## CORRELATE, CORRELATION - ZERO ORDER
Any variable
which is correlated with another variable. Age and sex are the two strongest correlates of
crime.
Correlation is a measure of
association between two variables. The variables are not designated as dependent variable
or independent variable. The value of a correlation
coefficient can vary from minus one to plus one. A minus one indicates a perfect negative
correlation, while a plus one indicates a perfect positive correlation.
When there is a negative correlation between two variables, as the value of one variable increases, the value of the other variable decreases, and vise versa. In other words, for a negative correlation, the variables work opposite each other. When there is a positive correlation between two variables, as the value of one variable increases, the value of the other variable also increases. The variables move together.
The two most popular correlation coefficients are: Spearman's correlation coefficient rho and Pearson's product-moment correlation coefficient. The standard error of a correlation coefficient is used to determine the confidence intervals around a true correlation of zero. If your correlation coefficient falls outside of this range, then it is significantly different than zero. The standard error can be calculated for interval or ratio-type data (i.e., only for Pearson's product-moment correlation). The significance (probability)
of the correlation coefficient is determined from the t-statistic. The probability of the
t-statistic indicates whether the observed correlation coefficient occurred by chance if
the true correlation is zero. In other words, it asks if the correlation is significantly
different than zero. When the t-statistic is calculated for Spearman's rank-difference
correlation coefficient, there must be at least 30 cases before the t-distribution can be
used to determine the probability. If there are fewer than 30 cases, you must refer to a
special table to find the probability of the correlation coefficient.
"Two variables can have a
causal relation even in the absence of a non-zero correlation. Zero-order correlations can
be spuriously small as well as spuriously large. This outcome is especially likely in the
complex causal networks that likely underlie real-world phenomena. Hence, the three
conditions for causal inference from correlational data are misspecified. They probably
reduce to two: temporal priority and a non-zero correlation after controlling for all
reasonable third variables." Alan & Bo's Correlation & Causality Blog Zero-order correlation matrices are used as the starting point in the analysis of causal structure inherent to the data. Theory of correlation - Zero-Order, Partial and Multiple Correlation Coefficients;. Correlation Ratios; Weighted Correlations. |