The HiTOP-BR instrument has 45 items and yields 7 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_hitopbr.
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 45 columns numbered
hbr01 to hbr45 containing each participant’s
rating on each item of the HiTOP-BR (on a numerical scale from 1 to
4).
data("ku_hitopbr")
ku_hitopbr
#> # A tibble: 411 × 47
#> participant biosex hbr01 hbr02 hbr03 hbr04 hbr05 hbr06 hbr07 hbr08 hbr09
#> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 P001 male 1 1 1 1 2 1 1 1 1
#> 2 P002 male 1 1 1 1 2 2 2 1 2
#> 3 P003 male 1 2 1 2 3 4 3 3 3
#> 4 P004 male 1 1 1 1 2 1 1 1 1
#> 5 P005 male 1 4 1 1 3 1 1 1 2
#> 6 P006 female 1 1 1 1 1 1 1 1 1
#> 7 P007 female 1 1 1 1 1 1 1 1 1
#> 8 P008 male 2 1 1 1 3 1 3 2 2
#> 9 P009 female 1 1 1 1 3 1 1 1 1
#> 10 P010 female 1 1 1 1 2 1 1 1 1
#> # ℹ 401 more rows
#> # ℹ 36 more variables: hbr10 <dbl>, hbr11 <dbl>, hbr12 <dbl>, hbr13 <dbl>,
#> # hbr14 <dbl>, hbr15 <dbl>, hbr16 <dbl>, hbr17 <dbl>, hbr18 <dbl>,
#> # hbr19 <dbl>, hbr20 <dbl>, hbr21 <dbl>, hbr22 <dbl>, hbr23 <dbl>,
#> # hbr24 <dbl>, hbr25 <dbl>, hbr26 <dbl>, hbr27 <dbl>, hbr28 <dbl>,
#> # hbr29 <dbl>, hbr30 <dbl>, hbr31 <dbl>, hbr32 <dbl>, hbr33 <dbl>,
#> # hbr34 <dbl>, hbr35 <dbl>, hbr36 <dbl>, hbr37 <dbl>, hbr38 <dbl>, …Basic Scoring
To turn these item-level ratings into mean scores on the 7 scales, we
can use the score_hitopbr() 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 47 so we can use
items = 3:47. I am going to also set
append = FALSE so that you can quickly see the scale
scores.
scores <- score_hitopbr(
data = ku_hitopbr,
items = 3:47,
append = FALSE
)
scores
#> # A tibble: 411 × 8
#> hbr_antagonism hbr_detachment hbr_disinhibition hbr_internalizing
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1.44 1.33 1.33 1.14
#> 2 1.33 1.33 1.33 2.43
#> 3 2.11 2.33 2.33 2.86
#> 4 1.11 1.17 1.33 1.14
#> 5 2.44 1.17 2.22 1.86
#> 6 1 1.17 1.22 1.14
#> 7 1 1 1 1
#> 8 1.67 1.5 1.33 1.86
#> 9 1.44 1.33 1.56 1.14
#> 10 1.33 1 1 1.29
#> # ℹ 401 more rows
#> # ℹ 4 more variables: hbr_somatoform <dbl>, hbr_thoughtDisorder <dbl>,
#> # hbr_externalizing <dbl>, hbr_pFactor <dbl>Appending
If I had instead set append = TRUE (or left it off, as
that is the default), we would get back the ku_hitopbr
tibble with the scale scores added to the end as extra columns. Notice
below how we now have 54 columns instead of 47.
scores <- score_hitopbr(
data = ku_hitopbr,
items = 3:47
)
scores
#> # A tibble: 411 × 55
#> participant biosex hbr01 hbr02 hbr03 hbr04 hbr05 hbr06 hbr07 hbr08 hbr09
#> <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 P001 male 1 1 1 1 2 1 1 1 1
#> 2 P002 male 1 1 1 1 2 2 2 1 2
#> 3 P003 male 1 2 1 2 3 4 3 3 3
#> 4 P004 male 1 1 1 1 2 1 1 1 1
#> 5 P005 male 1 4 1 1 3 1 1 1 2
#> 6 P006 female 1 1 1 1 1 1 1 1 1
#> 7 P007 female 1 1 1 1 1 1 1 1 1
#> 8 P008 male 2 1 1 1 3 1 3 2 2
#> 9 P009 female 1 1 1 1 3 1 1 1 1
#> 10 P010 female 1 1 1 1 2 1 1 1 1
#> # ℹ 401 more rows
#> # ℹ 44 more variables: hbr10 <dbl>, hbr11 <dbl>, hbr12 <dbl>, hbr13 <dbl>,
#> # hbr14 <dbl>, hbr15 <dbl>, hbr16 <dbl>, hbr17 <dbl>, hbr18 <dbl>,
#> # hbr19 <dbl>, hbr20 <dbl>, hbr21 <dbl>, hbr22 <dbl>, hbr23 <dbl>,
#> # hbr24 <dbl>, hbr25 <dbl>, hbr26 <dbl>, hbr27 <dbl>, hbr28 <dbl>,
#> # hbr29 <dbl>, hbr30 <dbl>, hbr31 <dbl>, hbr32 <dbl>, hbr33 <dbl>,
#> # hbr34 <dbl>, hbr35 <dbl>, hbr36 <dbl>, hbr37 <dbl>, hbr38 <dbl>, …Items as Strings
Alternatively, we could provide the item column names as a character
string. Typing out all 45 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 “hbr%02d” format and apply
that across the numbers 1 to 45, that will create the zero-padded column
names we need. If there was no zero-padding, we could have just used
“hbr%d”.
