The HiTOP-SR instrument has 405 items and yields 76 scale scores. To
demonstrate the ability of the package to calculate these scale scores,
we can use real example data (n=411) that was collected at the
University of Kansas (KU) by Girard & Gray in 2024–2025. This data
is stored in the package under the name ku_hitopsr.
First, we load the package into memory using the
library() function. If this doesn’t work, make sure you
installed the package properly (see the README on GitHub).
Next, we can load the example dataset from the package using the
data() function. It is a large tibble that contains a
participant column with a unique identifier for each
participant, a biosex column indicating whether each
participant is “female” or “male”, and then 405 columns numbered
hitop001 to hitop405 containing each
participant’s rating on each item of the HiTOP-SR (on a numerical scale
from 1 to 4).
data("ku_hitopsr")
ku_hitopsr
#> # A tibble: 411 × 407
#> participant biosex hsr001 hsr002 hsr003 hsr004 hsr005 hsr006 hsr007 hsr008
#> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 P001 male 1 1 1 1 1 2 1 2
#> 2 P002 male 2 1 1 1 2 1 1 1
#> 3 P003 male 1 2 2 3 2 1 1 1
#> 4 P004 male 1 1 2 1 1 1 1 1
#> 5 P005 male 1 2 1 1 3 1 1 1
#> 6 P006 female 1 1 1 1 1 1 1 1
#> 7 P007 female 1 1 1 1 1 1 1 1
#> 8 P008 male 1 1 1 1 1 1 1 1
#> 9 P009 female 3 2 3 1 1 1 1 1
#> 10 P010 female 1 1 1 1 1 1 1 1
#> # ℹ 401 more rows
#> # ℹ 397 more variables: hsr009 <dbl>, hsr010 <dbl>, hsr011 <dbl>, hsr012 <dbl>,
#> # hsr013 <dbl>, hsr014 <dbl>, hsr015 <dbl>, hsr016 <dbl>, hsr017 <dbl>,
#> # hsr018 <dbl>, hsr019 <dbl>, hsr020 <dbl>, hsr021 <dbl>, hsr022 <dbl>,
#> # hsr023 <dbl>, hsr024 <dbl>, hsr025 <dbl>, hsr026 <dbl>, hsr027 <dbl>,
#> # hsr028 <dbl>, hsr029 <dbl>, hsr030 <dbl>, hsr031 <dbl>, hsr032 <dbl>,
#> # hsr033 <dbl>, hsr034 <dbl>, hsr035 <dbl>, hsr036 <dbl>, hsr037 <dbl>, …Basic Scoring
To turn these item-level ratings into mean scores on the 76 scales,
we can use the score_hitopsr() function. It needs to know
what object contains the data and which columns contain the item-level
data. There are several ways we can specify the items. First, we can
provide the column numbers and use the : shortcut. In this
tibble, the items are from column 3 to column 407 so we can use
items = 3:407. I am going to also set
append = FALSE so that you can quickly see the scale
scores.
scores <- score_hitopsr(
data = ku_hitopsr,
items = 3:407,
append = FALSE
)
scores
#> # A tibble: 411 × 76
#> hsr_agoraphobia hsr_antisocialBehavior hsr_appetiteLoss hsr_bingeEating
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2 1.12 1 1.67
#> 2 1.4 1.75 1 2.67
#> 3 2.2 2.12 2 2.33
#> 4 1.2 1.25 1 2.33
#> 5 2 1.88 2 2.33
#> 6 1 1.25 1 1.33
#> 7 1 1 1.67 1.67
#> 8 1.6 1.62 1 1.33
#> 9 1.4 1.25 1.67 2
#> 10 1.2 1.38 1 1
#> # ℹ 401 more rows
#> # ℹ 72 more variables: hsr_bodilyDistress <dbl>, hsr_bodyDissatisfaction <dbl>,
#> # hsr_bodyFocus <dbl>, hsr_callousness <dbl>, hsr_checking <dbl>,
#> # hsr_cleaning <dbl>, hsr_cognitiveProblems <dbl>,
#> # hsr_conversionSymptoms <dbl>, hsr_counting <dbl>,
#> # hsr_dietaryRestraint <dbl>, hsr_difficultiesReachingOrgasm <dbl>,
#> # hsr_diseaseConviction <dbl>, hsr_dishonesty <dbl>, …Appending
If I had instead set append = TRUE (or left it off, as
that is the default), we would get back the ku_hitoppro
tibble with the scale scores added to the end as extra columns. Notice
below how we now have 483 columns instead of 407.
