You seem a trifle sensitive about models and modeling - statistics are just=
tools. Nearly every modern text book clearly points out that ANOVA, regres=
sion, etc are specific applications of a general mathematical approach but =
that each is a tool designed for a particular purpose. So, yes they are dif=
ferent in practice.=0A =0AIt makes no sense to say that something is wrong =
with the data. Either the program works for its intened purpose or it doesn=
't. If one of the statisticians who helped debug the program for SAS and an=
other professional statistician/programmer cannot get the program to work w=
ith a data set I'd say that the functionality of of the algorithm depends o=
n the data set - it is a tool that sometimes can't handle the data.=0A =0A =
I agree with you completely about the importance of real world variation. I=
think that too often the review process cleans up really messy data sets f=
or publication and we as scientists lose out on seeing good approaches to a=
range of difficult statistical issues as well as catching a glimpse of jus=
t darn good data. I did have one good experience in this area where I was a=
llowed to publish a figure that just included means and ranges of the data =
- sort of a retro analysis.=0A=0ABy efficient I mean the totality of the ex=
permient from using space at a field site or on a lab bench efficiently, th=
e cost in time and money of putting the experiment in the ground, the amoun=
t of useful data that the experiment produces, your ability to say somethin=
g interesting about the data. the time involved in analyzing the data, the =
time involved in writing it up, etc.=0A=0AWith regard to planned contrasts.=
If you designed the experiment right and you have some experience with the=
study system significant main effects and interactions are a given. What y=
ou really want to know is are your specific hypotheses correct. Things such=
as in environment 1 A>B>C and in environment 2 C>B>A are the critical thin=
gs that you want to know. Perhaps I have not been schooled properly but the=
se sorts of questions seem easier to answer using the ANOVA tool followed b=
y planned contrasts.=0A =0A=0A =0A----- Original Message ----=0AFrom: Highl=
and Statistics Ltd. <[EMAIL PROTECTED]>=0ATo: [EMAIL PROTECTED]
=0ASent: Tuesday, March 13, 2007 4:39:29 PM=0ASubject: Re: [ECOLOG-L] Deali=
ng with non-normal, ordinal data for 2-way ANOVA with interactions=0A=0A=0A=
>Date: Mon, 12 Mar 2007 15:35:18 -0700=0A>From: John Gerlach <gerlach=
[EMAIL PROTECTED]>=0A>Subject: Re: Dealing with non-normal, ordinal data for 2-=
way ANOVA =0Awith interactions=0A=0A>My short answer is that for controlled=
blocked factorial experiments where =3D=0A>interactions are important and =
where you have planned contrasts - since you=3D=0A>designed it you should k=
now what the important questions are - I'm not awa=3D=0A>re of any tool exc=
ept ANOVA that will suffice.=0A=0A=0AAm I missing something here?? ANOVA is=
linear regression...linear =0Aregression is GLM (generalised linear modell=
ing)....if you can set up =0Ayour explanatory variables in an ANOVA context=
(for interactions with =0Aplanned contrasts), you can do the same in a log=
istic regression =0Acontext, and for ordinal data. The only thing that is c=
hanging is the =0Aexact interpretation of the parameters if you swap famili=
es, but that =0Ashouldn't be a problem? We all seem to agree that the logis=
tic =0Aregression (or better: its extension to ordinal data) is a better =
=0Aapproach for your ordinal data. If your GLM software crashed for your =
=0Adata, then there is something wrong with your data or model, not with =
=0Athe software (provided it is decent software like SAS or R).=0A=0A=0A>up=
a design and a response variable. That said, you should use the correct =
=3D=0A>statistical tool but, where you have choices, ANOVA seems to be the =
most ef=3D=0A>ficient.=0A=0AWhat is your definition of "efficient"? I have=
n't seen many examples =0Afor which all the assumptions of linear regressio=
n/ANOVA were met. My =0Abelief is that everything in ecology is heterogeneo=
us....hence the =0Aonly thing I do is mixed modelling (or GLS). Heterogenei=
ty is part of =0Athe nature of the data, and should be taken into account..=
..not =0Ahidden behind a transformation. Chapter 5 in Pinheiro and Bates gi=
ves =0Aa good intro.=0A=0AAs to one of the other respondents to this postin=
g.....6-8 weeks ago =0Athere was a posting on the statistical newsgroup all=
stat that =0Asummarised 10-20 replies on the significance of main terms if =
the =0Ainteraction is also significant. It is not that trivial. I don't hav=
e =0Agood email access this week, hence can't provide the URL for the =0Asu=
mmary posting on allstat ; just google on "allstat significance main terms"=
=0A=0AAlain=0A=0A=0A=0ADr. Alain F. Zuur=0AFirst author of:=0A=0AAnalysing =
Ecological Data (2007). Zuur, AF, Ieno, EN and Smith, GM. =0ASpringer. 680=
p.=0AURL: www.springer.com/0-387-45967-7=0A=0AAnalysing Ecological data us=
ing GLMM and GAMM in R. (2008). Zuur, AF, =0AIeno, EN, Walker, N and Smith,=
GM=0ASpringer.=0A=0AOther books: http://www.brodgar.com/books.htm=0A=0ASta=
tistical consultancy, courses, data analysis and software=0AHighland Statis=
tics Ltd.=0A6 Laverock road=0AUK - AB41 6FN Newburgh=0ATel: 0044 1358 78817=
7=0AEmail: [EMAIL PROTECTED]: www.highstat.com=0AURL: www.brodgar=
.com
