Correct type of statistic for hypothesis testing on likert scale data dependancy

I'm new to this forum, I'm from Greece so please excuse my English :)
I had 20 people fill out a questionnaire for a paper I'm working on. This questionnaire contains 45 likert scale type questions and these questions are grouped in 7 groups.
In the context of my paper we hypothesise the dependancy of different groups with each other. For example, some of the groups are: Efficienty, Easy of use, Productivity etc. So, the 1st hypothesis is: "Easy of use has a positive effect on productivity" and so on for the rest of the groups.
I wanted to ask what kind of statistican analysis helps in the determination whethere these hypothesis are true or not, and how will the analysis determine it.
I would really appreciate any type of help, as I know very little about statistics and I am lost!
Thanks so much in advance!
So as I understand it, you want to see if one measureed item has an impact on another measured item.

The way I would approach this is to run a lot of correlations to look for significant relationships. If you go to the Bivariate Correlation test, you can enter in all your Likert test variables and it will generate a big table with all your variables and information as to whether or not they are related. Significant correlations mean they are in fact related.

You could stop there, but all you know from this is that as one item goes up another one consistently goes up or down. If you want to see if one of these variables has an effect on another, you can run linear regressions based on what correlations were significant. It's easier (and many people would say it's better science) if you are going into this with a specific question in mind, for example does A (independent variable) predict B (dependent variable). The correlation information will not tell you if A or B is an independent or dependent factor, so if you aren't going into this with a specific question in mind and being exploratory than you can run two analyses for each to see if there are differences in terms of one or another being a predictor.