Hello, I'm having problems with decimals on the MLR scaling correction factor. I've got two models which are nested, and the output from Mplus gives: 1: Chi-sq = 1794.786, df= 1246, scf = 1.118 2: Chi-sq = 1802.923, df = 1247, scf = 1.117 When I calculate the difference between the chi-squares, using the formula which incorporates the scaling correction factors, I get, chi-square = -56 (yes, negative). However, this appears to be due to rounding error. If I change the scaling factor on the second model to 1.1174, I get a chi-square difference of 22 (positive), a massive change. For some of the models where I'm running this, I can calculate the scaling correction factor 'by hand', by running the ML models and dividing the chi-squares. In which case I find that they are actually 1.117423 and 1.11754, and when I plug those two numbers in, I get a chi-square of 9. However, I'm running these models with and without weights, and when I run with weights, I can't calculate by hand, I have to use MLR. So, two questions: 1) Is there a way to make Mplus give more decimal places. 2) One solution that I've tried is to use the average of the two scaling correction factors for both - this works (and in the case that I gave, the average - 1.1175 is very close to both). However, is this going to give me the wrong results on occasion? Thanks, Jeremy
I have found a partial solution, which is to use the
results are results.dat
And then pulling the scaling correction factor from there. It's not especially easy (as it's not labelled), and it's not ideal, as I don't know when I need the extra precision, and when I don't need this extra precision. (Well, I kind of know that when my chi-square is high, I need lots of precision).