Produces item-total correlations, floor and ceiling effect proportions, and item means and standard deviations for each item within each scale.
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
#>
# }