Current stable release: 0.6.0.
Development plans: Roadmap · Future research · Open issues. Documentation: Package website · Changelog.
contentvalidR provides quantitative tools for substantive and content-oriented scale pretesting. The package provides six complementary workflows:
- Item sorting — Anderson & Gerbing (1991) Psa/Csv, exact item-level inference following Howard & Melloy (2016), and scale-level empirical norms from Colquitt et al. (2019).
- Construct ratings — Hinkin & Tracey (1999) fully crossed ratings, HTC/HTD, Greenhouse-Geisser-aware repeated-measures item screening, and Colquitt et al. (2019) scale-level norms.
- Expert panels — Aiken’s V, Lawshe CVR, CVI/modified kappa with score intervals, item-objective congruence, and panel-level agreement.
- Delphi rounds — consensus and stability across successive expert rounds: the share of experts agreeing against a threshold fixed before the study, and between-round weighted kappa following Holey et al. (2007), with the published alternatives selectable.
- Judge heterogeneity — generalizability theory for content-validity ratings following Crocker, Llabre & Miller (1988), judge severity and rater effects, and leave-one-judge-out influence diagnostics.
- Domain coverage — blueprint coverage, and expert-perceived content structure via the multidimensional scaling and clustering procedure of Sireci & Geisinger (1992).
The first four ask whether each item behaves as intended, or, in a Delphi study, whether the panel has settled on it. The last two ask questions no item-level index can reach: whether your conclusions depend on the particular judges you recruited, and whether your item set actually covers the domain you set out to measure. An item can only be rated if someone wrote it, so a perfect relevance index says nothing about the facet you forgot.
The design goal is interpretable output rather than coefficient
dumps. Recommended workflow functions summarize what the evidence
supports, flag items that need attention, and distinguish statistical
screening from substantive decisions. Printed output defines every index
it reports, so results can be read without first consulting the source
papers; see
vignette("reading-output").
Quantitative content-validity statistics are one part of a broader validity argument. They complement, rather than replace, construct definition, domain coverage, qualitative expert feedback, cognitive interviewing, and other evidence about relevance, comprehensiveness, and comprehensibility.
All six recommended workflows share a stable object contract. A fitted
sort_validity(), rating_validity(), expert_validity(),
delphi_validity(), judge_validity(), or domain_validity() object
always contains:
results— evidence at the workflow’s unit of analysis;scale_summary— target-scale or panel-level evidence;settings— analysis choices;design— sample-size, missingness, and design metadata; anddetails— method-specific supporting results.
The unit of analysis in results differs by workflow, which matters
when writing code against them: sort_validity(), rating_validity(),
expert_validity(), and delphi_validity() return one row per
item, judge_validity() one row per judge, and
domain_validity() one row per blueprint cell.
Every results table also includes a common status field with the
restrained categories Supported, Review, Insufficient data,
or Descriptive only. Method-specific recommendation wording is
retained alongside it—for example, item-sort and construct-rating
workflows still use Retain when their full statistical screening
criterion is met. This keeps the methods faithful to their evidentiary
role while making programmatic use consistent across workflows.
summary() uses the same common count fields across every workflow, and
print()/plot() retain method-appropriate displays. Compatibility
aliases such as rating_fit$contrasts and expert_fit$scale remain
available for code written before v0.0.6.
The six recommended workflows, the object contract they share, and the handoff to empirical validation are stable: breaking changes wait for a major version and never arrive without a deprecation cycle. Component indices and planning helpers are supported to the same standard and may gain arguments. Auxiliary and compatibility helpers may be deprecated with one minor release of warning. A changed default is treated as a breaking change, because it can silently change published numbers.
See ?contentvalidR for the full policy, including how deprecation
works and what the handoff schema guarantees a downstream package.
Install the current stable release from the JUhalt R-universe:
install.packages(
"contentvalidR",
repos = c(
"https://juhalt.r-universe.dev",
"https://cloud.r-project.org"
)
)Install the current development version directly from GitHub:
# install.packages("remotes")
remotes::install_github("JUhalt/contentvalidR")library(contentvalidR)
sort_dat <- data.frame(
item = rep(c("Clear 1", "Clear 2", "Needs review"), each = 20),
rater = rep(1:20, 3),
target_construct = "A",
assigned_construct = c(
rep("A", 18), rep("B", 2),
rep("A", 16), rep("B", 4),
rep("A", 12), rep("B", 8)
)
)
fit <- sort_validity(sort_dat)
fit
#> contentvalidR item-sort analysis
#> --------------------------------
#> Items: 3 | Raters: 20 | Target scales: 1
#> Item inference: Howard-Melloy exact target-count test (p0 = 0.50, alpha = 0.050)
#> Judges: naive
#>
#> 2 item(s) meet the exact target-assignment criterion; 1 item(s) are flagged for review.
#> Review: Needs review
#>
#> Item-level evidence:
#> item target n n_target competitor psa psa_low psa_high csv p_value
#> Clear 1 A 20 18 B 0.9 0.699 0.972 0.8 0.000
#> Clear 2 A 20 16 B 0.8 0.584 0.919 0.6 0.006
#> Needs review A 20 12 B 0.6 0.387 0.781 0.2 0.252
#> recommendation
#> Retain
#> Retain
#> Review
#>
#> 95% intervals for proportions: Wilson score (the default). Newcombe (1998)
#> compared seven methods and recommends score intervals over the Wald
#> interval. An interval reflects how few ratings an item received, not
#> whether the right judges were chosen.
#>
#> Scale-level Colquitt benchmark summary:
#> target n_items mean_psa psa_strength mean_csv csv_strength
#> A 3 0.767 Moderate 0.533 Moderate
#> benchmark_set
#> Overall (not correlation-normed)
#>
#> Colquitt labels are empirical percentile norms derived from scale-level averages,
#> not universal cutoffs or automatic scale-retention rules. They place a scale
#> against published scales; Psa and Csv sit on different scales, so their labels
#> are not comparable with each other.
