Everyday-Ageism
The Everyday Ageism Scale
Measuring: Measure routine ageism and distinguish it from other sources of everyday discrimination.
Number of Items: 10
| Item | Question Wording | TILDA Variable |
|---|---|---|
| 1 | I hear, see, and/or read jokes about old age, ageing, or older people. | SCQAgeism1 |
| 2 | I hear, see, and/or read things suggesting that older adults and ageing are unattractive and undesirable. | SCQAgeism2 |
| 3 | People insist on helping me with things I can do on my own. | SCQAgeism3 |
| 4 | People assume I have difficulty hearing and/or seeing things. | SCQAgeism4 |
| 5 | People assume I have difficulty remembering and/or understanding things. | SCQAgeism5 |
| 6 | People assume that I have difficulty with mobile phones and computers. | SCQAgeism6 |
| 7 | People assume I do not do anything important or valuable. | SCQAgeism7 |
| 8 | Feeling depressed, sad, or worried is part of getting older. | SCQAgeism8 |
| 9 | Feeling lonely is part of getting older. | SCQAgeism9 |
| 10 | Having health problems is part of getting older. | SCQAgeism10 |
Scoring Method:
Response options differ dependent on item. All 4-point Likert scale.
Items 1-7:
0 = Often
1 = Sometimes
2 = Rarely
3 = Never
Items 8 – 10
0 = Strongly agree
1 = Agree
2 = Disagree
3 = Strongly Disagree
All items are reverse coded.
A cumulative score is generated with higher scores representing higher exposure to everyday ageism (0-30).
Citation:
- Allen, J. O., Solway, E., Kirch, M., Singer, D., Kullgren, J. T., & Malani, P. N. (2022). The Everyday Ageism Scale: Development and Evaluation. Journal of aging and health, 34(2), 147–157. https://doi.org/10.1177/08982643211036131
Code
mvdecode (Ageism1 Ageism2 Ageism3 Ageism4 Ageism5 Ageism6 Ageism7 Ageism8 Ageism9 Ageism10), mv (-99=. \ -812=. \ -823=. \ -834 = . \ -845=. \ -856=. \ -867 = . \ -1=.)
foreach var in Ageism1 Ageism2 Ageism3 Ageism4 Ageism5 Ageism6 Ageism7 Ageism8 Ageism9 Ageism10 {
gen `var'_rev = 4 - `var'
}
fre Ageism1_rev Ageism2_rev Ageism3_rev Ageism4_rev Ageism5_rev Ageism6_rev Ageism7_rev Ageism8_rev Ageism9_rev Ageism10_rev
* Subscale: Exposure to Ageist Messages (0–6)
gen AgeismExposure = Ageism1_rev + Ageism2_rev
* Subscale: Interpersonal Ageism (0–15)
gen AgeismInterpersonal = Ageism3_rev + Ageism4_rev + Ageism5_rev + Ageism6_rev + Ageism7_rev
* Subscale: Internalized Ageism (0–9)
gen AgeismInternalised = Ageism8_rev + Ageism9_rev + Ageism10_rev
* Total Score (0–30)
gen AgeismTotal = AgeismExposure + AgeismInterpersonal + AgeismInternalised
codebook AgeismExposure
fre AgeismExposure
codebook AgeismInternalised
fre AgeismInternalised
codebook AgeismInterpersonal
fre AgeismInterpersonal
codebook AgeismTotal
fre AgeismTotal
lab var AgeismExposure "Everyday-Ageism scale: Exposure to ageist messaging"
lab var AgeismInterpersonal "Everyday-Ageism scale: Ageism in interpersonal interactions"
lab var AgeismInternalised "Everyday-Ageism scale: Internalised Ageism"
lab var AgeismTotal "Total scores of Everyday-Ageism Scale"ageism_items <- paste0("Ageism", 1:10)
special_missing <- c(-99, -812, -823, -834, -845, -856, -867, -1)
for (v in ageism_items) {
data[[v]][data[[v]] %in% special_missing] <- NA_real_
data[[paste0(v, "_rev")]] <- 4 - data[[v]]
}
rev <- paste0(ageism_items, "_rev")
for (v in rev) print(table(data[[v]], useNA = "ifany"))
# Ordinary addition matches the Stata source: any missing contributing item
# makes the corresponding subscale/total missing.
data$AgeismExposure <- data$Ageism1_rev + data$Ageism2_rev
data$AgeismInterpersonal <-
data$Ageism3_rev + data$Ageism4_rev + data$Ageism5_rev +
data$Ageism6_rev + data$Ageism7_rev
data$AgeismInternalised <-
data$Ageism8_rev + data$Ageism9_rev + data$Ageism10_rev
data$AgeismTotal <-
data$AgeismExposure + data$AgeismInterpersonal + data$AgeismInternalised
attr(data$AgeismExposure, "label") <-
"Everyday-Ageism scale: Exposure to ageist messaging"
attr(data$AgeismInterpersonal, "label") <-
"Everyday-Ageism scale: Ageism in interpersonal interactions"
attr(data$AgeismInternalised, "label") <-
"Everyday-Ageism scale: Internalised Ageism"
attr(data$AgeismTotal, "label") <-
"Total scores of Everyday-Ageism Scale"
summary(data[c(
"AgeismExposure", "AgeismInternalised", "AgeismInterpersonal", "AgeismTotal"
)])RECODE Ageism1 Ageism2 Ageism3 Ageism4 Ageism5 Ageism6 Ageism7 Ageism8 Ageism9 Ageism10
(-99=SYSMIS) (-812=SYSMIS) (-823=SYSMIS) (-834=SYSMIS)
(-845=SYSMIS) (-856=SYSMIS) (-867=SYSMIS) (-1=SYSMIS).
DO REPEAT src = Ageism1 Ageism2 Ageism3 Ageism4 Ageism5 Ageism6 Ageism7 Ageism8 Ageism9 Ageism10
/dst = Ageism1_rev Ageism2_rev Ageism3_rev Ageism4_rev Ageism5_rev Ageism6_rev Ageism7_rev Ageism8_rev Ageism9_rev Ageism10_rev.
COMPUTE dst = 4 - src.
END REPEAT.
FREQUENCIES VARIABLES=Ageism1_rev Ageism2_rev Ageism3_rev Ageism4_rev Ageism5_rev Ageism6_rev Ageism7_rev Ageism8_rev Ageism9_rev Ageism10_rev /MISSING=INCLUDE.
COMPUTE AgeismExposure = Ageism1_rev + Ageism2_rev.
COMPUTE AgeismInterpersonal =
Ageism3_rev + Ageism4_rev + Ageism5_rev + Ageism6_rev + Ageism7_rev.
COMPUTE AgeismInternalised =
Ageism8_rev + Ageism9_rev + Ageism10_rev.
COMPUTE AgeismTotal =
AgeismExposure + AgeismInterpersonal + AgeismInternalised.
VARIABLE LABELS
AgeismExposure "Everyday-Ageism scale: Exposure to ageist messaging"
AgeismInterpersonal "Everyday-Ageism scale: Ageism in interpersonal interactions"
AgeismInternalised "Everyday-Ageism scale: Internalised Ageism"
AgeismTotal "Total scores of Everyday-Ageism Scale".
DESCRIPTIVES VARIABLES=
AgeismExposure AgeismInternalised AgeismInterpersonal AgeismTotal.
EXECUTE.