**Correlation Analysis**

Correlation analysis is one of the topics in statistics frequently misunderstood and misused. It is a method used to identify relationships between two datasets or variables. Correlation analysis also checks how strong the relationship is. In the business world, especially in marketing research, correlation analysis is used to analyze quantitative data gathered through research methods such as polls and surveys. It is used to discover if there are significant patterns, connections, or trends between the two datasets.

In essence, correlation analysis helps scientists and researchers spot patterns in data. There are two types of correlation. A positive correlation where both variables increase in relation to each other and a negative correlation where one variable decreases while the other increases.

**Correlation coefficients**

According to Spearman, Kendall, and Pearson, there are three different ways of ranking statistical correlation. The result will be represented as r by each coefficient. The two widely used analytical formulas are the Spearman’s Rank and Pearson’s coefficient. The choice of the formula depends on the types of data the researchers have at hand.

**The Spearman’s Rank Correlation coefficient**

The spearman’s rank correlation coefficients determine if there is any significant relationship between the two datasets. It operates under the assumption that the data being used is ordinal. This means that the numbers do not signify quantity. Instead, they indicate a position of place of the subject’s standing. For example, 1^{st} 2^{nd} 3^{rd}, etc.

The spearman’s rank correlation coefficient demands a table of data that displays the raw data. It then ranks it and finds the difference between the two ranks. A scatter graph will show the squared difference between the two ranks. This will indicate if there is a positive, negative, or no correlation at all between the two variables. This coefficient works with a constraint that is -1 less or equal to r less or equal to +1. A result of 0 will mean that there is no relation whatsoever between the variables. If you are stuck with an assignment related to Spearman’s rank correlation coefficient, place your order with us and have our online tutors save the day for you.

**Pearson product-moment Coefficient**

It is a top-rated and widely used analysis formula. Pearson product-moment coefficient is used to measure the linear relationships between raw data from both variables. It does not measure their ranks. This coefficient has no data-related boundaries to be considered when conducting analyses with its formula. It is, for this reason, it is also called a dimensionless coefficient The Pearson product-moment coefficient is the first formula researchers try.

This coefficient will not accurately represent the relationship between two variables if the relationship between the data is not linear. Also, you cannot use it when Spearman’s rank should be implemented instead. When using the Pearson product-moment coefficient, the relevant data must be made to input into a table similar to that of the Spearman’s rank. However, there will be no ranks and the produced result will be in the numerical form. All correlation coefficients including the two discussed here must produce this kind of result

**So when do we use these coefficients?**

We can use the two methods discussed above depending on if there are parameters associated with the data gathered. You should watch out for the following two terms:

- Parametric

This applies to the Pearson’s coefficient where the data must be handled in relation to the parameters of probability distributions or populations. It is usually used with quantitative data that has already been set out within the said parameters.

- Non-parametric

This term applies to the Spearman’s rank coefficient. Where no assumptions can be made about the probability distribution. It is often used with qualitative data. Though it can also be used with quantitative data if the Spearman’s rank coefficient proves inadequate.

Statisticians recommend using the parametric methods in cases where both terms are applicable. This is because parametric methods tend to be more precise. However, this does not discredit non-parametric methods in situations where the data is not enough or where the result needed should be specified and accurate.

**Interpreting the results**

Visualizing the results on a scatter graph is the best way to gain generalized but more immediate interpretation. A strong positive correlation is indicated by a score from +0.5 to +1. This means that the variables are both increasing at the same time. On the other hand, a strong negative correlation is indicated by any score from -0.5 to -1. This means that as one variable increases, the other one decreases proportionally. No correlation is indicated by a score of zero, which means that there is no relationship between the two variables. If you want your result to be precise and accurate, then consider using a large sample size. These facts will stand true for all regardless of the formula you use

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