Dear all,
I am running a path model with a dummy variable (Gender) as an independent variable (there are more independent variables). The dependent variables are continuous.
Maximum Likelihood estimation assumes multivariate normality. But this assumption is violated using an independent dummy variable (or dichotomous variable).
I am looking for studies which have investigated the bias of an independent dummy variable on the maximum likelihood estimation when I use only continuous dependent variables.
Does anybody know such studies or some guiding rules?
[Due to sample design I can not use maximum likelihood parameter estimates with standard errors and a chi-square test statistic (when applicable) that are robust to non-normality and non-independence of observations.]
Thank you very much in advance.
Kind regards
Rico
Why do you think a dummy predictor variable affects ML estimation? The distributional assumptions apply to the response variable (conditional on the predictors), not the predictors.
also if you specify an appropriate model you can use ML estimation for non independent response data.
I am not sure if I have ever came across such materials but if you categorise your outcome variables as suggested using ML should be fine.
Obasanjo Bolarinwa I would not recommend categorizing an outcome variable as it will lose information and therefore statistical power.
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