Package: nomologR 0.9.0

nomologR: Guided Scale Development and Construct Validation

Provides an evidence-guided workflow for empirical scale development and construct validation, designed for graduate students and researchers. The package helps users examine item performance, dimensionality, measurement models, reliability, convergent and discriminant evidence, measurement invariance, and theory-specified nomological networks in the sense of Cronbach and Meehl (1955) <doi:10.1037/h0040957>. Methods are documented from historical to contemporary practice with literature references, and simulated teaching datasets with known population models accompany the examples. Following calls for transparent measurement practice such as Flake, Pek, and Hehman (2017) <doi:10.1177/1948550617693063>, it emphasizes transparent diagnostics, reproducible decision logging, and teaching-oriented guidance rather than automatic item deletion or binary declarations that a measure is valid.

Authors:Joshua Uhalt [aut, cre]

nomologR_0.9.0.tar.gz
nomologR_0.9.0.zip(r-4.7-any)nomologR_0.9.0.zip(r-4.6-any)nomologR_0.9.0.zip(r-4.5-any)
nomologR_0.9.0.tgz(r-4.6-any)nomologR_0.9.0.tgz(r-4.5-any)
nomologR_0.9.0.tar.gz(r-4.7-any)nomologR_0.9.0.tar.gz(r-4.6-any)
nomologR_0.9.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
nomologR/json (API)

# Install 'nomologR' in R:
install.packages('nomologR', repos = c('https://juhalt.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/juhalt/nomologr/issues

Pkgdown/docs site:https://juhalt.github.io

Datasets:

On CRAN:

Conda:

cfaconstruct-validityfactor-analysisfactor-scoreslavaanmeasurement-invariancepsychometricsreliabilityresearch-methodsscale-developmentstructural-equation-modeling

4.48 score 26 exports 52 dependencies

Last updated from:b72bc6d989 (on v0.9.0). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK263
source / vignettesOK446
linux-releaseOK280
macos-releaseOK168
macos-oldrelOK216
windows-develOK254
windows-releaseOK270
windows-oldrelOK251
wasm-releaseOK130

Exports:negativenegligiblenomo_apa_tablenomo_cfanomo_comparenomo_defaultsnomo_efanomo_factorsnomo_hierarchicalnomo_hypothesesnomo_invariancenomo_methodsnomo_missingnomo_modelnomo_networknomo_partialnomo_reliabilitynomo_reportnomo_revisenomo_runnomo_scoresnomo_screennomo_splitnomo_tablenomo_validitypositive

Dependencies:backportscheckmatecliclueclustercodetoolscpp11digestdplyrEFAtoolsfarverfuturefuture.applygenericsggplot2globalsglueGPArotationgtableisobandlabelinglatticelavaanlifecyclelistenvmagrittrMASSmnormtnlmenumDerivparallellypbivnormpillarpkgconfigprogressrpsychquadprogR6RColorBrewerRcppRcppArmadillorlangroptimS7scalessemToolstibbletidyselectutf8vctrsviridisLitewithr

Teaching with nomologR
Using the package in a course | 1. Find the weak item and the cross-loading item | 2. How many factors? | 3. Coefficient alpha or omega? | 4. Ordered categories | 5. A correlation is not a path | 6. Find the item that is not comparable across groups | 7. When content review and the data disagree | 8. An item-total correlation against which total?
Rendered fromteaching.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-09-28

Archiving a nomologR workflow with nomo_report()
A workflow to archive | Render a report | Tables for a manuscript | What the report contains | Reports from incomplete workflows | Plots and session information | Overwriting is explicit
Rendered fromreproducible-report.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-09-07

Research basis: from historical to contemporary practice
Purpose | The methods registry | How practice changed | 1. Item and data audit — nomo_screen() | 2. Dimensionality — nomo_factors() | 3. Exploratory structure — nomo_efa() | 4. Confirmatory measurement model — nomo_cfa() | 5. Reliability — nomo_reliability() | 6. Convergent and discriminant evidence — nomo_validity() | 7. Measurement invariance — nomo_invariance(), nomo_partial() | 8. Nomological network — nomo_hypotheses(), nomo_network() | 9. Workflow, scores, and reporting — nomo_run(), nomo_report() | References
Rendered fromresearch-basis.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-09-15

Get started with nomologR
Who nomologR is for | The question behind every stage | The workflow at a glance | Teaching datasets | A first look | How to read nomologR output | A suggested learning path | Where nomologR is heading | Citing nomologR
Rendered fromnomologR.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-09-15

From content review to empirical screening
What content review decided | The responses, and what each item was built to do | Screening the data as they arrive | Recoding as content review declared | The measurement model | Two cohorts | Where the two stages disagree | What each stage establishes
Rendered fromcontent-review.Rmdusingknitr::rmarkdown

Last update: 2026-09-28
Started: 2026-09-28

Guided workflow with nomo_run()
A fresh run | Resume with an explicit factor count | Confirmatory model handoff | Why pause after measurement evidence? | Optional invariance and network branches | Prespecified one-call workflow | Revising with a recorded lineage | Starting from content review | Reproducibility and provenance | Research basis
Rendered fromguided-workflow.Rmdusingknitr::rmarkdown

