APQ-17

Aging Perceptions Questionnaire 17 (APQ - 17) – Wave 3 / Wave 5 / Wave 6 Replenishment

Measuring: Self-perceptions of aging through 5 dimensions; Identity, Control, Consequences, Timeline, and Emotional Representations.

Number of Items: 17

Question Wording TILDA Variable Wave 3 TILDA Variable Wave 5 & 6r
I am always aware of my age. SCQAgePrc2
I always classify myself as old. SCQAgePrc3 SCQAgePrc3
I am always aware of the fact that I am getting older. SCQAgePrc4
I feel my age in everything that I do. SCQAgePrc5 SCQAgePrc5
As I get older I get wiser. SCQAgePrc6 SCQAgePrc6
As I get older I continue to grow as a person. SCQAgePrc7 SCQAgePrc7
As I get older I appreciate things more. SCQAgePrc8 SCQAgePrc8
I get depressed when I think about how ageing might affect the things that I can do. SCQAgePrc9 SCQAgePrc9
The quality of my social life in later years depends on me. SCQAgePrc10
The quality of my relationships with others in later life depends on me. SCQAgePrc11
Whether I continue living life to the full depends on me. SCQAgePrc12
Getting older makes me less independent. SCQAgePrc17 SCQAgePrc17
As I get older I can take part in fewer activities. SCQAgePrc19
As I get older I do not cope well with problems that arise. SCQAgePrc20 SCQAgePrc20
Slowing down with age is not something that I can control. SCQAgePrc21 SCQAgePrc21
How mobile I am in later life is not up to me. SCQAgePrc22
I have no control over the effects which getting older has on my social life. SCQAgePrc23
I get depressed when I think about getting older. SCQAgePrc25
I worry about the effects that getting older may have on my relationships. SCQAgePrc26
I go through cycles in which my experience of ageing gets better and worse. SCQAgePrc27
I feel angry when I think about getting older. SCQAgePrc29 SCQAgePrc29
I go through phases of feeling old. SCQAgePrc30
I go through phases of viewing myself as being old. SCQAgePrc32

Note Wave 3 17-item used different items to Wave 5 and Wave 6 Replenishment which have undergone factor analysis. The scales are not comparable.

Scoring Method:

Scored on a 7-point Likert scale.

1 = Strongly disagree

2 = Disagree

3 = Neither agree nor disagree

4 = Agree

5 = Strongly agree

Citation: Freeman, A. T., Santini, Z. I., Tyrovolas, S., Rummel-Kluge, C., Haro, J. M., & Koyanagi, A. (2016). Negative perceptions of ageing predict the onset and persistence of depression and anxiety: Findings from a prospective analysis of the Irish Longitudinal Study on Ageing (TILDA). Journal of affective disorders, 199, 132-138.

Example TILDA Papers:

Code for Wave 5/Wave 6r 17-item version (Wave 3 can be downloaded below)

* recode errors (multiple boxes ticked by R) and non-responders (-99) to missing
mvdecode SCQAgePrc*, mv(-99=. \ -812=. \ -823=. \ -834=. \ -845 =. \ 99=.)


gen recSCQAgePrc17=1 if SCQAgePrc17==5
replace recSCQAgePrc17=2 if SCQAgePrc17==4
replace recSCQAgePrc17=3 if SCQAgePrc17==3
replace recSCQAgePrc17=4 if SCQAgePrc17==2
replace recSCQAgePrc17=5 if SCQAgePrc17==1


gen recSCQAgePrc19=1 if SCQAgePrc19==5
replace recSCQAgePrc19=2 if SCQAgePrc19==4
replace recSCQAgePrc19=3 if SCQAgePrc19==3
replace recSCQAgePrc19=4 if SCQAgePrc19==2
replace recSCQAgePrc19=5 if SCQAgePrc19==1

gen recSCQAgePrc20=1 if SCQAgePrc20==5
replace recSCQAgePrc20=2 if SCQAgePrc20==4
replace recSCQAgePrc20=3 if SCQAgePrc20==3
replace recSCQAgePrc20=4 if SCQAgePrc20==2
replace recSCQAgePrc20=5 if SCQAgePrc20==1

gen recSCQAgePrc21=1 if SCQAgePrc21==5
replace recSCQAgePrc21=2 if SCQAgePrc21==4
replace recSCQAgePrc21=3 if SCQAgePrc21==3
replace recSCQAgePrc21=4 if SCQAgePrc21==2
replace recSCQAgePrc21=5 if SCQAgePrc21==1


gen recSCQAgePrc24=1 if SCQAgePrc24==5
replace recSCQAgePrc24=2 if SCQAgePrc24==4
replace recSCQAgePrc24=3 if SCQAgePrc24==3
replace recSCQAgePrc24=4 if SCQAgePrc24==2
replace recSCQAgePrc24=5 if SCQAgePrc24==1


