A simulated dataset illustrating typical expert ratings during the content validation of a 10-item depression screening instrument. Six expert clinicians rate each item's relevance on a 4-point scale.
Format
A 6 by 10 numeric matrix with rows representing expert raters
(expert1 through expert6) and columns representing candidate items
(item1 through item10). Values are on a 4-point relevance scale:
1: not relevant
2: somewhat relevant (item needs major revision)
3: quite relevant (item needs minor revision)
4: highly relevant
Details
The pattern of ratings is realistic: some items achieve universal agreement, most show strong but imperfect agreement, and a couple of items would be flagged for revision based on standard CVI cutoffs (e.g., items 5 and 9 in this example).
Examples
data(cvi_example)
icvi(cvi_example)
#> item1 item2 item3 item4 item5 item6 item7 item8
#> 1.0000000 1.0000000 1.0000000 0.8333333 0.6666667 1.0000000 0.8333333 1.0000000
#> item9 item10
#> 0.5000000 1.0000000
content_validity(cvi_example)
#> Content Validity Analysis
#> -------------------------
#> Experts: 6
#> Items: 10
#>
#> Item-level indices:
#> item icvi mod_kappa aiken_v gwet_ac1 gwet_ac2
#> item1 1.0000 1.0000 1.0000 1.0000 1.0000
#> item2 1.0000 1.0000 0.8889 1.0000 0.8964
#> item3 1.0000 1.0000 0.7778 1.0000 0.8964
#> item4 0.8333 0.8161 0.6667 0.5385 0.8286
#> item5 0.6667 0.5646 0.6111 0.0400 0.6940
#> item6 1.0000 1.0000 0.9444 1.0000 0.9494
#> item7 0.8333 0.8161 0.6667 0.5385 0.8286
#> item8 1.0000 1.0000 0.9444 1.0000 0.9494
#> item9 0.5000 0.2727 0.5000 -0.2000 0.8714
#> item10 1.0000 1.0000 0.9444 1.0000 0.9494
#>
#> Scale-level indices (overall):
#> scvi_ave scvi_ua mean_kappa mean_ac1 mean_ac2
#> 0.8833 0.6000 0.8470 0.6917 0.8864