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Hi I have just carried out a regression analysis using bootstrapping. I need to report the regression statistics for each DV and wonder whether there is a significance test for r2 that can be accessed in MPLUS. If not what would be the alternatvie to determining significance of r2. I managed to get the r2 and confidence intervals by using the following line in OUTPUT: standardized (all) sampstat cinterval (bcbootstrap) Thanks in advance for your time in replying to this query. Volker 


Mplus gives a standard error and significance test for Rsquare. 


Hi Linda, I have not been able to find this in the output. How do I obtain this? Answering the 'what if not' part of my query I determined the F value using the known pearson R2 as follows: F=(r2/k)/((1r2)/(Nk1)), df=k,Nk1 but does this hold for a bootstrapped regression? 


It looks like we don't give a standard error for Rsquare for bootstrap. The formula you give does not take bootstrapping into account. If you are interested in the siginficance of Rsquare, I recommend you use ESTIMATOR=BAYES which allows a nonnormally distributed Rsquare. You can look at the 95% credibility interval. 


Thank you Linda, This is helpful. Rerunning the analysis with estimator = Bayes is not possible with bootstrap but I suppose a Bayesian nonnormally distibuted Rsquare is an alternative to using bootstrapping in any case. My reason for using bootstrapping was to fix problems arising from violations of the normality assumption that error terms should be normally distibuted. Is it correct to say that using a Bayesian estimators 'corrects' for the violation that would otherwise occur? Interestingly the Bayesian estimator produces significant results when the Ftest I used initally does not, Also there are differences in the model output and size and significance of the weights, which suggests a different interpretation, so not trivial. It looks like I need to read up on use of bayesian estimators in regression, for my PhD defence. Could you suggest any references for me to check that discusses use of Bayesian estimation in regression? Many thanks again. 


Yes to paragraph one. Look at TECH8 to see how many iterations were used. Use the FBITERATIONS option to use twice as many iterations as were used in the first analysis. See Bayesian Analysis under Papers on the website in particular: Muthén, B. (2010). Bayesian analysis in Mplus: A brief introduction. Technical Report. Version 3. See also the UCONN 2011 Topic 9 course video and handout on the website. 


Excellent. Many thanks for your advice. 


Hello, I am running a regression between 8 categories and six factors from an EFA. When I run the regression in Mplus, I do not get the overall model statistics (i.e. F and R^2), like I would in other softwares. Is there a way to generate this information? VARIABLE: NAMES ARE Leadership Organization Altruism Creativity Analysis Production Adventure Erudition Factor1 Factor2 Factor3 Factor4 Factor5 Factor6...; USEVARIABLES ARE Leadership Organization Altruism Creativity Analysis Production Adventure Erudition Factor1 Factor2 Factor3 Factor4 Factor5 Factor6...; MISSING ARE all(99); Model: Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 ON Altruism 


When the model imposes no restrictions (leftout arrows), no overall test of fit is given. R2 is provided when you request a standardized solution. F values are not given. 


Hello Bengt, I read chapter three of the manual, on regression, and I did not see anything about arrows. What are you referring to? Also, What does Mplus offer in replacement of overall significance that is acceptable to report? Thank you for your time, Anna 


Arrows (regression relationships) are relevant for path analysis and general SEM; see those UG examples. In that context, regression models are considered justidentified/saturated and have no overall test of fit. The test of fit (typically) concerns how well the means and varcov of the variables are fit. In the regression setting, the usual Ftest checks if your model fits better than an intercept only model. In Mplus, you can test this by using Model Test to test if all slopes are zero but that is typically not very interesting. If you want the usual Ftest with ANOVA table results, you may want to do the regression in regressionspecific software. 

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