adhd_avg_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...
- Completed
- Partial
- No data
About statuses
ALSPAC | BIB | 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 | unmapped |
adhd_avg_1 | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | partial | unmapped | unmapped | partial | unmapped | unmapped |
adhd_avg_2 | unmapped | unmapped | unmapped | unmapped | complete | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | partial | unmapped | unmapped |
adhd_avg_3 | complete | unmapped | unmapped | unmapped | complete | unmapped | unmapped | unmapped | complete | partial | unmapped | unmapped | partial | unmapped | complete |
adhd_avg_4 | complete | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | complete | unmapped | unmapped | complete | unmapped | unmapped | complete |
adhd_avg_5 | complete | unmapped | complete | unmapped | complete | complete | complete | unmapped | complete | partial | unmapped | unmapped | complete | complete | complete |
adhd_avg_6 | complete | complete | unmapped | unmapped | complete | complete | complete | unmapped | complete | unmapped | unmapped | unmapped | complete | unmapped | unmapped |
adhd_avg_7 | complete | complete | unmapped | complete | complete | unmapped | complete | complete | complete | unmapped | unmapped | unmapped | complete | unmapped | unmapped |
adhd_avg_8 | complete | complete | unmapped | complete | complete | unmapped | complete | complete | complete | complete | unmapped | unmapped | complete | complete | unmapped |
adhd_avg_9 | complete | complete | unmapped | unmapped | unmapped | unmapped | complete | complete | complete | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped |
adhd_avg_10 | complete | complete | complete | unmapped | unmapped | unmapped | complete | complete | complete | unmapped | unmapped | unmapped | unmapped | complete | unmapped |
adhd_avg_11 | complete | complete | complete | complete | unmapped | unmapped | complete | unmapped | complete | unmapped | unmapped | unmapped | complete | unmapped | unmapped |
adhd_avg_12 | complete | complete | unmapped | complete | unmapped | unmapped | complete | unmapped | complete | unmapped | unmapped | unmapped | complete | unmapped | unmapped |
adhd_avg_13 | complete | complete | unmapped | complete | unmapped | unmapped | complete | unmapped | unmapped | unmapped | unmapped | complete | complete | complete | unmapped |
adhd_avg_14 | complete | complete | unmapped | complete | unmapped | unmapped | complete | unmapped | unmapped | unmapped | complete | unmapped | complete | unmapped | unmapped |
adhd_avg_15 | complete | unmapped | unmapped | unmapped | unmapped | unmapped | complete | unmapped | unmapped | unmapped | complete | unmapped | unmapped | unmapped | unmapped |
adhd_avg_16 | complete | unmapped | unmapped | unmapped | unmapped | unmapped | complete | unmapped | unmapped | unmapped | complete | unmapped | unmapped | complete | unmapped |
adhd_avg_17 | complete | unmapped | unmapped | unmapped | unmapped | unmapped | complete | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped |
adhd_avg_18 | unmapped | unmapped | unmapped | complete | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped |
adhd_avg_19 | unmapped | unmapped | unmapped | complete | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped | unmapped |
adhd_avg_20 | unmapped | 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 | unmapped |
Harmonisation details per Cohort
Select a Cohort to see the details of the harmonisation...
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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