#>
#> What these columns mean
#> psa -- Proportion of Substantive Agreement. Share of judges who assigned
#> the item to the construct it was written for. Higher means judges
#> recognized the item as belonging where you intended. (0 to 1; higher
#> is stronger)
#> psa_low/psa_high -- Interval for Psa. Lower and upper limits of an
#> interval around Psa. A wide interval means few judges sorted the
#> item, so a different sample of judges could plausibly give a quite
#> different Psa. (between 0 and 1; the method and level are named in
#> the output)
#> csv -- Coefficient of Substantive Validity. How much more often the item
#> went to its intended construct than to the alternative construct
#> judges chose most. It rewards being distinctly right, not merely
#> often right. (-1 to 1; 0 means the intended construct and its closest
#> rival were chosen equally often)
#> competitor -- Strongest competing construct. The construct, other than
#> the intended one, that judges chose most often for this item.
#> p_value -- Howard-Melloy exact test. Probability of seeing at least this
#> many target assignments if judges were assigning at the chance rate
#> p0. Small values mean the item's assignment pattern is unlikely to be
#> chance. (0 to 1; compared against alpha)
#>
#> What the status labels mean
#> Supported -- The evidence met the criteria set for this analysis.
#> Review -- Something here needs a closer look. This is not an instruction
#> to delete anything.
#> Insufficient data -- Too little usable data to reach a judgment.
#> Descriptive only -- Reported for description only; no decision rule was
#> applied.
#> Each workflow also uses its own wording in the recommendation column
#> (Retain, Strong support, Typical, Covered, and so on). Those words map
#> onto the shared statuses above.
#>
#> See `contentvalid_glossary()` for all terms, or set
#> `options(contentvalidR.show_key = FALSE)` to hide this key.
#>
#> 'Review' is not an automatic deletion decision. Use theory, construct-domain coverage,
#> item wording, and qualitative judge feedback alongside these statistics.
summary(fit)
#> Summary of item-sort content-validity evidence
#> -------------------------------------------
#> Retain: 2 of 3 item(s)
#> Review: 1 of 3 item(s)
#>
#> Target-scale evidence:
#> target n_items n_retain n_review mean_psa psa_strength mean_csv csv_strength
#> A 3 2 1 0.767 Moderate 0.533 Moderate
#> overall_strength
#> Moderate
#>
#> A: Generally supportive normative standing, with at least one dimension in the moderate range; review weaker items before finalizing.
#>
#> Items needing attention:
#> item target competitor psa csv p_value
#> Needs review A B 0.6 0.2 0.252
#> issue recommendation
#> Target favored, exact criterion not met Review
#>
#> Interpret scale norms and item flags alongside theory, domain coverage, and qualitative feedback.
#> This analysis does not by itself establish comprehensiveness or the full content-validity argument.The workflow deliberately separates two levels of evidence:
- Item level: Psa/Csv plus the exact Howard-Melloy target-count rule
produce Retain, Review, or Insufficient data flags. Psa is
reported with an interval (
psa_low,psa_high), so an item sorted by few judges does not look more settled than it is. The output also names the strongest competing construct so a weak item is diagnostically useful rather than just “non-significant.” - Target-scale level: Psa and Csv are averaged across the target scale’s items and interpreted using Colquitt et al. (2019)’s empirical percentile norms for definitional correspondence and distinctiveness. This matches how those norms were constructed.
Review deliberately does not mean automatic deletion. Likewise,
Colquitt categories such as Strong or Moderate are empirical
normative standing, not universal pass/fail cutoffs.
If substantive data provide the average correlation between a focal scale and its orbiting scales, the workflow can select Colquitt et al.’s correlation-conditional norm set:
sort_validity(sort_dat, orbiting_r = .42)$scale_summary
#> target n_items n_items_usable n_retain n_review mean_psa psa_strength
#> 1 A 3 3 2 1 0.7666667 Moderate
#> mean_csv csv_strength orbiting_r
#> 1 0.5333333 Moderate 0.42
#> benchmark_set benchmark_applicable
#> 1 More moderate focal-orbiting correlation (.35-.50) TRUE
#> overall_strength
#> 1 Moderate
#> evidence
#> 1 Generally supportive normative standing, with at least one dimension in the moderate range; review weaker items before finalizing.
colquitt_benchmarks("csv", orbiting_r = .42)
#> statistic benchmark_set benchmark_label
#> 1 csv moderate More moderate focal-orbiting correlation (.35-.50)
#> 2 csv moderate More moderate focal-orbiting correlation (.35-.50)
#> 3 csv moderate More moderate focal-orbiting correlation (.35-.50)
#> 4 csv moderate More moderate focal-orbiting correlation (.35-.50)
#> 5 csv moderate More moderate focal-orbiting correlation (.35-.50)
#> interpretation percentile minimum
#> 1 Very Strong 80th-99th 0.83
#> 2 Strong 60th-79th 0.61
#> 3 Moderate 40th-59th 0.52
#> 4 Weak 20th-39th 0.01
#> 5 Lack of 0th-19th -InfThe published norms were developed with naive judges representative of the target population. If the sort used expert judges, declare that explicitly; the package will suppress Colquitt labels rather than apply an unsupported benchmark:
sort_validity(sort_dat, judge_type = "expert")$scale_summary
#> target n_items n_items_usable n_retain n_review mean_psa psa_strength
#> 1 A 3 3 2 1 0.7666667 <NA>
#> mean_csv csv_strength orbiting_r benchmark_set
#> 1 0.5333333 <NA> NA Overall (not correlation-normed)
#> benchmark_applicable overall_strength
#> 1 FALSE <NA>
#> evidence
#> 1 Colquitt norms not applied because this workflow was marked as using expert judges.sort_power(N = c(20, 30, 40), true_p = c(.60, .70, .80))
#> Exact item-sort planning analysis
#> ---------------------------------
#> Retention rule: p0 = 0.50, alpha = 0.050
#>
#> N true_p critical_n_target minimum_observed_psa power
#> 20 0.6 15 0.750 0.126
#> 30 0.6 20 0.667 0.291
#> 40 0.6 26 0.650 0.317
#> 20 0.7 15 0.750 0.416
#> 30 0.7 20 0.667 0.730
#> 40 0.7 26 0.650 0.807
#> 20 0.8 15 0.750 0.804
#> 30 0.8 20 0.667 0.974
#> 40 0.8 26 0.650 0.992
#>
#> Power is the exact probability of reaching the required target-assignment count
#> under the assumed true target-assignment probability.sort_power() gives the exact probability of reaching the Howard-Melloy
retention count under each planned N and assumed true
target-assignment probability.
