What is Stress and Cognitive Failures?

Stress is a casual word that we often hear on our everyday life, usually it’s perceived as a state of mental or emotional tension. Stress can be positive (eustress), such as when it motivates you to take action to avoid danger. But stress can also be negative (distress), especially acute/chronic stress over a long period of time.

Perceived stress (PS) represents the psychological perception of environmental demands exceeding individual coping resources and is a core component of the stress process, resulting in adverse cognitive and physical outcomes. It is suggested that stress can affect brain and cognitive functions throughout one’s lifespan. Stress is usually considered to be a risk factor for cognitive decline and impairment.

Cognitive failures (CF) is a cognitive error (mental errors) occurring during the performance of a task that a person would normally execute successfully in everyday life. The common element of cognitive failures is departure from the normal smooth flow of thought function, where events do not proceed in accordance with intention. We see it in everyday slips and errors, where they may involve perceptual failures, or failures of memory, or actions which are misdirected.

It is important to see how someone’s perceived stress impact their cognitive process, especially their cognition failures.

About the Data

The data is obtained from Inter-university Consortium for Political and Social Research open-source repository. In this data, 435 Chinese adolescents were tested with perceived stress scale, cognitive failure scale, negative affect scale, and mobile phone addiction scale.

We can talk a little about two remaining variables that is also observed that is:

Negative affect (NA) more specifically, it is a construct that is defined by the common variance between anxiety, sadness, fear, anger, guilt and shame, irritability, and other unpleasant emotions.
Although there is still no certain definition when we talk about mobile phone addiction (PA), generally the term Problematic Smartphone Use are used for this construct instead. It involves addiction-like symptoms such as daily life disturbances, withdrawal, positive anticipation, overuse of mobile phone but it’s not clear if it’s a pathological construct.

Loading library

# Data Wrangling
library(haven)
library(dplyr)
library(tidyr)
library(labelled)
library(tidyverse)

# Plotting
library(ggplot2)
library(plotly)
library(glue)
library(GGally)

Reading data

stress <- read_sav("Perceived stress and cognitive failures/data.sav")
head(stress)

Data Wrangling

The data consists of individual score of each item for four construct scales (perceived stress scale, cognitive failure scale, negative affect scale, and mobile phone addiction scale). We will be taking only the cumulative score.

stress_cln <- stress %>%
    select(sex, age, PS, CF, `NA`, PA)

head(stress_cln)

Because from the head() function above we can see that the datas are already on their intended formats, we will proceed ahead. Next, we will be checking if there is a missing value in the data

colSums(is.na(stress_cln))

sex age  PS  CF  NA  PA 
  0   0   0   0   0   0

Because there is no missing data, we will do further data wrangling like unlabelling the coded sex data

stress_cln$sexc <- labelled(stress_cln$sex, c(Male = 1, Female = 2)) %>%
    to_factor()

Now we can see the brief summary of our data

summary(stress_cln %>%
    select(-sex))  # excluding the numeric coded sex

      age              PS              CF               NA       
 Min.   :14.00   Min.   :18.00   Min.   : 24.00   Min.   :10.00  
 1st Qu.:14.00   1st Qu.:37.00   1st Qu.: 53.50   1st Qu.:20.00  
 Median :15.00   Median :42.00   Median : 62.00   Median :24.00  
 Mean   :15.31   Mean   :41.16   Mean   : 61.44   Mean   :24.78  
 3rd Qu.:16.00   3rd Qu.:45.00   3rd Qu.: 69.50   3rd Qu.:30.00  
 Max.   :17.00   Max.   :64.00   Max.   :119.00   Max.   :50.00  
       PA            sexc    
 Min.   :17.00   Male  :236  
 1st Qu.:39.50   Female:199  
 Median :46.00               
 Mean   :47.48               
 3rd Qu.:55.00               
 Max.   :85.00

Inferences

Our data has no significant outliers, we can see the median and mean has no significant difference in value.
Although our male and female respondent is not exactly balanced, less than 50 participant differences is still acceptable.

