contentValidity 0.2.0

New features

• All relevance-scale indices (icvi(), mod_kappa(), aiken_v(), scvi_ave(), scvi_ua()) and cvr() now accept an optional ci = TRUE argument that returns bootstrap confidence intervals alongside the point estimate. Default ci = FALSE preserves the exact v0.1.0 return type (named numeric vector for item-level indices, scalar for scale-level indices), so existing code does not break.
• Bootstrap CIs support both the percentile method (default; Efron & Tibshirani, 1993) and the bias-corrected accelerated (BCa) method (DiCiccio & Efron, 1996). Default 2000 replicates, following Davison & Hinkley (1997) and Hesterberg (2015).
• Resampling is performed at the expert (row) level rather than the item (column) level, matching the standard inferential frame for inter-rater reliability analyses (Gwet, 2014).

New indices

• gwet_ac2() — Gwet’s AC2 weighted chance-corrected agreement coefficient (Gwet, 2008, 2014) for ordinal ratings. Unlike AC1 (which dichotomizes ratings before computing agreement), AC2 preserves the full ordinal information through a weight matrix. Supports weights = "quadratic" (default), "linear", "identity", or a custom q x q matrix; requires a categories argument to specify the full theoretical rating scale (e.g., 1:4 for a standard relevance scale). Implementation matches irrCAC::gwet.ac1.raw() exactly so that AC2 values are bit-for-bit reproducible against the canonical reference by Gwet (the original author).
• gwet_ac1() — Gwet’s AC1 chance-corrected agreement coefficient (Gwet, 2008). AC1 uses a marginal-adjusted chance-correction (p_e = 2pi(1-pi)), which is methodologically distinct from the fixed binomial null (C(N,A) * 0.5^N) used by Polit’s modified kappa. The two indices answer different questions about chance agreement and typically diverge when the prevalence of “relevant” ratings is far from 0.5; reporting both – alongside I-CVI – provides a more complete picture of inter-rater agreement than any single index. Wongpakaran et al. (2013, BMC Medical Research Methodology) compared AC1 with Cohen’s traditional kappa and recommended AC1 for high-prevalence rating contexts; the present package implements both AC1 and Polit’s modified kappa so the analyst can report whichever null model is most defensible. Accepts the bootstrap CI arguments described above.

Visualization

• plot.content_validity() — S3 plot method for content_validity objects. Produces an I-CVI vs. agreement-index scatter with reference lines at conventional adequacy cutoffs and automatic flagging of items outside the adequacy region. The y-axis index is selectable via y_index ("mod_kappa", "gwet_ac1", "gwet_ac2", or "aiken_v"). Parallel in spirit to the difficulty-discrimination scatter used in classical item analysis (mcqAnalysis).

Multi-dimensional / subscale analysis

• content_validity() accepts a new subscale argument that maps items to subscales (factors / domains). When supplied, the function computes scale-level indices (S-CVI/Ave, S-CVI/UA, mean kappa, mean AC1, mean AC2) per subscale in addition to the overall scale, and returns the per-subscale results as a $subscales data frame. Subscales with fewer than two items are reported with NA at the scale level. This reflects how multi-construct instruments are typically structured in practice (e.g., a depression scale with separate cognitive and somatic subscales).

Sample-size planning

• cv_sample_size_icvi() — computes the minimum number of expert raters required to estimate I-CVI within a specified confidence-interval half-width at a chosen confidence level. Supports both the closed-form Wald (normal approximation) and the Wilson score interval (Wilson, 1927; recommended by Newcombe, 1998 and Agresti & Coull, 1998 for proportion CIs near the boundary). Fills a documented gap in the content-validity literature: Lynn
  1986. and Polit & Beck (2006) recommend 5–10 experts as a rule of thumb without statistical justification, while this function gives a precision-based answer suitable for inclusion in study protocols and grant applications.

Wrapper updates

• content_validity() now includes gwet_ac1 and gwet_ac2 columns in its per-item table and mean_ac1 and mean_ac2 entries in its scale-level vector. New arguments: categories (defaults to seq(lo, hi) so AC2 works on the standard 4-point relevance scale with no extra input) and ac2_weights (defaults to "quadratic"). All existing fields are preserved unchanged for backward compatibility.
• apa_table() now includes “Gwet’s AC1” and “Gwet’s AC2” columns when the underlying content_validity object carries them, and silently omits them for v0.1.0-style objects that lack the fields.

Internals

• New internal helpers bootstrap_ci() and bca_ci() in R/bootstrap_helpers.R provide a single resampling engine reused by every index function (no code duplication across files).

contentValidity 0.1.0

Initial CRAN release.

Item-level indices

• icvi() — Item-level Content Validity Index (Lynn, 1986).
• mod_kappa() — Modified kappa adjusted for chance agreement (Polit, Beck, & Owen, 2007).
• aiken_v() — Aiken’s V coefficient using the full rating scale (Aiken, 1985).

Scale-level indices

• scvi_ave() — Scale-level Content Validity Index, average method (Polit & Beck, 2006).
• scvi_ua() — Scale-level Content Validity Index, universal agreement (Polit & Beck, 2006).

Lawshe’s Content Validity Ratio

• cvr() — Lawshe’s CVR (Lawshe, 1975).
• cvr_critical() — exact-binomial critical CVR values (Wilson, Pan, & Schumsky, 2012).

Reporting and convenience

• content_validity() — wrapper returning all relevance-scale indices in a tidy content_validity object, with a custom print() method.
• apa_table() — publication-ready APA-style tables in data frame, markdown, HTML, or LaTeX format.

Data

• cvi_example — simulated 6-expert by 10-item depression screening ratings for use in examples and the package vignette.