Score a TOCS-2 Questionnaire • sfsScorer

The Toronto Obsessive-Compulsive Scale 2 (TOCS-2) is a validated instrument for measuring Obsessive-Compulsive (OCD) traits. This article shows you how to score your TOCS-2 tests.

Quick Start

The code below shows how to score a TOCS-2 questionnaire using the score_tocs2() function.

library(sfsScorer)
#> sfsScorer Copyright (C) 2026 Jonathan Clutton
#> This program comes with ABSOLUTELY NO WARRANTY
#> This is free software, and you are welcome to redistribute it under certain
#> conditions
#> See conditions: <https://jclutton.github.io/sfsScorer/LICENSE.html>

#' Here's how we expect the data to be formatted
#' age (5-18)
#' gender 
#'  for non-gendered t-scores (Any gender values are acceptable)
#'  for gendered t-scores (1 = boy, 2 = girl)
#' p_respondent (1 = parent / caregiver responded, 0 = child / youth respondent)
#' All tocs questions 1-24 as tocs1...tocs24
head(random_data, 1)
#>   age gender p_respondent tocs1 tocs2 tocs3 tocs4 tocs5 tocs6 tocs7 tocs8 tocs9
#> 1  14      2            1    -1     2    -2     0     3    -1     0     1     2
#>   tocs10 tocs11 tocs12 tocs13 tocs14 tocs15 tocs16 tocs17 tocs18 tocs19 tocs20
#> 1      2      0     -2      2     -1     -1      2      0     -2     -2      2
#>   tocs21 tocs22 tocs23 tocs24
#> 1      1     -2      3     -3

# Score from a data frame
scores <- score_tocs2(df = random_data)
#> ✔ The model scored 5 observations.

# OR
# Score from a a csv or xlsx file
tocs_csv <- system.file("extdata", "sample_tocs.csv", package = "sfsScorer")
scores_csv <- score_tocs2(file = tocs_csv)
#> ✔ The model scored 5 observations.

# Data are validated by default
 df_mod <- random_data |> 
   dplyr::mutate(tocs1 = 6)
 try(scores_csv <- score_tocs2(df = df_mod))
#> There are 5 impossible values in the file.
#> The only valid values are -3, -2, -1, 0, 1, 2, 3, and NA.
#> 
#> Error in clean_file(df, test = "tocs", ignore_check = ignore_check) : 
#>   Please correct or remove these rows - "Row 1: tocs1 - 6", "Row 2: tocs1
#> - 6", "Row 3: tocs1 - 6", "Row 4: tocs1 - 6", and "Row 5: tocs1 - 6"
 
 # To ignore validation and introduce NAs use `ignore_check = TRUE`. 
 # This can impact scoring
 scores_csv <- score_tocs2(df = df_mod, ignore_check = TRUE)
#> ! 5 impossible values were changed to NA. This could impact scores. 
#> The only valid values are -3, -2, -1, 0, 1, 2, 3, and NA. To correct, review the following rows before running - "Row 1: tocs1 - 6", "Row 2: tocs1 - 6", "Row 3: tocs1 - 6", "Row 4: tocs1 - 6", and "Row 5: tocs1 - 6"
#> ✔ The model scored 0 observations.
#> ! 5 observations were not scored due to excessive missingness. 0 questions are allowed to be missing.
 
 # Allow more missingness
 # This will use prorated scores
  scores_csv <- score_tocs2(df = df_mod, ignore_check = TRUE, max_missing = 1)
#> ! 5 impossible values were changed to NA. This could impact scores. 
#> The only valid values are -3, -2, -1, 0, 1, 2, 3, and NA. To correct, review the following rows before running - "Row 1: tocs1 - 6", "Row 2: tocs1 - 6", "Row 3: tocs1 - 6", "Row 4: tocs1 - 6", and "Row 5: tocs1 - 6"
#> ✔ The model scored 5 observations.

Notes Before Starting

T-scores will be generated based on gendered and non-gendered norms. Please feel free to include children who are trans or non-binary in your dataset and leave the codes for their gender as appropriate for the individuals in your study. Non-gendered t-scores will be generated for all individuals. Gendered t-scores will only be generated when gender is coded as 1 or 2. It is recommended to use the non-gendered t-scores for trans or non-binary individuals.

Instructions

Formatting Your Data

Our first step is to prepare your raw TOCS-2 data.

Prepare a spreadsheet, preferably a .csv file, with a row for each of the tests you’d like to score.

Be sure the following columns are present in the spreadsheet and rename the columns to match the reference guide below. All of the following columns are necessary for the model. If age or p_respondent are missing from a row, the model will not return a t-score for that row.

