Sequential versus Partial Sums of Squares
In SPSS, the default mode is Type II/Type III Sums of Squares, also known as partial Sums of Squares (SS). In a partial SS model, the increased predictive power with a variable added is compared to the predictive power of the model with all the other variables except the one being tested. When conducting your test in this fashion, it does not matter in what order you tell Stata to test the variables. Since this is the default in Stata, it requires no additional specifications.
Like SPSS, Stata offers a second option, which is the Type I or Sequential Sums of Squares. Unlike partial SS, sequential SS builds the model variable-by-variable, assessing how much new variance is accounted for with each additional variable. If you choose to use sequential sums of squares, the order in which you enter variables matters. Specifically, the first variable entered will usually account for the most variance. Additionally, the last variable in a sequential model will look exactly like that same variable in the partial model. If this does not make sense, please consult with someone before running such analyses on your data.
Since partial SS is the default method, you will have to specify the sequential subcommand to run a sequential SS analysis. Thus the command to run one would look something like anova [dep var] [indep vars] , sequential
In a sequential SS mode, interaction terms work the same as single variables for analysis purposes, so anova intrinsi gpa*lang gender is different from anova intrinsi gender gpa*lang