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Apr 20, 2012 9:02 AM
(4790 views)

Hi, I am having problems trying to find the proper way to analyze a set of data and would greatly appreciate some help.

My data is about learning. It includes the response of an individual to two different stimuli (one rewarded, the other unrewarded) presented sequentially. I also have different pairs of stimuli. What I am interested in is to determine the effect of rewarding a stimulus, the effect of using different pairs of stimuli and how the probability changes overtime. I would like to be able to have logistic regressions for each treatment (pair of stimuli), to compare treatments and but at the same time be able to control for the fact that I am measuring the same individuals repeatedly. I know how to fit logistic regressions and how to make repeated measures analyses, but some how think that I need to have them together. Any ideas? I greatly appreciate any help!!

Andre

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Under Analyze, select "Modeling" then "Categorical". You can use "Repeated measures" if one row=one subject. If one subject is recorded across several rows, then you can use "multiple response by ID". Your treatments are the X grouping categories.

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What is the response you are measuring? Is it binomial, like your treatments?

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Hi mfisher,

Yes, it is binomial. I simply record whether the individual responds or not every time I present a stimulus.

Thanks for any help!

Andre

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Excellent! Thanks very much. I had never explored this tool of jmp before and seems to be very helpful not only for this but for other analyses. And a last question if you don't mind. Is it appropriate to pair the stats that I find using the Categorical analysis with graphs of log regressions like the ones found using X by Y plots?

Thanks so much again!

Andre J.

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I believe it is appropriate to *display* the relationships of your data with X by Y plots, but to use the stats from the repeated measures analysis. Any stats from the X by Y plots would not account for the repeated measures design and would be inaccurate, especially the variance and p-values.