I do not know how to do it in SPSS, but the setup of the contrasts is straightforward and can be used in any software. For subjects of group 1 the difference w1-w2 is an estimate of treatment difference C-T. So these differences score +1 on the contrastvector C-T; for group 3 the differences w1-w2 score -1 for C-T, the other groups will get score 0 on C-T. Equivalently, w1-w2 will get scores +1 an -1 for C-M in groups 2 and 5 respectively, and for T-M in groups 4 and 6. For the analysis you need only 2 of these contrast vectors. The contrast vector for sequence effect on C-T has +1 and -1 for group 1 and 3 respectively, and 0 for the other groups; the sequence effects for C-M and T-M can be modeled equivalently. No group factor should be included in the analysis of the differences (in a properly designed experiment there should be no differences between groups except those induced by the treatments; but even if there were group effects, these would be eliminated by taking differences).
Jos Jansen "Kasper Hornbaek" <[EMAIL PROTECTED]> schreef in bericht news:[EMAIL PROTECTED] > Hi, > > A while ago I received the suggestions below from this newsgroup, > which was most helpful. I proceeded to do an intrablock analysis, > which worked out fine. However, upon rereading the suggestions, I am > slightly puzzled by some of what was proposed, can anyone clarify. > > Jos Jansen wrote (see below for the full question and answer) "The > intrablock analysis can be performed by regression of the differences > using 2 contrast vectors for treatments differences (a third would be > redundant), and 3 contrast vectors for sequence effects (one for each > treatment combination)." > > I understand how to calculate differences (:->), but I am not sure > what "regression of the differences using 2 contrast vectors for > treatments differences" means. If I were to that in, say SPSS, how > would I proceed? I gather I should still use the grp factor (perhaps > encoded into some dummy variable?), but how could I set up the > contrasts that tests for treatment differences? > > If the above question is too basic, perhaps someone can point me to > information that can help answer it? > > Thanks in advance, > Kasper Hornb�k > > ---0--- > Fra:Jos Jansen ([EMAIL PROTECTED]) > Emne:Re: Analysis of design where subjects only use a selection of > treatment > View: Complete Thread (3 artikler) > Original Format > Nyhedsgruppe:sci.stat.edu, comp.soft-sys.stat.spss Dato:2004-02-21 > 04:06:37 PST > > > "Kasper Hornbaek" <[EMAIL PROTECTED]> schreef in bericht > news:[EMAIL PROTECTED] > > Hi, > > > > I have a question on how to analyse an exprimental design. > > > > We had three treatments (C, T, M) of which only two could be > > administred to each subject. The subjects receieved those treatments > > in two weeks following each other (W1, W2). I total there were six > > experimental groups, each defined by a treatment combination, i.e. > > > > grp w1 w2 > > 1 C T > > 2 C M > > 3 T C > > 4 T M > > 5 M C > > 6 M T > > > > We are interested in comparing the effects of treatments, e.g. C vs M. > > Right now I am considering a repeated measures analysis, with week and > > grp as factors. However, I cannot figure out how to compose the > > contrasts that would answer our questions. > > > > Any hints? Is there some other, conceptually easier approach I could > > take? Any litterature on this? > > This experimental design is a balanced incomplete block design, with > subjects being the blocks . A description of the analysis can be found > in any standard book on experimental design and the analysis can > probably best be performed by standard software for this type of > design. There are two possibilities: intrablock analysis, and analysis > with 'recovery of interblock information'. The latter is, in fact, a > mixed model analysis. > > For this special case one could equivalently proceed according to the > following lines (I suppose that treatment combinations have been > randomly assigned to subjects). Calculate differences and sums per > subject. The intrablock analysis can be performed by regression of the > differences using 2 contrast vectors for treatments differences (a > third would be redundant), and 3 contrast vectors for sequence effects > (one for each treatment combination). The interblock analysis can be > performed similarly by regression analysis of the sums. The estimates > from intra- and interblock analysis may eventually be combined, using > the inverse of estmated variances as weights; this may be not > worthwile, since the interblock estimates probably are much less > precise than the intrablock estimates. > > This analysis covers all aspects that have been touched in posts by > Jim Clark and Donald Burrill. > > 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/ . =================================================================
