Content Validity Indices for Instrument Development

Computes content validity indices commonly used in instrument development and questionnaire validation, including the Item-level Content Validity Index (I-CVI), Scale-level Content Validity Index (S-CVI), modified kappa adjusted for chance agreement, Aiken's V, and Lawshe's Content Validity Ratio (CVR). Methods follow Lynn (1986) , Polit and Beck (2006) , Aiken (1985) , and Lawshe (1975) .

contentValidity

contentValidity provides functions for computing content validity indices used in questionnaire and instrument development. It is intended for researchers in education, psychology, nursing, health sciences, and other fields where new measurement instruments must be evaluated by expert panels.

Features

Content validity indices

I-CVI -- Item-level Content Validity Index (Lynn, 1986)
S-CVI/Ave -- Scale-level CVI, average method (Polit & Beck, 2006)
S-CVI/UA -- Scale-level CVI, universal agreement (Polit & Beck, 2006)
Modified kappa -- I-CVI adjusted for chance agreement (Polit, Beck, & Owen, 2007)
Aiken's V -- coefficient using the full rating scale (Aiken, 1985)
Lawshe's CVR -- Content Validity Ratio (Lawshe, 1975) with corrected critical values from Wilson, Pan, and Schumsky (2012)
Gwet's AC1 -- binary chance-corrected agreement (Gwet, 2008) — new in v0.2.0
Gwet's AC2 -- ordinal weighted chance-corrected agreement, matching irrCAC::gwet.ac1.raw() (Gwet, 2008, 2014) — new in v0.2.0

Inference and planning

Bootstrap confidence intervals for every index, percentile and BCa methods, expert-level resampling — new in v0.2.0
Sample-size planning for I-CVI estimation via Wald and Wilson methods (cv_sample_size_icvi()) — new in v0.2.0

Convenience

One-call summary via content_validity() returning all eight indices in a tidy structure
Multi-dimensional / subscale support via the subscale argument for instruments structured into domains — new in v0.2.0
Publication-ready APA tables via apa_table(), supporting data frame, markdown, HTML, and LaTeX output, with selectable per-index interpretation columns
S3 plot method for the difficulty-discrimination-style scatter diagram, with selectable y-axis index and automatic flagging of items outside the adequacy region — new in v0.2.0

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Rafhq1403/contentValidity")

Quick example

library(contentValidity)

data(cvi_example)
result <- content_validity(cvi_example)
result
#> 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
#>   ...
#>
#> Scale-level indices (overall):
#>   scvi_ave    scvi_ua mean_kappa   mean_ac1   mean_ac2
#>     0.8833     0.6000     0.8470     0.6917     0.8864

# Publication-ready table for journal manuscripts
apa_table(result, format = "markdown")

# Visualize the item map
plot(result, y_index = "gwet_ac2")

# Sample-size planning
cv_sample_size_icvi(expected = 0.85, half_width = 0.10)
#> [1] 49

# Bootstrap confidence intervals
icvi(cvi_example, ci = TRUE, n_boot = 1000, seed = 1)