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# Correlational Analysis

The correlational analysis is a statistical evaluation method through which one can be able to study the strength of relationships between two variables such as height and weight. This method is used as an analysis technique mostly by researchers in their bit to identify connections between variables in their collected data. The functionality of correlation is that if two variables are said to correlate, then a change in one affects the other and that the two variables affect each other. Additionally, the correlation between variables is either positively or negatively. Correlating between two variables is said to be positive when there is a simultaneous increase between the two variable. This means that if one variable increases, the other also increases. A negative correlation, on the other hand, exists when there is only one variable being affected positively. This means that one variable is increasing while the other decreases.

Measurement in Correlation Analysis

Correlation is measured in Pearson’s product-moment coefficient and it is usually between +1 and -1. The closer the correlation is to +1 the stronger it is while weakness is observed with the closeness of correlation to -1. An indication of zero correlation means that there is no relationship between the measured variables.

Application of Correlation Analysis

In its application to real-life situations, correlational analysis is used by psychologists who however do not give perfect correlation between variables. Psychologists use this analysis to identify the correlation coefficient which they use to calculate probability through the use of statistical tables. The probability indicates whether there was a possibility of the results occurring at a random. Other applications of correlational analysis are in the insurance companies where the relationship between policyholders and claims are determined and considered to give a positive correlation. Further, nations through census use correlation analysis to determine the relationship between the increase in population and poverty effects.

In research, correlation analysis helps in identification of the extent to which two variables relate. This helps researchers developed clear conclusions of the researches. Additionally, the results obtained from two variables either positive or negative correlation opens researchers to more detailed and directional research.

Some of the major limitations of using correlational analysis are that one cannot how one variable affects the other despite the facts that correlating variables cause change to each other. Further, in a case where the correlation between two variable reads zero does not conclusively mean there is no relationship hence could result in the user of the analysis giving unclear reports