scores <- score_hitopsr(
data = ku_hitopsr,
items = 3:407
)
scores
#> # A tibble: 411 × 483
#> participant biosex hsr001 hsr002 hsr003 hsr004 hsr005 hsr006 hsr007 hsr008
#> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 P001 male 1 1 1 1 1 2 1 2
#> 2 P002 male 2 1 1 1 2 1 1 1
#> 3 P003 male 1 2 2 3 2 1 1 1
#> 4 P004 male 1 1 2 1 1 1 1 1
#> 5 P005 male 1 2 1 1 3 1 1 1
#> 6 P006 female 1 1 1 1 1 1 1 1
#> 7 P007 female 1 1 1 1 1 1 1 1
#> 8 P008 male 1 1 1 1 1 1 1 1
#> 9 P009 female 3 2 3 1 1 1 1 1
#> 10 P010 female 1 1 1 1 1 1 1 1
#> # ℹ 401 more rows
#> # ℹ 473 more variables: hsr009 <dbl>, hsr010 <dbl>, hsr011 <dbl>, hsr012 <dbl>,
#> # hsr013 <dbl>, hsr014 <dbl>, hsr015 <dbl>, hsr016 <dbl>, hsr017 <dbl>,
#> # hsr018 <dbl>, hsr019 <dbl>, hsr020 <dbl>, hsr021 <dbl>, hsr022 <dbl>,
#> # hsr023 <dbl>, hsr024 <dbl>, hsr025 <dbl>, hsr026 <dbl>, hsr027 <dbl>,
#> # hsr028 <dbl>, hsr029 <dbl>, hsr030 <dbl>, hsr031 <dbl>, hsr032 <dbl>,
#> # hsr033 <dbl>, hsr034 <dbl>, hsr035 <dbl>, hsr036 <dbl>, hsr037 <dbl>, …Items as Strings
Alternatively, we could provide the item column names as a character
string. Typing out all 405 item names would be a hassle, but luckily
this dataset named them consistently so we can build the names
automatically using sprintf(). If we use the “hitop%03d”
format and apply that across the numbers 1 to 405, that will create the
zero-padded column names we need. If there was no zero-padding, we could
have just used “hitop%d”.
scores <- score_hitopsr(
data = ku_hitopsr,
items = sprintf("hsr%03d", 1:405),
append = FALSE
)
scores
#> # A tibble: 411 × 76
#> hsr_agoraphobia hsr_antisocialBehavior hsr_appetiteLoss hsr_bingeEating
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2 1.12 1 1.67
#> 2 1.4 1.75 1 2.67
#> 3 2.2 2.12 2 2.33
#> 4 1.2 1.25 1 2.33
#> 5 2 1.88 2 2.33
#> 6 1 1.25 1 1.33
#> 7 1 1 1.67 1.67
#> 8 1.6 1.62 1 1.33
#> 9 1.4 1.25 1.67 2
#> 10 1.2 1.38 1 1
#> # ℹ 401 more rows
#> # ℹ 72 more variables: hsr_bodilyDistress <dbl>, hsr_bodyDissatisfaction <dbl>,
#> # hsr_bodyFocus <dbl>, hsr_callousness <dbl>, hsr_checking <dbl>,
#> # hsr_cleaning <dbl>, hsr_cognitiveProblems <dbl>,
#> # hsr_conversionSymptoms <dbl>, hsr_counting <dbl>,
#> # hsr_dietaryRestraint <dbl>, hsr_difficultiesReachingOrgasm <dbl>,
#> # hsr_diseaseConviction <dbl>, hsr_dishonesty <dbl>, …Scale Prefixes
Also note that each scale column has the prefix “hsr_” in its name.
You can change the prefix (e.g., setting it to "hitop_") or
even turn it off (e.g., setting it to "") using the
prefix argument.
scores <- score_hitopsr(
data = ku_hitopsr,
items = sprintf("hsr%03d", 1:405),
prefix = "hitop_",
append = FALSE
)
scores
#> # A tibble: 411 × 76
#> hitop_agoraphobia hitop_antisocialBeha…¹ hitop_appetiteLoss hitop_bingeEating
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2 1.12 1 1.67
#> 2 1.4 1.75 1 2.67
#> 3 2.2 2.12 2 2.33
#> 4 1.2 1.25 1 2.33
#> 5 2 1.88 2 2.33
#> 6 1 1.25 1 1.33
#> 7 1 1 1.67 1.67
#> 8 1.6 1.62 1 1.33
#> 9 1.4 1.25 1.67 2
#> 10 1.2 1.38 1 1
#> # ℹ 401 more rows
#> # ℹ abbreviated name: ¹hitop_antisocialBehavior
#> # ℹ 72 more variables: hitop_bodilyDistress <dbl>,
#> # hitop_bodyDissatisfaction <dbl>, hitop_bodyFocus <dbl>,
#> # hitop_callousness <dbl>, hitop_checking <dbl>, hitop_cleaning <dbl>,
#> # hitop_cognitiveProblems <dbl>, hitop_conversionSymptoms <dbl>,
#> # hitop_counting <dbl>, hitop_dietaryRestraint <dbl>, …Simple Standard Errors
In addition to calculating each scale score as the mean of its
corresponding items, we can also calculate each scale score’s standard
error as the SD of its corresponding items divided by the square root of
its number of items. These standard errors are especially useful when
plotting the scores as they can be converted into confidence intervals.