Researchers who need the component statistics directly can still use:
compute_psa(sort_dat)
#> item target n_total n n_missing n_target psa psa_low psa_high
#> 1 Clear 1 A 20 20 0 18 0.9 0.6989664 0.9721335
#> 2 Clear 2 A 20 20 0 16 0.8 0.5839826 0.9193423
#> 3 Needs review A 20 20 0 12 0.6 0.3865815 0.7811935
compute_csv(sort_dat)
#> item target n_total n n_missing n_target competitor n_other_max csv
#> 1 Clear 1 A 20 20 0 18 B 2 0.8
#> 2 Clear 2 A 20 20 0 16 B 4 0.6
#> 3 Needs review A 20 20 0 12 B 8 0.2
csv_binom_test(n_c = 15, N = 20)
#> $p.value
#> [1] 0.02069473
#>
#> $estimate
#> [1] 0.75
#>
#> $conf.int
#> [1] 0.5444176 1.0000000
#> attr(,"conf.level")
#> [1] 0.95
#>
#> $critical_n_target
#> [1] 15
#>
#> $passes_chance
#> [1] TRUE
#>
#> $decision
#> [1] "significant"
#>
#> $interpretation
#> [1] "Target assignments exceed the exact chance criterion."
interpret_colquitt(.70, "csv")
#> statistic value benchmark_set benchmark_label interpretation
#> 1 csv 0.7 overall Overall (not correlation-normed) Strong
#> applicable
#> 1 TRUE
#> note
#> 1 Empirical percentile norm from scale-level averages; not a universal cutoff.Missing assignments are excluded itemwise and are reported explicitly in
n_missing so the effective denominator is visible.
In the Hinkin-Tracey design, the same judge rates each item against
every construct definition. rating_validity() treats that dependence
explicitly rather than analyzing the ratings as independent groups.
set.seed(12)
rating_dat <- expand.grid(
item = c("A1", "A2", "B1"),
rater = 1:20,
construct = c("A", "B", "C")
)
rating_dat$target_construct <- ifelse(rating_dat$item == "B1", "B", "A")
rating_dat$rating <- ifelse(
rating_dat$construct == rating_dat$target_construct,
pmin(5, pmax(1, round(rnorm(nrow(rating_dat), 4.4, .6)))),
pmin(5, pmax(1, round(rnorm(nrow(rating_dat), 2.1, .7))))
)
rfit <- rating_validity(rating_dat, scale_min = 1, scale_max = 5)
rfit
#> contentvalidR construct-rating analysis
#> ---------------------------------------
#> Items: 3 | Raters: 20 | Target scales: 2 | Constructs: 3
#> Design: within-judge ratings | Scale: 1 to 5
#> Item inference: one-way repeated-measures ANOVA (Greenhouse-Geisser corrected omnibus p) plus planned paired target-versus-orbiting contrasts
#> Planned-contrast adjustment: none
#> Judges: naive
#>
#> 3 item(s) meet the full item-level screening criterion; 0 item(s) are flagged for review.
#>
#> Item-level evidence:
#> item target n_complete strongest_competitor htc htd p_value max_contrast_p
#> A1 A 20 C 0.88 0.619 0 0
#> A2 A 20 B 0.84 0.531 0 0
#> B1 B 20 C 0.89 0.637 0 0
#> recommendation
#> Retain
#> Retain
#> Retain
#>
#> Target-scale Colquitt benchmark summary:
#> target n_items n_htc n_htd mean_htc htc_strength mean_htd htd_strength
#> A 2 2 2 0.86 Moderate 0.575 Very Strong
#> B 1 1 1 0.89 Strong 0.637 Very Strong
#> benchmark_set
#> overall
#> overall
#>
#> Colquitt labels are empirical percentile norms for scale-level HTC/HTD averages, not universal cutoffs.
#> HTC is an average rating and HTD is a difference between ratings, so they sit on
#> different scales with different typical values. A high HTC can be labeled Weak in
#> the same analysis where a much smaller HTD is labeled Very Strong. Compare each
#> index against its own benchmark, never against the other index's number.
#>
#> What these columns mean
#> htc -- Hinkin-Tracey Correspondence. Average rating of the item against
#> its intended construct definition, expressed as a proportion of the
#> rating scale. (0 to 1; higher is stronger)
#> htd -- Hinkin-Tracey Distinctiveness. How far the intended construct's
#> average rating exceeds the best competing construct's, as a
#> proportion of the rating scale. It is a difference, so its typical
#> values are far smaller than HTC's. (usually a small positive number;
#> higher is stronger)
#>
#> What the status labels mean
#> Supported -- The evidence met the criteria set for this analysis.
#> Review -- Something here needs a closer look. This is not an instruction
#> to delete anything.
#> Insufficient data -- Too little usable data to reach a judgment.
#> Descriptive only -- Reported for description only; no decision rule was
#> applied.
#> Each workflow also uses its own wording in the recommendation column
#> (Retain, Strong support, Typical, Covered, and so on). Those words map
#> onto the shared statuses above.
#>
#> See `contentvalid_glossary()` for all terms, or set
#> `options(contentvalidR.show_key = FALSE)` to hide this key.
