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 × 25
#> 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 21 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 × 25
#> 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 21 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 rows