Score simulated PID-5-SF data
The PID-5-SF is a shorter version of the PID-5 with 100 items that still yields all domain and facet scores. The validity scales are still calculable but may have fewer items and their psychometric properties have not, to my knowledge, been examined with the FSF.
data("sim_pid5sf")
score_pid5(sim_pid5sf, items = 1:100, version = "SF", append = FALSE)
#> # A tibble: 100 × 30
#> pid_suspiciousness pid_impulsivity pid_submissiveness pid_callousness
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1.5 1.5 1 2.25
#> 2 2 1.25 1 2
#> 3 0.5 1.5 1.25 1.5
#> 4 2 1 2 1.25
#> 5 2.75 0.75 1 1.25
#> 6 0.75 1.5 2.75 1.5
#> 7 0.75 0 1.75 1
#> 8 0.5 0.75 1 2.25
#> 9 2.25 1.75 2 1.5
#> 10 1 1.25 1.75 1.5
#> # ℹ 90 more rows
#> # ℹ 26 more variables: pid_anhedonia <dbl>, pid_eccentricity <dbl>,
#> # pid_hostility <dbl>, pid_riskTaking <dbl>, pid_grandiosity <dbl>,
#> # pid_perceptualDysregulation <dbl>, pid_separationInsecurity <dbl>,
#> # pid_deceitfulness <dbl>, pid_perseveration <dbl>,
#> # pid_attentionSeeking <dbl>, pid_anxiousness <dbl>, pid_depressivity <dbl>,
#> # pid_withdrawal <dbl>, pid_restrictedAffectivity <dbl>, …
validity_pid5(sim_pid5sf, items = 1:100, version = "SF", append = FALSE)
#> ! A total of 96 observations (96.0%) met criteria for inconsistent responding on the INCS (0 missing).
#> ℹ Consider removing them with `dplyr::filter(df, pid_INCS < 8)`
#> ! Cut scores for the ORS-S, PRD-S, and SDTD-S have not been developed.
#> # A tibble: 100 × 5
#> pid_PNA pid_INCS pid_ORSS pid_PRDS pid_SDTDS
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 11 2 26 19
#> 2 0 14 3 17 10
#> 3 0 13 1 14 11
#> 4 0 16 3 26 16
#> 5 0 9 3 11 8
#> 6 0 15 2 21 13
#> 7 0 15 4 16 11
#> 8 0 17 1 28 11
#> 9 0 15 1 18 16
#> 10 0 15 1 16 10
#> # ℹ 90 more rowsScore real PID-5-SF data
We can repeat this process with real data that was collected at University of Kansas (KU). There should be fewer (but still some) validity problems since this is real data. We can also retain un-scored “ID” variables in the dataset.
data("ku_pid5sf")
score_pid5(
ku_pid5sf,
items = paste0("pid_", 1:100),
version = "SF",
append = FALSE
)
#> # A tibble: 386 × 30
#> pid_suspiciousness pid_impulsivity pid_submissiveness pid_callousness
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0.5 0
#> 2 0.5 0.25 1.5 0.5
#> 3 1.75 1.75 2 1.75
#> 4 0.25 1 0 0.25
#> 5 1.5 2.5 2 0.5
#> 6 0.75 0.75 0.75 0
#> 7 1.5 0.75 0.75 0.25
#> 8 0 0.25 1.25 0
#> 9 0 0 2.25 0
#> 10 0.5 0.5 2.5 0.75
#> # ℹ 376 more rows
#> # ℹ 26 more variables: pid_anhedonia <dbl>, pid_eccentricity <dbl>,
#> # pid_hostility <dbl>, pid_riskTaking <dbl>, pid_grandiosity <dbl>,
#> # pid_perceptualDysregulation <dbl>, pid_separationInsecurity <dbl>,
#> # pid_deceitfulness <dbl>, pid_perseveration <dbl>,
#> # pid_attentionSeeking <dbl>, pid_anxiousness <dbl>, pid_depressivity <dbl>,
#> # pid_withdrawal <dbl>, pid_restrictedAffectivity <dbl>, …
validity_pid5(
ku_pid5sf,
items = paste0("pid_", 1:100),
version = "SF",
append = FALSE
)
#> ! A total of 51 observations (13.2%) met criteria for inconsistent responding on the INCS (5 missing).
