| Title: | Package with functions to calculate components and sums for LCBC questionnaires |
|---|---|
| Description: | Creates summaries and factorials of answers to questionnaires. |
| Authors: | Athanasia Mo Mowinckel [aut, cre]
|
| Maintainer: | Athanasia Mo Mowinckel <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.0.3 |
| Built: | 2024-11-03 04:12:42 UTC |
| Source: | https://github.com/LCBC-UiO/questionnaires |
Beck Depression Inventory-II (BDI-II) is one of the most widely used instruments for measuring the severity of self-reported depression in adolescents and adults. As a general rule, BDI-II is administrated in LCBC to adults with an upper cut off around 60 years, while depression in older adults is assessed with the Geriatric Depression Scale (GDS). However, please consult the instructions for each project, as this guideline has been implemented at different time points across the projects.
The questionnaire consists of 21 statements, each reflecting a depression symptom or attitude which could be rated from 0 to 3 in terms of intensity. The answers should be based the participant´s feelings throughout the last week, including the day of filling out the form. The sum of the scores of the items (0-3) yields one total score, with a possible range between 0 and 62.
| Score | Category |
| 0-10 | These ups and downs are considered to be normal |
| 11-16 | Mild mood disturbance |
| 17 – 20 | Borderline clinical disturbance |
| 21 – 30 | Moderate depression |
| 31 – 40 | Severe depression |
| Above 40 | Extreme depression |
If a participant scores >= 17, we should consider contacting the participant to follow up on this and offer making a note for the participant's doctor describing the scores.
By default, the functions assume that columns have names in the manner of bdi_XX where XX is a zero-padded (i.e. zero in front of numbers below 9, eg. 09) question number of the inventory.
You may have column names in another format, but in that case you will need to supply to the functions the names of those columns using tidy-selectors (see the tidyverse packages for this).
The columns should adhere to some naming logic that is easy to specify.
The values in the columns should be the item number of the question that was answered (i.e. 0, 1, 2, or 3).
The inventory allows subjects to respond to several options per question, in the case of this, the mean of the responded alternatives should be applied.
Aaron T.Beck, Robert A.Steer, Margery G.Carbin (1988) Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation, Clinical Psychology Review, Volume 8, Issue 1, Pages 77-100, doi: 10.1016/0272-7358(88)90050-5
Robert A.Steer, David J.Rissmiller, Aaron T.Beck (2000) Use of the Beck Depression Inventory-II with depressed geriatric inpatients Behaviour Research and Therapy Volume 38, Issue 3,Pages 311-318, doi: https://doi.org/10.1016/S0005-7967(99)00068-6
Groth-Marnat G. (1990). The handbook of psychological assessment (2nd ed.). New York: John Wiley & Sons.
bdi_compute( data, cols = matches("bdi_[0-9][0-9]$"), max_missing = 0, prefix = "bdi_", keep_all = TRUE ) bdi_compute_sum(data, cols = matches("bdi_[0-9][0-9]$"), max_missing = 0) bdi_factorise(bdi_sum)bdi_compute( data, cols = matches("bdi_[0-9][0-9]$"), max_missing = 0, prefix = "bdi_", keep_all = TRUE ) bdi_compute_sum(data, cols = matches("bdi_[0-9][0-9]$"), max_missing = 0) bdi_factorise(bdi_sum)
data |
Data containing BDI data |
cols |
Columns that contain BDI data |
max_missing |
Maximum number of components allowed to be missing.
Defaults to "0", and will return |
prefix |
string to prefix column names of computed values |
keep_all |
logical, append to data.frame |
bdi_sum |
Sum of BDI questions, as summed by bdi_compute_sum |
data.frame
bdi_compute_sum(): Compute the BDI sum based on a data.frame containing the BDI data. Returns a numeric vector.
bdi_factorise(): Create a factor based on the BDI sum, with the cut-off points as described in original paper.
# Example of treatment of missing values library(dplyr) library(questionnaires) data <- tibble( bdi_01 = c(1, NA_real_, NA_real_, 2, 1), bdi_02 = c(1, 1, NA_real_, 2, NA_real_) ) # Row with all components missing, gets sum 0 bind_cols(data, bdi_sum = bdi_compute_sum(data)) # Do not allow any missing values bind_cols(data, bdi_sum = bdi_compute_sum(data, max_missing = 0)) # Allow one missing value bind_cols(data, bdi_sum = bdi_compute_sum(data, max_missing = 2))# Example of treatment of missing values library(dplyr) library(questionnaires) data <- tibble( bdi_01 = c(1, NA_real_, NA_real_, 2, 1), bdi_02 = c(1, 1, NA_real_, 2, NA_real_) ) # Row with all components missing, gets sum 0 bind_cols(data, bdi_sum = bdi_compute_sum(data)) # Do not allow any missing values bind_cols(data, bdi_sum = bdi_compute_sum(data, max_missing = 0)) # Allow one missing value bind_cols(data, bdi_sum = bdi_compute_sum(data, max_missing = 2))
If data come from Nettskjema, the structure is in wide format, with each question option as columns, creating 21*4 columns of data. This function allows you to gather and create single columns for questions.
bdi_restructure(data, cols = matches("[0-9]_[0-9]"), sep = "_")bdi_restructure(data, cols = matches("[0-9]_[0-9]"), sep = "_")
data |
Data containing BDI data |
cols |
Columns that contain BDI data |
sep |
separator to use for the column names |
The columns must adhere to some specific logic to work. It is recommended that the column names are in the format bdi_01_0 bdi_01_1 bdi_01_2 bdi_01_3, where the first two numbers are the question number, and the last number is the option number.
data frame
dat <- data.frame( ID = 1:4, bdi_01_0 = c(NA,1, NA, NA), bdi_01_1 = c(1, NA, 1, NA), bdi_01_2 = c(NA, NA, 1, NA), bdi_01_3 = c(NA, NA, NA, NA), bdi_02_0 = c(1, NA, NA, NA), bdi_02_1 = c(NA,NA, NA, NA), bdi_02_2 = c(NA,1, NA, NA), bdi_02_3 = c(NA, NA, NA, 1) ) bdi_restructure(dat)dat <- data.frame( ID = 1:4, bdi_01_0 = c(NA,1, NA, NA), bdi_01_1 = c(1, NA, 1, NA), bdi_01_2 = c(NA, NA, 1, NA), bdi_01_3 = c(NA, NA, NA, NA), bdi_02_0 = c(1, NA, NA, NA), bdi_02_1 = c(NA,NA, NA, NA), bdi_02_2 = c(NA,1, NA, NA), bdi_02_3 = c(NA, NA, NA, 1) ) bdi_restructure(dat)
The BFI-2 is a measure of the Big Five personality domains (which we label Extraversion, Agreeableness, Conscientiousness, Negative Emotionality, and Open-Mindedness) and 15 more-specific facet traits. The Big Five personality traits was the model to comprehend the relationship between personality and academic behaviors. This model was defined by several independent sets of researchers who used factor analysis of verbal descriptors of human behavior. These researchers began by studying relationships between a large number of verbal descriptors related to personality traits. They reduced the lists of these descriptors by 5–10 fold and then used factor analysis to group the remaining traits (using data mostly based upon people's estimations, in self-report questionnaire and peer ratings) in order to find the underlying factors of personality
Item numbers for the BFI-2 domain and facet scales are listed below. Reverse-keyed items are denoted by “R.” For more information about the BFI-2, visit the Colby Personality Lab website (http://www.colby.edu/psych/personality-lab/).
Extraversion: 1, 6, 11R, 16R, 21, 26R, 31R, 36R, 41, 46, 51R, 56 Agreeableness: 2, 7, 12R, 17R, 22R, 27, 32, 37R, 42R, 47R, 52, 57 Conscientiousness: 3R, 8R, 13, 18, 23R, 28R, 33, 38, 43, 48R, 53, 58R Negative Emotionality: 4R, 9R, 14, 19, 24R, 29R, 34, 39, 44R, 49R, 54, 59 Open-Mindedness: 5R, 10, 15, 20, 25R, 30R, 35, 40, 45R, 50R, 55R, 60
Sociability: 1, 16R, 31R, 46 Assertiveness: 6, 21, 36R, 51R Energy Level: 11R, 26R, 41, 56 Compassion: 2, 17R, 32, 47R Respectfulness: 7, 22R, 37R, 52 Trust: 12R, 27, 42R, 57 Organization: 3R, 18, 33, 48R Productiveness: 8R, 23R, 38, 53 Responsibility: 13, 28R, 43, 58R Anxiety: 4R, 19, 34, 49R Depression: 9R, 24R, 39, 54 Emotional Volatility: 14, 29R, 44R, 59 Intellectual Curiosity: 10, 25R, 40, 55R Aesthetic Sensitivity: 5R, 20, 35, 50R Creative Imagination: 15, 30R, 45R, 60
| Domain | Factor-pure facet | Complementary facets |
| E | Sociability | Assertiveness, Energy Level |
| A | Compassion | Respectfulness, Trust |
| C | Organization | Productiveness, Responsibility |
| N | Anxiety | Depression, Emotional Volatility |
| O | Aesthetic Sensitivity | Intellectual Curiosity, Creative Imagination |
The package functions expect the data to be named in a specific way, and to not contain data other than the BFI-2 data. Column names should be zero-leading two digits to indicate the question number, and they should end with these two digits. If this system is followed, then all functions work out of the box.
Examples that work:
bfi_01 bfi_02 ... bfi_59 bfi_60
big_five_01 big_five_02 ... big_five_59 bbig_five_60
Examples that won't work
bfi_1 bfi_2 ... bfi_59 bfi_60
big_five_01_trust big_five_02_change ... big_five_59_test bbig_five_60_lat
The data should be coded with the original scoring system 1-5. The data should not have implemented necessary reversal of answers for any of the questions, the functions will take care of this.
