Qiqi van der Kolk Unfortunately, I'm not familiar with Stata, but you can easily calculate adjusted R2 by hand with the following:

R2adj = 1 - [(1 - R2)(n - 1)/(n - k - 1)]

where n is the number of observations in the data set

k is the number of independent variables in your model (excluding the constant)

https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/adjusted-r2/

Thanks Blaine Tomkins for your help!

However, I'm wondering if I am allowed to calculate the adjusted R2 by hand and then report it in the results section of my thesis.

Do you know if it even makes sense to report the adjusted R-squared with a multivariate multiple regression?

I know it does with a multiple regression, but can't find any information about it when using a multivariate multiple regression

Qiqi van der Kolk Yes, it is common to report adjusted R2 when conducting a multivariate multiple regression because the more predictors you add to the model (unadjusted) R2 will increase, which may inflate the actual variance explained. Adjusted R2 takes the number of predictors into account. However, with multivariate regression, you will have multiple R2 values rather than just one. Specifically, you will have an R2 for each dependent variable (criterion). If you have, say, 3 dependent variables you will have 3 adjusted/R2 results.

I would assume it doesn't matter how you calculate the adjusted R2 for your thesis, unless you wanted to include the computer output from Stata directly into the paper - or - if your program has some requirement that results of statistical tests must be conducted via certain software? I'm confident adj. R2 can be calculated in Stata. It might just take a bit of digging. Below is a thread where you might find the answer:

https://www.statalist.org/forums/forum/general-stata-discussion/general/1620410-problems-with-multivariate-multiple-regression

Thank you very much for your help! Blaine Tomkins

Hello Qiqi !

There is no formal VIF value for determining presence of multicollinearity. And, on how to calculate the Adjusted R square (which is for the predictors, IVs not DVs), I would definitely go with @Blaine Tomkins.

For correlated responses (DVs) and a mixed Multivariate regression ( is this what you mean in MMR?), I would want to know if they are item response, ordinal scale, etc.

However, it is usually taken that Multicollinearity can be measured by variance inflation factors (VIF): "If VIF value exceeding 4.0, or by tolerance less than 0.2 then there is a problem with multicollinearity "(Hair et al., 2010).

And, on how to reduce VIF in regression?

Minitab support suggested any one of these:

1. Remove highly correlated predictors from the model. If you have two or more factors with a high VIF, remove one from the model.

2. Use Partial Least Squares Regression (PLS) or Principal Components Analysis, regression methods that cut the number of predictors to a smaller set of uncorrelated components. See, https://www.google.com/url?sa=t&source=web&rct=j&url=https://blog.minitab.com/en/understanding-statistics/handling-multicollinearity-in-regression-analysis&ved=2ahUKEwi4pL-9tpbyAhXwQEEAHSYWD9gQFnoECCIQBQ&usg=AOvVaw2xzwDCq1paE5s3zRDFbqRp

Hi Abiodun Christian Ibiloye,

Thanks for the advice!

With MMR I mean multivariate multiple regression.

I used the following procedure:

https://stats.idre.ucla.edu/stata/dae/multivariate-regression-analysis/

However, I have also a moderation effect and several control variables, that's the reason that I wrote multivariate multiple regression.

Regarding calculating Vif values, there is no command that enables me to calculate it after using 'mvreg'. Normally with a multiple regression, one can use estat vif in Stata after running a multiple lineaire regression.

I was wondering, maybe I can report the VIF values calculated after the multiple linear regression? Since coefficients, standard errors and R-squared are the same for a multiple and multivariate multiple regression (explained in the article of the added link)

Regarding my data, I conducted a randomised between subjects experiment. I am aware that there is a discussion about whether one can use OLS with likert data. However, I think I can use OLS, that's why I conduct a multivariate multiple regression.

Qiqi van der Kolk I've seen a few tutorials online simply use "vif" command. It may be worthwhile to get your hands on a recently published Stata guide.

The adjusted R-squared compares the explanatory power of regression models that contain different numbers of predictors.

Suppose you compare a five-predictor model with a higher R-squared to a one-predictor model. Does the five predictor model have a higher R-squared because it’s better? Or is the R-squared higher because it has more predictors? Simply compare the adjusted R-squared values to find out!

The adjusted R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model. The adjusted R-squared increases only if the new term improves the model more than would be expected by chance. It decreases when a predictor improves the model by less than expected by chance. The adjusted R-squared can be negative, but it’s usually not.  It is always lower than the R-squared.

In the simplified Best Subsets Regression output below, you can see where the adjusted R-squared peaks, and then declines. Meanwhile, the R-squared continues to increase.

for detailed information find the materials attached under

Thank you for your insights Teshita Uke Chikako