Internal consistency of a questionnaire's subscales

Cronbach's alpha per subscale, computed on the same recoded item matrix that score_questionnaire() aggregates – so reverse-coded items are already flipped, and a negative alpha means a genuine problem rather than a forgotten reversal. McDonald's omega is added when psych is installed, and is the better-behaved statistic when a subscale's items are not equally good indicators.

Usage

score_reliability(
  data,
  instrument,
  items = NULL,
  prefix = NULL,
  scale = NULL,
  reverse_items = NULL,
  verbose = TRUE
)

Arguments

data

A data frame with one row per respondent (or per respondent and condition) and one column per item.

instrument

Instrument key, e.g. "sus", "nasa_tlx", "ueq_s", "tia", "ssq". See list_questionnaires().

items

Optional. Either the item columns in the instrument's own order, or a character vector named after the item codes (c(mental = "tlx_md", effort = "tlx_ef", ...)), which is order-proof and the safer choice for a survey export you did not lay out yourself.

prefix

Optional column-name prefix selecting the item columns, e.g. "sus_". Matching columns are sorted numerically, so sus_2 comes before sus_10. Must select exactly as many columns as the instrument has items.

scale

Optional two-element vector giving the response range your survey used, e.g. c(1, 21) for the 21-point NASA-TLX sheet or c(0, 4) for a zero-based SUS. Responses are rescaled onto the instrument's own range before scoring. Defaults to the instrument's range; responses outside it are an error rather than a silent rescale.

reverse_items

Optional items to toggle the reverse-coding of, given as item numbers or item codes. Naming an item the scoring key already reverses un-reverses it, which is what an export that stores a pair the other way round needs.

verbose

Logical. If TRUE (default), emit the one-time mapping message described above.

Value

A data frame with one row per subscale: the number of items, the number of complete cases it was computed on, alpha, omega (NA without psych), and the mean inter-item correlation. Subscales of a single item yield NA – internal consistency is not defined for them.

Caution – verify the mapping against your own survey

Item numbers, item order and item polarity are properties of the sheet a study actually administered, not of the instrument in the abstract. Survey tools renumber items, translations reorder them, short forms drop them from the middle, and semantic differentials get printed with the poles the other way round.

This package applies each instrument's published scoring key, which is the right default and is still only a default. If the sheet your participants saw differed, the scores will be wrong – and wrong quietly, because a mismatched mapping raises no error and produces entirely plausible numbers.

So: run check_questionnaire() once per instrument per study and read the mapping it prints, and double-check any figure before it goes into a paper. The mapping used is also attached to the result as the "mapping" attribute, and summarised in a console note the first time each distinct mapping is scored in a session (silence it with options(colleyRstats.quiet_questionnaires = TRUE)).

A named items argument (items = c(mental = "tlx_md", ...)) removes the positional assumption altogether and is the safer choice for an export you did not lay out yourself.

See also

score_questionnaire()

Examples

set.seed(3)
trait <- rnorm(60)
d <- as.data.frame(lapply(1:10, function(i) {
  round(pmin(pmax(3 + trait + rnorm(60, sd = 0.6), 1), 5))
}))
names(d) <- paste0("sus_", 1:10)
# Items 2, 4, 6, 8, 10 are negatively worded on the real SUS, so flip them
d[paste0("sus_", c(2, 4, 6, 8, 10))] <- 6 - d[paste0("sus_", c(2, 4, 6, 8, 10))]
score_reliability(d, "sus", prefix = "sus_")
#>       subscale n_items n_complete     alpha     omega mean_item_cor
#> 1    Usability       8         60 0.9335244 0.9351592     0.6401712
#> 2 Learnability       2         60 0.7700840        NA     0.6274786