Solved – Dealing with model assumption violation (homogeneity of regression coefficients for ANCOVA)

ancovaassumptions

I have one continuous dependent, two categorical independent and one continuous covariate. How I can deal with the violation of homogeneity of regression coefficients which is the ANCOVA assumption. Do I need to check this assumption when I enter the interaction between two independent variables too?

Best Answer

  1. Ditch the ANCOVA and fit your model in a regression object.

  2. How bad is the heteroscedasticity? Could you include a scatterplot of fitted by residual values? Standard regression may be robust to the heteroscedasticity you have. Your heteroscedasticity could also be coming from a quadratic or cubic trend in your data—it's hard to tell without seeing the plot.

  3. The standard approach is to use robust standard errors, such as the Huber-White standard error. You can fit your model using the lm() function in R. There are good tutorials on getting standard errors that are robust to heteroskedasticity here and here.

  4. Another common approach is weighted least squares (WLS) regression instead of the standard ordinary least squares (OLS). You can set weights using the weight argument in the lm() function in R. That approach is discussed here.

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