Dear Abdelhamid,

My advice would be to concentrate on your second data type. You could check for overall differences in the scores using the different assessment tools using Friedman's ANOVA. This is available in SPSS under the nonparametric tests function.

I think you can go with pearson corelation test which is commonly done for these type of the studies. Impact of different methods 

Dont forget to include the baseline status in the test. In Peter Samuels suggestion it would be a anCova, where the 'C* is a covariate analysis. The reason is that a good writer at baseline will also be a good write after the course, so you might just be measuring the baseline difference after the cource and not the effect of the cource. If you use the same assesment before the cource and after the cource, you could use a t-test of the difference from before to after.

Regards Leif 

If you are interested in the improvement owing to the course as a whole, I suggest you look at the difference between the pre-test and the corresponding score after the course. To combine the information across the different occasions when you use a particular tool you could take the area under the curve (AUC). You could then do plots of improvement against each of the AUCs, find the correlation coefficients (if the plots indicate a linear relationship) and fit appropriate models to see which AUC best predicts the improvement. Combining the tests would not be a task for a beginner in statistics, and I would consult an expert here.

Try Chernoff faces

Ilya

Dear Colleagues

If I understand you correctly, you can use this statistical model.

See the example table: You can enter your numbers.

see this pege too: https://people.richland.edu/james/lecture/m170/ch13-2wy.html

Two way ANOVA with SAS

 Explain the question with an example

Two way ANOVA procedure

Run the test

Interpretate the result

Explain the question with an example

When there are one dependent variable and two or more independent variables, a two (or n) way ANOVA model sounds appropriate.

Iorn and Zinc fortification of milk-based fruit drinks are common practice. To better understand the effects of drink fortification on the cellular retentionof iron, researchers conducted an experiment by fortifying milk-based fruit drinks with low and high levels of iron (Fe) and zinc (Zn). The drinks were digested in a simulated gastroinatestinal tract and cellular iron retention was measured (mg Fe/mg cell protein). Data is available at Retention.csv .

For the study, there is one continuous dependent varialble (Fe retention), and two categorical independent variables, Fe (low/high) and Zn (low/high). A two-way ANOVA can be applied as follows.

Standard two-way ANOVA procedure

Open the data set from SAS. Or import with the following command.

data retention;

infile "H:\sas\data\retention.csv" dlm=',' firstobs=2;

input retention Fe $ Zn $;

run;

Then a two way ANOVA can be requested as following.

proc anova data=retention;

class Fe Zn;

model retention = Fe Zn Fe*Zn;

run;

The "model" statement lists retention as dependent variable, Fe and Ze as independent variable. Further more, Fe*Zn represents the interactions between Fe and Zn. If the interaction effect is proved insignificant, one can refit the data without the interaction effect Fe*Zn.

Reading the output

The ANOVA procedure

Dependent Variable: retention

Sum of

Source DF Squares Mean Square F Value Pr > F

Model 3 6.06863649 2.02287883 1082.46 F

Fe 1 4.40228628 4.40228628 2355.70

Look up Tukey's mean-difference plots that are used in the medical field. Also referred to s Bland - Altman plots. 

https://en.wikipedia.org/wiki/Bland%E2%80%93Altman_plot