Re: TRANSFORMATION OF IVS and DVs with reflection - multiple linear regression

Posted by Bruce Weaver on Nov 14, 2016; 2:35pm
URL: http://spssx-discussion.165.s1.nabble.com/TRANSFORMATION-OF-IVS-and-DVs-with-reflection-multiple-linear-regression-tp5733454p5733459.html

Re the normality assumption applying to the errors, HEAR, HEAR!  ;-)  

Perhaps the OP meant ordinal logistic regression?  

To the OP:  Please tell us what the variables are.  And please note that with n = 45 and 7 explanatory variables, you have a recipe for severe over-fitting, regardless of what type of model it is.  See Mike Babyak's nice article on over-fitting for more info about that.

  https://people.duke.edu/~mababyak/papers/babyakregression.pdf

HTH.

Jon Peck wrote

The normality assumption applies to the error terms, not the dependent
variable.  So while transformations of the dependent variable may be
appropriate, that does not follow from its nonnormality.

Second, you say that logistic regression would have been appropriate, but
logistic is for a dichotomous variable, which would, of course, have a
nonnormal distribution.  If that is what you have, then the transformations
described do not make sense, and avoiding logistic or other similar methods
is probably the wrong strategy.

On Mon, Nov 14, 2016 at 6:46 AM, researcher <[hidden email]> wrote:

> I had a dataset with 7 IVs and the DV : wanted to use linear multiple
> linear
> regression. The DV was very far from normal (accordion to usual tests).
> Very
> positive skew so I used reflection and SQRT transformation and (to pleasant
> surprise) the Dv then passed normality tests (p>.05). This was good but I
> noticed that the direction of the relationship between one of the IVs and
> the DV had changed - e.g. what had been a strongly positive beta (0.9) had
> become approx -.0.9.
>
> So I am wondering is it 'usual' to also transform the IVs when using this
> sort of transformation on the DV?
>
> Some other assumptions of MLR were met - no issues with multicollinearity,
> heteroscascidicty
>
> BTW I realise logistic regression would have been an option but there is a
> small N (45) and larger samples are generally recommended with logistic.
> Initial experiments with this gave a very large exp (B) for one IV but yet
> was still not sig...
>
> I am not a statistician -  I am a mixed methods researcher with some
> experience in quantitative research so highly technical answers may well
> blow over my head...
>
> Any hekp appreciated though
>
> Thanks
>
>
>
>
>
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--
Jon K Peck
[hidden email]

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