The interpretation of results from nominal data can be difficult except for the frequency of each variable. Your analysis shows:
Data set:
1. Q12 allows decimal - why
2. Measure for AGE is either scale or ordinal because it is in increasing order of 19, 20, 21
3. All other variables except "average" should be measured as "ordinal" because they are arranged in a certain order.
Statistical analyses
1) mean age = 2.02 cannot be explained. The statistic is flawed because theoretically you cannot have mean of nominal data. Mean is applicable to scale variable and is allowable for ordinal variables to some extent.
2) Cronbach's alpha for single-item questions pertaining to a construct is not reliable and should not be used in drawing conclusions. Cronbach's alpha is more correct statistic for reliability of a summated multi-item scale like in factor analysis.
3) Test statistics (e.g. sig., Chi-Square, df) are important and must be interpreted in relation to the research. You should use them to support your discussion of the results.
4) You have should explore your data: what are the relationships between the variables from the questionnaire.
Factor Analysis
If you have not already done so, you should run the full exploratory factor analysis as follows:
To do factor analysis, access the main dialogue box in SPSS by clicking on;
Analyse > Dimension Reduction >Factor
Factor Analysis screen will appear.
Transfer the variables from the box on the right to the box on the left of the screen labelled Variables