gen timeline_chronic_sf = (SCQAgePrc3 + SCQAgePrc4 + SCQAgePrc5)
gen consequenses_positive_sf = (SCQAgePrc6 + SCQAgePrc7 + SCQAgePrc8)
gen control_positive_sf = (SCQAgePrc10 + SCQAgePrc11 + SCQAgePrc12)  
gen consequenses_negative_sf = (recSCQAgePrc17 + recSCQAgePrc19 + recSCQAgePrc20)
gen control_negative_sf = (recSCQAgePrc21 + recSCQAgePrc24)
gen emotional_representation_sf = (SCQAgePrc9 + SCQAgePrc26 + SCQAgePrc29)

gen MHapq_tl_accr_sf = (SCQAgePrc3 + SCQAgePrc4 + SCQAgePrc5)
gen MHapq_cqpve_sf = (SCQAgePrc6 + SCQAgePrc7 + SCQAgePrc8)
gen MHapq_ctpve_sf = (SCQAgePrc10 + SCQAgePrc11 + SCQAgePrc12)  
gen MHapq_cqnve_sf = (recSCQAgePrc17 + recSCQAgePrc19 + recSCQAgePrc20)
gen MHapq_ctnve_sf = (recSCQAgePrc21 + recSCQAgePrc24)
gen MHapq_emo_sf = (SCQAgePrc9 + SCQAgePrc26 + SCQAgePrc29)

drop recSCQAgePrc*

order MHapq_cqnve_sf MHapq_emo_sf MHapq_ctpve_sf MHapq_ctnve_sf MHapq_cqpve_sf, after(MHapq_tl_accr_sf)
lab var MHapq_tl_accr_sf "APQ Short - Timeline Chronic/Acute"
lab var MHapq_emo_sf "APQ Short - Emotional Representations"
lab var MHapq_ctpve_sf "APQ Short - Control Positive"
lab var MHapq_ctnve_sf "APQ Short - Control Negative"
lab var MHapq_cqpve_sf "APQ Short - Consequences Positive"
lab var MHapq_cqnve_sf "APQ Short - Consequences Negative"

Download W5/W6r Stata .do file Download W3 Stata .do file

apq_vars <- grep("^SCQAgePrc", names(data), value = TRUE)
special_missing <- c(-99, -812, -823, -834, -845, 99)

for (v in apq_vars) {
  data[[v]][data[[v]] %in% special_missing] <- NA_real_
}

# Reverse-scored items in the executable source.
for (i in c(17, 19, 20, 21, 24)) {
  src <- paste0("SCQAgePrc", i)
  dst <- paste0("recSCQAgePrc", i)
  data[[dst]] <- NA_real_
  ok <- !is.na(data[[src]]) & data[[src]] %in% 1:5
  data[[dst]][ok] <- 6 - data[[src]][ok]
}

# Intermediate subscale variables retained from the source.
data$timeline_chronic_sf <-
  data$SCQAgePrc3 + data$SCQAgePrc4 + data$SCQAgePrc5
data$consequenses_positive_sf <-
  data$SCQAgePrc6 + data$SCQAgePrc7 + data$SCQAgePrc8
data$control_positive_sf <-
  data$SCQAgePrc10 + data$SCQAgePrc11 + data$SCQAgePrc12
data$consequenses_negative_sf <-
  data$recSCQAgePrc17 + data$recSCQAgePrc19 + data$recSCQAgePrc20
data$control_negative_sf <-
  data$recSCQAgePrc21 + data$recSCQAgePrc24
data$emotional_representation_sf <-
  data$SCQAgePrc9 + data$SCQAgePrc26 + data$SCQAgePrc29

# TILDA short-form output variables.
data$MHapq_tl_accr_sf <-
  data$SCQAgePrc3 + data$SCQAgePrc4 + data$SCQAgePrc5
data$MHapq_cqpve_sf <-
  data$SCQAgePrc6 + data$SCQAgePrc7 + data$SCQAgePrc8
data$MHapq_ctpve_sf <-
  data$SCQAgePrc10 + data$SCQAgePrc11 + data$SCQAgePrc12
data$MHapq_cqnve_sf <-
  data$recSCQAgePrc17 + data$recSCQAgePrc19 + data$recSCQAgePrc20
data$MHapq_ctnve_sf <-
  data$recSCQAgePrc21 + data$recSCQAgePrc24
data$MHapq_emo_sf <-
  data$SCQAgePrc9 + data$SCQAgePrc26 + data$SCQAgePrc29

attr(data$MHapq_tl_accr_sf, "label") <- "APQ Short - Timeline Chronic/Acute"
attr(data$MHapq_emo_sf, "label") <- "APQ Short - Emotional Representations"
attr(data$MHapq_ctpve_sf, "label") <- "APQ Short - Control Positive"
attr(data$MHapq_ctnve_sf, "label") <- "APQ Short - Control Negative"
attr(data$MHapq_cqpve_sf, "label") <- "APQ Short - Consequences Positive"
attr(data$MHapq_cqnve_sf, "label") <- "APQ Short - Consequences Negative"

