As Oyvind mentions, the difference in P-values in the two methods is in the expected direction. It is difficult to tell whether the magnitude of difference is 'reasonable' without knowing sample sizes and the number of landmarks. A nonparametric method requires larger sample sizes. In the discussions concern was mentioned about the assumption of normality. MANOVA is based on the assumption of homogeneity of within-group covariance matrices. That can be more of a problem that modest departures from normality (the central limit theorem works for multivariate data too).
In the original question it was mentioned that the data shows differences mostly along the PC1 axis. That implies that the explanation for the differences may be one-dimensional. If not size, then temperature, north-south gradient, etc. In such cases a general multidimensional method such as MANOVA will have less statistical power than a method such as multivariate regression on some possible explanatory variables. ------------------------ F. James Rohlf, Distinguished Professor Ecology & Evolution, Stony Brook University www: http://life.bio.sunysb.edu/ee/rohlf > -----Original Message----- > From: morphmet [mailto:[EMAIL PROTECTED] > Sent: Friday, November 09, 2007 9:35 AM > To: morphmet > Subject: Re: MANOVA vs npMANOVA > > Hi, it is interesting that you get so different > results between MANOVA and NPMANOVA. Would you > mind sending me the data set as saved from PAST > for me to look at? (I'm the author of PAST). > > The standard 'Iris' test data set of Fisher gives > quite similar results between MANOVA and NPMANOVA > of PAST. In general, the NPMANOVA is expected to > be less powerful (have higher p values) than MANOVA, > I believe. > > > Oyvind Hammer > Natural History Museum > University of Oslo > > > On Fri, 9 Nov 2007, morphmet wrote: > > > Dear colleagues, > > > > We would like to take advantage of Dr. Slice's comments about MANOVA > to > > assess differences among species or biological groups. There are few > > multivariate normality tests available, however it is often common > that > > Partial Warps (and therefore Relative Warps) from morphological > > structures do not fit normality. Thus, we think this is the main > reason > > why Dr. Slice recommends the non-parametric alternatives for MANOVA. > > More specifically our problem is that we obtain different results > when > > applying either of both parametric and non-parametric MANOVAS. > > We are at the moment assessing the relationships between > environmental > > variables (remote sensing data) and morphological characters > (geometric > > morphometrics) in a group of Neotropical bats. Neither of both types > of > > variables fit normality very well. > > However, what currently puzzle us is the fact that the MANOVA > performed > > in SPSS give us significant differences, while the np-MANOVA in PAST > > gives us non significant differences. The contrast in magnitude > > between both p-values is extreme. We haven?t yet looked at > additional > > indexes of overlap, confidence or robustness for p-values. > > We will be grateful with any comments or suggestions. > > Pablo Menendez > > Pablo Jarrin > > > > > > > > -- > > Replies will be sent to the list. > > For more information visit http://www.morphometrics.org > > > > > > -- > Replies will be sent to the list. > For more information visit http://www.morphometrics.org -- Replies will be sent to the list. For more information visit http://www.morphometrics.org