Column Name	Additional Information	Example
age	The child’s age	5 - 18 Note: Age in a numeric form, i.e. with decimals, will provide a more accurate t-score compared to an integer-based age
gender	The child’s gender. - A full range of gender options can be included with the questionnaire. - If you want to generate non-gendered norms, any gender identities can be included and coded in any way (e.g., boy, girl, transgender, non-binary, etc). - If you want to generate gendered norms, gender must be binary and coded as 1 = boy and 2 = girl	Non-gendered t-scores: any value Gendered t-scores: 1 = Boy, 2 = Girl Note: Any gender not coded as 1 or 2 will not receive a gendered t-score.
p_respondent	Whether the survey was filled out by the parent or the child	1 = Parent / Caregiver Respondent, 0 = Child / Youth Self-Respondent
tocs1	TOCS-2 Question #1	-3 (Far Below) to 3 (Far Above)
tocs2	TOCS-2 Question #2	-3 (Far Below) to 3 (Far Above)
tocs3	TOCS-2 Question #3	-3 (Far Below) to 3 (Far Above)
tocs4	TOCS-2 Question #4	-3 (Far Below) to 3 (Far Above)
tocs5	TOCS-2 Question #5	-3 (Far Below) to 3 (Far Above)
tocs6	TOCS-2 Question #6	-3 (Far Below) to 3 (Far Above)
tocs7	TOCS-2 Question #7	-3 (Far Below) to 3 (Far Above)
tocs8	TOCS-2 Question #8	-3 (Far Below) to 3 (Far Above)
tocs9	TOCS-2 Question #9	-3 (Far Below) to 3 (Far Above)
tocs10	TOCS-2 Question #10	-3 (Far Below) to 3 (Far Above)
tocs11	TOCS-2 Question #11	-3 (Far Below) to 3 (Far Above)
tocs12	TOCS-2 Question #12	-3 (Far Below) to 3 (Far Above)
tocs13	TOCS-2 Question #13	-3 (Far Below) to 3 (Far Above)
tocs14	TOCS-2 Question #14	-3 (Far Below) to 3 (Far Above)
tocs15	TOCS-2 Question #15	-3 (Far Below) to 3 (Far Above)
tocs16	TOCS-2 Question #16	-3 (Far Below) to 3 (Far Above)
tocs17	TOCS-2 Question #17	-3 (Far Below) to 3 (Far Above)
tocs18	TOCS-2 Question #18	-3 (Far Below) to 3 (Far Above)
tocs19	TOCS-2 Question #19	-3 (Far Below) to 3 (Far Above)
tocs20	TOCS-2 Question #20	-3 (Far Below) to 3 (Far Above)
tocs21	TOCS-2 Question #21	-3 (Far Below) to 3 (Far Above)
tocs22	TOCS-2 Question #22	-3 (Far Below) to 3 (Far Above)
tocs23	TOCS-2 Question #23	-3 (Far Below) to 3 (Far Above)
tocs24	TOCS-2 Question #24	-3 (Far Below) to 3 (Far Above)

Generate Scores

Use the code below to generate your t-scores. First, you will be prompted to select your file. Second, we will check that your data are formatted properly. Third, t-scores will be generated.

If you receive an error, please correct the issue in your file, save your file, then run the score_tocs2() function again.

library(sfsScorer)

# The score_tocs2 function checks that your data are formatted correctly and generates the t-scores
tocs <- score_tocs2()

Additional options

You have the option to…


# Score from a data.frame in case the data do not exist in a csv file
tocs <- score_tocs2(df = tocs)

# Specify the input file
tocs <- score_tocs2(file = here("test_scores.csv"))

# Export results to an output folder
tocs <- score_tocs2(output_folder = file.path("C:","Users",..."yourpath"))

# Change the number of allowed missingness. 
# Default missinness is 0 questions. You can use the max_missing variable to allow more missingness
# Doing so will use a prorated score to generate a t-score. This can produce outliers and issues
tocs <- score_tocs2(file = here("test_scores.csv"), max_missing = 2)

Understanding the Output

Summary Values

Column	Description
tocs_tot	A summed score of the answered questions
tocs_miss	A count of missing values across
tocs_pro	A prorated score by dividing swan_tot by the number of answered questions

T-scores

Column	Description
tocs_gender_tscores	A t-score normed to the Spit for Science sample that adjusts for age, respondent, and gender
tocs_tscores	A t-score normed to the Spit for Science that adjusts for age and respondent