Correlation Analysis

Correlation Analysis

Sometimes in statistics, we are interested to know the strength of the relationship between two variables under study. It doesn’t matter whether the data is ordinal, qualitative, or quantitative. We can determine the strength of the relationship between the two variables before we can proceed with model buying. Correlation analysis is always performed on such data to assess the strength of the relationship. In this article, we try to understand what correlation is, how we can interpret the creation coefficient, the different types of correlation, and where you can get correlation analysis assignment help.

What is correlation?

Correlation is a way of studying the strength of relationships of the data we are analyzing.  A data is said to be highly correlated if it has a strong relationship, and it’s weakly correlated if it has a weak relationship.

How to interpret the formation coefficient.

When conducting a correlation analysis, it often yields a value based on which  we derive our interpretation. The value ranges from -1 to 1.  A value close  to 1 implies  robust positive correlation while a value closer to -1 implies d solid negative relationship. A positive correlation means that a decrease or increases in one variable will cause a similar decrease or increase. Conversely, a negative correlation implies that a rise in one variable will cause a reduction in the other and vice verse. Two variables with no correlation will yield a correlation of zero, which implies neutrality. There is no effect on the changes in variables.

Let’s say we were conducting a correlation analysis on a dataset with variables x and y. The interpretation of the correlation analysis could be:-

X and Y have a robust positive  relationship.

X and Y have a robust negative relationship.

X and Y have no relationship.

Types of correlation analysis

There are different types of correlation techniques applied to data. The standard type which you will find in SPSS is the Pearson correlation coefficient and Spearman rank correlation coefficient. Let us look at the two in detail.

Pearson correlation coefficient

Pearson correlation is the most widely used. It’s adaptable for continuous data and if we are evaluating the degree of the strength of a linear relationship. The data here needs to be quantitative. Examples of data that we can analyze using the Pearson correlation is the relationship between height and weight, the relationship between different stock price movement and the relationship between prices and purchases.

The Pearson correlation assumes that the data must be normally distributed, there is a linear relationship between the two, and the data is equally distributed around the regression line.

It is always advisable to plot a scatter plot, which will indicate if the data is linear or not before performing the Pearson correlation coefficient.

Spearman rank correlation coefficient.

Spearman rank correlation is used for ordinal data and where normality is not a requirement. Ordinal data refers to the data type, which is both qualitative and quantitative. An example is using 1 and 0 to  represent male and female.  It does not carry any assumption of the distribution of the data. Overall it’s as if spearman rank correlation and Pearson rank are complementary to each other.

Correlational analysis and regression analysis.

It’s easy to confuse these two forms of analysis given that they are used to explore the relationship between variables, and a positive correlation could imply a positive gradient of the regression line. In case you find the two confusing, here is how you can note the differences.