inconsistently.

Non-normal data can be analyzed using non-parametric analyses or other analysis that require less strict assumptions. Additionally sometimes it is beneficial to transform non-normal quantitative data into categorical data which can be normally distributed by using ranges of values for categories.

Ideally there is less than 5% missing data, but the importance and impact of missing data depends upon what data is missing, what type of data is collected, the intended analysis and the relationship between variables that are missing large amounts of data.