I have done a correlation in R and I'm unsure on the output? I'm new to stats and have made this correlation table. I'm specifically looking at sdsa (Social desirability scale) to see if my variables (confa = confidence, conta = Control, consa = constancy) were influenced by socially desired responses. I was told to make a correlation matrix to interpret results can I use this information?

 A: Since you're new to statistics, I will be a bit more verbose to make sure you're aware of what you're doing and some possible mistakes in what you were asked.

I'm specifically looking at sdsa (Social desirability scale) to see if
my variables (confa = confidence, conta = Control, consa = constancy)
were influenced by socially desired responses.

Correlation, or a correlation matrix, won't tell you if variable $a_i$ influences variable $a_j$ or how much one influences the other. Just to start with, $a_{ij}$ in your matrix does not even tell you the specific amount of correlation between $a_i$ and $a_j$, considering all other possible confounding factors (look up for partial correlation, if you're interested in this). They can even be independent, and you could still find some number there. What a correlation matrix tells you is the Pearson correlation coefficient between a set of variables, that is, the strength of a linear relationship between these variables, not considering other variables. P-values may be useful in your interpretation of such correlation results (check cor.test function in R).
If it's not clear to you what I mean by confounding factors, imagine that the weather can cause many things. Very hot weather can change consumption patterns, for example, and you would therefore during the hot season measure some correlation between some item's purchase patterns. However, this could be entirely due to a confounding factor: weather. During other times of the year, the consumption of such items occurs in a way that is independent of each other, which ideally would give you a Pearson correlation coefficient of $0$. Depending on what you want, this matrix correlation can be enough, but just bear in mind that you are not measuring how much one variable influences the other. Correlation does not imply causation.
A: Correlation matricies are symmteric matrices in which each entry is the (usually Pearson) correlation between the row and column variable. The diagonals, which have row and column variables the same, have a 100% correlation, of course. So in your case, you would look at the the column for $sdsa$ and you could see if there are any high-absolute-value entries which would indicate a strong correlation—negative or positive depending on the sign—with the other variables. From the table above, it seems that $consa$ has the "strongest" correlation with $sdsa$. Whether a negative 25% correlation is significant or not is something you would have to decide or research in the context of your subject matter.
