I have a dataset of this nature:
stage temp published reconstructed
one 15 19.12 13.164
one 18 12.86 10.862
one 20 9.67 5.857
two 15 167.78 79.211
two 18 96.68 57.359
two 20 90.12 31.757
I applied MANOVA on the data in a bid to see how the means between published and reconstructed differ with regard to temp and stage.
questions:
1) Is MANOVA the right technique or there is a better option
2) If MANOVA, How do I do means separation afterwards? (to see in which temp for each stage differs significantly)
I am using R
Dear Dorcas,
I am not entirely sure how your model looks like, but if published and reconstructed are your outcomes and you model stage and temp as fixed effects, your approach might be right. Concerning your second question: You will need to add an interaction term stage*temp and take a look at the least square means of the differences. Unfortunately I work with SAS in general, so I cannot help you with R precisely. Maybe this link helps (?):
https://cran.r-project.org/web/packages/phia/vignettes/phia.pdf
This looks like factorial ANCOVA to me. You can set this up in various ways, but one simple (but powerful) model is to use the change score (reconstructed - published) as outcome Y stage as a factor and temp and baseline (published) as covariates. This model has no repeated measures or DVs and assumes that published and reconstructed are on the same scale.
Interpretation is helped by entering temp and baseline.
It is MANOVA with 2 factor stage and tem .use another program like GenestT 12 and use dancan for comparison between means
Thank you Ann Christina. That really helps. I included the interaction term followed by LSD but I am still getting an empty result.
Thom, Thanks. Yes, they are on the same scale. Let me try ancova and see.
Isam, that's exactly what I am asking. How to do it. Let me try what has been suggested
It could also be t - test (or Mann - Whitney). First, perform a factor analysis (or principal component analysis), create a factor of the three (dependent variables) and then compare means for the levels of the factor. T - test if the distribution of the factor is normal otherwise, Mann - Whitney.
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