Non-Parametic ANCOVA

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  • #2804
    Donna M
    Participant

    Hi Andy

     

    Does the bootstrapping function on SPSS include the ability to do an ANCOVA type analysis on non-parametric data?

     

    Or are there any other ways to go about this? I looked at the R plug in and it seemed really complex so I was looking for a more straight forward approach.

     

    Thanks,

    Donna.

    #2812
    Jeremy Miles
    Participant

    Data aren’t really non-parametric, tests are.

    ANCOVA is inherently parametric.  Bootstrapping is parametric (because you’re estimating parameters). 

    But to answer your question, ues, you can bootstrap an ancova which means you can relax the normality assumptions.

    #2811
    Donna M
    Participant

    Thanks Jeremy for your response, that’s great.

     

    I had heard that the bootstrapping functionality in SPSS is not a standard part of the programme but that it is sold as a separate package – do you know if this is the case? 

    #2810
    Anonymous
    Inactive

    now… why is it that you’d like to try a non-parametric analogue of ANCOVA? the normality assumption to perform inference in the case of any linear model is on the residuals and not on the variables themselves… could you perhaps tell us a little bit more about the data you are working with?

    #2809
    Donna M
    Participant

    My data is not normally distributed and I’d like to be able compare results for 2 groups on a measure whilst controlling for another variable.  

    #2808
    Anonymous
    Inactive

    once again, the assumption of normality for infernce is not on the variables themselves but on the residuals after you’ve performed your analysis… have you tested those to see whether they look normal or not?

    #2807
    Donna M
    Participant

    The tests of normality I have undertaken is the Kolmogorov-Smirnov test on the distribution of the results for the variable as a whole, rather than the residual.

    #2806
    Anonymous
    Inactive

    well, test the residuals then and we’ll take it from there if they violate the normality assumption…

    #2805

    As oscar says, test the residuals (remember with tests like K-S if you have a large sample then they’re pointless because even small deviations will be deemed significant, and with small samples the tests have low power, so really graphs and values of skew/kurtosis are probably most useful).

    If you have a problem, then from what I recall the bootstrapping module (it was a separate module, but I think under IBM it is integrated into the core package) only bootraps SEs on the mean for things like ANCOVA.

    You have more options in R to be honest, but if you want to use R I wouldn’t bother with the SPSS R plugin because you may as well just use R directly.

    good luck,

    a.

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