"Marlies" <[EMAIL PROTECTED]> schreef in bericht news:[EMAIL PROTECTED] > Hello statistics-group,
> ---------------Explanation of problem ------------------------- > > I have a set of data, obtained from 21 biological specimen. In each > specimen we obtained eight samples, which are positioned like the > corners of a cube, meaning every sample has one unique position within > the specimen based on three 'directional' parameters: Front-Back; > Top_Down and Left-Right. > > I am interested in 'checking' whether or not the eight samples taken > from one and the same specimen can be considered equal. I started with > comparing every sample positions with all sample positions (one-way > ANOVA) but this gives too much information (27 pairs of positions) to > conclude anything comprehensive. > > So now we try to compare the data according to the three axes of > symmetry. > Front-Back differences > Top-Bottom differences > Left-Right differences > > I'll use the Front-Back differences as an example. > <snip> > > My idea was to use a two-way ANOVA, containing two factors: > 'plane' (levels: Back and Front) > 'position' (levels: TL, TR, DL, DR) > <snip> > -------------Questions ------------------------------------- > > Essentially I have four questions: > > - Is the test I prescribed (two-way ANOVA) a good way to perform such > an analysis This analysis is not valid. You should take into account the effect of specimen as a block effect; failing to do this results in an estimate of error which may be too large and has too many degrees of freedom. > - Is it 'allowable' to ignore significant influences because you > 'know' it is irrelevant what is means. There is nothing wrong in observing significant effects and explaining why you are not interested in these effects. > And if this tests is allowed > - Could each test between planes (Front-Back; Left-Right, Top-Down) be > regarded as an independent test (suggesting a Bonferroni-adjustment > per test of p/4 for each single contrast) > - Or should all tests be regarded as dependent, suggesting a > Bonferroni adjustment of p/12 for each single contrast? > (Personally I think I should go for the last option) The sum of squares for position effects may be decomposed and tested in any way you like. But the most straightforward and comprehensive analysis would be a decomposition according to that of a full 2^3 factorial with specimens as a blocking factor. Main effects (Front-Back; Left-Right, Top-Down) and interactions between these may be tested independently and no Bonferroni adjustments are required. The results may be presented in tables or plots of means, with corresponding LSD's (least significant differences) as yardsticks indicating their precision. Jos Jansen . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
