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 table

2 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 semester

3 State Your Hypothesis

There will be a significant relationship between stress, pandemic anxiety, and loneliness. Specifically, all three variables will be positively related.

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':    674 obs. of  7 variables:
##  $ X          : int  520 2814 3146 3295 717 6056 4753 5365 2044 1965 ...
##  $ age        : chr  "1 under 18" "1 under 18" "1 under 18" "1 under 18" ...
##  $ mhealth    : chr  "none or NA" "none or NA" "none or NA" "none or NA" ...
##  $ pss        : num  2.75 2.25 3 2 1.75 2 1 1.25 3 1.25 ...
##  $ pas_covid  : num  3 3.44 4.67 2.44 1.56 ...
##  $ isolation_c: num  1 1 1 1 1 1 1 1 1 1 ...
##  $ mfq_26     : num  2.7 4.55 4.8 3.8 4.5 4 5.8 4.2 4.5 5.25 ...
# 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(isolation_c, pas_covid, pss))

# 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
## isolation_c    1 674 2.29 0.81   2.25    2.30 1.11   1 3.5   2.5 -0.02    -1.24
## pas_covid      2 674 3.24 0.68   3.33    3.26 0.66   1 5.0   4.0 -0.29     0.16
## pss            3 674 3.13 0.94   3.25    3.15 1.11   1 5.0   4.0 -0.13    -0.73
##               se
## isolation_c 0.03
## pas_covid   0.03
## pss         0.04
# NOTE: Our fake variable has high kurtosis, which we'll ignore for the lab because we created it to be problematic. 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$isolation_c)

hist(d$pas_covid)

hist(d$pss)

# 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$isolation_c, d$pas_covid)

plot(d$isolation_c, d$pss)

plot(d$pss, d$pas_covid)

5 Check Your Assumptions

5.1 Pearson’s Correlation Coefficient Assumptions

  • Should have all measurement 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$isolation_c <- scale(d2$isolation_c, center=T, scale=T)
hist(d2$isolation_c)

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

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

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

5.2 Issues with My Data

Two of my variables meet all of the assumptions of Pearson’s correlation coefficient. One variable, pandemic anxiery had 1 outlier. 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$isolation_c, d2$pss)

7 View Single Correlation

corr_output
## Call:corr.test(x = d2$isolation_c, y = d2$pss)
## Correlation matrix 
##      [,1]
## [1,] 0.68
## Sample Size 
## [1] 674
## 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 
##             isolation_c pas_covid  pss
## isolation_c        1.00      0.30 0.68
## pas_covid          0.30      1.00 0.34
## pss                0.68      0.34 1.00
## Sample Size 
## [1] 674
## Probability values (Entries above the diagonal are adjusted for multiple tests.) 
##             isolation_c pas_covid pss
## isolation_c           0         0   0
## pas_covid             0         0   0
## pss                   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 stress, pandemic anxiety, and loneliness would be correlated with one another, we calculated a series of Pearson’s correlation coefficients. Two of the variables (stress and loneliness) met the required assumptions of the test, with both meeting the standards of normality and containing no outliers. One variable, pandemic anxiety, had 1 outlier; so any significant results involving fakeness should be evaluated carefully.

As predicted, we found that all three variables were significantly correlated (all ps < 0.001). The effect sizes of all correlations were large and moderate (rs > 0.50; Cohen, 1988). Additionally, stress, pandemic anxiety, and loneliness were all positively correlated to one another, as predicted. 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
Stress 3.13 0.94
Pandemic Anxiety 3.24 0.68 .34**
[.28, .41]
Loneliness 2.29 0.81 .68** .30**
[.64, .72] [.23, .37]
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.