## CORRELATE, CORRELATION - ZERO ORDER
Any variable which is correlated with another variable. Age and sex are the two strongest correlates of crime. A correlation of zero order means there is no relationship between the two variables. 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.
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. Criminologists from an empiricist perspective tend to look at the social world in terms of variables. Everyone in your class is a student so that is a constant, however, there is a great deal of variation by factors like sex, age, income, program, GPA, religion, ethnic heritage. If one gathers information from the whole class on these variables we might begin to see that some variables vary in patterned ways. People with a particular ethnic heritage may tend to be more religious than those from other heritages. This would suggest a correlation; as one variable varies, so does the other. If there were more students of that particular ethnic heritage in the class then religiosity for the group would also increase. As one goes up, so does the other. This is referred to as a positive correlation. If one variable goes up and the other down, this is called a negative relationship. As age goes up, the crime rate goes down, is a negative correlation. A correlation does not mean that one variable causes the other.
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. 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. |