Running a Correlation
Megan Ford
2024-11-07
1 Loading Libraries
#install.packages("apaTables")
#install.packages("kableExtra")
library(psych) # for the describe() command and the corr.test() command
library(apaTables) # to create our correlation table
library(kableExtra) # to create our correlation table2 Importing Data
d <- read.csv(file="Data/projectdata.csv", header=T)
# For HW, import the your project dataset you cleaned previously; this will be the dataset you'll use throughout the rest of the semester3 State Your Hypothesis
We predict that the need to belong and life satisfaction will be correlated with each other. We also predict that social media use and the need to belong will be correlated with each other.
4 Check Your Variables
# you only need to check the variables you're using in the current analysis
# it's always a good idea to look them to be sure that everything is correct
str(d)## 'data.frame': 557 obs. of 7 variables:
## $ ResponseId: chr "R_12G7bIqN2wB2N65" "R_3lLnoV2mYVYHFvf" "R_1gTNDGWsqikPuEX" "R_3G1XvswZmPZTkMU" ...
## $ age : chr "1 between 18 and 25" "1 between 18 and 25" "1 between 18 and 25" "1 between 18 and 25" ...
## $ disability: chr "psychiatric" "other" "learning" "psychiatric" ...
## $ socmeduse : int 34 37 26 23 35 30 40 34 38 42 ...
## $ npi : num 0.0769 0.7692 0 0 0.0769 ...
## $ belong : num 3.6 2.4 3 3.5 3.3 2.7 3.8 2.6 3.8 4 ...
## $ swb : num 1.83 5.83 5.33 5 2.67 ...# We're going to create a fake variable for this lab, so that we have four variables.
# NOTE: YOU WILL SKIP THIS STEP FOR THE HOMEWORK!
#d$fake <- (d$unhappy*d$worry)/d$life_satis
# Since we're focusing only on our continuous variables, we're going to subset them into their own dataframe. This will make some stuff we're doing later on easier.
d2 <- subset(d, select=c(socmeduse, belong, swb))
# You can use the describe() command on an entire dataframe (d) or just on a single variable (d$pss)
describe(d2)## vars n mean sd median trimmed mad min max range skew kurtosis
## socmeduse 1 557 33.66 8.83 34.00 33.92 8.90 11.0 55 44.0 -0.29 0.16
## belong 2 557 3.28 0.61 3.30 3.29 0.59 1.5 5 3.5 -0.18 -0.32
## swb 3 557 4.00 1.41 4.17 4.04 1.48 1.0 7 6.0 -0.18 -0.79
## se
## socmeduse 0.37
## belong 0.03
## swb 0.06# NOTE: Our fake variable has high kurtosis, which we'll ignore for the lab. You don't need to discuss univariate normality in the results write-ups for the labs/homework, but you will need to discuss it in your final project manuscript.
# also use histograms to examine your continuous variables
hist(d$socmeduse)
hist(d$belong)
hist(d$swb)
# last, use scatterplots to examine your continuous variables together, for each pairing
plot(d$belong, d$swb)
plot(d$belong, d$socmeduse)
5 Check Your Assumptions
5.1 Pearson’s Correlation Coefficient Assumptions
- Should have two measurements for each participant
- Variables should be continuous and normally distributed
- Outliers should be identified and removed
- Relationship between the variables should be linear
5.1.1 Checking for Outliers
Note: You are not required to screen out outliers or take any action based on what you see here. This is something you will check and then discuss in your write-up.
# We are going to standardize (z-score) all of our variables, and check them for outliers.
d2$belong <- scale(d2$belong, center=T, scale=T)
hist(d2$belong)
sum(d2$belong < -3 | d2$belong > 3)## [1] 0d2$socmeduse <- scale(d2$socmeduse, center=T, scale=T)
hist(d2$socmeduse)
sum(d2$socmeduse < -3 | d2$socmeduse > 3)## [1] 0d2$swb <- scale(d2$swb, center=T, scale=T)
hist(d2$swb)
sum(d2$swb < -3 | d2$swb > 3)## [1] 05.2 Issues with My Data
Three of my variables meet all of the assumptions of Pearson’s correlation coefficient. I have no outliers. Outliers can distort the relationship between two variables and sway the correlation in their direction.
[Make sure to revise the above paragraph for your HW.]
6 Run a Single Correlation
corr_output <- corr.test(d2$belong, d2$socmeduse)7 View Single Correlation
corr_output## Call:corr.test(x = d2$belong, y = d2$socmeduse)
## Correlation matrix
## [,1]
## [1,] 0.29
## Sample Size
## [1] 557
## These are the unadjusted probability values.
## The probability values adjusted for multiple tests are in the p.adj object.
## [,1]
## [1,] 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option8 Create a Correlation Matrix
Strong: Between |0.50| and |1| Moderate: Between |0.30| and |0.49| Weak: Between |0.10| and |0.29| Trivial: Less than |0.09|
Remember, Pearson’s r is also an effect size!
corr_output_m <- corr.test(d2)9 View Test Output
corr_output_m## Call:corr.test(x = d2)
## Correlation matrix
## socmeduse belong swb
## socmeduse 1.00 0.29 0.17
## belong 0.29 1.00 -0.14
## swb 0.17 -0.14 1.00
## Sample Size
## [1] 557
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## socmeduse belong swb
## socmeduse 0 0 0
## belong 0 0 0
## swb 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option5# Remember to report the p-values from the matrix that are ABOVE the diagonal## [1] 510 Write Up Results
To test our hypothesis that the need to belong, life satisfaction and social media use would be correlated with one another, we calculated a series of Pearson’s correlation coefficients. Three of the variables (social media use, need to belong, and life satisfaction) met the required assumptions of the test, with all three meeting the standards of normality and containing no outliers.
As predicted, we found that all four variables were significantly correlated (all ps < .001). The effect sizes of all correlations were small (rs < .5; Cohen, 1988). Our second hypothesis was also supported, that social media use would be correlated in participants who reported the need to belong, as can be seen by the correlation coefficients reported in Table 1.
[In your HW, revise the above two paragraphs to fit your results. Make sure to discuss ALL predicted correlations and if sig or not.]
| Variable | M | SD | 1 | 2 | 3 |
|---|---|---|---|---|---|
| satisfaction with life | 4.00 | 1.41 | |||
| the need to belong | 3.28 | 0.61 | -.14** | ||
| [-.22, -.06] | |||||
| social media use | 33.66 | 8.83 | .17** | .29** | |
| [.09, .25] | [.21, .36] | ||||
| narcissistic personality inventory | 0.27 | 0.30 | -.06 | -.03 | .06 |
| [-.14, .03] | [-.11, .05] | [-.02, .14] | |||
| Note: | |||||
| M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval. The confidence interval is a plausible range of population correlations that could have caused the sample correlation. | |||||
| * indicates p < .05 | |||||
| ** indicates p < .01. |
References
Cohen J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York, NY: Routledge Academic.