Method "A" describes biological samples using multivariate "fingerprints" that consist of about 30 different variables. Different variables show different typical distribution and many of them closely correlate one with another. From prior experience it is assumed that we cannot transform many of the variables to normal distribution.

Method "B" is designed to be an improved version of method "A" and we wish to compare the repeatabilty of these two methods. If we were dealing with single variable, we would perform independent analyses of several samples and use ANOVA in order to compare within-method to between-methods variability. But here we are dealing with multivariate outputs and we do not wish to perform one analysis per variable. What are the correct approaches to this question?

#Resolution# The answer by gui11aume's answer, provides useful and valuable information. I will adapt the "downstream application" from gui11aume's answer following by 7 one-way analyses as suggested by AdamO.

Method "A" describes biological samples using multivariate "fingerprints" that consist of about 30 different variables. Different variables show different typical distribution and many of them closely correlate one with another. From prior experience it is assumed that we cannot transform many of the variables to normal distribution.

Method "B" is designed to be an improved version of method "A" and we wish to compare the repeatabilty of these two methods. If we were dealing with single variable, we would perform independent analyses of several samples and use ANOVA in order to compare within-method to between-methods variability. But here we are dealing with multivariate outputs and we do not wish to perform one analysis per variable. What are the correct approaches to this question?

Resolution

The answer by gui11aume's answer, provides useful and valuable information. I will adapt the "downstream application" from gui11aume's answer following by 7 one-way analyses as suggested by AdamO.

Method "A" describes biological samples using multivariate "fingerprints" that consist of about 30 different variables. Different variables show different typical distribution and many of them closely correlate one with another. From prior experience it is assumed that we cannot transform many of the variables to normal distribution.

Method "B" is designed to be an improved version of method "A" and we wish to compare the repeatabilty of these two methods. If we were dealing with single variable, we would perform independent analyses of several samples and use ANOVA in order to compare within-method to between-methods variability. But here we are dealing with multivariate outputs and we do not wish to perform one analysis per variable. What are the correct approaches to this question?

#Resolution# The answer by gui11aume's answer, provides useful and valuable information. I will adapt the "downstream application" from gui11aume's answer following by 7 one-way analyses as suggested by AdamO.

Method "A" describes biological samples using multivariate "fingerprints" that consist of about 30 different variables. Different variables show different typical distribution and many of them closely correlate one with another. From prior experience it is assumed that we cannot transform many of the variables to normal distribution.

Method "B" is designed to be an improved version of method "A" and we wish to compare the repeatabilty of these two methods. If we were dealing with single variable, we would perform independent analyses of several samples and use ANOVA in order to compare within-method to between-methods variability. But here we are dealing with multivariate outputs and we do not wish to perform one analysis per variable. What are the correct approaches to this question?

#Resolution# The answer by gui11aume's answer, provides useful and valuable information. I will adapt the "downstream application" from gui11aume's answer following by 7 one-way analyses as suggested by AdamO.

Method "A" describes biological samples using multivariate "fingerprints" that consist of about 30 different variables. Different variables show different typical distribution and many of them closely correlate one with another. From prior experience it is assumed that we cannot transform many of the variables to normal distribution.

Method "B" is designed to be an improved version of method "A" and we wish to compare the repeatabilty of these two methods. If we were dealing with single variable, we would perform independent analyses of several samples and use ANOVA in order to compare within-method to between-methods variability. But here we are dealing with multivariate outputs and we do not wish to perform one analysis per variable. What are the correct approaches to this question?

Method "A" describes biological samples using multivariate "fingerprints" that consist of about 30 different variables. Different variables show different typical distribution and many of them closely correlate one with another. From prior experience it is assumed that we cannot transform many of the variables to normal distribution.

Method "B" is designed to be an improved version of method "A" and we wish to compare the repeatabilty of these two methods. If we were dealing with single variable, we would perform independent analyses of several samples and use ANOVA in order to compare within-method to between-methods variability. But here we are dealing with multivariate outputs and we do not wish to perform one analysis per variable. What are the correct approaches to this question?

#Resolution# The answer by gui11aume's answer, provides useful and valuable information. I will adapt the "downstream application" from gui11aume's answer following by 7 one-way analyses as suggested by AdamO.