adhd_avg_0

average of available items comprising the total raw score of adhd_raw_0

average of available items comprising the total raw score of adhd_raw_0...

Description

Format: continuous
N repeats: 21

Harmonisation status per Cohort

Overview of the harmonisation status per Cohort

Overview of the harmonisation status per Cohort...

Completed
Partial
No data

	ALSPAC	CHOP	DNBC	EDEN	ELFE	ELSPAC	GenR	INMA	MoBa	NFBC1986	NINFEA	PELAGIE	RAINE	RHEA
adhd_avg_0	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped
adhd_avg_1	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	partial	unmapped	unmapped	partial	unmapped	unmapped
adhd_avg_2	unmapped	unmapped	unmapped	complete	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	partial	unmapped	unmapped
adhd_avg_3	complete	unmapped	unmapped	complete	unmapped	unmapped	unmapped	complete	partial	unmapped	unmapped	partial	unmapped	complete
adhd_avg_4	complete	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	complete	unmapped	unmapped	complete	unmapped	unmapped	complete
adhd_avg_5	complete	complete	unmapped	complete	complete	complete	unmapped	complete	partial	unmapped	unmapped	complete	complete	complete
adhd_avg_6	complete	unmapped	unmapped	complete	complete	complete	unmapped	complete	unmapped	unmapped	unmapped	complete	unmapped	unmapped
adhd_avg_7	complete	unmapped	complete	complete	unmapped	complete	complete	complete	unmapped	unmapped	unmapped	complete	unmapped	unmapped
adhd_avg_8	complete	unmapped	complete	complete	unmapped	complete	complete	complete	complete	unmapped	unmapped	complete	complete	unmapped
adhd_avg_9	complete	unmapped	unmapped	unmapped	unmapped	complete	complete	complete	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped
adhd_avg_10	complete	complete	unmapped	unmapped	unmapped	complete	complete	complete	unmapped	unmapped	unmapped	unmapped	complete	unmapped
adhd_avg_11	complete	complete	complete	unmapped	unmapped	complete	unmapped	complete	unmapped	unmapped	unmapped	complete	unmapped	unmapped
adhd_avg_12	complete	unmapped	complete	unmapped	unmapped	complete	unmapped	complete	unmapped	unmapped	unmapped	complete	unmapped	unmapped
adhd_avg_13	complete	unmapped	complete	unmapped	unmapped	complete	unmapped	unmapped	unmapped	unmapped	complete	complete	complete	unmapped
adhd_avg_14	complete	unmapped	complete	unmapped	unmapped	complete	unmapped	unmapped	unmapped	complete	unmapped	complete	unmapped	unmapped
adhd_avg_15	complete	unmapped	unmapped	unmapped	unmapped	complete	unmapped	unmapped	unmapped	complete	unmapped	unmapped	unmapped	unmapped
adhd_avg_16	complete	unmapped	unmapped	unmapped	unmapped	complete	unmapped	unmapped	unmapped	complete	unmapped	unmapped	complete	unmapped
adhd_avg_17	complete	unmapped	unmapped	unmapped	unmapped	complete	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped
adhd_avg_18	unmapped	unmapped	complete	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped
adhd_avg_19	unmapped	unmapped	complete	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped
adhd_avg_20	unmapped	unmapped	complete	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped
adhd_avg_21	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped	unmapped

Harmonisation details per Cohort

Select a Cohort to see the details of the harmonisation

Select a Cohort to see the details of the harmonisation...

ALSPAC

CHOP

DNBC

EDEN

ELFE

ELSPAC

GenR

INMA

MoBa

NFBC1986

NINFEA

PELAGIE

RAINE

RHEA

Name: adhd_avg_0
Harmonisation status: No data
Description: None
Variables used: None
Syntax: None

Name: adhd_avg_1
Harmonisation status: No data
Description: None
Variables used: None
Syntax: None

Name: adhd_avg_2
Harmonisation status: No data
Description: None
Variables used: None
Syntax: None

Name: adhd_avg_3
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_4
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_5
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_6
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_7
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_8
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_9
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_10
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_11
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_12
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_13
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_14
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_15
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_16
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_17
Harmonisation status: Completed
Description: Reverse-scored items correctly coded prior to harmonisation. Harmonisation works in three steps: (i) variable lists are created of the variables contributing to the score at each questionnaire age, (ii) the average score used to pro-rate score is derived by taking the mean score for each item (dropping missing values), (iii) the "exactAge" function is used to recode the intermediate questionnaire score to correct age bands based on the exact age of the child at assessment.
Variables used: tc4001
kq334
ta7009
ku700
tc4009
tc4024
tc4014
kq340
j550
n8354
j544
j531
n8364
n8341
j539
ku694
ku689
j554
ku681
ta7020
kq344
ta7024
ta7001
ku704
tc4020
n8360
kq329
kq321
n8349
ta7014
Syntax

## Create variable lists
sdq_adhd_j.vars <- c("j531", "j539", "j544", "j550", "j554")
sdq_adhd_kq.vars <- c("kq321", "kq329", "kq334", "kq340", "kq344")
sdq_adhd_n.vars <- c("n8341", "n8349", "n8354", "n8360", "n8364")
sdq_adhd_ku.vars <- c("ku681", "ku689", "ku694", "ku700", "ku704")
sdq_adhd_kw.vars <- c("kw6501", "kw6509", "kw6514", "kw6520", "kw6524")
sdq_adhd_ta.vars <- c("ta7001", "ta7009", "ta7014", "ta7020", "ta7024")
sdq_adhd_tc.vars <- c("tc4001", "tc4009", "tc4014", "tc4020", "tc4024")

## Calculate based on average questionnaire age
wp6_high.data %<>% 
  mutate(
    adhd_avg_j = rowMeans(.[, sdq_adhd_j.vars], na.rm = TRUE),
    dhd_avg_kq = rowMeans(.[, sdq_adhd_kq.vars], na.rm = TRUE),
    adhd_avg_n = rowMeans(.[, sdq_adhd_n.vars], na.rm = TRUE),
    adhd_avg_ku = rowMeans(.[, sdq_adhd_ku.vars], na.rm = TRUE),
    adhd_avg_kw = rowMeans(.[, sdq_adhd_kw.vars], na.rm = TRUE),
    adhd_avg_ta = rowMeans(.[, sdq_adhd_ta.vars], na.rm = TRUE),
    adhd_avg_tc = rowMeans(.[, sdq_adhd_tc.vars], na.rm = TRUE))

## Convert to correct age bands
wp6_high.data <- exactAge(
    data = wp6_high.data, lc_prefix = "adhd_avg", quest = "both", sep = "_", 
    grouping = "age", order = "ascending", create_blank = FALSE,
    wp6_age_out = TRUE)

Name: adhd_avg_18
Harmonisation status: No data
Description: None
Variables used: None
Syntax: None

Name: adhd_avg_19
Harmonisation status: No data
Description: None
Variables used: None
Syntax: None

Name: adhd_avg_20
Harmonisation status: No data
Description: None
Variables used: None
Syntax: None

Name: adhd_avg_21
Harmonisation status: No data
Description: None
Variables used: None
Syntax: None