For the continuous variables you can use a t-test if you are comparing two conditions or an ANOVA if you are comparing three or more conditions.

For the categorical variables you can use a Chi-Square test, either a goodness of fit test if you are looking at a single categorical variable, or a test of independence if looking at two categorical variables.

I recommend the book "SPSS Survival Manual: A Step by Step Guide to Data Analysis Using IBM SPSS" for a useful, easy to understand guide, on when and how to use these and other analyses.

I wrote you a long answer but it disappeared so take a look at the attached paper and compute sums of your dependent variables with an alpha about .70 for each of variables (compassion satisfaction, burnout, and secondary traumatic stress) so that you delete those items that do not contribute to the one dependent variable you explain with age, trauma..... and so on

Then use linear regression analysis. Independent explanatory variables can be categorical also. Your age variable is a continuous variable and does not have to be grouped but can be grouped.

Student nurses and the general population in Sweden: Trends in attitudes towards mental illness

Full-text available on RG

• July 2012
• Nordic Journal of Psychiatry
• 📷Béatrice Marianne Ewalds-Kvist
• 📷Torbjörn Högberg
• 📷Kim Lützén

Likert scales are considered as continous variable. So you have categorical IVs and continous DVs. I think your data is cross-sectional. In that case, you can simply use regression analysis. You can added all categorical independent variables at the same step (or maybe you can try stepwise regression if it is more suitable for your research aim) in regression analysis. So you should repeat regression analysis for each I DVs (sub-scale of ProQol-5).