How to test the effect of pre-post, treatment-control intervention with three age groups, two dependent variables and gender effects? The hypothesis of my research project is “a certain  training improves the ability in Subject A” .  
Sample details
Groups: 1A (25), 1B (21), 2A (25), 2B(27), 3A(23), 3B (23).
The number within brackets represent the number in each group.
Control/experimental: 1A, 2A, 3A are experimental groups and 1B, 2B, 3B are control groups.
Group labelled (1A, 1B); (2A,2B); (3A,3B) fall under 3 different age groups respectively. 
Data collected was as follows:


*

*Pre-test and post-test scores for each group taken using subject A ability test  

*Subject A and Subject B achievement scores for each grade (5-6 scores taken at different intervals  in an academic year  of class unit tests given by the teacher).  


Statistical analysis
With limited knowledge in experimental statistics, pre-test and post-test results for all the groups were taken to test Subject A ability. I conducted a paired t-test for each group separately.
Subject B unit test scores were also taken just to have a check on the effect of training on one other subject achievement, different from subject A.  
Questions :
Apart from paired t-test what other statistical tool will help in line with my hypothesis?
1. I wish to compare the Subject A ability scores between control and experiment groups
2. Compare the Subject A ability scores on the 3 different age groups
3. Compare the unit test scores conducted in class separately for subject A and Subject B
4. Compare ability scores on the subject A for  male and female in each group and overall  
PS: I will be using SPSS for statistical analysis.
 A: For the most part it is similar to a standard pre-post treatment-control design. As such most of the responses to this question would be relevant. The main difference is that you have three age groups. 
I don't quite understand how you can have unit scores taken at different intervals and still have a pure pre-post design. 
Nonetheless, one way of implementing your analysis is as a 3 x 2 x 2 mixed ANOVA with 3 levels of age, 2 levels of condition, and 2 levels of time.
This can readily be tested using a GLM - repeated measures in SPSS with time as your repeated measures factors and age and condition as your between subjects variables. 
Furthermore, it sounds like you have two different dependent variables. One for Subject A and one for subject B. If you are happy you could just run two separate ANOVAs, one for each DV.
Regarding your specific questions:


*

*Presumably you are actually interested in the pre-post change scores, which would be represented by the condition by time interaction.

*The main effect of age and possibly with some post hoc tests would test this.

*I don't quite understand how your unit scores work and how these relate to pre-post measures. Are they different types of tests? Or are they just a way of measuring change over time?

*You could just add gender as a between subjects factor and look at the effect of gender.


More generally, you might find it useful to read through a book like Statistical Methods for Psychology. This book provides extensive discussion of how to analyse complex psychological experiments including mixed ANOVAs and follow-up tests.
I should also mention that these are just some quick thoughts. Your data is of moderate complexity and basically you are asking about how to design an analysis plan for an entire data analysis project. This is a complex undertaking requiring many reasoned decisions. This site generally works best when you ask about more discrete questions.
