By the way, ordinal regression would require huge datasets because my dependent variable has around 20 different responses... but again, one might say that with so many  ordinal responses, it is as if we have a linear/interval variable, right? I just hoped that there would be a two-way kruskal-wallis or something like that. On the other hand, what is going to happen if I (1) bootstrap data from all cells of my design and average the rank ordering of the data of every cell? And then (2) do the same but using data from a uniform/normal distribution so that I assume that there is no difference between the cells? From point (1) I will find the statistical value and from point (2) the expectation and then with a third step (3) I can run a chi-square on the observed/expected values. Would this be reasonable? But again, how can I distinguish between main and interaction effects?

Dr. Iasonas Lamprianou





Assistant Professor (Educational Research and Evaluation)

Department of Education Sciences

European University-Cyprus

P.O. Box 22006

1516 Nicosia

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Tel.: +357-22-713178

Fax: +357-22-590539





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Tel. 0044  161 275 3485

iasonas.lamprianou@manchester.ac.uk

--- On Tue, 7/9/10, Dennis Murphy <djmuser@gmail.com> wrote:

From: Dennis Murphy <djmuser@gmail.com>
Subject: Re: [R] two questions
To: "juan xiong" <xiongjuan2000@gmail.com>
Cc: "David Winsemius" <dwinsemius@comcast.net>, r-help@r-project.org, "Iasonas Lamprianou" <lamprianou@yahoo.com>
Date: Tuesday, 7 September, 2010, 4:47

Hi:

On Mon, Sep 6, 2010 at 5:26 PM, juan xiong <xiongjuan2000@gmail.com> wrote:

Maybe Friedman test

The Friedman test corresponds to randomized complete block designs, not general two-way classifications. David's advice is sound, but also investigate proportional odds models (e.g., lrm in Prof. Harrell's rms package) in case the 'usual' approach comes up short. It would be helpful to know the number of response categories and some idea of the number of cities-of-birth under study, though...


HTH,
Dennis




On Mon, Sep 6, 2010 at 4:47 PM, David Winsemius <dwinsemius@comcast.net>wrote:



> The usual least-squares methods are fairly robust to departures from

> normality. Furthermore, it is the residuals that are assumed to be normally

> distributed (not the marginal distributions that you are probably looking

> at) , so it does not sound as though you have yet examined the data

> properly. Tell us what the descriptive stats (say the means, variance, 10th

> and 90th percentiles) are on the residuals within cells cross-classified by

> the gender and city-of-birth variables (say the means, variance, 10th and

> 90th percentiles).

>

>

> On Sep 6, 2010, at 4:34 PM, Iasonas Lamprianou wrote:

>

>

>> Dear friends, two questions

>>

>> (1) does anyone know if there are any non-parametric equivalents of the

>> two-way ANOVA in R? I have an ordinal non-normally distributed dependent

>> variable and two factors (gender and city of birth). Normally, one would try

>> a two-way anova, but if R has any non-parametric equivalents, that might be

>> great.

>>

>

> There is an entire task view page on robust methods if you decide to press

> on with this quest.

>

>

>  (2) Also, if the interaction of gender and city of birth is statistically

>> significant, which post-hoc tests should I run?

>>

>

> How many cities are we talking about?

>

>

>  Thanks

>>

>> Jason

>>

>>

>> Dr. Iasonas Lamprianou

>>

>

> --

>

> David Winsemius, MD

> West Hartford, CT

>

>

> ______________________________________________

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> http://www.R-project.org/posting-guide.html

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>



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