Hi all,
Hope someone can help with this as I seem to be going round in circles. I'm
looking at Task Switching using some novel verbal based tasks. On the task
people have to manipulate two, then three, then four verbal categories.
They are scored on RT and accuracy. On this particular study there are two
different types of task (basically using different types of verbal
categories). So we are looking at what effect the number and type of
categories being used has on Rt and accuracy.
The study is repeated measures - IV1 = Test Type (A & B), IV2 = Number of
Categories (2, 3 or 4), and I have 2 DVs of RT and accuracy.
Try as I might I can't think of how to get this into one model. I've been
reading through Tabachnik & Fidell and I'm looking at profile analysis /
doubly-MANOVA design (and I'm a bit shaky on that!), but I can't see how to
include the second IV of test type as well. I wonder if I may be able to
change the way I have arranged my dataset but I really can't see what to do
differently. Currently I have 12 main variables of interest representing
the various combinations of the IVs and two DV measures for each person.
Oh and there are a bunch of covariates thrown in for good measure as well!
Previously I have used separate repeated measures ANOVAs (with adjusted
alpha) for RT and for accuarcy but it's not really telling me what I want
to know. As well as looking for main effects and interaction of the IVs I
want to see how the two DVs relate to each other through these various
combinations. I could do a profile analysis for both DVs on each test type
separately - is this as close as I'm going to get?
Many thanks in anticipation :)
Fiona
--
Tel: 01707 284 761
E-mail: [log in to unmask]
Fiona Essig
Research Student
Room E384
Research Huts
School of Psychology
University of Hertfordshire
College Lane
Hatfield
Herts AL10 9AB
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