#>
#> 'Review' is not an automatic deletion decision. Consider construct definitions, item wording,
#> orbiting-construct choice, domain coverage, and qualitative judge feedback.
summary(rfit)
#> Summary of construct-rating content-validity evidence
#> ---------------------------------------------------
#> Retain: 3 of 3 item(s)
#> Review: 0 of 3 item(s)
#>
#> Target-scale evidence:
#> target n_items n_htc n_htd n_retain n_review mean_htc htc_strength mean_htd
#> A 2 2 2 2 0 0.86 Moderate 0.575
#> B 1 1 1 1 0 0.89 Strong 0.637
#> htd_strength overall_strength
#> Very Strong Moderate
#> Very Strong Strong
#>
#> A: Generally supportive normative standing, with at least one content-validity dimension in the moderate range; inspect weaker items and construct overlap before finalizing the scale.
#> B: Strong normative standing on the weaker of definitional correspondence (HTC) and distinctiveness (HTD).
#>
#> All analyzed items met the item-level inferential screening criterion.
#>
#> Interpret these results alongside theory, domain coverage, and qualitative feedback.
#> The analysis does not by itself establish comprehensiveness or the full content-validity argument.The workflow combines two descriptive indices with direct item-level screening:
- HTC (Hinkin-Tracey correspondence): how strongly ratings match the intended definition;
- HTD (Hinkin-Tracey distinctiveness): how much intended-definition ratings exceed orbiting-definition ratings;
- a one-way repeated-measures ANOVA for each item; and
- planned paired contrasts comparing the target definition with each orbiting definition.
As with the item-sort workflow, Retain and Review are screening
labels rather than automatic editorial decisions. The output names the
strongest orbiting competitor so a weak item tells the researcher
where the conceptual overlap appears. Colquitt HTC/HTD labels are
applied to target-scale averages, not treated as universal item-level
cutoffs.
Low-level components remain available:
htc(rating_dat, scale_min = 1, scale_max = 5)
#> item target n_target target_mean anchors htc
#> 1 A1 A 20 4.40 5 0.88
#> 2 A2 A 20 4.20 5 0.84
#> 3 B1 B 20 4.45 5 0.89
htd(rating_dat, scale_min = 1, scale_max = 5)
#> item target n_complete n_pairs target_mean_complete strongest_competitor
#> 1 A1 A 20 40 4.40 C
#> 2 A2 A 20 40 4.20 B
#> 3 B1 B 20 40 4.45 C
#> competitor_mean anchors htd
#> 1 1.95 5 0.61875
#> 2 2.10 5 0.53125
#> 3 2.00 5 0.63750
anova_content(rating_dat)
#> item target design n_raters n_complete n_constructs target_mean
#> 1 A1 A within 20 20 3 4.40
#> 2 A2 A within 20 20 3 4.20
#> 3 B1 B within 20 20 3 4.45
#> strongest_competitor competitor_mean F df1 df2 p
#> 1 C 1.95 108.55245 2 38 1.941920e-16
#> 2 B 2.10 66.92593 2 38 3.531492e-13
#> 3 C 2.00 94.20683 2 38 1.873865e-15
#> epsilon_gg df1_gg df2_gg p_gg p_screen partial_eta2
#> 1 0.8571129 1.714226 32.57029 2.109513e-14 2.109513e-14 0.8510417
#> 2 0.7606524 1.521305 28.90479 1.544388e-10 1.544388e-10 0.7788793
#> 3 0.9532879 1.906576 36.22494 7.805392e-15 7.805392e-15 0.8321656
#> min_mean_diff max_contrast_p contrast_pass posthoc_pass
#> 1 2.45 4.238082e-10 TRUE TRUE
#> 2 2.10 6.543223e-08 TRUE TRUE
#> 3 2.45 2.290289e-10 TRUE TRUEExpert panels answer several different questions, so
expert_validity() uses an explicit mode rather than pretending that
Aiken V, CVR, CVI, and IOC are interchangeable.
expert_ratings <- matrix(
c(4,4,4,4,4,4,
4,4,4,3,4,4,
4,3,4,4,3,4),
nrow = 6,
dimnames = list(NULL, c("Item1", "Item2", "Item3"))
)
efit <- expert_validity(
expert_ratings,
mode = "relevance",
lo = 1, hi = 4,
seed = 1
)
efit
#> contentvalidR expert-panel analysis
#> -----------------------------------
#> Mode: relevance
#> Items: 3 | Experts/item: 6
#> Mean Aiken V: 0.944 | S-CVI/Ave: 1 | S-CVI/UA: 1
#> Strong support: 3 | Support: 0 | Review: 0
#> Panel agreement, Krippendorff's alpha (ordinal): 0.018 (95% interval -0.133
#> to 0.15). Identical rating pairs: 71.1%
#>
#> item N V ci_low ci_high I_CVI I_CVI_low I_CVI_high kappa_mod
#> Item1 6 1.000 0.824 1.000 1 0.61 1 1
#> Item2 6 0.944 0.742 0.990 1 0.61 1 1
#> Item3 6 0.889 0.672 0.969 1 0.61 1 1
#> recommendation
#> Strong support
#> Strong support
#> Strong support
#>
#> ci_low and ci_high bound Aiken's V (Penfield-Giacobbi score interval);
#> I_CVI_low and I_CVI_high bound I-CVI.
#> 95% intervals for proportions: Wilson score (the default). Newcombe (1998)
#> compared seven methods and recommends score intervals over the Wald
#> interval. An interval reflects how few ratings an item received, not
#> whether the right judges were chosen.
#>
#> Panel agreement is one coefficient for the whole panel, whereas kappa_mod
#> describes each item. Alpha can be low when nearly every rating is the same
#> value, even on a panel that agrees closely, so read it beside the share of
#> identical rating pairs. A low alpha with many identical pairs is not by
#> itself evidence of a poor panel. Print `details$agreement` for the full
#> explanation and interval details.
#>
#> CVI thresholds shown by the workflow are common panel-size guidelines, not universal validity cutoffs.
