Only then do we have to decide how to 'prove' the truth or fallacy of our 'eyeball judgement'. Common sense and a clear head can tell us without mathematics if the first answers are 'men answer the same as women', etc, or we immediately know why the answers are different. It is easier for most non-statistical types to start at the bottom with.what was the question, the possible answers, how did my sample actually respond, and were their differences between how men and women answered, different age groups, different cultural backgrounds, etc. Much of questionnaire data is not going to be 'parametric' in any sense of the word, so much traditional 'what statistical test should I use' answers are simply going to be confusing. as well as SPSS), and goes through analyzing the data, along with good descriptions of when to use what specific statistical tests.

Looker Looker is a cloud-based business intelligence and data analytics platform. The graphical interface allows for point-and-click analysis and modeling.

It starts with creating a data base (which will work in excel. KNIME is great for data scientists who need to integrate and process data for machine learning and other statistical models but don't necessarily have strong programming skills.

One of the good resources, which is written mostly in common English rather than statistical jargon, is Pallant's SPSS Survival Manual. Some of the statistical suggestions above were based, I think, on the premise that no validation or previous research on the questionnaire items had been done.

Good tools for data analysis