Correlations have two properties: strength and direction. The strength of a correlation is determined by its numerical (absolute) value. The direction of the correlation is determined by the sign of the correlation coefficient 'r', regardless of whether the correlation is positive or negative. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Correlation standardizes the measure of interdependence between two variables and, as a result, tells you how close the two variables are. A correlation coefficient is the covariance divided by the product of the standard deviation of each variable. The measure of correlation, i.e. the correlation coefficient, will always take on a value between 1 and – 1: If the correlation coefficient is one, the variables have a perfect positive correlation. This means that if one variable moves by a certain amount, the second moves proportionally in the same direction. A positive correlation coefficient less than one indicates a less-than-perfect positive correlation, with the strength of the correlation increasing as the number approaches one. If the correlation coefficient is zero, there is no relationship between the variables. If one variable moves, you cannot make predictions about the movement of the other variable; they are uncorrelated. If the correlation coefficient is -1, the variables are perfectly negatively correlated (or inversely correlated) and move in opposition to each other. If one variable increases, the other variable decreases proportionally. A negative correlation coefficient greater than –1 indicates a less-than-perfect negative correlation with the strength of the correlation increasing as the number approaches –1. There are two types of correlation: bivariate and partial. A bivariate correlation is a correlation between two variables while a partial correlation examines the relationship between two variables while "controlling" for the effect of one or more additional variables. Pearson product moment correlation coefficient (r): Evaluates the linear relationship between two continuous variables. A relationship is linear when the change in one variable is associated with a proportional change in the other variable. Pearson correlation is a parametric statistic and requires interval data for both variables. To verify its significance we assume the normality of both variables. For example, you could use a Pearson correlation to evaluate whether increases in temperature at your manufacturing facility are associated with decreases in chocolate coating thickness. Spearman rank order correlation coefficient (ρ): Also called Spearman rho, Spearman correlation evaluates the monotonic correlation relationship between two continuous or ordinal variables. In a monotonic relationship, the variables tend to change together, but not necessarily at a constant rate. The Spearman correlation coefficient, a nonparametric statistic, is based on the ranked (ordinal) values ​​for each variable rather than the raw data. Spearman correlation is often used to evaluate relationships involving ordinal variables. For example, you might use a Spearman correlation to evaluate whether the order in which employees complete a test exercise correlates with the number of months on the job. Please note: this is just an example. Get a custom paper from our expert writers now. Get a custom essay Kendall correlation coefficient, tau (τ): nonparametric statistic like Spearman's rs but probably better for small samples.