#>
#> What these columns mean
#> V -- Aiken's V. Relevance index that rescales the experts' average rating
#> to run from 0 to 1 given the bounds of the rating scale used. (0 to
#> 1; higher is stronger)
#> I_CVI -- Item-level Content Validity Index. Proportion of experts who
#> rated the item as relevant, after applying the relevance cut. (0 to
#> 1; compared against a panel-size guideline)
#> I_CVI_low/I_CVI_high -- Interval for I-CVI. Lower and upper limits of an
#> interval around I-CVI. Expert panels are usually small, so these
#> intervals are often wide: a single I-CVI value can look more settled
#> than the number of experts behind it supports. (between 0 and 1; the
#> method and level are named in the output)
#> kappa_mod -- Modified kappa. I-CVI adjusted for the chance that experts
#> would have agreed even if rating at random. With small panels, chance
#> agreement is substantial, which is why the raw I-CVI alone can
#> overstate consensus. (0 to 1; higher is stronger)
#> agreement -- Panel-level agreement. One coefficient describing how
#> consistently the whole panel rated the item set: Krippendorff's alpha
#> by default, or Gwet's AC1 if chosen. It is separate from modified
#> kappa, which describes one item at a time. (1 is perfect agreement
#> and 0 is agreement no better than chance; it can be low on a
#> close-agreeing panel whose ratings cluster on one value)
#>
#> What the status labels mean
#> Supported -- The evidence met the criteria set for this analysis.
#> Review -- Something here needs a closer look. This is not an instruction
#> to delete anything.
#> Insufficient data -- Too little usable data to reach a judgment.
#> Descriptive only -- Reported for description only; no decision rule was
#> applied.
#> Each workflow also uses its own wording in the recommendation column
#> (Retain, Strong support, Typical, Covered, and so on). Those words map
#> onto the shared statuses above.
#>
#> See `contentvalid_glossary()` for all terms, or set
#> `options(contentvalidR.show_key = FALSE)` to hide this key.
#>
#> Use quantitative indices alongside expert comments, construct coverage, and comprehensibility review.
summary(efit)
#> Summary of expert-panel content-validity evidence
#> ---------------------------------------------
#> Mode: relevance
#> Supported: 3 | Review: 0
#> Panel agreement, Krippendorff's alpha (ordinal): 0.018 (95% interval -0.133
#> to 0.15). Identical rating pairs: 71.1%
#> No items were flagged by the workflow's quantitative review rules.
#>
#> These summaries support, but do not replace, qualitative content review.Relevance mode reports Aiken’s V with the Penfield-Giacobbi score confidence interval, I-CVI with its own interval, Polit-Beck-Owen modified kappa, S-CVI/Ave, S-CVI/UA, and panel-level agreement. The workflow displays common panel-size CVI guidelines as review aids, not universal validity cutoffs. Aiken V is not converted into an automatic deletion rule.
Panel-level agreement is one coefficient for how consistently the panel
rated the whole item set; modified kappa still describes each item. The
default is Krippendorff’s alpha on the ordinal ratings (Hayes &
Krippendorff, 2007), and agreement_level switches to nominal or
interval. Its bootstrap interval resamples items with all of their
ratings, following Zapf et al. (2016); set seed to make it
reproducible. In the example above, alpha is near zero even though most
rating pairs are identical: the three items were rated almost the same,
so the ratings barely distinguish one item from another. That is why the
output reports identical rating pairs beside alpha. Gwet’s AC1 is
available through agreement = "ac1" but is never the default, and its
output repeats the critique in Vach and Gerke (2023).
panel_agreement() runs the same analysis on any rater-by-item matrix.
I-CVI here, like Psa in sort_validity(), is a proportion of a small
panel, so it comes with an interval. The Wilson score interval is the
default, following Newcombe (1998). Agresti-Coull and Clopper-Pearson
exact intervals are available through proportion_ci for studies that
need to match earlier work, and the printed output names whichever
method ran:
exact_fit <- expert_validity(expert_ratings, mode = "relevance",
lo = 1, hi = 4, proportion_ci = "exact")
exact_fit$results[, c("item", "I_CVI", "I_CVI_low", "I_CVI_high")]
#> item I_CVI I_CVI_low I_CVI_high
#> 1 Item1 1 0.5407419 1
#> 2 Item2 1 0.5407419 1
#> 3 Item3 1 0.5407419 1The CVI relevance threshold is explicit and can be changed when a study uses a different rating convention:
expert_validity(expert_ratings, mode = "relevance",
lo = 1, hi = 4, relevance_cut = 3, seed = 1)
#> contentvalidR expert-panel analysis
#> -----------------------------------
#> Mode: relevance
#> Items: 3 | Experts/item: 6
#> Mean Aiken V: 0.944 | S-CVI/Ave: 1 | S-CVI/UA: 1
#> Strong support: 3 | Support: 0 | Review: 0
#> Panel agreement, Krippendorff's alpha (ordinal): 0.018 (95% interval -0.133
#> to 0.15). Identical rating pairs: 71.1%
#>
#> item N V ci_low ci_high I_CVI I_CVI_low I_CVI_high kappa_mod
#> Item1 6 1.000 0.824 1.000 1 0.61 1 1
#> Item2 6 0.944 0.742 0.990 1 0.61 1 1
#> Item3 6 0.889 0.672 0.969 1 0.61 1 1
#> recommendation
#> Strong support
#> Strong support
#> Strong support
#>
#> ci_low and ci_high bound Aiken's V (Penfield-Giacobbi score interval);
#> I_CVI_low and I_CVI_high bound I-CVI.
#> 95% intervals for proportions: Wilson score (the default). Newcombe (1998)
#> compared seven methods and recommends score intervals over the Wald
#> interval. An interval reflects how few ratings an item received, not
#> whether the right judges were chosen.