#> ℹ Consider removing them with `dplyr::filter(df, pid_INCS < 8)`
#> ! Cut scores for the ORS-S, PRD-S, and SDTD-S have not been developed.
#> # A tibble: 386 × 5
#> pid_PNA pid_INCS pid_ORSS pid_PRDS pid_SDTDS
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0
#> 2 0 2 0 7 7
#> 3 0 9 0 22 14
#> 4 0 4 1 10 14
#> 5 0 3 1 13 9
#> 6 0 6 0 3 3
#> 7 0 5 0 10 8
#> 8 0 4 0 2 5
#> 9 0 5 0 5 5
#> 10 0 7 0 14 7
#> # ℹ 376 more rowsSimple Standard Errors
In addition to calculating each scale score as the mean of its
corresponding items, we can also calculate a standard error for each
scale score. For the 25 facets, this is the SD of the items the
respondent actually answered divided by the square root of how many of
those items they answered. The 5 domain scores are means of three facet
scores rather than means of items, so their standard errors are taken
one level up: the SD of the three contributing facet scores divided by
the square root of 3. A standard error is NA wherever its
scale score is NA. We turn this on using
calc_se.
Note what these numbers do and do not describe. Each one summarizes how much a respondent’s answers varied within a scale; it is not an estimate of how precisely the scale measures the underlying trait, so it does not give a confidence interval for a respondent’s true score. For measurement precision, see the reliability coefficients below.
score_pid5(
ku_pid5sf,
items = paste0("pid_", 1:100),
version = "SF",
calc_se = TRUE,
append = FALSE
)
#> # A tibble: 386 × 60
#> pid_suspiciousness pid_impulsivity pid_submissiveness pid_callousness
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0.5 0
#> 2 0.5 0.25 1.5 0.5
#> 3 1.75 1.75 2 1.75
#> 4 0.25 1 0 0.25
#> 5 1.5 2.5 2 0.5
#> 6 0.75 0.75 0.75 0
#> 7 1.5 0.75 0.75 0.25
#> 8 0 0.25 1.25 0
#> 9 0 0 2.25 0
#> 10 0.5 0.5 2.5 0.75
#> # ℹ 376 more rows
#> # ℹ 56 more variables: pid_anhedonia <dbl>, pid_eccentricity <dbl>,
#> # pid_hostility <dbl>, pid_riskTaking <dbl>, pid_grandiosity <dbl>,
#> # pid_perceptualDysregulation <dbl>, pid_separationInsecurity <dbl>,
#> # pid_deceitfulness <dbl>, pid_perseveration <dbl>,
#> # pid_attentionSeeking <dbl>, pid_anxiousness <dbl>, pid_depressivity <dbl>,
#> # pid_withdrawal <dbl>, pid_restrictedAffectivity <dbl>, …Note how there are now 60 columns instead of 30. The extra columns
aren’t all shown in the preview above, but they are named with the
_se suffix, e.g., pid_anhedonia_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 estimate reliability with the reliability_pid5()
function, which returns a tibble with one row per scale and columns for
the number of items and the requested coefficients. By default it
computes both alpha and omega; for the latter,
we will need the lavaan package installed (set
omega = FALSE to skip it).
reliability_pid5(
data = ku_pid5sf,
items = sprintf("pid_%d", 1:100),
version = "SF"
)
#> # A tibble: 25 × 4
#> scale nItems alpha omega
#> <chr> <int> <dbl> <dbl>
#> 1 Suspiciousness 4 0.663 0.665
#> 2 Impulsivity 4 0.804 0.808
#> 3 Submissiveness 4 0.818 0.818
#> 4 Callousness 4 0.816 0.816
#> 5 Anhedonia 4 0.864 0.865
#> 6 Eccentricity 4 0.832 0.833
#> 7 Hostility 4 0.748 0.762
#> 8 Risk Taking 4 0.782 0.785
#> 9 Grandiosity 4 0.763 0.772
#> 10 Perceptual Dysregulation 4 0.690 0.689
#> # ℹ 15 more rowsNormative Scores
The pid_norms dataset carries the normative score
distributions published by Markon et al. (2024), including a set built
on the short form. The norm_pid5() function looks scored
columns up in those tables and returns, for each one, the T score and
percentile printed against the nearest tabled raw score. It converts
scores rather than computing them, so we hand it the output of
score_pid5() — and of validity_pid5(), if we
want those scales converted too.