# Drop temporary reverse-scored variables.
data[grep("^recSCQAgePrc", names(data), value = TRUE)] <- NULL

# Match the Stata order command for the six labelled short-form variables.
ordered <- c("MHapq_tl_accr_sf", "MHapq_cqnve_sf", "MHapq_emo_sf",
             "MHapq_ctpve_sf", "MHapq_ctnve_sf", "MHapq_cqpve_sf")
rest <- setdiff(names(data), ordered)
insert_after <- match("MHapq_tl_accr_sf", names(data))
# If the variables already exist, place the six together in the requested order.
data <- data[c(rest[!rest %in% ordered], ordered)]

Download W5/W6r R file Download W3 R file

* Recode special missing/error values on the 17 items used in this short form.
RECODE SCQAgePrc3 SCQAgePrc4 SCQAgePrc5 SCQAgePrc6 SCQAgePrc7 SCQAgePrc8 SCQAgePrc9 SCQAgePrc10 SCQAgePrc11 SCQAgePrc12 SCQAgePrc17 SCQAgePrc19 SCQAgePrc20 SCQAgePrc21 SCQAgePrc24 SCQAgePrc26 SCQAgePrc29
  (-99 = SYSMIS) (-812 = SYSMIS) (-823 = SYSMIS)
  (-834 = SYSMIS) (-845 = SYSMIS) (99 = SYSMIS).

* The Stata source has a naming typo for item 24
* (recSCQAgePrc24w1 versus recSCQAgePrc24).
* This translation uses recSCQAgePrc24 so the intended score can run.

RECODE SCQAgePrc17 (5=1)(4=2)(3=3)(2=4)(1=5)(ELSE=SYSMIS) INTO recSCQAgePrc17.
RECODE SCQAgePrc19 (5=1)(4=2)(3=3)(2=4)(1=5)(ELSE=SYSMIS) INTO recSCQAgePrc19.
RECODE SCQAgePrc20 (5=1)(4=2)(3=3)(2=4)(1=5)(ELSE=SYSMIS) INTO recSCQAgePrc20.
RECODE SCQAgePrc21 (5=1)(4=2)(3=3)(2=4)(1=5)(ELSE=SYSMIS) INTO recSCQAgePrc21.
RECODE SCQAgePrc24 (5=1)(4=2)(3=3)(2=4)(1=5)(ELSE=SYSMIS) INTO recSCQAgePrc24.

COMPUTE timeline_chronic_sf =
  SCQAgePrc3 + SCQAgePrc4 + SCQAgePrc5.
COMPUTE consequenses_positive_sf =
  SCQAgePrc6 + SCQAgePrc7 + SCQAgePrc8.
COMPUTE control_positive_sf =
  SCQAgePrc10 + SCQAgePrc11 + SCQAgePrc12.
COMPUTE consequenses_negative_sf =
  recSCQAgePrc17 + recSCQAgePrc19 + recSCQAgePrc20.
COMPUTE control_negative_sf =
  recSCQAgePrc21 + recSCQAgePrc24.
COMPUTE emotional_representation_sf =
  SCQAgePrc9 + SCQAgePrc26 + SCQAgePrc29.

COMPUTE MHapq_tl_accr_sf =
  SCQAgePrc3 + SCQAgePrc4 + SCQAgePrc5.
COMPUTE MHapq_cqpve_sf =
  SCQAgePrc6 + SCQAgePrc7 + SCQAgePrc8.
COMPUTE MHapq_ctpve_sf =
  SCQAgePrc10 + SCQAgePrc11 + SCQAgePrc12.
COMPUTE MHapq_cqnve_sf =
  recSCQAgePrc17 + recSCQAgePrc19 + recSCQAgePrc20.
COMPUTE MHapq_ctnve_sf =
  recSCQAgePrc21 + recSCQAgePrc24.
COMPUTE MHapq_emo_sf =
  SCQAgePrc9 + SCQAgePrc26 + SCQAgePrc29.

VARIABLE LABELS
  MHapq_tl_accr_sf "APQ Short - Timeline Chronic/Acute"
  MHapq_emo_sf "APQ Short - Emotional Representations"
  MHapq_ctpve_sf "APQ Short - Control Positive"
  MHapq_ctnve_sf "APQ Short - Control Negative"
  MHapq_cqpve_sf "APQ Short - Consequences Positive"
  MHapq_cqnve_sf "APQ Short - Consequences Negative".

DELETE VARIABLES
  recSCQAgePrc17 recSCQAgePrc19 recSCQAgePrc20 recSCQAgePrc21 recSCQAgePrc24.

EXECUTE.

Download W5/W6r SPSS .sps file Download W3 SPSS .sps file