Soto, C. J., & John, O. P. (2017). The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113(1), 117–143. https://doi.org/10.1037/pspp0000096
bfi_compute( data, type = c("domains", "facets"), keep_all = FALSE, prefix = "bfi_" ) bfi_compute_domains( data, domains = c("extraversion", "agreeableness", "conscientiousness", "negative emotionality", "open-mindedness"), keep_all = FALSE, prefix = "domain_" ) bfi_compute_facets( data, facets = c("sociability", "assertiveness", "energy", "compassion", "respectful", "trust", "organization", "productive", "responsibility", "anxiety", "depression", "emotional volatility", "intellectual curiosity", "aesthetic sensebility", "creative imagination"), keep_all = FALSE, prefix = "facet_" )bfi_compute( data, type = c("domains", "facets"), keep_all = FALSE, prefix = "bfi_" ) bfi_compute_domains( data, domains = c("extraversion", "agreeableness", "conscientiousness", "negative emotionality", "open-mindedness"), keep_all = FALSE, prefix = "domain_" ) bfi_compute_facets( data, facets = c("sociability", "assertiveness", "energy", "compassion", "respectful", "trust", "organization", "productive", "responsibility", "anxiety", "depression", "emotional volatility", "intellectual curiosity", "aesthetic sensebility", "creative imagination"), keep_all = FALSE, prefix = "facet_" )
data |
data.frame containing bfi data |
type |
Choose domains or facets. Default is both |
keep_all |
logical, append to data.frame |
prefix |
string to prefix column names of computed values |
domains |
string vector of domains to compute |
facets |
string vector of facets to compute |
data.frame with calculated scores
bfi_compute_domains(): Compute BFI-2 domains and return in a data.frame
bfi_compute_facets(): Compute BFI-2 domains and return in a data.frame
library(dplyr) # Making some test data test_data <- tibble( id = rep(1:10, each = 60), name = rep(sprintf("bfi_%02d", 1:60), 10), value = lapply(1:10, function(x){ sample(1:5, size = 60, replace = TRUE) }) %>% unlist() ) %>% tidyr::pivot_wider() bfi_compute(test_data) bfi_compute(test_data, prefix = "bfi_")library(dplyr) # Making some test data test_data <- tibble( id = rep(1:10, each = 60), name = rep(sprintf("bfi_%02d", 1:60), 10), value = lapply(1:10, function(x){ sample(1:5, size = 60, replace = TRUE) }) %>% unlist() ) %>% tidyr::pivot_wider() bfi_compute(test_data) bfi_compute(test_data, prefix = "bfi_")
Calculate the domains of the BFI-2
bfi_domain_extravers( data, cols = matches("01$|06$|11$|16$|21$|26$|31$|36$|41$|46$|51$|56$"), reverse = TRUE, ... ) bfi_domain_agreeable( data, cols = matches("02$|07$|12$|17$|22$|27$|32$|37$|42$|47$|52$|57$"), reverse = TRUE, ... ) bfi_domain_conscient( data, cols = matches("03$|08$|13$|18$|23$|28$|33$|38$|43$|48$|53$|58$"), reverse = TRUE, ... ) bfi_domain_negemotion( data, cols = matches("04$|09$|14$|19$|24$|29$|34$|39$|44$|49$|54$|59$"), reverse = TRUE, ... ) bfi_domain_openminded( data, cols = matches("05$|10$|15$|20$|25$|30$|35$|40$|45$|50$|55$|60$"), reverse = TRUE, ... )bfi_domain_extravers( data, cols = matches("01$|06$|11$|16$|21$|26$|31$|36$|41$|46$|51$|56$"), reverse = TRUE, ... ) bfi_domain_agreeable( data, cols = matches("02$|07$|12$|17$|22$|27$|32$|37$|42$|47$|52$|57$"), reverse = TRUE, ... ) bfi_domain_conscient( data, cols = matches("03$|08$|13$|18$|23$|28$|33$|38$|43$|48$|53$|58$"), reverse = TRUE, ... ) bfi_domain_negemotion( data, cols = matches("04$|09$|14$|19$|24$|29$|34$|39$|44$|49$|54$|59$"), reverse = TRUE, ... ) bfi_domain_openminded( data, cols = matches("05$|10$|15$|20$|25$|30$|35$|40$|45$|50$|55$|60$"), reverse = TRUE, ... )
data |
data.frame with BFI data in |
cols |
tidyselector(s) of which columns data are in |
reverse |
logical. If reversal is needed (default) or not |
... |
other arguments to |
a vector with computed domain values.
bfi_domain_extravers(): Calculate the extraversion domain.
bfi_domain_agreeable(): Calculate the agreeableness domain.
bfi_domain_conscient(): Calculate the conscientiousness domain.
bfi_domain_negemotion(): Calculate the negative emotionality domain.
bfi_domain_openminded(): Calculate the open-minded domain.
library(dplyr) # Making some test data test_data <- dplyr::tibble( id = rep(1:10, each = 60), name = rep(sprintf("bfi_%02d", 1:60), 10), value = lapply(1:10, function(x){ sample(1:5, size = 60, replace = TRUE) }) %>% unlist() ) %>% tidyr::pivot_wider() bfi_domain_extravers(test_data) bfi_domain_conscient(test_data)library(dplyr) # Making some test data test_data <- dplyr::tibble( id = rep(1:10, each = 60), name = rep(sprintf("bfi_%02d", 1:60), 10), value = lapply(1:10, function(x){ sample(1:5, size = 60, replace = TRUE) }) %>% unlist() ) %>% tidyr::pivot_wider() bfi_domain_extravers(test_data) bfi_domain_conscient(test_data)
Calculate the facets of the BFI-2
bfi_facet_sociability( data, cols = matches("01$|16$|31$|46$"), reverse = TRUE, ... ) bfi_facet_assertive( data, cols = matches("06$|21$|36$|51$"), reverse = TRUE, ... ) bfi_facet_energy(data, cols = matches("11$|26$|41$|56$"), reverse = TRUE, ...) bfi_facet_compassion( data, cols = matches("02$|17$|32$|47$"), reverse = TRUE, ... ) bfi_facet_respectful( data, cols = matches("07$|22$|37$|52$"), reverse = TRUE, ... ) bfi_facet_trust(data, cols = matches("12$|27$|42$|57$"), reverse = TRUE, ...) bfi_facet_organization( data, cols = matches("03$|18$|33$|48$"), reverse = TRUE, ... ) bfi_facet_productive( data, cols = matches("08$|23$|38$|53$"), reverse = TRUE, ... ) bfi_facet_responsibility( data, cols = matches("13$|28$|43$|58$"), reverse = TRUE, ... ) bfi_facet_anxiety(data, cols = matches("04$|19$|34$|49$"), reverse = TRUE, ...) bfi_facet_depression( data, cols = matches("09$|24$|39$|54$"), reverse = TRUE, ... ) bfi_facet_emovolatility( data, cols = matches("14$|29$|44$|59$"), reverse = TRUE, ... ) bfi_facet_intcuriosity( data, cols = matches("10$|25$|40$|55$"), reverse = TRUE, ... ) bfi_facet_aestheticsens( data, cols = matches("05$|20$|35$|50$"), reverse = TRUE, ... ) bfi_facet_imagination( data, cols = matches("15$|30$|45$|60$"), reverse = TRUE, ... )bfi_facet_sociability( data, cols = matches("01$|16$|31$|46$"), reverse = TRUE, ... ) bfi_facet_assertive( data, cols = matches("06$|21$|36$|51$"), reverse = TRUE, ... ) bfi_facet_energy(data, cols = matches("11$|26$|41$|56$"), reverse = TRUE, ...) bfi_facet_compassion( data, cols = matches("02$|17$|32$|47$"), reverse = TRUE, ... ) bfi_facet_respectful( data, cols = matches("07$|22$|37$|52$"), reverse = TRUE, ... ) bfi_facet_trust(data, cols = matches("12$|27$|42$|57$"), reverse = TRUE, ...) bfi_facet_organization( data, cols = matches("03$|18$|33$|48$"), reverse = TRUE, ... ) bfi_facet_productive( data, cols = matches("08$|23$|38$|53$"), reverse = TRUE, ... ) bfi_facet_responsibility( data, cols = matches("13$|28$|43$|58$"), reverse = TRUE, ... ) bfi_facet_anxiety(data, cols = matches("04$|19$|34$|49$"), reverse = TRUE, ...) bfi_facet_depression( data, cols = matches("09$|24$|39$|54$"), reverse = TRUE, ... ) bfi_facet_emovolatility( data, cols = matches("14$|29$|44$|59$"), reverse = TRUE, ... ) bfi_facet_intcuriosity( data, cols = matches("10$|25$|40$|55$"), reverse = TRUE, ... ) bfi_facet_aestheticsens( data, cols = matches("05$|20$|35$|50$"), reverse = TRUE, ... ) bfi_facet_imagination( data, cols = matches("15$|30$|45$|60$"), reverse = TRUE, ... )
data |
data.frame with BFI data in |
cols |
tidyselector(s) of which columns data are in |
reverse |
logical. If reversal is needed (default) or not |
... |
other arguments to |
a vector with computed domain values.
bfi_facet_sociability(): Calculate the sociability facet.
bfi_facet_assertive(): Calculate the assertive facet.
bfi_facet_energy(): Calculate the energy level facet.
bfi_facet_compassion(): Calculate the compassion facet.
bfi_facet_respectful(): Calculate the respectfulness facet.
bfi_facet_trust(): Calculate the trust facet.
bfi_facet_organization(): Calculate the organization facet.
bfi_facet_productive(): Calculate the productive facet.
bfi_facet_responsibility(): Calculate the responsibility facet.
bfi_facet_anxiety(): Calculate the anxiety facet.
bfi_facet_depression(): Calculate the depression facet.
bfi_facet_emovolatility(): Calculate the emotional volatiliity facet.
bfi_facet_intcuriosity(): Calculate the intellectual curiosity facet.
bfi_facet_aestheticsens(): Calculate the aesthetic sensibility facet.
bfi_facet_imagination(): Calculate the creative imagination facet.
