The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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.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.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).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).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
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.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).Initial CRAN release.
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).scvi_ave() — Scale-level Content Validity Index,
average method (Polit & Beck, 2006).scvi_ua() — Scale-level Content Validity Index,
universal agreement (Polit & Beck, 2006).cvr() — Lawshe’s CVR (Lawshe, 1975).cvr_critical() — exact-binomial critical CVR values
(Wilson, Pan, & Schumsky, 2012).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.cvi_example — simulated 6-expert by 10-item depression
screening ratings for use in examples and the package vignette.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.