#>
#> Panel agreement is one coefficient for the whole panel, whereas kappa_mod
#> describes each item. Alpha can be low when nearly every rating is the same
#> value, even on a panel that agrees closely, so read it beside the share of
#> identical rating pairs. A low alpha with many identical pairs is not by
#> itself evidence of a poor panel. Print `details$agreement` for the full
#> explanation and interval details.
#>
#> CVI thresholds shown by the workflow are common panel-size guidelines, not universal validity cutoffs.
#>
#> What these columns mean
#> V -- Aiken's V. Relevance index that rescales the experts' average rating
#> to run from 0 to 1 given the bounds of the rating scale used. (0 to
#> 1; higher is stronger)
#> I_CVI -- Item-level Content Validity Index. Proportion of experts who
#> rated the item as relevant, after applying the relevance cut. (0 to
#> 1; compared against a panel-size guideline)
#> I_CVI_low/I_CVI_high -- Interval for I-CVI. Lower and upper limits of an
#> interval around I-CVI. Expert panels are usually small, so these
#> intervals are often wide: a single I-CVI value can look more settled
#> than the number of experts behind it supports. (between 0 and 1; the
#> method and level are named in the output)
#> kappa_mod -- Modified kappa. I-CVI adjusted for the chance that experts
#> would have agreed even if rating at random. With small panels, chance
#> agreement is substantial, which is why the raw I-CVI alone can
#> overstate consensus. (0 to 1; higher is stronger)
#> agreement -- Panel-level agreement. One coefficient describing how
#> consistently the whole panel rated the item set: Krippendorff's alpha
#> by default, or Gwet's AC1 if chosen. It is separate from modified
#> kappa, which describes one item at a time. (1 is perfect agreement
#> and 0 is agreement no better than chance; it can be low on a
#> close-agreeing panel whose ratings cluster on one value)
#>
#> What the status labels mean
#> Supported -- The evidence met the criteria set for this analysis.
#> Review -- Something here needs a closer look. This is not an instruction
#> to delete anything.
#> Insufficient data -- Too little usable data to reach a judgment.
#> Descriptive only -- Reported for description only; no decision rule was
#> applied.
#> Each workflow also uses its own wording in the recommendation column
#> (Retain, Strong support, Typical, Covered, and so on). Those words map
#> onto the shared statuses above.
#>
#> See `contentvalid_glossary()` for all terms, or set
#> `options(contentvalidR.show_key = FALSE)` to hide this key.
#>
#> Use quantitative indices alongside expert comments, construct coverage, and comprehensibility review.expert_validity(
c(10, 8, 6),
mode = "essentiality",
N = 12
)
#> contentvalidR expert-panel analysis
#> -----------------------------------
#> Mode: essentiality
#> Items: 3 | Experts/item: 12
#> Method: Lawshe CVR with exact binomial critical values
#>
#> item ne N cvr p_value critical_ne recommendation
#> Item1 10 12 0.667 0.019 10 Supported
#> Item2 8 12 0.333 0.194 10 Review
#> Item3 6 12 0.000 0.613 10 Review
#>
#> What these columns mean
#> cvr -- Lawshe's Content Validity Ratio. How far the panel leans toward
#> calling the item essential rather than merely useful. (-1 to 1; above
#> 0 means more than half the panel called it essential)
#>
#> What the status labels mean
#> Supported -- The evidence met the criteria set for this analysis.
#> Review -- Something here needs a closer look. This is not an instruction
#> to delete anything.
#> Insufficient data -- Too little usable data to reach a judgment.
#> Descriptive only -- Reported for description only; no decision rule was
#> applied.
#> Each workflow also uses its own wording in the recommendation column
#> (Retain, Strong support, Typical, Covered, and so on). Those words map
#> onto the shared statuses above.
#>
#> See `contentvalid_glossary()` for all terms, or set
#> `options(contentvalidR.show_key = FALSE)` to hide this key.
#>
#> Use quantitative indices alongside expert comments, construct coverage, and comprehensibility review.The CVR workflow derives the item-specific critical essential count directly from the exact binomial distribution, following the logic revisited by Ayre and Scally (2014). Judge-by-item 0/1 matrices are also accepted, including itemwise missingness when explicitly requested.
ioc_dat <- expand.grid(
item = c("I1", "I2"),
judge = 1:4,
objective = c("A", "B")
)
ioc_dat$target_objective <- ifelse(ioc_dat$item == "I1", "A", "B")
ioc_dat$score <- ifelse(
ioc_dat$objective == ioc_dat$target_objective, 1, -1
)
expert_validity(ioc_dat, mode = "congruence")
#> contentvalidR expert-panel analysis
#> -----------------------------------
#> Mode: congruence
#> Items: 2 | Experts/cell: 4 | Objectives: 2
#> Method: Rovinelli-Hambleton item-objective congruence
#>
#> item target target_ioc strongest_competitor competitor_ioc margin
#> I1 A 1 B -1 2
#> I2 B 1 A -1 2
#> recommendation
#> Target favored
#> Target favored
#> interpretation
#> The intended objective has the highest IOC; use the margin and expert comments to judge practical distinctiveness.
#> The intended objective has the highest IOC; use the margin and expert comments to judge practical distinctiveness.
#> status
#> Supported
#> Supported
#>
#> What these columns mean
#> ioc -- Item-Objective Congruence. How consistently experts linked the
#> item to the objective it was written for rather than to another
#> objective. (-1 to 1; higher is stronger)
#>
#> What the status labels mean
#> Supported -- The evidence met the criteria set for this analysis.
#> Review -- Something here needs a closer look. This is not an instruction
#> to delete anything.
#> Insufficient data -- Too little usable data to reach a judgment.
#> Descriptive only -- Reported for description only; no decision rule was
#> applied.
#> Each workflow also uses its own wording in the recommendation column
#> (Retain, Strong support, Typical, Covered, and so on). Those words map
#> onto the shared statuses above.
#>
#> See `contentvalid_glossary()` for all terms, or set
#> `options(contentvalidR.show_key = FALSE)` to hide this key.