We turn this on using calc_se.
scores <- score_hitopsr(
data = ku_hitopsr,
items = sprintf("hsr%03d", 1:405),
calc_se = TRUE,
append = FALSE
)
scores
#> # A tibble: 411 × 152
#> hsr_agoraphobia hsr_antisocialBehavior hsr_appetiteLoss hsr_bingeEating
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2 1.12 1 1.67
#> 2 1.4 1.75 1 2.67
#> 3 2.2 2.12 2 2.33
#> 4 1.2 1.25 1 2.33
#> 5 2 1.88 2 2.33
#> 6 1 1.25 1 1.33
#> 7 1 1 1.67 1.67
#> 8 1.6 1.62 1 1.33
#> 9 1.4 1.25 1.67 2
#> 10 1.2 1.38 1 1
#> # ℹ 401 more rows
#> # ℹ 148 more variables: hsr_bodilyDistress <dbl>,
#> # hsr_bodyDissatisfaction <dbl>, hsr_bodyFocus <dbl>, hsr_callousness <dbl>,
#> # hsr_checking <dbl>, hsr_cleaning <dbl>, hsr_cognitiveProblems <dbl>,
#> # hsr_conversionSymptoms <dbl>, hsr_counting <dbl>,
#> # hsr_dietaryRestraint <dbl>, hsr_difficultiesReachingOrgasm <dbl>,
#> # hsr_diseaseConviction <dbl>, hsr_dishonesty <dbl>, …Note how there are now 152 columns instead of 76. The extra columns
aren’t shown in the preview above, but they are named with the
_se suffix, e.g., hsr_agoraphobia_se.
Scale Reliability
As we compute scale scores, we can also estimate their inter-item reliability using Cronbach’s α (alpha) or McDonald’s ω (omega total). α is fast and widely used, but it assumes tau-equivalence (all items load equally on a single factor); violations can make α under- or over-estimate reliability. ω is based on a congeneric single-factor model, allowing items to have different loadings and error variances; it typically provides a more accurate reliability estimate for unit-weighted sums. Both assume the scale is essentially unidimensional; α and ω coincide when tau-equivalence holds.
We can just add one or more of the following arguments to
score_hitopsr(): alpha and omega.
For the latter, we will need the lavaan package
installed. If requested, a table of reliability results will be printed
as a side-effect of the function (alongside any warnings from lavaan
about convergence of the factor analysis models that omega is based
on).
scores <- score_hitopsr(
data = ku_hitopsr,
items = sprintf("hsr%03d", 1:405),
alpha = TRUE,
omega = TRUE
)
#> scale alpha omega
#> 1 Agoraphobia 0.4188 0.431
#> 2 Antisocial Behavior 0.5446 0.553
#> 3 Appetite Loss 0.3666 NA
#> 4 Binge Eating 0.1103 0.218
#> 5 Bodily Distress 0.3960 0.469
#> 6 Body Dissatisfaction 0.2935 NA
#> 7 Body Focus 0.3939 0.436
#> 8 Callousness 0.4816 0.490
#> 9 Checking 0.6014 0.605
#> 10 Cleaning 0.4800 0.526
#> 11 Cognitive Problems 0.4744 0.508
#> 12 Conversion Symptoms 0.4546 0.487
#> 13 Counting 0.2714 0.333
#> 14 Dietary Restraint 0.5214 0.543
#> 15 Difficulties Reaching Orgasm 0.4128 0.447
#> 16 Disease Conviction 0.5616 0.574
#> 17 Dishonesty 0.5520 0.579
#> 18 Disorganization 0.4663 0.491
#> 19 Dissociation 0.4384 0.456
#> 20 Distress Dysphoria 0.7408 0.753
#> 21 Domineering 0.3650 0.416
#> 22 Eccentricity 0.5838 0.594
#> 23 Emotionality 0.7418 0.755
#> 24 Entitlement 0.3712 0.403
#> 25 Excessive Exercise 0.5155 0.553
#> 26 Excoriation 0.4598 0.487
#> 27 Exhibitionism 0.4215 0.441
#> 28 Fantasy Proneness 0.5749 0.592
#> 29 Food Selectivity 0.4096 0.431
#> 30 Gambling 0.3668 0.440
#> 31 Gaming 0.3686 0.372
#> 32 Grandiosity 0.3269 0.345
#> 33 Health Anxiety 0.4414 0.457
#> 34 Hoarding 0.5728 0.616
#> 35 Hyperdeliberation 0.2873 0.357
#> 36 Hypervigilance 0.6377 0.662
#> 37 Insomnia 0.4203 0.440
#> 38 Low Sexual Arousal 0.3140 0.458
#> 39 Low Sexual Interest 0.6008 0.674
#> 40 Manic Energy 0.3561 0.392
#> 41 Mistrust 0.4914 0.527
#> 42 Muscle Building 0.3529 0.487
#> 43 Nssi 0.4611 0.495
#> 44 Nightmares 0.2203 NA
#> 45 Non Persistence 0.6345 0.672
