A difference of 100 cases in 4500, i.e. about 2%, looks as the likely effect

of rounding, and therefore you should not worry too much about it. The

results, i.e. the decision based on the K-W test, would have been most

probably the same if the weighted number of cases would have been 4500

instead of 4600 (except if you are almost exactly over the edge of

non-significance, in which case you would end up still near the edge but

probably on the other side of it).

Hector

-----Mensaje original-----

De: SPSSX(r) Discussion [mailto:

[hidden email]] En nombre de

Andreas Schneider

Enviado el: Monday, August 07, 2006 10:42 AM

Para:

[hidden email]
Asunto: Re: Weighting and Nonparametric Tests

Dear ViAnn, Hector and others,

thank you so far for your first comments.

We are working with a dataset of 4.500 novice drivers. Using K-W-test

with weighted data expands the number of cases by about 100. Our main

problem is the question whether the significance test using weighted

data or the one using unweighted data is the correct one. The p-values

are different, and sometimes they show significance using the weighted

data but show no significance using unweighted data.

Thanks in advance for your help.

Greetings Andreas

Hector Maletta schrieb:

> I think ViAnn is correct. This is not the only instance in which

fractional

> weights cause some similar problem. However, this should not create a

large

> difference in the number of cases since the rounding is random, so cases

of

> rounding up should be (approximately) offset by cases of rounding down,

and

> the final difference should be small or nil, even with relatively small

> samples. Perhaps Andreas may explain his case in somewhat fuller terms.

> Hector

>

>

> -----Mensaje original-----

> De: SPSSX(r) Discussion [mailto:

[hidden email]] En nombre de

> Beadle, ViAnn

> Enviado el: Monday, August 07, 2006 9:55 AM

> Para:

[hidden email]
> Asunto: Re: Weighting and Nonparametric Tests

>

> Nonparametric tests require "whole" cases. When non-integer weights are

used

> with any of the tests generated by the NPAR TESTS command, they are

randomly

> rounded up or down to create integer weights. If you have lots of cases

you

> might not even notice except when re-running the test unless you set the

> seed on the SET command.

>

> I can't speak to the statistical question of weights and non-parametric

> tests.

>

> -----Original Message-----

> From: SPSSX(r) Discussion [mailto:

[hidden email]] On Behalf Of

> Andreas Schneider

> Sent: Monday, August 07, 2006 7:19 AM

> To:

[hidden email]
> Subject: Weighting and Nonparametric Tests

>

> Dear listers,

>

> we have a dataset of about 4.500 respondents and weighted it by a

> weighting variable which is standardized by the number of cases, so that

> the number of cases in the weighted and the unweighted dataset is the

same.

>

> Calculating nonparametric tests like Kruskal-Wallis, however, provides a

> "wrong" (too large) number of cases for the weighted data.

>

> Does this mean nonparametric tests can or should not be used with

> weighted datasets?

>

> Thanks in advance

>

> Andreas

>

>

> --

> Andreas H. Schneider

> Dipl.-Sozialwirt

>

> Institut für empirische Soziologie

> an der Friedrich-Alexander-Universität Erlangen-Nürnberg

>

> Marienstr. 2

> 90402 Nürnberg

>

> Tel.: 0911 23565 -41

> Fax: 0911 23565 -50

>

>

--

Andreas H. Schneider

Dipl.-Sozialwirt

Institut für empirische Soziologie

an der Friedrich-Alexander-Universität Erlangen-Nürnberg

Marienstr. 2

90402 Nürnberg

Tel.: 0911 23565 -41

Fax: 0911 23565 -50