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

We predict that there will be a significant relationship between independence as a marker of adulthood, adult roles as a marker of adulthood, and experiences of emerging adulthood. 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':    3050 obs. of  7 variables:
##  $ ResponseID      : chr  "R_BJN3bQqi1zUMid3" "R_2TGbiBXmAtxywsD" "R_12G7bIqN2wB2N65" "R_39pldNoon8CePfP" ...
##  $ sibling         : chr  "at least one sibling" "at least one sibling" "at least one sibling" "at least one sibling" ...
##  $ usdream         : chr  "american dream is important and achievable for me" "american dream is important and achievable for me" "american dream is not important and maybe not achievable for me" "american dream is not important and maybe not achievable for me" ...
##  $ moa_independence: num  3.67 3.67 3.5 3 3.83 ...
##  $ moa_role        : num  3 2.67 2.5 2 2.67 ...
##  $ idea            : num  3.75 3.88 3.75 3.75 3.5 ...
##  $ swb             : num  4.33 4.17 1.83 5.17 3.67 ...
# 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(moa_independence, moa_role, idea))

# 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
## moa_independence    1 3050 3.54 0.46   3.67    3.61 0.49   1   4     3 -1.43
## moa_role            2 3050 2.97 0.72   3.00    3.00 0.74   1   4     3 -0.33
## idea                3 3050 3.58 0.38   3.62    3.62 0.37   1   4     3 -1.51
##                  kurtosis   se
## moa_independence     2.47 0.01
## moa_role            -0.84 0.01
## idea                 4.34 0.01
# 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$moa_independence)

hist(d$moa_role)

hist(d$idea)

# 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$moa_independence, d$moa_role)

plot(d$moa_independence, d$idea)

plot(d$moa_role, d$idea)

5 Check Your Assumptions

5.1 Pearson’s Correlation Coefficient Assumptions

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

sum(d2$moa_independence < -3 | d2$moa_independence > 3)
## [1] 49
d2$moa_role <- scale(d2$moa_role, center=T, scale=T)
hist(d2$moa_role)

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

sum(d2$idea < -3 | d2$idea > 3)
## [1] 33

5.2 Issues with My Data

One of my variables meets all of the assumptions of Pearson’s correlation coefficient. The variable independence had high kurtosis (2.47) and 49 outliers, and the variable idea had high kurtosis (4.34) and 33 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$moa_independence, d2$moa_role)

7 View Single Correlation

corr_output
## Call:corr.test(x = d2$moa_independence, y = d2$moa_role)
## Correlation matrix 
##      [,1]
## [1,] 0.64
## Sample Size 
## [1] 3050
## 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 
##                  moa_independence moa_role idea
## moa_independence             1.00     0.64 0.14
## moa_role                     0.64     1.00 0.07
## idea                         0.14     0.07 1.00
## Sample Size 
## [1] 3050
## Probability values (Entries above the diagonal are adjusted for multiple tests.) 
##                  moa_independence moa_role idea
## moa_independence                0        0    0
## moa_role                        0        0    0
## idea                            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 independence as a marker of adulthood, adult roles as a marker of adulthood, and experience of emerging adulthood would be positively correlated with one another, we calculated a series of Pearson’s correlation coefficients. One of the variables (adult roles as a marker of adulthood) met the required assumptions of the test, meeting the standards of normality and containing no outliers. The variable, independence, had high kurtosis (2.47) and 49 outliers, and the variable, idea, had high kurtosis (4.34) and 33 outliers, so any significant results involving independence and idea should be evaluated carefully.

As predicted, we found that all three variables were correlated (all ps < .001). The effect size of the correlation between independence and role was large (r > 0.50; Cohen, 1988). However, the effect size of independence and idea was weak (r = .14), and the effect size of role and idea was trivial (r = .07). Additionally, all variables were found to be positively related, 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
Independence as a Marker of Adulthood 3.54 0.46
Adult Roles as a Marker of Adulthood 2.97 0.72 .64**
[.62, .66]
Experiences of Emerging Adulthood 3.58 0.38 .14** .07**
[.11, .18] [.03, .10]
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.