#>
#> Use quantitative indices alongside expert comments, construct coverage, and comprehensibility review.When a target objective is supplied, the workflow reports the intended IOC, strongest competing objective, and target-minus-competitor margin. Without a target mapping, IOC cells are returned descriptively instead of manufacturing a pass/fail claim.
Low-level functions remain available for researchers who need the components directly:
aikens_v(expert_ratings, lo = 1, hi = 4)
#> item N n_missing V ci_low ci_high ci_method
#> 1 Item1 6 0 1.0000000 0.8241208 1.0000000 Penfield-Giacobbi score
#> 2 Item2 6 0 0.9444444 0.7424270 0.9901248 Penfield-Giacobbi score
#> 3 Item3 6 0 0.8888889 0.6720023 0.9689805 Penfield-Giacobbi score
cvr(essential = c(8, 10, 5), N = 12)
#> item ne N cvr p_value critical_ne critical_cvr pass
#> 1 Item1 8 12 0.3333333 0.19384766 10 0.6666667 FALSE
#> 2 Item2 10 12 0.6666667 0.01928711 10 0.6666667 TRUE
#> 3 Item3 5 12 -0.1666667 0.80615234 10 0.6666667 FALSE
cvi(expert_ratings >= 3)
#> Content Validity Index (CVI)
#> ----------------------------
#> Items analyzed: 3
#> Judges per item: 6
#> S-CVI/Ave: 1.000
#> S-CVI/UA : 1.000
#>
#> Item-level results (modified kappa is chance-corrected):
#> item A N I_CVI I_CVI_low I_CVI_high Pc kappa_mod
#> Item1 6 6 1 0.61 1 0.016 1
#> Item2 6 6 1 0.61 1 0.016 1
#> Item3 6 6 1 0.61 1 0.016 1
#>
#> 95% intervals for proportions: Wilson score (the default). Newcombe (1998)
#> compared seven methods and recommends score intervals over the Wald
#> interval. An interval reflects how few ratings an item received, not
#> whether the right judges were chosen.
#>
#> Interpretation should consider panel size, item purpose, and qualitative expert feedback;
#> CVI statistics alone do not establish comprehensive content validity.
ioc(ioc_dat[c("item", "judge", "objective", "score")])
#> item objective n_total n_judges n_missing ioc
#> 1 I1 A 4 4 0 1
#> 2 I1 B 4 4 0 -1
#> 3 I2 A 4 4 0 -1
#> 4 I2 B 4 4 0 1Content-validity evidence ends where response data begin.
content_handoff() packages a finished workflow’s item decisions — the
items that survived review, a per-item evidence table, and the
provenance of the analysis — so the item set and its reasons travel
together instead of being retyped:
handoff <- content_handoff(efit)
handoff$items
#> [1] "Item1" "Item2" "Item3"
handoff$item_evidence[, c("item", "carried", "status", "n_judges", "rule")]
#> item carried status n_judges
#> 1 Item1 TRUE Supported 6
#> 2 Item2 TRUE Supported 6
#> 3 Item3 TRUE Supported 6
#> rule
#> 1 I-CVI >= 0.78 (common panel-size guideline for 6 experts); modified kappa > 0.74 for strong support
#> 2 I-CVI >= 0.78 (common panel-size guideline for 6 experts); modified kappa > 0.74 for strong support
#> 3 I-CVI >= 0.78 (common panel-size guideline for 6 experts); modified kappa > 0.74 for strong supportThe object carries the carried item names, a construct mapping
(scales, when the design has one), a per-item evidence table, the
statistics behind each decision, and the provenance of the analysis.
Only items with a Supported status travel by default; keep widens
that when a protocol carries items flagged for review. Items held back
stay in the table with carried = FALSE, because review is not
deletion.
Once responses are collected, the item set carries into the empirical
stage. In nomologR, the companion
package for that stage, that is
nomo_screen(responses, items = handoff). The object shape is agreed
between the two packages as schema version 1, and every field is a base
type, so neither package depends on the other. Carrying an item forward
is not a prediction that it will perform: a clearly relevant item can
still correlate poorly with its construct or load on an unintended
factor, which is what the empirical analysis tests. See
vignette("handoff-to-empirical-validation").
Aggregate indices average heterogeneity away. judge_validity() asks
whether your conclusions depend on the particular judges who happened to
serve, and returns one row per judge.
judge_ratings <- rbind(
c(4, 4, 4, 3, 2, 2), c(4, 4, 3, 4, 2, 1), c(4, 3, 4, 4, 1, 2),
c(3, 4, 4, 4, 2, 2), c(4, 4, 4, 4, 2, 1), c(4, 3, 4, 3, 1, 2),
c(4, 4, 3, 4, 2, 2), c(2, 2, 2, 2, 1, 1)
)
dimnames(judge_ratings) <- list(paste0("Judge", 1:8), paste0("Item", 1:6))
judge_fit <- judge_validity(judge_ratings, lo = 1, hi = 4)
judge_fit$results[, c("judge", "mean_rating", "severity_raw",
"differentiation", "n_items_flipped", "recommendation")]
#> judge mean_rating severity_raw differentiation n_items_flipped
#> 1 Judge1 3.166667 -0.2708333 0.9136465 0
#> 2 Judge2 3.000000 -0.1041667 1.1754383 0
#> 3 Judge3 3.000000 -0.1041667 1.1754383 0
#> 4 Judge4 3.166667 -0.2708333 0.9136465 0
#> 5 Judge5 3.166667 -0.2708333 1.2351428 0
#> 6 Judge6 2.833333 0.0625000 1.0863535 0
#> 7 Judge7 3.166667 -0.2708333 0.9136465 0
#> 8 Judge8 1.666667 1.2291667 0.4798707 0
#> recommendation
#> 1 Typical
#> 2 Typical
#> 3 Typical
#> 4 Typical
#> 5 Typical
#> 6 Typical
#> 7 Typical
#> 8 SevereSeverity is signed so positive means harsher. n_items_flipped is the
influence diagnostic: how many items would change review status if that
judge were removed. A judge flagged here is not a judge to delete —
a dissenting expert may be the one reading the construct definition
correctly.
