Produces, for each item within each scale, the item-rest correlation, floor and ceiling proportions, and the item mean and standard deviation.
Value
An object of class sframe_item_report: a named list with one
element per scale, each a list holding scale_id, label and
diagnostics, a data frame with one row per item and columns item_id,
mean, sd, item_rest_r, floor_pct, ceiling_pct and n_missing.
as.data.frame() stacks every scale's diagnostics into one table.
Details
Diagnostics use the scale's scoring orientation, so an item the scale
reverse-codes is reversed first, as in score_scales() and
reliability_report(). The item-rest correlation is the correlation
between an item and the sum of the scale's other items. It is computed
on respondents who answered every item in the scale, the same rows
reliability_report() uses, and n_missing counts the item's own missing
values in data.
Floor and ceiling are the proportions at the item's declared lowest and
highest response, taken from its choice set, slider limits or rating
maximum. They are NA for an item that declares no bounds, since the
sample's own extremes say nothing about a floor or ceiling effect.
Examples
# \donttest{
demo <- sframe_demo_data()
ir <- item_report(demo$responses, demo$instrument)
print(ir)
#> Item diagnostics: digital_marketing (Digital marketing effectiveness)
#>
#> item_id mean sd item_rest_r floor_pct ceiling_pct n_missing
#> 1 dm_1 3.141667 0.9982828 0.6877922 0.05000000 0.10000000 0
#> 2 dm_2 3.125000 0.9663455 0.7061626 0.05000000 0.07500000 0
#> 3 dm_3 3.191667 0.9982828 0.7026148 0.05833333 0.08333333 0
#>
#> Item diagnostics: service_quality (Service quality)
#>
#> item_id mean sd item_rest_r floor_pct ceiling_pct n_missing
#> 1 sq_1 3.008333 1.041136 0.6873656 0.06666667 0.09166667 0
#> 2 sq_2 3.100000 1.007535 0.7302414 0.04166667 0.09166667 0
#> 3 sq_3 3.058333 1.031405 0.7124990 0.05833333 0.07500000 0
#>
#> Item diagnostics: sustainability (Sustainability perception)
#>
#> item_id mean sd item_rest_r floor_pct ceiling_pct n_missing
#> 1 sus_1 3.133333 0.8786289 0.6296654 0.008333333 0.07500000 0
#> 2 sus_2 3.241667 0.9437618 0.6296654 0.033333333 0.09166667 0
#>
#> Item diagnostics: satisfaction (Tourist satisfaction)
#>
#> item_id mean sd item_rest_r floor_pct ceiling_pct n_missing
#> 1 sat_1 3.325000 1.167936 0.6904532 0.06666667 0.1916667 0
#> 2 sat_2 3.258333 1.103819 0.6904532 0.03333333 0.1583333 0
#>
#> Item diagnostics: behavioural_intention (Behavioural intention)
#>
#> item_id mean sd item_rest_r floor_pct ceiling_pct n_missing
#> 1 bi_1 3.1 1.125712 0.7299236 0.07500000 0.1250000 0
#> 2 bi_2 3.1 1.133152 0.7299236 0.08333333 0.1333333 0
#>
# one scale's diagnostics
ir[[1]]$diagnostics
#> item_id mean sd item_rest_r floor_pct ceiling_pct n_missing
#> 1 dm_1 3.141667 0.9982828 0.6877922 0.05000000 0.10000000 0
#> 2 dm_2 3.125000 0.9663455 0.7061626 0.05000000 0.07500000 0
#> 3 dm_3 3.191667 0.9982828 0.7026148 0.05833333 0.08333333 0
# every scale in one table
as.data.frame(ir)
#> scale_id item_id mean sd item_rest_r floor_pct
#> 1 digital_marketing dm_1 3.141667 0.9982828 0.6877922 0.050000000
#> 2 digital_marketing dm_2 3.125000 0.9663455 0.7061626 0.050000000
#> 3 digital_marketing dm_3 3.191667 0.9982828 0.7026148 0.058333333
#> 4 service_quality sq_1 3.008333 1.0411357 0.6873656 0.066666667
#> 5 service_quality sq_2 3.100000 1.0075346 0.7302414 0.041666667
#> 6 service_quality sq_3 3.058333 1.0314046 0.7124990 0.058333333
#> 7 sustainability sus_1 3.133333 0.8786289 0.6296654 0.008333333
#> 8 sustainability sus_2 3.241667 0.9437618 0.6296654 0.033333333
#> 9 satisfaction sat_1 3.325000 1.1679365 0.6904532 0.066666667
#> 10 satisfaction sat_2 3.258333 1.1038194 0.6904532 0.033333333
#> 11 behavioural_intention bi_1 3.100000 1.1257117 0.7299236 0.075000000
#> 12 behavioural_intention bi_2 3.100000 1.1331521 0.7299236 0.083333333
#> ceiling_pct n_missing
#> 1 0.10000000 0
#> 2 0.07500000 0
#> 3 0.08333333 0
#> 4 0.09166667 0
#> 5 0.09166667 0
#> 6 0.07500000 0
#> 7 0.07500000 0
#> 8 0.09166667 0
#> 9 0.19166667 0
#> 10 0.15833333 0
#> 11 0.12500000 0
#> 12 0.13333333 0
# }