Do you need multiple comparisons for a correlation matrix?

Do you need multiple comparisons for a correlation matrix?

Does the computation of an N x N correlation matrix for N unrelated variables require multiple comparisons correction for all the computed pairwise correlations (assuming each computed correlation is a ‘comparison’ in the sense of being a ‘statistical test’)?

Can a multiple correlation coefficient be more than one variable?

These definitions may also be expanded to more than two independent variables. With just one independent variable the multiple correlation coefficient is simply r. Unfortunately, R is not an unbiased estimate of the population multiple correlation coefficient, which is evident for small samples.

What is the problem of multiple comparisons in statistics?

If many data series are compared, similarly convincing but coincidental data may be obtained. In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.

How do you calculate multiple correlation in Excel?

These definitions can be extended to more than three variables as described in Advanced Multiple Correlation. E.g. if R1 is an m × n data range containing the data for n variables then the supplemental function RSquare (R1, k) calculates the multiple coefficient of determination for the kth variable with respect to the other variables in R1.

Can a correlation coefficient be assessed with missing data?

If some data are missing, it is not possible to assess the correlation in the usual way. Here we demonstrate two approaches to assessing the correlation coefficient between two variables in the presence of missing data. First, we load in a data file in which some values are missing (denoted as “NA”).

When do you need to correction for multiple comparisons?

If you will declare success/significance if any of the correlations are significant (fishing for significance) then yes, you need to do a correction for multiple comparisons because if the truth is nothing is correlated, but you add a bunch of random noise variables and don’t adjust, then you will likely see something significant by chance.

Is there a correlation function in are that accepts matrix?

One correlation function supported by R’s stats package that can remove the NAs is cor.test (). However, this function only runs correlation on a pair of vectors and does NOT accept a data.frame/matrix as its input (to run correlation on the columns of the data frame and build a a pairwise correlation matrix accordingly).

When to use multiple correlations and Bonferroni’s correction?

Multiple correlations and Bonferroni’s correction Correlation coefficients between biological measurements and clinical scales are often calculated in psychiatric research. Calculating numerous correlations increases the risk of a type I error, i.e., to erroneously conclude the presence of a significant correlation.

What happens if you calculate too many correlations?

Calculating numerous correlations increases the risk of a type I error, i.e., to erroneously conclude the presence of a significant correlation. To avoid this, the level of statistical significance of correlation coefficients should be adjusted.