library(dplyr) # Making some test data test_data <- tibble( id = rep(1:10, each = 60), name = rep(sprintf("bfi_%02d", 1:60), 10), value = lapply(1:10, function(x){ sample(1:5, size = 60, replace = TRUE) }) %>% unlist() ) %>% tidyr::pivot_wider() bfi_facet_sociability(test_data) bfi_facet_assertive(test_data)library(dplyr) # Making some test data test_data <- tibble( id = rep(1:10, each = 60), name = rep(sprintf("bfi_%02d", 1:60), 10), value = lapply(1:10, function(x){ sample(1:5, size = 60, replace = TRUE) }) %>% unlist() ) %>% tidyr::pivot_wider() bfi_facet_sociability(test_data) bfi_facet_assertive(test_data)
Big-5 Item reversals
bfi_reversal(data, ...)bfi_reversal(data, ...)
data |
Data with big-5 columns |
... |
Column selection to reverse |
data.frame with specified columns reversed
data <- dplyr::tibble( col_01 = c(1:5, 3, 5, 4), col_02 = c(1:5, 3, 5, 4) ) bfi_reversal(data, col_01)data <- dplyr::tibble( col_01 = c(1:5, 3, 5, 4), col_02 = c(1:5, 3, 5, 4) ) bfi_reversal(data, col_01)
Compiles education from participant, mother or father depending on source availability. Made for ease of testing and reporting education SES of family
edu_compile(data, participant, mother, father)edu_compile(data, participant, mother, father)
data |
MOAS-like data.frame |
participant |
unquoted column of 4 category education for participant |
mother |
unquoted column of 4 category education for participant's mother |
father |
unquoted column of 4 category education for participant's father |
dataframe with three new columns
Other edu_functions:
edu_compute(),
edu_factorise(),
edu_levels2name(),
edu_levels(),
edu_reduce(),
edu_to_years()
edu <- data.frame( edu4 = c("3", "High school", 1, NA, "University/University college (> 4 years)", NA, "University/University college (< 4 years)"), edu9 = c(7,7,8,NA,"Primary school (6 years)",5, 9), edu_years = c(NA, 12, 9, NA, 19, 19, NA), mother = c("3", "High school", 1, NA, "University/University college (> 4 years)", "University/University college (> 4 years)", "University/University college (< 4 years)"), father = c(7,7,8,4,"Primary school (6 years)",5, 10), stringsAsFactors = FALSE ) library(dplyr) edu %>% mutate( mother = ifelse(mother == "3", NA, mother), mother = edu4_factorise(mother), father = edu9_reduce(edu9_factorise(father)) ) %>% edu_compile( participant = edu4, mother = mother, father = father )edu <- data.frame( edu4 = c("3", "High school", 1, NA, "University/University college (> 4 years)", NA, "University/University college (< 4 years)"), edu9 = c(7,7,8,NA,"Primary school (6 years)",5, 9), edu_years = c(NA, 12, 9, NA, 19, 19, NA), mother = c("3", "High school", 1, NA, "University/University college (> 4 years)", "University/University college (> 4 years)", "University/University college (< 4 years)"), father = c(7,7,8,4,"Primary school (6 years)",5, 10), stringsAsFactors = FALSE ) library(dplyr) edu %>% mutate( mother = ifelse(mother == "3", NA, mother), mother = edu4_factorise(mother), father = edu9_reduce(edu9_factorise(father)) ) %>% edu_compile( participant = edu4, mother = mother, father = father )
Using existing data in the MOAS, fills in gaps, converts from on type of coding to another etc.
edu_compute( data, edu4 = edu_coded4, edu9 = edu_coded10, edu_years = edu_years, prefix = "edu_", keep_all = TRUE )edu_compute( data, edu4 = edu_coded4, edu9 = edu_coded10, edu_years = edu_years, prefix = "edu_", keep_all = TRUE )
data |
MOAS-like data |
edu4 |
unquoted column containing Education coded in 4 categories |
edu9 |
unquoted column containing Education coded in 4 categories |
edu_years |
unquoted column containing Education in years to highest completed |
prefix |
string to prefix column names of computed values |
keep_all |
logical, append to data.frame |
a data.frame
Other edu_functions:
edu_compile(),
edu_factorise(),
edu_levels2name(),
edu_levels(),
edu_reduce(),
edu_to_years()
edu <- data.frame( edu4 = c("3", "High school", 1, NA, "University/University college (> 4 years)", NA, "University/University college (< 4 years)"), edu9 = c(7,7,8,NA,"Primary school (6 years)",5, 9), edu_years = c(NA, 12, 9, NA, 19, 19, NA), mother = c("3", "High school", 1, NA, "University/University college (> 4 years)", "University/University college (> 4 years)", "University/University college (< 4 years)"), father = c(7,7,8,4,"Primary school (6 years)",5, 10), stringsAsFactors = FALSE ) edu_compute(edu, edu4 = edu4, edu9 = edu9, edu_years = edu_years)edu <- data.frame( edu4 = c("3", "High school", 1, NA, "University/University college (> 4 years)", NA, "University/University college (< 4 years)"), edu9 = c(7,7,8,NA,"Primary school (6 years)",5, 9), edu_years = c(NA, 12, 9, NA, 19, 19, NA), mother = c("3", "High school", 1, NA, "University/University college (> 4 years)", "University/University college (> 4 years)", "University/University college (< 4 years)"), father = c(7,7,8,4,"Primary school (6 years)",5, 10), stringsAsFactors = FALSE ) edu_compute(edu, edu4 = edu4, edu9 = edu9, edu_years = edu_years)
Will convert even a mixed character vector (combining numbers and text) of education levels 10 and 4 to a factor.
edu_factorise(x, levels) edu4_factorise(x) edu9_factorise(x)edu_factorise(x, levels) edu4_factorise(x) edu9_factorise(x)
x |
character vector |
levels |
levels returned from the edu_levels() function |
Specialized returns
edu_factorise - with option to choose number of levels
edu4_factorise - directly transform vector coded in 4-level scheme
edu9_factorise - directly transform vector coded in 9-levels scheme
factor
Other edu_functions:
edu_compile(),
edu_compute(),
edu_levels2name(),
edu_levels(),
edu_reduce(),
edu_to_years()
edu9 <- c("7", "7", "8", NA, "Primary school (6 years)", "5", "9") edu_factorise(edu9, 9) edu9_factorise(edu9)edu9 <- c("7", "7", "8", NA, "Primary school (6 years)", "5", "9") edu_factorise(edu9, 9) edu9_factorise(edu9)
Keeping track of the different educational coding schemes at LCBC can be tricky. This formula contains the two current types of coding schemas employed by LCBC.
edu_levels(levels = 4) edu4_levels() edu9_levels()edu_levels(levels = 4) edu4_levels() edu9_levels()
levels |
how many levels to return (either 4 or 9) |
Specialized returns
edu_levels - returns named numeric vector for levels specified
edu4_levels - returns named numeric vector for 4-levels scheme
edu9_levels - returns named numeric vector for 9-levels scheme
named numeric vector
Other edu_functions:
edu_compile(),
edu_compute(),
edu_factorise(),
edu_levels2name(),
edu_reduce(),
edu_to_years()
edu_levels(4) edu_levels(9) edu4_levels() edu9_levels()edu_levels(4) edu_levels(9) edu4_levels() edu9_levels()
Change educational coded levels to names of the levels
edu_levels2name(x, levels) edu4_levels2name(x) edu9_levels2name(x)edu_levels2name(x, levels) edu4_levels2name(x) edu9_levels2name(x)
x |
vector containing levels |
levels |
numeric of number of levels (4 or 10) |
Specialized returns
edu_levels2name - transforms levels to names for levels specified
edu4_levels2name - transforms levels to names for 4-levels scheme
edu9_levels2name - transforms levels to names for 9-levels scheme
character vector
Other edu_functions:
edu_compile(),
edu_compute(),
edu_factorise(),
edu_levels(),
edu_reduce(),
edu_to_years()
edu4 <- c(9, 9, 16, 19) edu_levels2name(edu4, 4) # does the same as edu4_levels2name(edu4) edu9 <- c(0, 6, 21, 16) edu_levels2name(edu9, 9) # does the same as edu9_levels2name(edu9)edu4 <- c(9, 9, 16, 19) edu_levels2name(edu4, 4) # does the same as edu4_levels2name(edu4) edu9 <- c(0, 6, 21, 16) edu_levels2name(edu9, 9) # does the same as edu9_levels2name(edu9)
Converting from a high-level educational
coding to a lower level one is cumbersome.
This function bases it self in any coding
scheme specified in edu_levels
and tries
creating a conversion table between two
specified schemas.
edu_map(from = 9, to = 4) edu_map_chr(from = 9, to = 4) edu_map_num(from = 9, to = 4)edu_map(from = 9, to = 4) edu_map_chr(from = 9, to = 4) edu_map_num(from = 9, to = 4)
from |
schema levels to convert from |
to |
shcema levels to convtert to |
Specialized returns
edu_map - returns a data.frame of two named vectors
edu_map_chr - returns a data.frame with two character vectors
edu_map_num - returns a data.frame with two numeric vectors
New nettskjema data requires codebook to not have special characters, and as such the old and new coding scheme does not fit. This function turns new coding scheme into the old, wanted one
edu_recode(x, names = TRUE)edu_recode(x, names = TRUE)
x |
character vector of old scheme |
names |
logical. toggle return of names rather than numbers |
character
eds <- c(NA, "UnderGrad_BA", "HighSchool_Initial", "PostGrad_MA", "PostGrad_PhD", "HighSchool", "Junior-HighSchool", "HighSchool_addition") edu_recode(eds) eds <- c(1,5,8,2,6,9,1,10) edu_recode(eds, names = FALSE)eds <- c(NA, "UnderGrad_BA", "HighSchool_Initial", "PostGrad_MA", "PostGrad_PhD", "HighSchool", "Junior-HighSchool", "HighSchool_addition") edu_recode(eds) eds <- c(1,5,8,2,6,9,1,10) edu_recode(eds, names = FALSE)
These functions will aid in converting one education scheme into another. While you may attempt to go from a low level to a high (from 4 to 9), there is no way to actually do that in a consistent way that will correctly reflect the underlying data.
