Correlation analysis is a statistical technique that allows researchers to investigate cause and effect relationships between two or more variables. In other words, correlation analysis gives an indication of whether a change in one variable causes a change in another variable, or vice versa. The strength of the relationship between these two variables can be measured by calculating their statistical correlation coefficient. In this article, we will discuss the concept of correlation, its significance in research, its applications, and how positive and negative correlation impacts the research.

**Concept of Correlation**

The concept of correlation is widely used in research because it permits us to investigate cause and effect relationships between various factors without having any prior knowledge about the processes underlying these relationships.

**Applications of Correlation Analysis**

Correlation analysis has been found useful in many fields such as biology, physics and sociology. Correlation analysis allows researchers to investigate cause and effect relationships between two or more variables. The process of correlation analysis is simple and easy, as it helps us identify whether there is a linear or non-linear relationship between various factors.

In marketing research, it is mainly used to measure the impact of advertising on sales volume or sales revenue. The results obtained from using it can help in planning strategies for future advertising activities so as to achieve better results than before. They also help in reducing costs by avoiding ineffective advertising campaigns.

Correlation analysis is commonly used in PhD research to see if there is a relationship between the variables under study and if this relationship is strong enough to be considered significant. If you are also using correlation and getting irrelevant and insignificant results, then consider getting PhD dissertation help from online experts.

**Significance of Correlation Analysis**

The significance of correlation analysis lies in its ability to show whether there is a relationship between two variables or not. It lets us measure whether there is a relationship between two variables or not and how strong this relationship is by calculating an estimate called coefficient of correlation (r). The higher the value of r, the stronger is the relationship between two variables; therefore, it would be safe to assume that there exists a definite relationship between these two variables

**Type of Correlation**

Correlation can be positive or negative, depending on the direction of change in one variable with respect to another variable. It also indicates how closely related two variables are and how much variation on one side (X) will affect variation on the other side (Y). The strength of correlation can be calculated by Pearson’s r coefficient or Spearman’s rank order coefficient.

There are many types of correlation such as bivariate correlation matrix, partial correlation coefficient matrix, etc. All based on different kinds of causal relationships between two variables. Correlation analysis is used extensively in psychology research studies because it provides information about qualitative data using numerical values. Which can then be used for testing hypothesis based on this data. The two main types of correlation relationship are:

- Positive Correlation
- Negative Correlation

**Positive & Negative Correlation**

**Positive Correlation**

**Positive Correlation**

When two variables are positively correlated, it means that as one variable increases, the other variable also increases. For example, if you were to look at the relationship between basketball players’ height and their points scored per game. You would most likely find a positive correlation between these two variables. In other words, taller people tend to score more points than shorter people because they have an advantage on offense due to their height advantage over defenders.

**Negative Correlation**

**Negative Correlation**

A negative correlation means that as one variable increases in value or magnitude by some amount (such as increased time spent studying). The other variable decreases in value or magnitude by some amount (such as decreased test scores). For example, if you were looking at how much time students spend studying for exams vs. their grades on those exams. Then there would be a negative correlation between these two variables. Because students who study less will usually get lower grades than students who study more.

**The Impact of Negative Correlation on Research and Experimentation**

Let’s say you are investigating the correlation between school funding and test scores. As you might imagine, a negative correlation would indicate that as school funding decreased, test scores increased.

This is not to say that there is a definite cause-and-effect relationship between these two variables; rather, it means that there is more than one way in which they can be connected. For example:

- There may be some factors other than the variables themselves that cause both lower school funding and higher test scores.
- It may be possible for schools with low levels of money on hand to provide better educational opportunities than those with greater resources.
- Some schools may simply have stronger curricula than others regardless of their financial situation

**The Impact of Positive Correlation on Research and Experimentation**

Positive correlation is a relationship between two variables in which both variables increase or decrease together. This means that if one variable increases, the other variable will also increase; and if one decreases, the other will decrease.

Creating a positive correlation between two variables can be done by increasing the value of one while maintaining its standard deviation. A positive linear (direct) correlation exists when there is an increasing slope on a scatter diagram. And all points are above their respective regression lines.

If this happens, then we say that there is a direct relationship between them because every point on an x-y plane moves up. As you move towards right from the origin on both axes, i.e., the movement towards right means higher values for both x & y coordinates at the same time so we can say that these two variables are positively correlated. Similarly, movements towards left mean lower values for both x & y coordinates at the same time. So we can say that these two are negatively correlated or anti-correlated with each other

**Conclusion**

The goal of correlation analysis is to determine whether there exists a relationship between the variables or not. However, what really counts is the strength of the relationship that can help us predict an event in the future. Correlation is however affected due to two variable types, positive and negative correlation. At times it may also be affected based on other factors. But this article is focused only on the impact of negative and positive correlations on research.