Measuring Social Class
Want to define a scale that incorporates multiple factors, acknowledging financial, occupational and educational factors, as well as the social construct (alignment) of social class. Below are the current factors:
| - NSECC Occupation Type |
| - SIMD Deprivation Index |
| - Education Level (ordinal scale) |
| - Household Income (ordinal scale)… potentially problematic |
| - Self-identification (ordinal) |
| **I have also considered parental occupation, but not in below example… |
Could have given weightings (proportions) of these 5 separate factors to create a final score. From this final score, we can assign them with a given social class?
‘What is your highest educational level?’
Can create an ordinal scale from this
1 (Did not leave high school with qualifications)
2 High school qual
3 College Qual
4 Undergraduate degree
5 Postgraduate degree
6 PhD
‘How would you describe your social class’
I have asked this in open-box question before and it leaves a lot of deliberation. Was more successful when given a drop-down box to choose.
Could be more fine-grained boxes with: lower WC, upper WC, lower MC, upper MC, lower UC, upper UC, not sure
From this, assigned ordinal values to be used.
sc <- data.frame(
PPS = c("LJ", "SW", "KJ", "RJ", "TJ"),
NSECC = c(7, 4, 3, 8, 2), # this will need to be inversed.
SIMD = c(1, 1, 9, 3, 7),
Education = c(5, 5, 4, 2, 6),
HI = c(4, 4, 5, 1, 3),
Self = c(2, 2, 3, 6, 1)
)
# Print the dataframe
print(sc)## PPS NSECC SIMD Education HI Self
## 1 LJ 7 1 5 4 2
## 2 SW 4 1 5 4 2
## 3 KJ 3 9 4 5 3
## 4 RJ 8 3 2 1 6
## 5 TJ 2 7 6 3 1# Inverse the NSECC score so that 1 = lowest, rather than current 9 = lowest
sc$NSECC <- (9 + 1) - sc$NSECC
# Standardising the scores across the ranking systems
# Maximum possible scores for each factor
max_scores <- c(
NSECC = 9,
SIMD = 10,
Education = 6,
Parent_occ = 9,
HI = 5,
Self = 6
)#Normalising the scores
sc$NSECC_norm <- sc$NSECC / max_scores["NSECC"]
sc$SIMD_norm <- sc$SIMD / max_scores["SIMD"]
sc$Education_norm <- sc$Education / max_scores["Education"]
sc$HI_norm <- sc$HI / max_scores["HI"]
sc$Self_norm <- sc$Self / max_scores["Self"]Note, I’m not sure whether we need to give self-identification a higher weighting or not?
Can go down the rabbit hole here of social class as a performative identity, and self-realisation of class will inherently have an impact on projected semiotic resources speaker believes to be aligning with their class (linguistic variables/perception)
# Assign weights of factors as percentages
weights <- c(
NSECC = 0.3, # Occupation = 30%
SIMD = 0.2, # Deprivation index = 30%
HI = 0.15, # Household income = 15%
#Parent_occ = 0.10,
Education = 0.15, # Education level = 15%
Self = 0.20 # self-identification = 10% - this will need to raise as its a performative measure
)##Ensure it does indeed count to 1, because I struggle with basic maths!
print(sum(weights)) #hoorah!## [1] 1# Calculate the weighted score using normalized values
sc$FinalScore <- with(sc,
NSECC_norm * weights["NSECC"] +
SIMD_norm * weights["SIMD"] +
Education_norm * weights["Education"] +
HI_norm * weights["HI"] +
Self_norm * weights["Self"]
)
print(sc)## PPS NSECC SIMD Education HI Self NSECC_norm SIMD_norm Education_norm HI_norm
## 1 LJ 3 1 5 4 2 0.3333333 0.1 0.8333333 0.8
## 2 SW 6 1 5 4 2 0.6666667 0.1 0.8333333 0.8
## 3 KJ 7 9 4 5 3 0.7777778 0.9 0.6666667 1.0
## 4 RJ 2 3 2 1 6 0.2222222 0.3 0.3333333 0.2
## 5 TJ 8 7 6 3 1 0.8888889 0.7 1.0000000 0.6
## Self_norm FinalScore
## 1 0.3333333 0.4316667
## 2 0.3333333 0.5316667
## 3 0.5000000 0.7633333
## 4 1.0000000 0.4066667
## 5 0.1666667 0.6800000Current thinking: that upper 20% (0:80 above) will be upper class. I then have 4 categories for working class and middle class that people will be assigned to.
*Now let’s classify our participants’ social class, based on these updated rules below:
sc$SocialClass <- ifelse(
sc$FinalScore >= 0.80, "Upper-Class", # Upper Class for FinalScores 0.81 and above
ifelse(sc$FinalScore <= 0.2, "Lower_Working-Class", # Lower Working Class
ifelse(sc$FinalScore <= 0.4, "Upper_Working-Class", # Upper Working Class
ifelse(sc$FinalScore <= 0.6, "Lower_Middle-Class", # Lower Middle Class
"Upper_Middle-Class" # Upper Middle Class for 0.61 to 0.80
)
)
)
)#Tadaaaaa… Here, participants are given a final scoring, and then a respective ranking of Social Class. I have inputted my own data (LJ) and seems to be quite accurate. But we could wiggle around the weightings of factors, and add/delete some of the factors.
# Print the dataframe with SocialClass assignment
print(sc)## PPS NSECC SIMD Education HI Self NSECC_norm SIMD_norm Education_norm HI_norm
## 1 LJ 3 1 5 4 2 0.3333333 0.1 0.8333333 0.8
## 2 SW 6 1 5 4 2 0.6666667 0.1 0.8333333 0.8
## 3 KJ 7 9 4 5 3 0.7777778 0.9 0.6666667 1.0
## 4 RJ 2 3 2 1 6 0.2222222 0.3 0.3333333 0.2
## 5 TJ 8 7 6 3 1 0.8888889 0.7 1.0000000 0.6
## Self_norm FinalScore SocialClass
## 1 0.3333333 0.4316667 Lower_Middle-Class
## 2 0.3333333 0.5316667 Lower_Middle-Class
## 3 0.5000000 0.7633333 Upper_Middle-Class
## 4 1.0000000 0.4066667 Lower_Middle-Class
## 5 0.1666667 0.6800000 Upper_Middle-Class