edu_reduce(x, from, to) edu9_reduce(x, to = 4)edu_reduce(x, from, to) edu9_reduce(x, to = 4)
x |
character vector |
from |
factor level to transform from |
to |
factor level to transform to |
Always go from a higher level scheme to a lower one (currently from 9 to 4 only)
Specialized returns
edu_reduce - reduce with own to and from specification
edu9_reduce - directly reduce from 9 to 4
factor
Other edu_functions:
edu_compile(),
edu_compute(),
edu_factorise(),
edu_levels2name(),
edu_levels(),
edu_to_years()
edu9 <- c("7", "7", "8", NA, "Primary school (6 years)", "5", "9") edu_reduce(edu9, 9, 4) edu9_reduce(edu9)edu9 <- c("7", "7", "8", NA, "Primary school (6 years)", "5", "9") edu_reduce(edu9, 9, 4) edu9_reduce(edu9)
Turn education data to years
edu_to_years(x, levels) edu4_to_years(x) edu9_to_years(x)edu_to_years(x, levels) edu4_to_years(x) edu9_to_years(x)
x |
character vector |
levels |
levels returned from the edu_levels() function |
Specialized returns
edu_to_years - Alter education to years specifying number of levels
edu4_to_years - directly alter 4-level coded education to years
edu9_to_years - directly alter 9-level coded education to years
vector of integers
Other edu_functions:
edu_compile(),
edu_compute(),
edu_factorise(),
edu_levels2name(),
edu_levels(),
edu_reduce()
edu4 <- c("3", "High school", "1", NA, "University/University college (> 4 years)", NA, "University/University college (< 4 years)") edu_to_years(edu4, 4) edu4_to_years(edu4) edu9 <- c("7", "7", "8", NA, "Primary school (6 years)", "5", "9") edu_to_years(edu9, 9) edu9_to_years(edu9)edu4 <- c("3", "High school", "1", NA, "University/University college (> 4 years)", NA, "University/University college (< 4 years)") edu_to_years(edu4, 4) edu4_to_years(edu4) edu9 <- c("7", "7", "8", NA, "Primary school (6 years)", "5", "9") edu_to_years(edu9, 9) edu9_to_years(edu9)
Since the coding we have often uses negative numbers to indicate left-hand preferences, a specialized function is here to return a vector with only the values asked for.
ehi_change(x, direction = 1)ehi_change(x, direction = 1)
x |
numeric vector |
direction |
either 1 for positive, -1 for negative |
If direction is set to 1, returns only positive numbers, negative and 0 returns as NA. If direction is set to -1, returns only negative numbers, positive and 0 returns as NA.
numeric vector
Compute all variables of ehi, using other functions in this package. Will return the given data.frame with three additional columns, the laterality quotient (LQ), the laterality factor (Coded), and the nominal laterality code (Nominal).
ehi_compute( data, cols = matches("^ehi_[0-9][0-9]$"), writing = ehi_01, ..., keep_all = TRUE, prefix = "ehi_" )ehi_compute( data, cols = matches("^ehi_[0-9][0-9]$"), writing = ehi_01, ..., keep_all = TRUE, prefix = "ehi_" )
data |
data.frame containing ehi data |
cols |
tidyselected columns of all ehi data |
writing |
numeric vector of writing preference (-2,-1,0,1,2) |
... |
additional arguments to ehi_factorise_lqa |
keep_all |
logical, append to data.frame |
prefix |
string to prefix column names of computed values |
The Edinburgh Handedness Inventory is a measurement scale used to assess the dominance of a person's right or left hand in everyday activities, sometimes referred to as laterality. The inventory can be used by an observer assessing the person, or by a person self-reporting hand use. The latter method tends to be less reliable due to a person over-attributing tasks to the dominant hand.
The EHI has several measures that can help assess a person's laterality.
| answer | value | nominal | lq | lq_cat | lqa_cat |
| Left dominance | -2 | left | -100 | left | left |
| Left preference | -1 | left | -40 | left | ambidexter |
| No preference | 0 | ambidexter | 0 | right | ambidexter |
| Right preference | 1 | right | 40 | right | ambidexter |
| Right dominance | 2 | right | 100 | right | right |
The easiest measure from the EHI is the nominal laterality value, which is just the answer to the first question on hand preference when writing. This simple index just treat negative answers as "left" dominance, positive number as "right" dominance, and a 0 as ambidextrous. Note: The original paper by Oldfield (1971) does not explicitly state a category for "Ambidextrous". It is very rare that a person does not have a clear preference on writing hand, even if they can write with both hands. This category is only added in this package to handle the possible case of someone answering "No preference".
| min | max | category |
| -2 | -1 | left |
| 0 | 0 | ambidexter |
| 1 | 2 | right |
The total score of the EHI is more than just summing the values for each answer. The laterality quotient (LQ) uses the answers to all the questions. The LQ can take values from -100 to 100, and is calculated by taking the sum of all positive answers subtracting the sum of absolute values of the negative answers, divided by the sum of both, and multiplied by 100.
katex::katex_html(equation)
The laterality index is based on the laterality quotient (above) and categorises answers into to categories, Left and Right.
The Oldfield (1971) paper mentions "indeterminate handedness" a couple of times in the paper, but the case for "true" ambidextrous is not made, and as such the inventory does not have official categories for that.
As the index is based on the quotient, that ranges from -100 to 100, getting a perfect 0 LQ is very unlikely, and as indicated in the paper, such score is assumed to belong to the Right hand part of the scale.
| min | max | category |
| -100 | -1 | left |
| 0 | 100 | right |
An alternate laterality index is also often employed, where scores between -40 and 40 are treated as ambidextrous.
One row of data should refer to a single questionnaire answered, and as such, if a person has answered multiple times, these should appear on separate rows with columns identifying ID and time point per observation.
For ease, we recommend naming the columns in a consistent way, so the functions in this package become easier to use.
The LCBC database follows a naming scheme that prefixes all columns with ehi_ and ends with a zero-padded double digit indicator of the question number.
The cell values in the data should be coded from -2 through 0 to 2, and there should be a single value per question.
| value | category |
| -2 | Left hand dominance |
| -1 | Left hand preference |
| 0 | No preference |
| 1 | Right hand preference |
| 2 | Right hand dominance |
Oldfield, RC (March 1971) The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia. 9 (1): 97–113. doi:10.1016/0028-3932(71)90067-4
Verdino, M; Dingman, S (April 1998). Two measures of laterality in handedness: the Edinburgh Handedness Inventory and the Purdue Pegboard test of manual dexterity. Perceptual and Motor Skills. 86 (2): 476–8. doi:10.2466/pms.1998.86.2.476
Knecht, S; Dräger, B; Deppe, M; Bobe, L; Lohmann, H; Flöel, A; Ringelstein, E-B; Henningsen, H (December 2000). Handedness and hemispheric language dominance in healthy humans. Brain. 123 (12): 2512–8. doi:10.1093/brain/123.12.2512.
data.frame
Other ehi_functions:
ehi_compute_lq(),
ehi_factorise_lq(),
ehi_factorise_nominal()
The laterality quotient is calculated using all the answers on the ehi, with the formula: (pos-neg)/(pos+neg)*100 )
The Edinburgh Handedness Inventory is a measurement scale used to assess the dominance of a person's right or left hand in everyday activities, sometimes referred to as laterality. The inventory can be used by an observer assessing the person, or by a person self-reporting hand use. The latter method tends to be less reliable due to a person over-attributing tasks to the dominant hand.
The EHI has several measures that can help assess a person's laterality.
| answer | value | nominal | lq | lq_cat | lqa_cat |
| Left dominance | -2 | left | -100 | left | left |
| Left preference | -1 | left | -40 | left | ambidexter |
| No preference | 0 | ambidexter | 0 | right | ambidexter |
| Right preference | 1 | right | 40 | right | ambidexter |
| Right dominance | 2 | right | 100 | right | right |
The easiest measure from the EHI is the nominal laterality value, which is just the answer to the first question on hand preference when writing. This simple index just treat negative answers as "left" dominance, positive number as "right" dominance, and a 0 as ambidextrous. Note: The original paper by Oldfield (1971) does not explicitly state a category for "Ambidextrous". It is very rare that a person does not have a clear preference on writing hand, even if they can write with both hands. This category is only added in this package to handle the possible case of someone answering "No preference".
| min | max | category |
| -2 | -1 | left |
| 0 | 0 | ambidexter |
| 1 | 2 | right |
The total score of the EHI is more than just summing the values for each answer. The laterality quotient (LQ) uses the answers to all the questions. The LQ can take values from -100 to 100, and is calculated by taking the sum of all positive answers subtracting the sum of absolute values of the negative answers, divided by the sum of both, and multiplied by 100.
katex::katex_html(equation)
The laterality index is based on the laterality quotient (above) and categorises answers into to categories, Left and Right.
The Oldfield (1971) paper mentions "indeterminate handedness" a couple of times in the paper, but the case for "true" ambidextrous is not made, and as such the inventory does not have official categories for that.
As the index is based on the quotient, that ranges from -100 to 100, getting a perfect 0 LQ is very unlikely, and as indicated in the paper, such score is assumed to belong to the Right hand part of the scale.
| min | max | category |
| -100 | -1 | left |
| 0 | 100 | right |
An alternate laterality index is also often employed, where scores between -40 and 40 are treated as ambidextrous.
One row of data should refer to a single questionnaire answered, and as such, if a person has answered multiple times, these should appear on separate rows with columns identifying ID and time point per observation.
For ease, we recommend naming the columns in a consistent way, so the functions in this package become easier to use.
The LCBC database follows a naming scheme that prefixes all columns with ehi_ and ends with a zero-padded double digit indicator of the question number.
