Calculate scale scores on the Personality Inventory for DSM-5: full version (PID-5, 220 items), short form version (PID-5-SF, 100 items), or brief form version (PID-5-BF, 25 items) from item-level data.
Arguments
- data
A data frame containing (at least) all the PID items (numerically scored and in order).
- items
A vector of column names (as strings) or numbers (as integers) corresponding to the PID items in order. Items must be supplied in instrument order; a misordered mapping silently scores the wrong items, so a warning is issued when the names share a common prefix and trailing number but those numbers are not ascending. Duplicated entries are an error.
- version
A string indicating the version of the PID to score: "FULL", "SF", or "BF". Will be automatically capitalized. (default =
"FULL")- srange
An optional numeric vector specifying the minimum and maximum values of the items, used for reverse-coding. (default =
c(0, 3))- prefix
An optional string to add before each scale column name. If no prefix is desired, set to an empty string
"". (default ="pid_")- missing
A string selecting how missing item responses are handled when computing scale scores.
"apa"(the default) follows the published APA scoring key: a facet or domain-item scale with more than 25% of its items unanswered is set toNA, and otherwise the raw score is prorated to the full item count and rounded to the nearest whole number before averaging (a FULL/SF domain isNAif any one of its three contributing facets isNA)."available"averages whatever items are present (rowMeans(na.rm = TRUE))."complete"returnsNAfor any scale with a missing item (rowMeans(na.rm = FALSE)). With no missing items the three agree. (default ="apa")- calc_se
An optional logical indicating whether to calculate the standard error of each scale score. Standard errors are
NAwherever their scale score isNA. (default =FALSE)- append
An optional logical indicating whether the new columns should be added to the end of the
datainput. (default =TRUE)
Value
A tibble containing all scale scores and standard
errors (if requested) and all original data columns (if requested)
Details
For the FULL and SF versions, the output includes the 25 facet
scores followed by the 5 personality-trait domain scores. Following the APA
scoring key (Step 3), each domain score is the mean of the average scores of
its 3 primary facets (the map is stored in pid_domains). The BF version
scores its 5 domains directly from its items, and adds a total score. By
default (missing = "apa") all versions apply the APA missing-data and
proration rule; use missing = "available" or missing = "complete" for
the traditional rowMeans() behaviors. For per-scale reliability estimates
(Cronbach's alpha, McDonald's omega), use reliability_pid5().
The PID-5-BF total score
version = "BF" returns a total column after its 5 domains. Markon et al.
(2024, p. 23) define it as the item-level mean over all 25 items, not
the mean of the 5 domain means: the total "can be computed by averaging the
overall score by the total number of items in the measure (i.e., 25)". With
five equal-sized domains the two definitions coincide on complete data and
differ only when items are missing, where the published rule above governs.
The total is scored like any other scale, so missing applies to it at the
25-item level. Under missing = "apa" that means it is NA when more than
a quarter of the 25 items are unanswered (7 or more) and prorated otherwise,
independently of the domains. Because a 5-item domain is dropped at 2
unanswered items while the total tolerates 6, a total can be reported
alongside one or more NA domains (at most 3 of the 5; blanking all five
requires 10 unanswered items, which blanks the total as well). This is the
published rule applied as written, not an oversight.
The FULL and SF versions have no total score: the PID-5 book defines one only for the brief form.
References
Krueger, R. F., Derringer, J., Markon, K. E., Watson, D., & Skodol, A. E. (2012). Initial construction of a maladaptive personality trait model and inventory for DSM-5. Psychological Medicine, 42, 1879-1890. doi:10.1017/s0033291711002674
Anderson, J. L., Sellbom, M., & Salekin, R. T. (2016). Utility of the Personality Inventory for DSM-5-Brief Form (PID-5-BF) in the measurement of maladaptive personality and psychopathology. Assessment, 25(5), 596–607. doi:10.1177/1073191116676889
Markon, K. E., Fossati, A., Somma, A., & Krueger, R. F. (2024).
Understanding the Personality Inventory for DSM-5 (PID-5). American
Psychiatric Association Publishing. The source for the PID-5-BF total
score's definition (p. 23) and for the normative tables in pid_norms.
Maples, J. L., Carter, N. T., Few, L. R., Crego, C., Gore, W. L., Samuel, D. B., Williamson, R. L., Lynam, D. R., Widiger, T. A., Markon, K. E., Krueger, R. F., & Miller, J. D. (2015). Testing whether the DSM-5 personality disorder trait model can be measured with a reduced set of items: An item response theory investigation of the personality inventory for DSM-5. Psychological Assessment, 27(4), 1195–1210. doi:10.1037/pas0000120
Examples
# Score the full PID-5 (25 facets + 5 domains) from the simulated data
score_pid5(sim_pid5, items = 1:220, version = "FULL", append = FALSE)
#> # A tibble: 100 × 30
#> pid_anhedonia pid_suspiciousness pid_riskTaking pid_impulsivity
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1.25 1.71 1.36 2.33
#> 2 1.38 1.57 1.43 2
#> 3 1.88 1 1.29 1.83
#> 4 1.25 2.43 1.21 1.5
#> 5 1.12 1.57 1.64 2.5
#> 6 2.12 1 1.79 1.83
#> 7 1.38 1.14 1.86 1.17
#> 8 1.5 1.71 1.86 0.667
#> 9 1.12 1.14 1.86 1.67
#> 10 1.38 1.86 2.07 2
#> # ℹ 90 more rows
#> # ℹ 26 more variables: pid_eccentricity <dbl>, pid_distractibility <dbl>,
#> # pid_restrictedAffectivity <dbl>, pid_submissiveness <dbl>,
#> # pid_withdrawal <dbl>, pid_callousness <dbl>,
#> # pid_separationInsecurity <dbl>, pid_attentionSeeking <dbl>,
#> # pid_emotionalLability <dbl>, pid_depressivity <dbl>, pid_hostility <dbl>,
#> # pid_irresponsibility <dbl>, pid_rigidPerfectionism <dbl>, …
# Short form, using the item column names instead of positions
score_pid5(sim_pid5sf, items = sprintf("pid_%d", 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>, …
# Brief form (5 domains + the total) with standard errors
score_pid5(sim_pid5bf, items = 1:25, version = "BF", calc_se = TRUE,
append = FALSE)
#> # A tibble: 100 × 12
#> pid_disinhibition pid_detachment pid_psychoticism pid_negativeAffectivity
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1.8 1.6 2 1.8
#> 2 2.2 2.2 2.2 1.4
#> 3 2.4 1.2 1.8 1.6
#> 4 2.4 2.2 0.8 0.8
#> 5 2.2 1.2 1.4 2.8
#> 6 1.8 0.6 2.2 1.2
#> 7 1 2 1.6 1.4
#> 8 1.4 1.8 1.2 1.8
#> 9 1.6 0.8 2.2 0.8
#> 10 1.2 1.8 1.4 0.6
#> # ℹ 90 more rows
#> # ℹ 8 more variables: pid_antagonism <dbl>, pid_total <dbl>,
#> # pid_disinhibition_se <dbl>, pid_detachment_se <dbl>,
#> # pid_psychoticism_se <dbl>, pid_negativeAffectivity_se <dbl>,
#> # pid_antagonism_se <dbl>, pid_total_se <dbl>