1 Loading Libraries

#install.packages("apaTables")
#install.packages("kableExtra")
#install.packages("psych")

library(psych) # for the describe() command and the corr.test() command
## Warning: package 'psych' was built under R version 4.5.2
library(apaTables) # to create our correlation table
## Warning: package 'apaTables' was built under R version 4.5.2
library(kableExtra) # to create our correlation table
## Warning: package 'kableExtra' was built under R version 4.5.2

2 Importing Data

d <- read.csv(file="Data/projectdata.csv", header=T)

3 State Your Hypothesis

We predict there will be a significant relationship between social media use, perceived social support, and satisfaction with life. Specifically, social media use will be positively related to perceived social support but negatively related to satisfaction with life, and perceived social support will be positively related to satisfaction with life.

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':    2162 obs. of  7 variables:
##  $ ResponseID: chr  "R_BJN3bQqi1zUMid3" "R_2TGbiBXmAtxywsD" "R_12G7bIqN2wB2N65" "R_39pldNoon8CePfP" ...
##  $ gender    : chr  "f" "m" "m" "f" ...
##  $ age       : chr  "1 between 18 and 25" "1 between 18 and 25" "1 between 18 and 25" "1 between 18 and 25" ...
##  $ socmeduse : int  47 23 34 35 37 13 37 43 37 29 ...
##  $ support   : num  6 6.75 5.17 5.58 6 ...
##  $ swb       : num  4.33 4.17 1.83 5.17 3.67 ...
##  $ belong    : num  2.8 4.2 3.6 4 3.4 4.2 3.9 3.6 2.9 2.5 ...
# 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, support, 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 2162 34.25 8.60  35.00   34.52 7.41  11  55    44 -0.30     0.19
## support      2 2162  5.53 1.13   5.75    5.65 0.99   0   7     7 -1.08     1.30
## swb          3 2162  4.43 1.33   4.50    4.49 1.48   1   7     6 -0.35    -0.49
##             se
## socmeduse 0.18
## support   0.02
## swb       0.03
# If you have high skew or kurtosis for any of your project variables, you will need to discuss it below in the Issues with My Data and Write up Results sections, as well as in your final project manuscript if your data does not meet the normality assumption.

# also use histograms to examine your continuous variables
# Because we are looking at 3 variables, we will have 3 histograms.

hist(d$socmeduse)

hist(d$support)

hist(d$swb)

# last, use scatterplots to examine your continuous variables together, for each pairing
# because we are looking at 3 variables, we will have 3 pairings/plots. 

plot(d$socmeduse, d$support)

plot(d$socmeduse, d$swb)

plot(d$support, d$swb)

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: For correlations, you will NOT screen out outliers or take any action based on what you see here. This is something you will simply check and then discuss in your write-up.We will learn how to removed outliers in later analyses.

# We are going to standardize (z-score) all of our 3 variables, and check them for outliers.

d2$socmeduse <- scale(d2$socmeduse, center=T, scale=T)
hist(d2$socmeduse)

sum(d2$socmeduse < -3 | d2$socmeduse > 3)
## [1] 0
d2$support <- scale(d2$support, center=T, scale=T)
hist(d2$support)

sum(d2$support < -3 | d2$support > 3)
## [1] 27
d2$swb <- scale(d2$swb, center=T, scale=T)
hist(d2$swb)

sum(d2$swb < -3 | d2$swb > 3)
## [1] 0

5.2 Issues with My Data

Two of my variables meet all of the assumptions of Pearson’s correlation coefficient. One variable, perceived social support, had 27 outliers. Outliers can distort the relationship between two variables and sway the correlation in their direction.

6 Run a Single Correlation

corr_output <- corr.test(d2$socmeduse, d2$support)

7 View Single Correlation

corr_output
## Call:corr.test(x = d2$socmeduse, y = d2$support)
## Correlation matrix 
##      [,1]
## [1,] 0.19
## Sample Size 
## [1] 2162
## 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 option

8 Create a Correlation Matrix

corr_output_m <- corr.test(d2)

9 View Test Output

corr_output_m
## Call:corr.test(x = d2)
## Correlation matrix 
##           socmeduse support  swb
## socmeduse      1.00    0.19 0.09
## support        0.19    1.00 0.46
## swb            0.09    0.46 1.00
## Sample Size 
## [1] 2162
## Probability values (Entries above the diagonal are adjusted for multiple tests.) 
##           socmeduse support swb
## socmeduse         0       0   0
## support           0       0   0
## swb               0       0   0
## 
##  To see confidence intervals of the correlations, print with the short=FALSE option
# Remember to report the p-values from the matrix that are ABOVE the diagonal!

Remember, Pearson’s r is also an effect size! We don’t report effect sizes for non-sig correlations.

  • 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|

10 Write Up Results

To test our hypothesis that social media use, perceived social support, and satisfaction with life would be correlated with one another, we calculated a series of Pearson’s correlation coefficients. Two of the variables (social media use and satisfaction with life) met the required assumptions of the test, with both meeting the standards of normality and containing no outliers. One variable, perceived social support, had 27 outliers; so any significant results involving perceived social support should be evaluated carefully.

As predicted, we found that all three variables were significantly correlated (all ps < 0.001). Our hypothesis was partially supported, as Social media use was found to be positively related to perceived social support, and perceived social support was positively related to satisfaction with life as we predicted. However, contrary to the predictions of our hypothesis, social media use was positively related to satisfaction with life. The relationship between social media use and perceived social support had a small effect (0.10 < r < 0.29; Cohen, 1988), the relationship between social media use and satisfaction with life had a trivial effect (r < 0.10), and the relationship between perceived social support and satisfaction with life had a medium effect (0.30 < r < 0.49). Please refer to the correlation coefficients reported in Table 1.

Table 1: Means, standard deviations, and correlations with confidence intervals
Variable M SD 1 2
Social Media Use 34.25 8.60
Perceived Social Support 5.53 1.13 .19**
[.15, .23]
Satisfaction with Life 4.43 1.33 .09** .46**
[.05, .13] [.43, .49]
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.