Testing several means I am currently working on cross country responses to a survey question. More specifically, the questions asks 'are you satisfied with your job?' and lets respondents select a number from 0 (strongly dissatisfied) to 4 (strongly satisfied). The question was asked to people in Germany, United Kingdom, Spain and Italy. The sample size in every country is 2000. I am interested in determining whether the responses are statistically different between countries. What is the best way to do so?  
 A: Let's apply common sense and a little statistical understanding to cut through the complications.
In a comment you write

The mean response in Italy was 1.54 ; in Spain it was 2.01; in the UK it was 2.39; in Germany it was 3.51.

As a mathematical proposition, the variance in a group of numbers bounded by $0$ and $5$ with a mean of $\mu$ cannot exceed $(\mu-0)(5-\mu),$ whence the sampling variance of the mean of $n=2000$ such numbers cannot exceed
$$\frac{\mu(5-\mu)}{2000}.$$
Applying this formula to the given mean response gives the corresponding set of maximal standard deviations of approximately
$$0.052, 0.055, 0.056, 0.051.$$
The closest difference between any two successive means is $2.39- 2.01 = 0.38,$ whereas the sampling standard deviation for that difference would be $\sqrt{0.055^2 + 0.056^2} = 0.078,$ making that difference exceed
$$\frac{0.38}{0.078} = 4.8$$
standard errors.  The Z-scores for the other two successive differences are larger still.
Assuming an approximately Normal distribution of differences, that would produce a p-value less than one in a million.  Let's adjust that conservatively by multiplying it by all the possible between-group comparisons we might make, which is six.  The result is still tiny.  That's strong enough to conclude, without any more ado, that

(1) there are significant differences in mean survey responses and (2) every pairwise difference is significant, too.

Comments and discussion
If you're not convinced, you may extend this analysis to construct a lower bound on the between-group sum of squares and thereby obtain a lower bound on the one-way ANOVA F-ratio statistic.  It will have an extremely small p-value.
We might worry a little about the implicit use of ANOVA underlying this reasoning.  However, none of these four sample means is close enough to the extremes of $0$ and $5$ to be overly concerned about the effects of possible skewness in the group distributions and the bounded responses guarantee that high kurtosis will not be an issue.  The usual Normal-theory distribution calculations are going to be quite accurate.
There are legitimate questions surrounding such a result.  Did the surveys really measure the same thing if they were presented in different languages?  How does one interpret a difference of, say, $0.38:$ what does that say about the responses?  But if we accept that each of the four surveys was accurately conducted on a truly random sample of a well-defined population, then it is incontrovertible that the differences observed are extremely unlikely to be due solely to the random selection of subjects.  That's what "statistically significant" means.
A: You might try the Kruskal-Wallis test. This is similar to an ANOVA but considers rank instead of actual value. If you find a significant difference you can then look at pair-wise Mann-Whitney U tests to determine which countries are significantly different from one another.
