Positive correlation can be defined as the direct relationship between two variables, i.e., when the value of one variable increases, the value of the other increases too. This post explains this concept in psychology, with the help of some examples.

“The consumption of ice cream (pints per person) and the number of murders in New York are positively correlated. That is, as the amount of ice cream sold per person increases, the number of murders increases. Strange but true!”**― Deborah J. Rumsey, Statistics For Dummies®**

Psychology uses various methods for its research, and one of them is studying the correlation between any two variables. Correlation is nothing but the measure of degree of relation between two variables. It can be plotted graphically to show the relationship between them.

Correlation studies the relationship between two variables, and its coefficient can range from -1 to 1. A positively inclining relationship is nothing but positive correlation. Its value can range from 0 to 1. Positive correlation implies there is a positive relationship between the two variables, i.e., when the value of one variable increases, the value of other variable also increases, and the opposite happens when the value of one variable decreases. Correlation is used in many fields, such as mathematics, statistics, economics, psychology, etc.

Let’s take a **hypothetical example**, where a researcher is trying to study the relationship between two variables, namely ‘x’ and ‘y’. The example will help you understand what is positive correlation.

Let ‘x’ be the number of hours that a student has studied, and ‘y’ be his score in a test (maximum marks: 120). The researcher picks up 20 students from a class, and records the number of hours they studied for the test. The researcher then records the marks scored by the students in the test. We try to compare the relationship between the number of hours the student has devoted in studying, and his corresponding score.

x (Number of Hours) |
y (Score in Test) |

4 | 47 |

2 | 23 |

3 | 31 |

5 | 55 |

6 | 66 |

5.5 | 65 |

8 | 82 |

3.5 | 48 |

10 | 94 |

9.5 | 80 |

8.5 | 80 |

6.7 | 62 |

8.9 | 84 |

2.5 | 38 |

4.7 | 35 |

3.3 | 43 |

5.2 | 51 |

10.1 | 101 |

7.6 | 84 |

9.3 | 70 |

► The given data is of two variables ‘x’ and ‘y’. There are 20 observations recorded by the researcher. We will plot these points on a graph.

► After plotting the points on the graph, we get a scatter diagram. The scatter diagram indicates the trend, and displays whether the correlation is positive or negative.

► An upward trend usually indicates a positive correlation, and on the other hand, a downward trend usually indicates a negative correlation. The degree of relation will however differ every time. Thus, the scatter diagram helps us visualize the correlation.

► In psychology, correlation can be helpful in studying behavioral patterns. For example, if you want to study whether those students who are depressed fail in their examinations or score poorly, you can plot your observations and study the association between them. If there is a positive association, it implies that depressed students are more prone to fail in their examinations.

**Graphical Representation of Data: Scatter Diagram**

### What Do We Observe?

► After plotting the points on the graph, we can notice the upward/rising trend of the scatter diagram. This indicates that as the value of variable ‘x’ increases, the value of ‘y’ also increases. Thus, this indicates that the students who have put in more hours of study have scored better in the test.

► However, this survey method has its own limitations. This data is based on the statistics of 20 students in a class with different IQ levels. Though the trend here observed is positive, there are high chances that the IQ level of that student can play an important contributing factor too. The inference that more the hours you study, better the score, might hold true, if it is assumed that the IQ level of all the students is similar, on an average. However, there are other variables that cannot be ruled out, such as the level of concentration of the students, which can influence the scores.

### Examples of Positive Correlation in Real Life

► If I walk more, I will burn more calories.

► With the growth of the company, the market value of company stocks increase.

► When demand increases, price of the product increases (at same supply level).

► When you study more, you score high in the exams.

► When you pay more to your employees, they’re motivated to perform better.

► With increase in consumption of junk food, there is increase in obesity.

► When you meditate more, your concentration level increases.

► Couples who spend more time together have a healthier and long-lasting relationship.

It must be noted that correlation does not imply causation. A direct relationship or positive relationship does not imply that they are the cause and effect of each other. A correlation between two variables aids the researcher in determining the association between them. However, statistical data is based on a sample, and hence, can sometimes lead to misleading results. A strong positive correlation does not imply there is necessarily a relationship between them; it might be due to an unknown external variable. Hence, researchers have to be careful about the statistical data while drawing inferences.