To transform or not to transform and how to transform data?

My study:
- 2 groups- 1. Not allowed to work; 2. Allowed to work (control)
- compare the groups on various aspects like quality of life, depression, anxiety, etc on standardized quantitative questionnaires
- compare if coping ability (2 standardized quantitative scales) predict scores on above measures

Currently I'm running preliminary analysis on collected data (n=103+) which is still ongoing. Have checked for normality.
Some questionnaires' data was normal, on others it wasn't:

Questionnaire 1- normally distributed; Skewness- normal but negative; Kurtosis- normal but negative

Quest. 2- not normal; skew- normal but negative; kurtosis- negative and not normal (more than 1.96)

quest. 3- not normal; skew- positive; kurtosis- normal

quest. 4- not normal; skew- positive; kurtosis- normal

quest. 5- not normal; skew- positive; kurtosis- not normal

quest. 6- not normal; skew- negative but normal; kurtosis- negative but normal

quest. 7- normal; skew-negative but normal; kurtosis- negative but normal

quest. 8 (can be ignored as of now as not sure of scoring)- normal, skew- negative but normal; negative but normal

so, it's quite a mix of normally and not-normally distributed data. i went ahead and did log transformation and it didn't help much- varied result once again.
I also later realized I had done it wrong as I didn't account for negative skewness of some questionnaires.
Also, some questionnaires have 'zero' values as well.

My questions now:
1. Do I need to transform data?
2. How do I transform it, given negative skewness on some questionnaires (Andy Field doesn't go into much detail on this in his book :( i.e. using SPSS)
3. Would the same procedure be used for all questionnaires given the variety of results- positively skewed-kurtosis/negatively skewed-kurtosis/ not skewed-kurtosis?

Please help!
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