The cell values in the data should be coded from -2 through 0 to 2, and there should be a single value per question.
| value | category |
| -2 | Left hand dominance |
| -1 | Left hand preference |
| 0 | No preference |
| 1 | Right hand preference |
| 2 | Right hand dominance |
Oldfield, RC (March 1971) The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia. 9 (1): 97–113. doi:10.1016/0028-3932(71)90067-4
Verdino, M; Dingman, S (April 1998). Two measures of laterality in handedness: the Edinburgh Handedness Inventory and the Purdue Pegboard test of manual dexterity. Perceptual and Motor Skills. 86 (2): 476–8. doi:10.2466/pms.1998.86.2.476
Knecht, S; Dräger, B; Deppe, M; Bobe, L; Lohmann, H; Flöel, A; Ringelstein, E-B; Henningsen, H (December 2000). Handedness and hemispheric language dominance in healthy humans. Brain. 123 (12): 2512–8. doi:10.1093/brain/123.12.2512.
ehi_compute_lq(data, cols = matches("^ehi_[0-9][0-9]$"))ehi_compute_lq(data, cols = matches("^ehi_[0-9][0-9]$"))
data |
data.frame containing ehi data |
cols |
tidyselected columns of all ehi data |
numeric
Other ehi_functions:
ehi_compute(),
ehi_factorise_lq(),
ehi_factorise_nominal()
While the laterality quotient is nice to use if your sample and variance is large enough for analyses, in most cases you will need to report the categories of laterality your participants fall within. This function takes the laterality quotient as computed by ehi_compute_lq and creates a factor using common specifications.
ehi_factorise_lq(lq = ehi_lq) ehi_factorise_lqa( lq, min = -70, max = 70, levels = c("left", "ambidexter", "right") )ehi_factorise_lq(lq = ehi_lq) ehi_factorise_lqa( lq, min = -70, max = 70, levels = c("left", "ambidexter", "right") )
lq |
numeric vector calculated by ehi_compute_lq |
min |
minimum value for ambidexter specification (default = -70) |
max |
maximum value for ambidexter specification (default = 70) |
levels |
the levels for the lq component. Usually c("left", "ambidexter", "right"). |
The Edinburgh Handedness Inventory is a measurement scale used to assess the dominance of a person's right or left hand in everyday activities, sometimes referred to as laterality. The inventory can be used by an observer assessing the person, or by a person self-reporting hand use. The latter method tends to be less reliable due to a person over-attributing tasks to the dominant hand.
The EHI has several measures that can help assess a person's laterality.
| answer | value | nominal | lq | lq_cat | lqa_cat |
| Left dominance | -2 | left | -100 | left | left |
| Left preference | -1 | left | -40 | left | ambidexter |
| No preference | 0 | ambidexter | 0 | right | ambidexter |
| Right preference | 1 | right | 40 | right | ambidexter |
| Right dominance | 2 | right | 100 | right | right |
The easiest measure from the EHI is the nominal laterality value, which is just the answer to the first question on hand preference when writing. This simple index just treat negative answers as "left" dominance, positive number as "right" dominance, and a 0 as ambidextrous. Note: The original paper by Oldfield (1971) does not explicitly state a category for "Ambidextrous". It is very rare that a person does not have a clear preference on writing hand, even if they can write with both hands. This category is only added in this package to handle the possible case of someone answering "No preference".
| min | max | category |
| -2 | -1 | left |
| 0 | 0 | ambidexter |
| 1 | 2 | right |
The total score of the EHI is more than just summing the values for each answer. The laterality quotient (LQ) uses the answers to all the questions. The LQ can take values from -100 to 100, and is calculated by taking the sum of all positive answers subtracting the sum of absolute values of the negative answers, divided by the sum of both, and multiplied by 100.
katex::katex_html(equation)
The laterality index is based on the laterality quotient (above) and categorises answers into to categories, Left and Right.
The Oldfield (1971) paper mentions "indeterminate handedness" a couple of times in the paper, but the case for "true" ambidextrous is not made, and as such the inventory does not have official categories for that.
As the index is based on the quotient, that ranges from -100 to 100, getting a perfect 0 LQ is very unlikely, and as indicated in the paper, such score is assumed to belong to the Right hand part of the scale.
| min | max | category |
| -100 | -1 | left |
| 0 | 100 | right |
An alternate laterality index is also often employed, where scores between -40 and 40 are treated as ambidextrous.
One row of data should refer to a single questionnaire answered, and as such, if a person has answered multiple times, these should appear on separate rows with columns identifying ID and time point per observation.
For ease, we recommend naming the columns in a consistent way, so the functions in this package become easier to use.
The LCBC database follows a naming scheme that prefixes all columns with ehi_ and ends with a zero-padded double digit indicator of the question number.
The cell values in the data should be coded from -2 through 0 to 2, and there should be a single value per question.
| value | category |
| -2 | Left hand dominance |
| -1 | Left hand preference |
| 0 | No preference |
| 1 | Right hand preference |
| 2 | Right hand dominance |
Oldfield, RC (March 1971) The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia. 9 (1): 97–113. doi:10.1016/0028-3932(71)90067-4
Verdino, M; Dingman, S (April 1998). Two measures of laterality in handedness: the Edinburgh Handedness Inventory and the Purdue Pegboard test of manual dexterity. Perceptual and Motor Skills. 86 (2): 476–8. doi:10.2466/pms.1998.86.2.476
Knecht, S; Dräger, B; Deppe, M; Bobe, L; Lohmann, H; Flöel, A; Ringelstein, E-B; Henningsen, H (December 2000). Handedness and hemispheric language dominance in healthy humans. Brain. 123 (12): 2512–8. doi:10.1093/brain/123.12.2512.
ehi_factorise_lq - returns original two-factor specification
ehi_factorise_lqa - returns commonly used three-factor specification
factor
Other ehi_functions:
ehi_compute_lq(),
ehi_compute(),
ehi_factorise_nominal()
LQ <- c(1, 40, 70, -20, 0, 100, -90) ehi_factorise_lq(LQ) ehi_factorise_lqa(LQ) ehi_factorise_lqa(LQ, min = -40, max = 60)LQ <- c(1, 40, 70, -20, 0, 100, -90) ehi_factorise_lq(LQ) ehi_factorise_lqa(LQ) ehi_factorise_lqa(LQ, min = -40, max = 60)
Using the answers to the first question on writing from the Edinburgh handedness inventory, a nominal scale of three factors can be returned.
ehi_factorise_nominal(writing = ehi_01)ehi_factorise_nominal(writing = ehi_01)
writing |
numeric vector of writing preference (-2,-1,0,1,2) |
factor
Other ehi_functions:
ehi_compute_lq(),
ehi_compute(),
ehi_factorise_lq()
writing <- c(2, 2, -1, 0, 1, -2) ehi_factorise_nominal(writing)writing <- c(2, 2, -1, 0, 1, -2) ehi_factorise_nominal(writing)
Calculate the sum on non-NA values in all columns in the specified direction( 1 == sum all positives, -1 sum absolutes values of negatives)
ehi_values(data, cols = matches("^ehi_[0-9][0-9]$"), direction = 1)ehi_values(data, cols = matches("^ehi_[0-9][0-9]$"), direction = 1)
data |
data.frame containing ehi data |
cols |
tidy-selection of all ehi columns |
direction |
sum positive or negatives (1 for positive, -1 for negative) |
numeric vector
Necessary step for computing the total score
gds_alter_values( data, values = gds_values(), reverse = FALSE, cols = matches("01$|05$|07$|09$|15$|19$|21$|27$|29$|30$") )gds_alter_values( data, values = gds_values(), reverse = FALSE, cols = matches("01$|05$|07$|09$|15$|19$|21$|27$|29$|30$") )
data |
data.frame with GDS data in it |
values |
named vector of 2 providing the coding for Yes and No answers c(Yes = 1, No = 2) |
reverse |
reverse logic |
cols |
GDS data columns |
Other gds_functions:
gds_binary(),
gds_compute_sum(),
gds_values()
internal function to make all "yes" answers equal to 1, and all "no" to 0. This for convenience of calculations later.
gds_binary(x, values = gds_values())gds_binary(x, values = gds_values())
x |
vector of yes and no coding |
values |
named vector of 2 providing the coding for Yes and No answers c(Yes = 1, No = 2) |
vector of 0's and 1's
Other gds_functions:
gds_alter_values(),
gds_compute_sum(),
gds_values()
gds_binary(c(1,1,0,NA,1), gds_values(1,0)) gds_binary(c("y","y","n",NA,"y"), gds_values(yes = "y", no = "n"))gds_binary(c(1,1,0,NA,1), gds_values(1,0)) gds_binary(c("y","y","n",NA,"y"), gds_values(yes = "y", no = "n"))
The Geriatric Depression Scale (GDS) is an instrument designed specifically for rating depression in the elderly. It can be administrated to healthy, medically ill, and mild to moderately cognitively impaired older adults. As a general rule, GDS is administrated in LCBC to older adults with a lower cut off around 60 years. However, please consult the instructions for each project, as this guideline has been implemented at different time points across the projects.
The questionnaire consists of 30 questions tapping into a wide variety of topics relevant to depression, including cognitive complaints, motivation, thoughts about the past and the future, self-image, and mood itself. The answers should be based the participants' feelings throughout the last week.
Twenty of the questions indicate the presence of depression when answered positively, while the ten remaining indicate depression when answered negatively (see scoring instructions below). The questionnaire is scored accordingly, giving one point for each statement that affirms a depressive symptom. The sum of these scores yields one total score, with a possible range between 0 and 30. ##Scoring The GDS is quite straight forward in its format, a series of 30 questions that take a yes or no answer. This binary coding makes it quite easy to work with. Several of the questions, however, are formulated in such a way that they require a reversal of the coding before the total score can be summed. The questions which require reversal of coding are, 01, 05, 07, 09, 15, 19, 21, 27, 29, 30, meaning answering "yes" to these should be altered to 0, and "no" altered to 1, before calculating the sum score. The total GDS score is after reversal, a simple addition of all the answers into a single score.
One point is given for any “No” answered to the following questions: 1, 5, 7, 9, 15, 19, 21, 27, 29 and 30
and one point is given for every “Yes” answered on the following questions: 2, 3, 4, 6, 8, 10, 11, 12, 13, 14, 16, 17, 18, 20, 22, 23, 24, 25, 26, 28
There are 3 categories of severity for the GDS total score.
Below or equal to 9 is "Normal", above 19 is "Severe depression", and the remaining fall within "Mild depression".
| GDS score | Depression category |
| 0-9 | Normal |
| 10-19 | Mild depressive |
| 20-30 | Severe depressive |
The easiest is to have data coded as in the NOAS, as this will let you use default values for the arguments.