Categorizing and Visualizing Our Data

Usually, we can do empirical means to categorize how high/low someone’s score on a certain scale. We will do that to each respective scale. For psychological variables we usually do:

High: X > Mean + 1SD
Middle: High < X < Low
Low: X < Mean - 1SD

sort_func <- function(x, mean_value, sd_value) {
    upper_limit <- mean_value + sd_value
    lower_limit <- mean_value - sd_value

    if (x > upper_limit) {
        return("High")
    } else if (x < lower_limit) {
        return("Low")
    } else {
        return("Middle")
    }
}

# Perceived Stress (PS)
mean_ps <- mean(stress_cln$PS)
sd_ps <- sd(stress_cln$PS)

stress_cln$ps_labelled <- sapply(X = stress_cln$PS, FUN = sort_func, mean_value = mean_ps,
    sd_value = sd_ps)

# Cognitive Failure (CF)
mean_cf <- mean(stress_cln$CF)
sd_cf <- sd(stress_cln$CF)

stress_cln$cf_labelled <- sapply(X = stress_cln$CF, FUN = sort_func, mean_value = mean_cf,
    sd_value = sd_cf)

# Negative Emotion (NE)
mean_ne <- mean(stress_cln$`NA`)
sd_ne <- sd(stress_cln$`NA`)

stress_cln$ne_labelled <- sapply(X = stress_cln$`NA`, FUN = sort_func, mean_value = mean_ne,
    sd_value = sd_ne)

# Phone Addiction (PA)
mean_pa <- mean(stress_cln$PA)
sd_pa <- sd(stress_cln$PA)

stress_cln$pa_labelled <- sapply(X = stress_cln$PA, FUN = sort_func, mean_value = mean_pa,
    sd_value = sd_pa)

Let’s visualize each of our variable!

Correlation for each variables

Now let’s see if each variables are correlated via plot

ggcorr(stress_cln %>%
    select(sex, age, PS, CF, `NA`, PA))

Inferences

Sex and Age are not correlated with any of our observed variables
Our observed variables (perceived stress, cognitive failure, negative emotion, phone addiction) are correlated one another

To make sure, let’s check the number

round(cor(stress_cln[, c("PS", "CF", "NA", "PA")]), 3) %>%
    kable(format = "html", table.attr = "style='width:70%;'") %>%
    kable_styling()

	PS	CF	NA	PA
PS	1.000	0.418	0.584	0.396
CF	0.418	1.000	0.521	0.424
NA	0.584	0.521	1.000	0.449
PA	0.396	0.424	0.449	1.000

Inferences

Variables with strongest correlation (>0.5) is Perceived Stress and Negative Emotion also Cognitive Failures and Negative Emotion
The higher our Negative Affect, the higher our Perceived Stress and Cognitive Failures are

Conclusions

These findings might just show the important role that negative emotions plays in perceived stress and cognitive failures. But in our everyday life, we can’t just avoid negative emotions because arguably negative emotions are unavoidable. So the key here lies on how we can manage our negative emotions as to avoid heightened perceived stress and cognitive failures in our day to day life. Knowledge of how to manage our emotions well, especially the negative ones, is something that needs to be taught whether it’s from one family member to the other, a teacher to their students or from HR to employees.

Reference

Broadbent, D. E., Cooper, P. F., FitzGerald, P., & Parkes, K. R. (1982). The Cognitive Failures Questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21(1), 1–16. https://doi.org/10.1111/j.2044-8260.1982.tb01421.x
Chen, Y., Liang, Y., Zhang, W., Crawford, J. C., Sakel, K. L., & Dong, X. (2019). Perceived stress and cognitive decline in chinese‐american older adults. Journal of the American Geriatrics Society, 67(S3). https://doi.org/10.1111/jgs.15606
Tiro, J., Lee, S. J., Lipshultz, S. E., Miller, T. L., Wilkinson, J. D., Mestre, M. A., Resnick, B., Miller, D., Fernandez, C. A., Lee, D. J., Hall, M. H., Young-Hyman, D. L., Young-Hyman, D. L., Pellowski, J., Resnick, B., Bustillo, N. E., Pellowski, J., Bryant, V., Frankel, A., … Gambert, S. (2013). Negative affect. Encyclopedia of Behavioral Medicine, 1303–1304. https://doi.org/10.1007/978-1-4419-1005-9_606
Zhang, Yingying. 2020 Perceived Stress and Cognitive Failure in Chinese Adolescents. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-27. https://doi.org/10.3886/E127521V1

Perceived Stress and Cognitive Failure

Ifa

2023-08-14

What is Stress and Cognitive Failures?

About the Data

Data Wrangling

Categorizing and Visualizing Our Data

Correlation for each variables

Now let’s see if each variables are correlated via plot

To make sure, let’s check the number

Conclusions

Reference