scores <- score_hitopbr(
data = ku_hitopbr,
items = sprintf("hbr%02d", 1:45),
append = FALSE
)
scores
#> # A tibble: 411 × 8
#> hbr_antagonism hbr_detachment hbr_disinhibition hbr_internalizing
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1.44 1.33 1.33 1.14
#> 2 1.33 1.33 1.33 2.43
#> 3 2.11 2.33 2.33 2.86
#> 4 1.11 1.17 1.33 1.14
#> 5 2.44 1.17 2.22 1.86
#> 6 1 1.17 1.22 1.14
#> 7 1 1 1 1
#> 8 1.67 1.5 1.33 1.86
#> 9 1.44 1.33 1.56 1.14
#> 10 1.33 1 1 1.29
#> # ℹ 401 more rows
#> # ℹ 4 more variables: hbr_somatoform <dbl>, hbr_thoughtDisorder <dbl>,
#> # hbr_externalizing <dbl>, hbr_pFactor <dbl>Scale Prefixes
Also note that each scale column has the prefix “hbr_” in its name.
You can change the prefix (e.g., setting it to "hitopbr_")
or even turn it off (e.g., setting it to "") using the
prefix argument.
scores <- score_hitopbr(
data = ku_hitopbr,
items = sprintf("hbr%02d", 1:45),
prefix = "",
append = FALSE
)
scores
#> # A tibble: 411 × 8
#> antagonism detachment disinhibition internalizing somatoform thoughtDisorder
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.44 1.33 1.33 1.14 1.25 1
#> 2 1.33 1.33 1.33 2.43 1.25 1
#> 3 2.11 2.33 2.33 2.86 2.88 1.83
#> 4 1.11 1.17 1.33 1.14 1.38 1
#> 5 2.44 1.17 2.22 1.86 1.25 1
#> 6 1 1.17 1.22 1.14 1 1
#> 7 1 1 1 1 1 1
#> 8 1.67 1.5 1.33 1.86 1.75 1.17
#> 9 1.44 1.33 1.56 1.14 1.38 1
#> 10 1.33 1 1 1.29 1 1
#> # ℹ 401 more rows
#> # ℹ 2 more variables: externalizing <dbl>, pFactor <dbl>Simple Standard Errors
Finally, 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_hitopbr(
data = ku_hitopbr,
items = sprintf("hbr%02d", 1:45),
calc_se = TRUE,
append = FALSE
)
scores
#> # A tibble: 411 × 16
#> hbr_antagonism hbr_detachment hbr_disinhibition hbr_internalizing
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1.44 1.33 1.33 1.14
#> 2 1.33 1.33 1.33 2.43
#> 3 2.11 2.33 2.33 2.86
#> 4 1.11 1.17 1.33 1.14
#> 5 2.44 1.17 2.22 1.86
#> 6 1 1.17 1.22 1.14
#> 7 1 1 1 1
#> 8 1.67 1.5 1.33 1.86
#> 9 1.44 1.33 1.56 1.14
#> 10 1.33 1 1 1.29
#> # ℹ 401 more rows
#> # ℹ 12 more variables: hbr_somatoform <dbl>, hbr_thoughtDisorder <dbl>,
#> # hbr_externalizing <dbl>, hbr_pFactor <dbl>, hbr_antagonism_se <dbl>,
#> # hbr_detachment_se <dbl>, hbr_disinhibition_se <dbl>,
#> # hbr_internalizing_se <dbl>, hbr_somatoform_se <dbl>,
#> # hbr_thoughtDisorder_se <dbl>, hbr_externalizing_se <dbl>,
#> # hbr_pFactor_se <dbl>Note how there are now 14 columns instead of 7. The extra columns
aren’t shown in the preview above, but they are named with the
_se suffix, e.g., hbr_somatoform_se.