# Tag Info

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It is generally thought nowadays that testing for baseline differences in randomized experiments is misleading. Stephen Senn's book Statistical Issues in Drug Development discusses this. One of the many issues involved is that you never know when to stop. How many uncollected variables do you go back and collect in order to test for balance? Couple that ...

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Your data definitely has missing values. See output of with(ez_data,table(id,cost,msg)), or (following the suggestion in the error message) ezDesign(data=ez_data,y=id,x=cost,col=msg) Ah, I see now that you likely posted merely a subset of your data. Still, as you note "Participants were randomly assigned to both cost and msg at two points in time, so ...

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I think I have the answers for these questions: Answers for questions (1), (2) and (3) Since you want to compare averages between treatments, I would recommend to you to try first Tukey's test that is the most rigorous among the existent tests. Tukey's test is good if you want to avoid type I erros (reject null hypothesis, when null hypothesis is true). ...

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This is not actually an answer to your question but the logic behind such tests seems fundamentally misguided, no matter what the specifics are. If treatment assignment is not or cannot be properly randomized, showing that both groups have approximately the same characteristics on some arbitrary set of variables is not going to replace randomization. If ...

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Here's a really basic design for $s=4$ systems. Create a factorial set of trials: $m$ different inputs $p=6 = (s-1)(s)/2= 3(4)/2$ comparisons (i.e., System A with B, A with C, A with D, B with C, B with D, C with D) Create $pm$ trials and present in random order to $n$ participants. For each participant, record the proportion of the time that system A, ...

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James K. Lindsey has several books in similar vein. Perhaps the one nearest to your goal is http://www.amazon.co.uk/Introduction-Applied-Statistics-Modelling-Approach/dp/0198528957/

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The total number of sample combinations you have is $2\times 3 \times 2 \times 3 \times 3 = 108$ (or what ever). Depending on your experiment (and the difficulty of taking samples), you should ideally just sample everything. If not, there a a few other options. You can't technically do standard LHC sampling, or orthogonal sampling, because it requires each ...

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Use Simplex-lattice, Simplex-Centroid designs, or something like that. First, from your question I guess this is a mixture design in which you change proportions of the components of the muffin (or some of it's components) and measure several responses, i.e. texture, acceptance, etc. I suppose that you have some knowledge in the difference between ANOVA ...

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