The column names in the NOAS all start with gds_ and then are followed by a two-digit numbering of the question:
gds_01, gds_02, gds_03, ... gds_28, gds_29, gds_30
If your data is coded differently, a consistent naming scheme should help you use the functions anyway.
Each row of data should belong to a single answer to the entire questionnaire.
Meaning if you have multiple answers to the questionnaire over time, these should be placed in another row, duplicating the participant ID, together with a column indicating the timepoint the data was collected in.
Data values are binary yes and no answers to the GDS.
While the functions are made in such a way that any type of binary coding works well, the default is set to be yes = 1, no = 0.
These can be altered by applying the gds_values functions to the other functions asking for the coding schema.
Depression Screening Scale: A Preliminary Report, J Psychiatr Res, 17 (1), 37-49, doi: 10.1016/0022-3956(82)90033-4
E L Lesher 1, J S Berryhill (1994), Validation of the Geriatric Depression Scale – Short Form Among Inpatients, J Clin Psychol, 50 (2), 256-60, doi: 10.1002/1097-4679(199403)50:2<256::aid-jclp2270500218>3.0.co;2-e
gds_compute_sum( data, cols = dplyr::matches("[0-3][0-9]$"), cols_rev = dplyr::matches("01$|05$|07$|09$|15$|19$|21$|27$|29$|30$"), values = gds_values() ) gds_factorise(gds_sum) gds_compute( data, cols = dplyr::matches("[0-9][0-9]$"), cols_rev = dplyr::matches("01$|05$|07$|09$|15$|19$|21$|27$|29$|30$"), values = gds_values(), prefix = "gds_", keep_all = TRUE )gds_compute_sum( data, cols = dplyr::matches("[0-3][0-9]$"), cols_rev = dplyr::matches("01$|05$|07$|09$|15$|19$|21$|27$|29$|30$"), values = gds_values() ) gds_factorise(gds_sum) gds_compute( data, cols = dplyr::matches("[0-9][0-9]$"), cols_rev = dplyr::matches("01$|05$|07$|09$|15$|19$|21$|27$|29$|30$"), values = gds_values(), prefix = "gds_", keep_all = TRUE )
data |
data.frame with GDS data in it |
cols |
GDS data columns |
cols_rev |
Columns for reversal of binary code |
values |
named vector of 2 providing the coding for Yes and No answers c(Yes = 1, No = 2) |
gds_sum |
numeric vector of GDS sums |
prefix |
string to prefix column names of computed values |
keep_all |
logical, append to data.frame |
numeric
factor
data frame
gds_compute_sum(): Calculate the total GDS score
gds_factorise(): Create a factor from the sum of the GDS scores
Other gds_functions:
gds_alter_values(),
gds_binary(),
gds_values()
Other gds_functions:
gds_alter_values(),
gds_binary(),
gds_values()
Other gds_functions:
gds_alter_values(),
gds_binary(),
gds_values()
Function to easily set the response coding used in the GDS data.
gds_values(yes = 1, no = 0)gds_values(yes = 1, no = 0)
yes |
value indicating a positive answer |
no |
value indicating a negative answer |
list of yes and no values
Other gds_functions:
gds_alter_values(),
gds_binary(),
gds_compute_sum()
gds_values() gds_values(yes = "YES", no = "NO")gds_values() gds_values(yes = "YES", no = "NO")
Older collected income data for LCBC collected income information in 7 bins. Newer data collects continuous income data. This function converts binned income data from these 7 categories into the mean income value for each bin.
income_bin2nok(x)income_bin2nok(x)
x |
income bin vector |
numeric
x <- c("< 200k", "600k - 699k", "400k - 499k", "> 700k", "500k - 599k", "300k - 399k", "200k - 299k") income_bin2nok(x)x <- c("< 200k", "600k - 699k", "400k - 499k", "> 700k", "500k - 599k", "300k - 399k", "200k - 299k") income_bin2nok(x)
In order to compare income in Norway to other countries, currency conversions might be necessary. This function multiplies with the rate provided.
income_nok2other(x, rate = 0.1)income_nok2other(x, rate = 0.1)
x |
currency |
rate |
currency translation rate (defaul 0.10 for euro) |
numeric
income_nok2other(c(100, 2930, 13649)) income_nok2other(c(100, 2930, 13649), 0.5)income_nok2other(c(100, 2930, 13649)) income_nok2other(c(100, 2930, 13649), 0.5)
The purpose of the International Physical Activity questionnaires (IPAQ) is to provide a set of well-developed instruments that can be used internationally to obtain comparable estimates of physical activity. There are two versions of the questionnaire. The short version is suitable for use in national and regional surveillance systems and the long version provide more detailed information often required in research work or for evaluation purposes.
Scoring of the IPAQ is based on a metric called METs, which are multiples of the resting metabolic rate. The IPAQ scoring description can be found here
Expressed as MET-min per week: MET level x minutes of activity/day x days per week
MET levels:
Light - 3.3 METs
Moderate - 4.0 METs
Vigorous - 8.0 METs
Total MET-minutes/week = Light (3.3 x min x days) + Mod (4.0 x min x days) + Vig (8.0 x min x days)
Three levels (categories) of physical activity are proposed:
This is the lowest level of physical activity. Those individuals who not meet criteria for categories 2 or 3 are considered low/inactive.
Any one of the following 3 criteria:
3 or more days of vigorous activity of at least 20 minutes per day OR
5 or more days of moderate-intensity activity or walking of at least 30 minutes per day OR
5 or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum of at least 600 MET-min/week.
Any one of the following 2 criteria:
Vigorous-intensity activity on at least 3 days and accumulating at least 1500 MET-minutes/ week OR
7 or more days of any combination of walking, moderate-intensity or vigorous intensity activities achieving a minimum of at least 3000 MET-minutes/week
Depression Screening Scale: A Preliminary Report, J Psychiatr Res, 17 (1), 37-49, doi: 10.1016/0022-3956(82)90033-4
E L Lesher 1, J S Berryhill (1994), Validation of the Geriatric Depression Scale – Short Form Among Inpatients, J Clin Psychol, 50 (2), 256-60, doi: 10.1002/1097-4679(199403)50:2<256::aid-jclp2270500218>3.0.co;2-e
ipaq_compute_met(minutes = ipaq_2, days = ipaq_1b, met = ipaq_mets()$light) ipaq_compute_sum(vigorous, moderate, light) ipaq_compute( data, mets = ipaq_mets(), light_days = ipaq_5b, light_mins = ipaq_6, mod_days = ipaq_3b, mod_mins = ipaq_4, vig_days = ipaq_1b, vig_mins = ipaq_2, prefix = "ipaq_", keep_all = TRUE )ipaq_compute_met(minutes = ipaq_2, days = ipaq_1b, met = ipaq_mets()$light) ipaq_compute_sum(vigorous, moderate, light) ipaq_compute( data, mets = ipaq_mets(), light_days = ipaq_5b, light_mins = ipaq_6, mod_days = ipaq_3b, mod_mins = ipaq_4, vig_days = ipaq_1b, vig_mins = ipaq_2, prefix = "ipaq_", keep_all = TRUE )
minutes |
vector of numeric minutes |
days |
vector of numeric days |
met |
met number (light = 3.3, moderate = 4.0, vigorous = 8) |
vigorous |
Vector with vigorous met calculated |
moderate |
Vector with moderate met calculated |
light |
Vector with light met calculated |
data |
data.frame containing all the ipaq data |
mets |
list generated with ipaq_mets() (default = ipaq_mets()) |
light_days |
column with the days of light activity |
light_mins |
column with the minutes of light activity |
mod_days |
column with the days of moderate activity |
mod_mins |
column with the minutes of moderate activity |
vig_days |
column with the days of vigorous activity |
vig_mins |
column with the minutes of vigorous activity |
prefix |
string to prefix column names of computed values |
keep_all |
logical, append to data.frame |
data.frame
ipaq_compute_met(): Calculate mets of an activity type
ipaq_compute_sum(): Calculate the IPAQ sum based on activities and mets
Other ipaq_functions:
ipaq_mets(),
ipaq_time_alter()
ipaq_vig_mins <- c(60, 20, 60, 25, 90, 20, 0, 75, 60, 30) ipaq_vig_days <- c(1, 3, 2, 5, 6, 1, 1, 2, 2, 4) ipaq_compute_met(ipaq_vig_mins, ipaq_vig_days, met = 8.0) light = c(1300, 300) moderate = c(200, 400) vigorous = c(0, 1300) ipaq_compute_sum(vigorous , moderate, light)ipaq_vig_mins <- c(60, 20, 60, 25, 90, 20, 0, 75, 60, 30) ipaq_vig_days <- c(1, 3, 2, 5, 6, 1, 1, 2, 2, 4) ipaq_compute_met(ipaq_vig_mins, ipaq_vig_days, met = 8.0) light = c(1300, 300) moderate = c(200, 400) vigorous = c(0, 1300) ipaq_compute_sum(vigorous , moderate, light)
IPAQ calculations require specification of met (resting metabolic rate), which are not necessarily static values. While there are defaults for each of the three categories, there should be the possibility to alter these with newer research.
ipaq_mets(light = 3.3, moderate = 4, vigorous = 8)ipaq_mets(light = 3.3, moderate = 4, vigorous = 8)
light |
numeric. default 3.3 |
moderate |
numeric. default 4.0 |
vigorous |
numeric. default 8.0 |
This is a convenience function if users need to alter the default values for one or more of the categories and is compatible with the remaining IPAQ functions in this package.
