The Personality Inventory for DSM-5 (PID-5) instrument has 220 items and yields 25 facet scales, 5 domain scales, and 5 validity scales. We can demonstrate the package’s functionality using some simulated data.
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).
Score simulated PID-5 data
The sim_pid5 dataset is built into the package and can be loaded using the data() function. It contains 100 rows (each representing a simulated participant) and 220 columns named pid_1 to pid_220 (each representing an item from the PID-5).
data("sim_pid5")
sim_pid5
#> # A tibble: 100 × 220
#> pid_1 pid_2 pid_3 pid_4 pid_5 pid_6 pid_7 pid_8 pid_9 pid_10 pid_11 pid_12
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 0 3 2 1 1 3 1 3 3 0 0 3
#> 2 3 3 0 3 0 3 2 2 1 2 0 0
#> 3 3 2 3 2 3 3 0 3 3 2 3 3
#> 4 1 3 0 2 1 0 2 0 3 2 3 2
#> 5 0 1 3 2 3 1 0 1 2 2 2 2
#> 6 2 1 1 3 3 2 2 0 1 3 1 3
#> 7 1 1 3 3 1 3 1 0 1 1 0 2
#> 8 2 0 3 0 3 2 0 1 3 1 2 0
#> 9 1 1 3 0 1 1 2 3 1 1 3 1
#> 10 0 3 2 3 3 0 1 2 1 3 0 2
#> # ℹ 90 more rows
#> # ℹ 208 more variables: pid_13 <int>, pid_14 <int>, pid_15 <int>, pid_16 <int>,
#> # pid_17 <int>, pid_18 <int>, pid_19 <int>, pid_20 <int>, pid_21 <int>,
#> # pid_22 <int>, pid_23 <int>, pid_24 <int>, pid_25 <int>, pid_26 <int>,
#> # pid_27 <int>, pid_28 <int>, pid_29 <int>, pid_30 <int>, pid_31 <int>,
#> # pid_32 <int>, pid_33 <int>, pid_34 <int>, pid_35 <int>, pid_36 <int>,
#> # pid_37 <int>, pid_38 <int>, pid_39 <int>, pid_40 <int>, pid_41 <int>, …To turn these item-level data into scale scores on the 25 facets and 5 domains, we can use the score_pid5() function. We will need to tell the function which columns contain our items and which version of the PID this is. 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 1 to column 220 so we can use items = 1:220. I am going to also set append = FALSE so that you can quickly see the scale scores. I also can set the version to "FULL" (or leave that argument off, as that is the default, shown in the example example) to let it know we are using the full 220-item version.
scores <- score_pid5(sim_pid5, items = 1:220, version = "FULL", append = FALSE)
scores
#> # A tibble: 100 × 25
#> 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 21 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>, …If I had instead set append = TRUE (or left it off, as that is the default), we would get back the sim_pid5 tibble with the scale scores added to the end as extra columns. Notice below how we now have 245 columns instead of 220 or 25.
scores <- score_pid5(sim_pid5, items = 1:220)
scores
#> # A tibble: 100 × 245
#> pid_1 pid_2 pid_3 pid_4 pid_5 pid_6 pid_7 pid_8 pid_9 pid_10 pid_11 pid_12
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 0 3 2 1 1 3 1 3 3 0 0 3
#> 2 3 3 0 3 0 3 2 2 1 2 0 0
#> 3 3 2 3 2 3 3 0 3 3 2 3 3
#> 4 1 3 0 2 1 0 2 0 3 2 3 2
#> 5 0 1 3 2 3 1 0 1 2 2 2 2
#> 6 2 1 1 3 3 2 2 0 1 3 1 3
#> 7 1 1 3 3 1 3 1 0 1 1 0 2
#> 8 2 0 3 0 3 2 0 1 3 1 2 0
#> 9 1 1 3 0 1 1 2 3 1 1 3 1
#> 10 0 3 2 3 3 0 1 2 1 3 0 2
#> # ℹ 90 more rows
#> # ℹ 233 more variables: pid_13 <int>, pid_14 <int>, pid_15 <int>, pid_16 <int>,
#> # pid_17 <int>, pid_18 <int>, pid_19 <int>, pid_20 <int>, pid_21 <int>,
#> # pid_22 <int>, pid_23 <int>, pid_24 <int>, pid_25 <int>, pid_26 <int>,
#> # pid_27 <int>, pid_28 <int>, pid_29 <int>, pid_30 <int>, pid_31 <int>,
#> # pid_32 <int>, pid_33 <int>, pid_34 <int>, pid_35 <int>, pid_36 <int>,
#> # pid_37 <int>, pid_38 <int>, pid_39 <int>, pid_40 <int>, pid_41 <int>, …Alternatively, we could provide the item column names as a character string. Typing out all 220 item names would be a hassle, but luckily this data named them consistently so we can build the names automatically using sprintf(). If we use the “pid_%d” format and apply that across the numbers 1 to 220, that will create the column names we need.
