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What is a Pearson Correlation Analysis?
Correlation describes the strength and direction of the linear relationship between two variables. Pearson’s correlation coefficient is used for interval variables, and Pearson’s correlation coefficient can only take values from -1 to +1. The sign indicates whether the correlation is positive (both variables decrease or increase) or negative (one variable decreases when the other increases and vice versa). A perfect correlation, 1 or -1, indicates that the value of one variable can be accurately determined when we know the value of the other. A correlation equal to 0 indicates no relationship between the two variables.
When Should a Pearson Correlation be Used?
An Example Of Pearson Correlation Test
For example, a teacher wants to examine the relationship between the level of anxiety and the exam score. Then we have two variables: the level of anxiety and the exam score.
What are the use of null and alternative hypothesis for the correlation analysis?
Therefore, we test the following hypotheses:
Null hypothesis: There is no significant effect relationship between the level of anxiety and exam score.
Alternative hypothesis: There is a significant effect relationship between the level of anxiety and exam score
R function to Compute Pearson Correlation
The code to run a Pearson Correlation Analysis using R is as follows:
cor.test (x, y, method=(“pearson”, “kendall”, “spearman”), data = dataframe)
x, y: numeric vectors with the same length
method: correlation method
In this section, we will show you how to run the Pearson correlation analysis using the r studio program and how to interpret the test results after we obtain the result of the test. In the first part, we present the r program code and function for the correlation analysis. Next, you will see the outputs as a result of running the r codes. In the last section, you can find the interpretation of the Pearson correlation in APA format.
# LOAD DATA
library(readxl)
Data <- read_excel(“Data.xlsx“)
# VIEW DATA
View(Data)
# NAME VARIABLES
data <- Data
anxiety <- data$anxiety
exams <- data$exams
# PERFORM PEARSON CORRELATION ANALYSIS
cor.test(anxiety, exams, method=c(“pearson”))
# SCATTERPLOT
plot(anxiety, exams, main=”Scatter plot”, xlab=”Anxiety level”, ylab=”Exam score”, pch=19)
abline(lm(exams~anxiety), col=”black”)
> cor.test(anxiety, exams, method=c("pearson"))##
## Pearson's product-moment correlation
##
## data: anxiety and exams
## t = 1.5724, df = 97, p-value = 0.1191
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.0410285 0.3443544
## sample estimates:
## cor
## 0.1576603
> plot(anxiety, exams, main="Scatter plot", xlab="Anxiety level", ylab="Exam score", pch=19)
> abline(lm(exams~anxiety), col="black")
Reporting Pearson Correlation in R
Pearson correlation coefficient was computed to determine the relationship between anxiety level and exam score. The results indicate a non-significant negative weak relationship, r(97) = 0.16, p = 0.12. We, therefore, fail to reject the null hypothesis and conclude that there is no significant relationship between anxiety level and exam score.
GET HELP FROM US
There is a lot of statistical software out there, but R is one of the most popular. If you’re a student who needs help with R Studio, there are a few different resources you can turn to. We prepared a page for R tutorial for Beginners. All contents can guide you through Step-by-step R data analysis tutorials and you can see Basic Statistical Analysis Using the R Statistical Package.
The second option is that you can get help from us, we give R Studio help for students with their assignments, dissertation, or research. Doing it yourself is always cheaper, but it can also be a lot more time-consuming. If you’re not the best at SPSS, then this might not be a good idea. It can take days just to figure out how to do some of the easier things in SPSS. So paying someone to do your R task will save you a ton of time and make your life a lot easier.
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