list of three
Other ipaq_functions:
ipaq_compute_met(),
ipaq_time_alter()
ipaq_mets() ipaq_mets(moderate = 5.1)ipaq_mets() ipaq_mets(moderate = 5.1)
Time is often punched as HH:MM in order to preserve correct time calculations. The ipaq calculation recure time to be in decimal minutes. This function easily changes HH:MM into decminal minutes in a data.frame It alters columns directly in the data.frame
ipaq_time_alter(data, cols = c(ipaq_2, ipaq_4, ipaq_6, ipaq_7))ipaq_time_alter(data, cols = c(ipaq_2, ipaq_4, ipaq_6, ipaq_7))
data |
data with columns to alter |
cols |
columns to alter, in tidyselect format |
data.frame
Other ipaq_functions:
ipaq_compute_met(),
ipaq_mets()
dat <- data.frame( time_1 = c("12:34", "09:33", "22:14"), time_2 = c("10:55", "16:45", "18:02") ) ipaq_time_alter(dat, cols = c(time_1, time_2))dat <- data.frame( time_1 = c("12:34", "09:33", "22:14"), time_2 = c("10:55", "16:45", "18:02") ) ipaq_time_alter(dat, cols = c(time_1, time_2))
is_hm locates columns that are time (hm) classes
is_hm(x)is_hm(x)
x |
vector |
logical vector of length==ncol(data)
## Not run: is_hm(data) ## End(Not run)## Not run: is_hm(data) ## End(Not run)
is_hms locates columns that are time (hms) classes
is_hms(x)is_hms(x)
x |
vector |
logical vector of length==ncol(data)
## Not run: is_hms(data) ## End(Not run)## Not run: is_hms(data) ## End(Not run)
Despite the prevalence of sleep complaints among psychiatric patients, few questionnaires have been specifically designed to measure sleep quality in clinical populations. The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire which assesses sleep quality and disturbances over a 1-month time interval. Nineteen individual items generate seven “component” scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score. ##Scoring
| component | name | description |
| 1 | subjective sleep quality | Answer to q 6 |
| 2 | sleep latency | Scaled sum of number of minutes before sleep (q 2) and evaluation of sleep within 30min (q 5a), scaled to a 5 point scale |
| 3 | sleep duration | Scaled score of number of hours before one falls asleep (q 4), scaled to a 5 point scale |
| 4 | habitual sleep efficiency | hours of sleep (q 4) divided by bedtime (q 1) subtracted from rising time (q 3), and scaled to a 5 point scale |
| 5 | sleep disturbances | Sum of evaluation of sleep within 30min (q 5a) and all remaining questions on sleep problems (q 5b-j), scaled to a 5 point scale |
| 6 | use of sleeping medication | Answer to question on use of sleep medication (q 7) |
| 7 | daytime dysfunction | Sum of evaluation of staying awake (q 8) and evaluation of keeping enthusiastic (q 9), scaled to a 5-point scale |
| global score | sum of the above. | If any of the above is not possible to calculate, the global sum is also not calculated |
Questions with multiple subquestions should be named in a similar manner, suffixed by the alphabetical index (psqi_5a, psqi_5b etc.). For questions 5j and 10j, the frequency of occurence should have the names psqi_5j and psqi_10e, and the freehand explanations should have any type of suffix after this to indicate a text answers (i.e. psqi_5j_Desc or psqi_5j_string, psqi_5j_freehand). As an example, LCBC has the following set-up:
psqi_1
psqi_2
psqi_3
psqi_4
psqi_5a psqi_5b psqi_5c psqi_5d psqi_5e psqi_5f psqi_5g psqi_5h psqi_5i psqi_5j psqi_5j_Coded psqi_5j_Desc
psqi_6
psqi_7
psqi_8
psqi_9
psqi_10 psqi_10a psqi_10b psqi_10c psqi_10d psqi_10e psqi_10e_Desc psqi_10e_Coded
psqi_11a psqi_11b psqi_11c psqi_11d
All 4-option questions need to be coded 0-3, not 1-4.
For question 1, 3 and 4 (bedtime, rising time, hours of sleep), data should be punched as "HH:MM". Question 2 should be punched as minutes in numbers.
Buysse et al. (1989) The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research, Psychiatry Research, 28:2, 193-213
psqi_compute_comp2(min_before_sleep, no_sleep_30min) psqi_compute_comp3(hours_sleep) psqi_compute_comp4(hours_sleep, bedtime, risingtime, ...) psqi_compute_comp5(data, sleep_troubles = matches("^psqi_05[b-j]$")) psqi_compute_comp7(keep_awake, keep_enthused) psqi_compute_global(data, cols = matches("comp[1-7]+_"), max_missing = 0) psqi_compute( data, components = 1:7, bedtime = psqi_01, min_before_sleep = psqi_02, risingtime = psqi_03, hours_sleep = psqi_04, no_sleep_30min = psqi_05a, sleepquality = psqi_06, medication = psqi_07, keep_awake = psqi_08, keep_enthused = psqi_09, sleep_troubles = matches("^psqi_05[b-j]$"), max_missing = 0, ..., prefix = "psqi_", keep_all = TRUE )psqi_compute_comp2(min_before_sleep, no_sleep_30min) psqi_compute_comp3(hours_sleep) psqi_compute_comp4(hours_sleep, bedtime, risingtime, ...) psqi_compute_comp5(data, sleep_troubles = matches("^psqi_05[b-j]$")) psqi_compute_comp7(keep_awake, keep_enthused) psqi_compute_global(data, cols = matches("comp[1-7]+_"), max_missing = 0) psqi_compute( data, components = 1:7, bedtime = psqi_01, min_before_sleep = psqi_02, risingtime = psqi_03, hours_sleep = psqi_04, no_sleep_30min = psqi_05a, sleepquality = psqi_06, medication = psqi_07, keep_awake = psqi_08, keep_enthused = psqi_09, sleep_troubles = matches("^psqi_05[b-j]$"), max_missing = 0, ..., prefix = "psqi_", keep_all = TRUE )
min_before_sleep |
column name with no. minutes before sleep (numeric) (psqi_02) |
no_sleep_30min |
column name with evaluation of sleep within 30min (0-3) (psqi_05a) |
hours_sleep |
column name with hours of sleep (decimal hours) (psqi_04) |
bedtime |
column name with bedtime (HH:MM:SS) (psqi_01) |
risingtime |
column name with rising time (HH:MM:SS) (psqi_03) |
... |
other arguments to |
data |
data frame |
sleep_troubles |
columns containing sleep problem evaluations (0-3) (psqi_05(b-j) ) |
keep_awake |
column name with evaluation of staying awake (0-3) (psqi_08) |
keep_enthused |
column name with evaluation of keeping enthusiastic (0-3) (psqi_09) |
cols |
columns containing the components |
max_missing |
Integer specifying the number of missing values to accept in the PSQI components,
before the global PSQI value is set to missing. Defaults to 0. If |
components |
integer vector of components to calculate. If all 7, global is added also |
sleepquality |
column name with evaluation of sleep quality (0-3) (psqi_06) |
medication |
column name with use of sleep mediation (0-3) (psqi_07) |
prefix |
string to prefix column names of computed values |
keep_all |
logical, append to data.frame |
a data.frame containing only the calculated components
psqi_compute_comp2(): calculate the component 2 (sleep latency)
psqi_compute_comp3(): calculate the component 3 (sleep duratione)
psqi_compute_comp4(): calculate the component 4 (habitual sleep efficiency)
psqi_compute_comp5(): calculate the component 5 (sleep disturbance)
psqi_compute_comp7(): calculate the component 7 (daytime dysfunction)
psqi_compute_global(): calculate the global scores, sum of all components
PSQI compute time in bed
psqi_compute_time_in_bed( risingtime, bedtime, risingtime_func = lubridate::hm, bedtime_func = lubridate::hm )psqi_compute_time_in_bed( risingtime, bedtime, risingtime_func = lubridate::hm, bedtime_func = lubridate::hm )
risingtime |
column name with rising time (HH:MM:SS) (psqi_03) |
bedtime |
column name with bedtime (HH:MM:SS) (psqi_01) |
risingtime_func |
function to convert time to |
bedtime_func |
function to convert time to |
Compute the TAS factors
tas_compute( data, reverse_cols = c(tas_04, tas_05, tas_10, tas_18), identify_cols = c(tas_01, tas_03, tas_06, tas_07, tas_09, tas_13, tas_14), describe_cols = c(tas_02, tas_04, tas_11, tas_12, tas_17), thinking_cols = c(tas_05, tas_08, tas_10, tas_15, tas_16, tas_18, tas_19, tas_20), prefix = "tas_", keep_all = TRUE )tas_compute( data, reverse_cols = c(tas_04, tas_05, tas_10, tas_18), identify_cols = c(tas_01, tas_03, tas_06, tas_07, tas_09, tas_13, tas_14), describe_cols = c(tas_02, tas_04, tas_11, tas_12, tas_17), thinking_cols = c(tas_05, tas_08, tas_10, tas_15, tas_16, tas_18, tas_19, tas_20), prefix = "tas_", keep_all = TRUE )
data |
Data containing TAS data |
reverse_cols |
Columns that need reversing |
identify_cols |
Columns for the "identify feeling" factor |
describe_cols |
Columns for the "describing feelings" factor |
thinking_cols |
Columns for the "externally oriented thinking" factor |
prefix |
string to prefix column names of computed values |
keep_all |
logical, append to data.frame |
Turn strings of H:M to time
time_alter(x, unit = "minute", time_func = lubridate::hm)time_alter(x, unit = "minute", time_func = lubridate::hm)
x |
string of class lubridate::hms "HH:MM:SS" |
unit |
unit to convert to |
time_func |
a function to convert x to a lubridate time vector. Default is lubridate::hms |
time <- c("02:33") time_alter(time) time_alter(time, "minute")time <- c("02:33") time_alter(time) time_alter(time, "minute")
Turn strings in class hms into periods of time.
time_deci2period(x, unit = "hour", type = "hm")time_deci2period(x, unit = "hour", type = "hm")
x |
string of class lubridate::hms "HH:MM:SS" |
unit |
unit to convert to |
type |
hms or hm |
Period
time_deci2period(8.5) time_deci2period(1.25, "minute")time_deci2period(8.5) time_deci2period(1.25, "minute")
Takes a vector of HH:MM (HH:MM:SS) information and categorizes these by a 4 level factor of time of day.