Generalizability theory answers the planning question of how many judges the design actually needs:
gt <- gtheory_content(judge_ratings)
gt$coefficients
#> n_judges g_coefficient phi_coefficient rel_error_var abs_error_var
#> 1 8 0.9682114 0.941527 0.03087798 0.05840774
gt$judges_needed
#> target n_judges_relative n_judges_absolute
#> 1 0.7 1 2
#> 2 0.8 2 2
#> 3 0.9 3 5The dependability coefficient (phi_coefficient) concerns the absolute
level of ratings and is penalized by judge severity differences, which
is usually what content-validity decisions rest on. NA in
judges_needed means no realistic panel reaches that target, which
happens when judges barely distinguished the items.
Relevance indices describe items that exist. They cannot reveal a facet
nobody wrote an item for. domain_validity() returns one row per
blueprint cell.
assignments <- data.frame(
item = paste0("I", 1:7),
construct = c("Autonomy", "Autonomy", "Autonomy", "Autonomy",
"Competence", "Competence", "Relatedness"),
stringsAsFactors = FALSE
)
domain_fit <- domain_validity(
assignments,
cell_col = "construct",
domain = c("Autonomy", "Competence", "Relatedness", "Belonging")
)
domain_fit$results[, c("cell", "n_items", "share", "recommendation")]
#> cell n_items share recommendation
#> 1 Autonomy 4 0.5714286 Over-represented
#> 2 Competence 2 0.2857143 Covered
#> 3 Relatedness 1 0.1428571 Thinly covered
#> 4 Belonging 0 0.0000000 Not coveredBelonging is the point: the blueprint asks for it and nothing
addresses it. Detecting that requires passing the full cell list through
domain, since an empty cell leaves no trace in the item assignments.
Omit it and the output says coverage gaps could not be detected rather
than implying complete coverage.
Where experts rated item similarity, content_structure() tests whether
they group items the way the blueprint claims, using multidimensional
scaling and clustering with a chance-corrected adjusted Rand index.
similarity_from_sort() derives those similarities from an existing
sorting task.
expert_power() replaces “use about six experts” with a question that
has an answer:
expert_power(n_experts = 3:8, prob = 0.9)$results
#> n_experts prob required_endorsements power
#> 1 3 0.9 3 0.7290000
#> 2 4 0.9 4 0.6561000
#> 3 5 0.9 5 0.5904900
#> 4 6 0.9 5 0.8857350
#> 5 7 0.9 6 0.8503056
#> 6 8 0.9 7 0.8131047Note the step. The common I-CVI guideline requires unanimity up to five experts and 0.78 from six, so a fourth or fifth expert lowers the probability of clearing while a sixth raises it sharply. That is a property of the guideline, not of the items, and the package reports it rather than smoothing it away.
compare_rounds() compares successive pretest rounds and, critically,
checks whether the analysis settings changed between them — so a relaxed
criterion cannot read as item improvement.
For a Delphi study, where the same panel rates items over several rounds
with feedback in between, delphi_validity() reports two questions
separately. Consensus is the share of experts agreeing, judged against a
threshold you fix before the study. Stability is the share of experts
who kept their rating, shown beside weighted kappa between rounds. Kappa
carries no verbal labels, because it falls as a panel converges. The
published alternatives are selectable, each with its limits printed:
Chaffin and Talley’s lambda and chi-square, Dajani et al.’s chi-square,
and Scheibe et al.’s 15% rule. See vignette("delphi-rounds").
content_report() builds manuscript-ready tables as a data frame or as
Markdown for Quarto and R Markdown, with no reporting dependency added
to the package.
content_report(fit, include = "flagged")
#> item target n n_target competitor psa psa_low psa_high csv p_value
#> 1 Needs review A 20 12 B 0.6 0.39 0.78 0.2 0.25
#> recommendation status
#> 1 Review ReviewThere is deliberately no helper returning “the items that passed.”
Filtering on status is a substantive decision that belongs in your own
visible code.
The workflow objects include dependency-free base-R graphics designed around the substantive questions in each method:
plot(fit, type = "map")
plot(rfit, type = "map")
plot(rfit, type = "profile")
plot(efit)
The sort and rating maps jointly display definitional correspondence and definitional distinctiveness, with target-scale means distinguished from item points. The rating profile plot shows the intended-definition mean against the strongest competitor for every item. Expert-panel plots use Aiken score intervals, panel-specific CVR criteria, or target-versus-competitor IOC gaps as appropriate. The plots intentionally avoid converting scale-level empirical norms into item-level cutoffs.
plot(sort_power(N = seq(10, 50, by = 5), true_p = c(.60, .70, .80)))
The package ships five deterministic, human-readable CSV examples
covering the item-sort, construct-rating, relevance, essentiality, and
IOC/congruence input shapes. They are installed under inst/extdata and
are regenerated from the base-R provenance script in
data-raw/build-example-data.R. This keeps the worked examples
inspectable outside R as well as reproducible inside the package.
For example:
sort_path <- system.file("extdata", "sort_example.csv", package = "contentvalidR")
bundled_sort <- utils::read.csv(sort_path, stringsAsFactors = FALSE)
sort_validity(bundled_sort)See vignette("reporting-examples", package = "contentvalidR") for
conservative manuscript-ready methods/results scaffolds, table-building
examples, and a minimum reproducibility statement. Package citation
metadata are available with citation("contentvalidR"); the method
bibliography is installed as REFERENCES.bib.
The diagnostic, simulation, and Q-factor helpers remain available as
auxiliary functions, but they are not recommended workflows. The six
recommended workflows are sort_validity(), rating_validity(),
expert_validity(), delphi_validity(), judge_validity(), and
domain_validity().
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