scores <- score_pid5(sim_pid5, items = sprintf("pid_%d", 1:220))
scores
#> # A tibble: 100 × 245
#> pid_1 pid_2 pid_3 pid_4 pid_5 pid_6 pid_7 pid_8 pid_9 pid_10 pid_11 pid_12
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 0 3 2 1 1 3 1 3 3 0 0 3
#> 2 3 3 0 3 0 3 2 2 1 2 0 0
#> 3 3 2 3 2 3 3 0 3 3 2 3 3
#> 4 1 3 0 2 1 0 2 0 3 2 3 2
#> 5 0 1 3 2 3 1 0 1 2 2 2 2
#> 6 2 1 1 3 3 2 2 0 1 3 1 3
#> 7 1 1 3 3 1 3 1 0 1 1 0 2
#> 8 2 0 3 0 3 2 0 1 3 1 2 0
#> 9 1 1 3 0 1 1 2 3 1 1 3 1
#> 10 0 3 2 3 3 0 1 2 1 3 0 2
#> # ℹ 90 more rows
#> # ℹ 233 more variables: pid_13 <int>, pid_14 <int>, pid_15 <int>, pid_16 <int>,
#> # pid_17 <int>, pid_18 <int>, pid_19 <int>, pid_20 <int>, pid_21 <int>,
#> # pid_22 <int>, pid_23 <int>, pid_24 <int>, pid_25 <int>, pid_26 <int>,
#> # pid_27 <int>, pid_28 <int>, pid_29 <int>, pid_30 <int>, pid_31 <int>,
#> # pid_32 <int>, pid_33 <int>, pid_34 <int>, pid_35 <int>, pid_36 <int>,
#> # pid_37 <int>, pid_38 <int>, pid_39 <int>, pid_40 <int>, pid_41 <int>, …There are other useful arguments to the function that you can read about using its documentation by typing the following into your R console: ?score_pid5 or through the package website.
Validity Scales for the PID-5
There are also several validity scales that have been developed for the full PID-5, including measures of overreporting, inconsistent responding, and positive impression management. We can use the simulated data to demonstrate the ability of the validity_pid5() function to calculate these scores and flag issues. The function arguments will be consistent with what we just learned. Note that, because the data is fake, we would expect there to be lots of validity issues.
validity_pid5(sim_pid5, items = 1:220, append = FALSE)
#> ! A total of 99 observations (99.0%) met criteria for inconsistent responding on the INC (0 missing).
#> ℹ Consider removing them with `dplyr::filter(df, pid_INC < 17)`
#> ! A total of 53 observations (53.0%) met criteria for overreporting on the ORS (0 missing).
#> ℹ Consider removing them with `dplyr::filter(df, pid_ORS < 3)`
#> ! A total of 92 observations (92.0%) met criteria for defensiveness on the SDTD (0 missing).
#> ℹ Consider removing them with `dplyr::filter(df, pid_SDTD < 19)`
#> # A tibble: 100 × 5
#> pid_PNA pid_INC pid_ORS pid_PRD pid_SDTD
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 25 2 40 26
#> 2 0 18 2 34 31
#> 3 0 32 2 34 29
#> 4 0 29 3 34 31
#> 5 0 24 0 36 17
#> 6 0 23 2 35 36
#> 7 0 42 2 31 19
#> 8 0 17 1 28 21
#> 9 0 25 4 41 24
#> 10 0 30 5 31 29
#> # ℹ 90 more rows