time_factor(x, time_func = lubridate::hms, tod = time_of_day())time_factor(x, time_func = lubridate::hms, tod = time_of_day())
x |
character vector of times |
time_func |
a function to convert x to a lubridate time vector. Default is lubridate::hms |
tod |
list defining when the breakpoints for the various time of day distinctions. |
factor vector
time_factor(c("12:23", "15:59", "22:10", "8:13"))time_factor(c("12:23", "15:59", "22:10", "8:13"))
Turn string time into decimal
time_hms2deci(x, unit = "hour")time_hms2deci(x, unit = "hour")
x |
string of class lubridate::hms "HH:MM:SS" |
unit |
unit to convert to |
numeric vector
time <- lubridate::hms("02:33:12") time_hms2deci(time) time_hms2deci(time, "minute")time <- lubridate::hms("02:33:12") time_hms2deci(time) time_hms2deci(time, "minute")
Create list of time of day break points
time_of_day(morning = c(5, 12), afternoon = c(12, 17), evening = c(17, 21))time_of_day(morning = c(5, 12), afternoon = c(12, 17), evening = c(17, 21))
morning |
vector of two for the hours where morning start or end in 24H |
afternoon |
vector of two for the hours where afternoon start or end in 24H |
evening |
vector of two for the hours where evening start or end in 24H |
list of fours times of day classifying the 24H of the day
time_of_day()time_of_day()
This note contains a brief description of the algorithm used to determine zygocity in recruitment in the 2000s.
| Name | Answer questions about... | Used for |
| Drop | You and your twin were like two drops of water in childhood | Pairs and singles |
| Stranger | Strangers had trouble telling the difference when you were children | Pairs and singles |
| Eye | Similarity in terms of eye color | Pairs |
| Voice | Similarity in terms of voice | Single |
| Dexter | Similarity in Dexterity | Pairs and Singles |
| Belief | What you believe yourself | Pairs and Singles |
"Single" twins here means those who have responded alone, i.e. there is no data available for both in the pair. The similarity questions that are not found in the table above, e.g. whether or not family members had problems distinguishing the twins is not used in the classification.
During calculations of the entire zygocity score, weights are applied to the different categories, depending on whether one or both twins have responded to the questionnaire.
| Name | Answer questions about... | Factor single | Factor pair |
| Drop | You and your twin were like two drops of water | 1.494 | 2.111 |
| Stranger | Strangers had trouble seeing the difference | 0.647 | 0.691 |
| Eye | Similarity in terms of eye color | 0.394 | |
| Voice | Similarity in terms of voice | 0.347 | |
| Dexter | Dexterity Similarity | 0.458 | 0.366 |
| Belief | What you believe yourself | 0.417 | 0.481 |
| Constant term in the formula | 0.007 | - 0.087 | |
zygo_calc(x, type, n = "single", recode = TRUE)zygo_calc(x, type, n = "single", recode = TRUE)
x |
integer vector of answers to one of the questionnaire questions. Should not be longer than 2. |
type |
type of question the vector is from. "drop", "stranger, "dexterity", "voice", "eye", or "belief". |
n |
string indicating number of twins in the pair available. Either "single" or "pair". |
recode |
logical indicating if data should be recoded from 1-5(7) to -1. 0. 1. |
single value of calculated score based on recoded vector and multiplied with correct factor weight.
zygo_calc(c(1), type = "eye") zygo_calc(c(1,3), type = "belief", n = "pair") zygo_calc(c(4), type = "voice")zygo_calc(c(1), type = "eye") zygo_calc(c(1,3), type = "belief", n = "pair") zygo_calc(c(4), type = "voice")
The Zygocity questionnaire was developed by the Norwegian Public Health Institute (FHI; Folkehelseinstituttet) for their twin registry studies. Its a series of questions probing the similarities between twins, to determine if they are mono- or dizygotic.
zygo_compute( data, twin_col, cols, recode = TRUE, prefix = "zygo_", keep_all = FALSE )zygo_compute( data, twin_col, cols, recode = TRUE, prefix = "zygo_", keep_all = FALSE )
data |
dara.frame with the relevant data |
twin_col |
column that codes for twin pairs. Each twin should have the same identifier here. |
cols |
columns that contain the zygocity data. Use tidy-selectors |
recode |
logical indicating if data should be recoded from 1-5(7) to -1. 0. 1. |
prefix |
string to prefix column names of computed values |
keep_all |
logical, append to data.frame |
This note contains a brief description of the algorithm used to determine zygocity in recruitment in the 2000s.
| Name | Answer questions about... | Used for |
| Drop | You and your twin were like two drops of water in childhood | Pairs and singles |
| Stranger | Strangers had trouble telling the difference when you were children | Pairs and singles |
| Eye | Similarity in terms of eye color | Pairs |
| Voice | Similarity in terms of voice | Single |
| Dexter | Similarity in Dexterity | Pairs and Singles |
| Belief | What you believe yourself | Pairs and Singles |
"Single" twins here means those who have responded alone, i.e. there is no data available for both in the pair. The similarity questions that are not found in the table above, e.g. whether or not family members had problems distinguishing the twins is not used in the classification.
During calculations of the entire zygocity score, weights are applied to the different categories, depending on whether one or both twins have responded to the questionnaire.
| Name | Answer questions about... | Factor single | Factor pair |
| Drop | You and your twin were like two drops of water | 1.494 | 2.111 |
| Stranger | Strangers had trouble seeing the difference | 0.647 | 0.691 |
| Eye | Similarity in terms of eye color | 0.394 | |
| Voice | Similarity in terms of voice | 0.347 | |
| Dexter | Dexterity Similarity | 0.458 | 0.366 |
| Belief | What you believe yourself | 0.417 | 0.481 |
| Constant term in the formula | 0.007 | - 0.087 | |
"Form value" is the value the answer option has in the data file. "Score value" is the value used in the algorithm when zygocity is calculated.
| Variable | Answer option | Form value | Score value |
| Drop | Like two drops of water | 1 | 1 |
| Like most siblings | 2 | -1 | |
| Don't know | 3 | 0 | |
| Stranger | Often | 1 | 1 |
| Occasionally | 2 | 0 | |
| Never | 3 | -1 | |
| Don't know | 4 | 0 | |
| Belief | Monozygotic | 1 | 1 |
| Dizygotic | 2 | -1 | |
| Don't know | 3 | 0 | |
| Eye, Voice & Dexter | Exactly the same | 1 | 1 |
| Almost like | 2 | 0 | |
| Different | 3 | -1 | |
| Don't know | 4 | 0 | |
No answer option is used directly in the calculations, only the score values. In the following, it is these values (-1, 0 or 1) that are used in the algorithms. E.g. has Drop in the formula value 1 for a positive answer to whether the twins were equal to two drops of water.
The higher the absolute value of the final score, the more certain / clearer the classification. For answers that reveal greater uncertainty about the similarity (e.g. a greater proportion of "almost" and "don't know"), the value will be closer to zero.
For pairs where both have answered, the pair's average values for all score values are first calculated. That is Drop = (Drop1 + Drop2) / 2, etc., where Drop1 is the score value of the response from twin 1 and Drop2 is the score value of the response from twin 2 in the same pair.
The sign of this "pair score" is then used to determine zygocity in the same way as for "single": Negative value means double, positive value means single.
If only one twin in the pair has responded, the following is calculated:
The sign of this "single score" is then used to determine the zygocity: Negative value means double egg, positive value means single egg.
By default, the functions assume that columns have names in the manner of zygocity_XX where XX is a zero-padded (i.e. zero in front of numbers below 9, eg. 09) question number of the inventory.
You may have column names in another format, but in that case you will need to supply to the functions the names of those columns using tidy-selectors (see the tidyverse packages for this).
The columns should adhere to some naming logic that is easy to specify.
The values in the columns should be the item number of the question that was answered (i.e. 1, 2, or 3, and for some questions also 4).
data.frame with computed values
"Form value" is the value the answer option has in the data file. "Score value" is the value used in the algorithm when zygocity is calculated.
| Variable | Answer option | Form value | Score value |
| Drop | Like two drops of water | 1 | 1 |
| Like most siblings | 2 | -1 | |
| Don't know | 3 | 0 | |
| Stranger | Often | 1 | 1 |
| Occasionally | 2 | 0 | |
| Never | 3 | -1 | |
| Don't know | 4 | 0 | |
| Belief | Monozygotic | 1 | 1 |
| Dizygotic | 2 | -1 | |
| Don't know | 3 | 0 | |
| Eye, Voice & Dexter | Exactly the same | 1 | 1 |
| Almost like | 2 | 0 | |
| Different | 3 | -1 | |
| Don't know | 4 | 0 | |
No answer option is used directly in the calculations, only the score values. In the following, it is these values (-1, 0 or 1) that are used in the algorithms. E.g. has Drop in the formula value 1 for a positive answer to whether the twins were equal to two drops of water.
zygo_recode(x, type)zygo_recode(x, type)
x |
vector of numbers, either 1:3 or 1:4 |
type |
Type of question to recode. Can either be 05, 06, 07 or 08, or drop, stranger, dexterity, voice, eye or belief. |
return a vector with 0, -1 or 1.
zygo_recode(c(1:4, NA), type = "eye") zygo_recode(c(1:4, NA), type = "voice") zygo_recode(c(1:3, NA), type = "drop")zygo_recode(c(1:4, NA), type = "eye") zygo_recode(c(1:4, NA), type = "voice") zygo_recode(c(1:3, NA), type = "drop")
The zygocity calculations are different depending on wheather both twins have answered the questionnaire or not. This convenience function help determine, based on the column coding for twin pairs, if one or two twins are present in the data with complete viable data. If both twins are in the data, but one twin has incomplete data, the function will return "single" for the remaining twin.
zygo_type(data, twin_col, cols = starts_with("zygo"))zygo_type(data, twin_col, cols = starts_with("zygo"))
data |
dara.frame with the relevant data |
twin_col |
column that codes for twin pairs. Each twin should have the same identifier here. |
cols |
columns that contain the zygocity data. Use tidy-selectors |
full data frame with twin type appended
Calculate the item score of a question. Function takes a single vector, with information on the question type and the twin type ('single' or 'pair') and calculates the zygocity item score.
zygo_weighted(x, type, n = "single")zygo_weighted(x, type, n = "single")
x |
vector or recoded zygocity data (-1, 0, 1) |
type |
string. one of 'drop', 'stranger', 'dexterity', 'voice', 'eye' or 'belief.' |
n |
string, 'pair' if both twins have answered, 'single' if not. |
numeric vector of weighted data