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”, whether the correlation is positive or negative. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay. Correlation standardizes the measurement of interdependence between two variables and, therefore, tells you how much the two variables are changing. A correlation coefficient is the covariance divided by the product of the standard deviation of each variable. The correlation measure, that is to say the correlation coefficient, will always take 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 given amount, the second moves proportionally in the same direction. A positive correlation coefficient less than one indicates an imperfect positive correlation, with the strength of the correlation increasing as the number approaches one. If the correlation coefficient is zero, no relationship exists between the variables. If one variable moves, you can't make any prediction about the movement of the other variable; they are not correlated. If the correlation coefficient is –1, the variables are perfectly negatively correlated (or inversely correlated) and evolve in opposition to each other. If one variable increases, the other variable decreases proportionally. A negative correlation coefficient greater than –1 indicates an imperfect 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 a change in one variable is associated with a proportional change in the other variable. Pearson's correlation is a parametric statistic and requires interval data for both variables. To test its significance, we assume the normality of the two variables. For example, you can use a Pearson correlation to assess whether temperature increases in your production facility are associated with a decrease in the thickness of your chocolate coating. Spearman's order correlation coefficient (ρ): Also called Spearman's rho, Spearman's correlation evaluates the monotonic relationship between two continuous or ordinal variables. In a monotonic relationship, variables tend to change together, but not necessarily at a constant rate. Spearman's correlation coefficient, a nonparametric statistic, is based on the ranked (ordinal) values ​​of each variable rather than the raw data. Spearman's correlation is often used to evaluate relationships involving ordinal variables. For example, you can use a Spearman correlation to assess whether the order in which employees complete a testing exercise is related to the number of months they have been employed. Keep in mind: this is just a sample. Get a personalized paper now from our expert writers.Get a personalized essayKendall's correlation coefficient, tau (τ): non-parametric statistic like Spearman's rs but probably better for small.