#> 46 Non Planfulness 0.5382 0.561
#> 47 Oppositionality 0.3296 0.323
#> 48 Panic 0.1326 0.244
#> 49 Paraphilias 0.4004 0.392
#> 50 Perfectionism 0.5222 0.536
#> 51 Premature Orgasm 0.3370 NA
#> 52 Problematic Shopping 0.3732 0.411
#> 53 Purging 0.2713 0.300
#> 54 Reality Distortion 0.6772 0.687
#> 55 Restlessness 0.5616 0.598
#> 56 Restricted Affectivity 0.1401 0.153
#> 57 Restricted Eating 0.5266 0.576
#> 58 Rigidity 0.3968 0.448
#> 59 Risk Aversion 0.5158 0.574
#> 60 Risk Taking 0.5511 0.570
#> 61 Risky Sex 0.1348 0.203
#> 62 Romantic Disinterest -0.0803 0.200
#> 63 Sex Related Substance Use 0.3688 0.403
#> 64 Sexual Distress 0.5135 0.526
#> 65 Sexual Pain 0.4416 0.458
#> 66 Social Aggression 0.5386 0.555
#> 67 Social Aloofness 0.5550 0.599
#> 68 Social Anxiety 0.3877 0.482
#> 69 Somatic Preoccupation 0.5924 0.661
#> 70 Specific Phobia Index 0.5931 0.598
#> 71 Submissiveness 0.1241 NA
#> 72 Suicidality 0.4398 0.466
#> 73 Trauma Reactions 0.3306 0.357
#> 74 Trichotillomania 0.1963 NA
#> 75 Well Being 0.6629 0.680
#> 76 Workaholism 0.4852 0.502Renaming Item Columns
If your dataset was collected before the item numbers were
standardized or used arbitrary variable names, you must rename your
columns to the standard format before running the scoring engine. The
rename_hitopsr_items() function handles this preparation
via two approaches.
Method 1: Legacy “Original” Pool Names
If your columns are labeled with older pool names (e.g., HiTOP_659 or
Ext_432), use method = "original". The function scans your
data frame columns, matches them against the package’s built-in item
database, and renames them to standard names.
# Setup a mock dataset with older legacy column names
legacy_data <- data.frame(
HiTOP_659 = c(1, 2, 4),
HiTOP_301 = c(3, 4, 2),
ParticipantID = c(101, 102, 103)
)
# Rename legacy columns using the native pipe
standardized_legacy <-
legacy_data |>
rename_hitopsr_items(method = "original", prefix = "HSR_")
#> Warning: Only 2 out of 405 HiTOP-SR items were successfully matched and renamed.
#> ℹ Note: If you plan to use `score_hitopsr()`, ensure uncollected items exist in
#> the data frame as `NA` columns.
standardized_legacy
#> HSR_1 HSR_2 ParticipantID
#> 1 1 3 101
#> 2 2 4 102
#> 3 4 2 103Method 2: Matching via Literal Item Text
If your dataset uses completely customized column names but you
tracked the exact question prompts presented to your participants, use
method = "text". Provide a vector of your current column
names alongside a matching vector of literal item text strings.
# Setup a mock dataset with completely custom column names
custom_data <- data.frame(
q_party = c(1, 2, 1),
q_flawless = c(4, 3, 4),
Age = c(21, 25, 30)
)
# Define the mapping pairs
my_cols <- c("q_party", "q_flawless")
my_texts <- c(
"I preferred to stay home than to go to a party.",
"I felt that my work must be flawless."
)
# Rename custom columns based on text matching
standardized_custom <- custom_data |>
rename_hitopsr_items(
method = "text",
item_cols = my_cols,
item_text = my_texts,
prefix = "HSR_"
)
#> Warning: Only 2 out of 405 HiTOP-SR items were successfully matched and renamed.
#> ℹ Note: If you plan to use `score_hitopsr()`, ensure uncollected items exist in
#> the data frame as `NA` columns.
standardized_custom
#> HSR_1 HSR_2 Age
#> 1 1 4 21
#> 2 2 3 25
#> 3 1 4 30Note: Because these small mock examples contain only a subset of
the full item pool, rename_hitopsr_items() will safely
issue a cli warning letting you know that fewer than 405 items were
matched. This perfectly accommodates researchers intentionally
administering short forms or separate diagnostic modules.