scored <- score_pid5(ku_pid5sf, items = paste0("pid_", 1:100), version = "SF")
scored <- validity_pid5(scored, items = paste0("pid_", 1:100), version = "SF")For the short form the published tables cover the five domain scales,
all 25 facet scales, and the INC-S inconsistency scale. Each converted
scale gains a _ptl column, and those whose tables print T
scores also gain a _t column. INC-S is distributed as
percentiles only, so it gets no _t column. Note that
version = "SF" selects the short-form tables — the same raw
score converts differently across forms.
norm_pid5(
scored,
scores = paste0(
"pid_",
c("negativeAffectivity", "detachment", "antagonism", "disinhibition",
"psychoticism", "INCS")
),
version = "SF",
append = FALSE
)
#> Warning: ! 0 observations below and 6 above the printed range were capped to the nearest
#> printed row.
#> ℹ A capped score's T and percentile are the end row's printed values, not an
#> extrapolation.
#> # A tibble: 386 × 11
#> pid_negativeAffectivity_t pid_negativeAffectivity_ptl pid_detachment_t
#> <int> <dbl> <int>
#> 1 38 0 39
#> 2 63 0.89 59
#> 3 65 0.89 68
#> 4 54 0.72 74
#> 5 53 0.67 55
#> 6 51 0.58 42
#> 7 56 0.76 59
#> 8 53 0.67 43
#> 9 50 0.58 48
#> 10 72 0.97 64
#> # ℹ 376 more rows
#> # ℹ 8 more variables: pid_detachment_ptl <dbl>, pid_antagonism_t <int>,
#> # pid_antagonism_ptl <dbl>, pid_disinhibition_t <int>,
#> # pid_disinhibition_ptl <dbl>, pid_psychoticism_t <int>,
#> # pid_psychoticism_ptl <dbl>, pid_INCS_ptl <dbl>Every number returned is a cell of a published table: the nearest
printed row is selected and nothing is interpolated. A score that falls
outside a printed range is capped to the nearest end rather than
extrapolated, and a warning reports how many observations that happened
to. A scale the tables do not cover returns NA in both
columns with a warning naming it. Every report this function makes is a
warning, so a single suppressWarnings() call silences
it.
If the items were answered on a four-option response scale that
starts somewhere other than 0 — 1 to 4, say — pass that range as
srange and each score is reconciled to the published 0–3
metric before it is looked up, with a warning naming which scales were
adjusted and which were left alone. The per-scale formulas are given in
?norm_pid5.
Profile Plots
Once a respondent’s scores are normed, plot_pid5() draws
them as a profile against the published metric. It takes one respondent
— a profile plot shows one person — so we norm the whole dataset and
hand it a single row. Passing version = "SF" builds the
plot against the short form’s own tables.
domains <- paste0(
"pid_",
c("negativeAffectivity", "detachment", "antagonism", "disinhibition",
"psychoticism")
)
normed <- norm_pid5(scored, scores = domains, version = "SF")
plot_pid5(normed[1, ], version = "SF")
The dashed line marks T = 50, the normative sample’s mean, and the score axis spans the range the published short-form tables actually print for these scales — so the axis does not rescale from respondent to respondent and two short-form profiles are directly comparable. Nothing on the plot says whether a score is high, low, or concerning: {hitop} presents scores against norms and leaves the interpreting to you.
The short form scores all 25 facets, so level = "facet"
plots those instead, grouped into a panel per domain. The APA key ties
three facets to each domain; the remaining ten define no domain and are
grouped separately rather than dropped.
facets <- paste0("pid_", pid_scales[["SF"]]$camelCase)
normed_facets <- norm_pid5(scored, scores = facets, version = "SF")
plot_pid5(normed_facets[1, ], version = "SF", level = "facet")
Set metric = "percentile" for a percentile axis instead
of T scores; the full-form vignette shows one. The result is an ordinary
ggplot object, so you can restyle it with any ggplot2 layer.