Re-check boxplot after outlier removal I have a sample of 608 subjects and I need to remove outliers for age. In R, the boxplot appears like this:

It shows 74 outliers:
> length(boxplot(mydata)$out)
[1] 74

After I have removed these outliers, should I take a new look at the boxplot with the new data? If I do that, the boxplot still contains other outliers:

Questions:
1. Is this a problem?
2. Is this method appropriate for removing outliers for age?
EDIT: I will not use age as a variable in a regression model. I want just to remove outliers for age in order to obtain a more uniform sample (this is a students sample). For example, I have one subject 60 years old, while the mean age of my sample is 26.6. For this reason, I was also thinking to remove outliers not by boxplot but by ± 3 standard deviations from the mean. From my sample, I then will select two groups of subjects for further testing.
 A: If you have that many outliers, they aren't outliers; you have a non-normal distribution. 
How are you going to be using the age variable? One possibility is that it is to be used as an independent variable in a regression. In this case, this distribution is not necessarily a problem - regression makes assumptions about the error (as measured by the residuals) not about the distribution of the independent variables. 
(Also, @Doug 's answer is good, and you should tell us that, too). 
A: Answers 1: maybe, 2: depends.  We need a little more information on why you want to remove these outliers.  If you could provide a histogram, it might be possible to transform the data and eliminate some of the outliers, but it all depends on the research questions.  Please tell us more about 1) your research questions, 2) your participants, and 3O) how you are defining outliers (or are you allowing the boxplots to define them for you).
A: Ok here is what I learned, It is enough to pick out the outliers once from your dataset. If you continue to do so IQR changes respectively which will keep giving you new outliers. If you do not want to see the outliers once you picked them out just add the code, "outline=F", to avoid seeing the new outliers. Hope this helps.
A: when you remove outliers no of data changes thus its quantile changes means lower range and upper range changes thus it is again showing outliers
If you observe both box plot carefully your upper range for first in nearly 38
after removing outlier it become nearly 32