Last update: 2026-09-27
Started: 2026-09-06

From item audit to exploratory structure
Why this workflow is staged | A teaching dataset with a known answer | Step 1: audit the items | Careless responding | Step 2: investigate factor retention | Historical note: eigenvalues greater than one | Step 3: hand the retention evidence into EFA | Read the item table as evidence, not a deletion list | Pattern versus structure matrices | Residual strain | Researcher control remains explicit | Ordered response categories | What should happen next? | Research basis
Rendered fromexploratory-workflow.Rmdusingknitr::rmarkdown

Last update: 2026-09-27
Started: 2026-09-04

From CFA to a defensible measurement model
Why measurement evidence is staged | A healthy two-factor model | Good global fit does not guarantee good measurement | Strong reliability does not guarantee construct separation | Ordered indicators: keep the score scale explicit | Continuing the exploratory example | Comparing models with a recorded rationale | A cross-loading suggested by the results | Is a weak item needed? | Does a result depend on how missing data were handled? | When the strategies disagree | What the comparison cannot tell you | Reading the evidence as an argument | Research basis
Rendered frommeasurement-model-evidence.Rmdusingknitr::rmarkdown

Last update: 2026-09-25
Started: 2026-09-05

Scoring a measurement model with nomo_scores()
A model to score | Adding items is a model, not arithmetic | What a score is, and is not | Scoring changes the answer | One design recovers a regression | Choosing a method | What is never done | References
Rendered fromscoring.Rmdusingknitr::rmarkdown

Last update: 2026-09-23
Started: 2026-09-22

Total and subscale scores: bifactor and higher-order models
Two ways to model a general factor | An example with a general factor | How much of each score is general? | Can you score the factors? | Comparing structures | Why fit cannot choose the structure | Higher-order models and Schmid-Leiman | Ordered indicators | What nomologR does not do | References
Rendered fromhierarchical-models.Rmdusingknitr::rmarkdown

Last update: 2026-09-21
Started: 2026-09-18

Theory-Specified Nomological Networks with nomologR
Why theory comes before the network | The teaching study | Specify theory before looking at results | Fit the latent network | Read relation-level evidence | A marginal association is not a structural prediction | Negligible predictions and equivalence evidence | Criterion and predictive evidence | Measurement context | Calibration and validation | Figures | Interpretation rule | Research basis
Rendered fromnomological-network.Rmdusingknitr::rmarkdown

Last update: 2026-09-18
Started: 2026-09-06

Measurement Invariance with nomologR
What invariance is for | A teaching example with a known answer | Continuous indicators | Localizing strain | Researcher-controlled partial invariance | Ordered indicators | Tables and figures | Research basis
Rendered frommeasurement-invariance.Rmdusingknitr::rmarkdown

Last update: 2026-09-15
Started: 2026-09-06

Readme and manuals

Help Manual

Help pageTopics
Manuscript-ready tables in APA stylenomo_apa_table
Guided confirmatory factor analysisnomo_cfa
Compare confirmatory measurement modelsnomo_compare
Default guidance settings for nomologRnomo_defaults
Simulated two-factor item data with known teaching featuresnomo_demo_continuous
Simulated multi-construct validation studynomo_demo_network
Simulated five-category ordered item datanomo_demo_ordinal
Shared walkthrough data from content review to empirical screeningnomo_demo_walkthrough nomo_demo_walkthrough_items
Guided exploratory factor analysisnomo_efa
Specify directional or negligible theoretical expectationsnegative negligible nomo_expectations positive
Evaluate evidence about the number of latent factorsnomo_factors
Evaluate general-factor strength in bifactor and higher-order modelsnomo_hierarchical
Specify a priori expectations for a nomological networknomo_hypotheses
Evaluate measurement invariance across groupsnomo_invariance
The methods registry: what nomologR computes, and where it comes fromnomo_methods
Missing-data sensitivity: does a result depend on how missing data were handled?nomo_missing nomo_missing.nomo_cfa nomo_missing.nomo_network
Build confirmatory factor-analysis syntaxnomo_model
Evaluate a theory-specified nomological networknomo_network
Specify researcher-controlled partial-invariance releasesnomo_partial
Model-based reliability evidencenomo_reliability
Render a reproducible nomologR analysis reportnomo_report
Revise a guided workflow and keep its lineagenomo_revise
Run the guided nomologR workflownomo_run
Score a measurement model, with the evidence for the scoring choicenomo_scores
Audit item-level data before factor modelingnomo_screen
Create a reproducible calibration/validation splitnomo_split
Extract report-ready evidence tablesnomo_table
Convergent and discriminant construct-validity evidencenomo_validity
Plot confirmatory factor-analysis evidenceplot.nomo_cfa
Plot a measurement-model comparisonplot.nomo_compare
Plot exploratory factor-analysis evidenceplot.nomo_efa
Plot factor-retention evidenceplot.nomo_factors
Plot hierarchical measurement evidenceplot.nomo_hierarchical
Plot measurement-invariance evidenceplot.nomo_invariance
Plot nomological-network evidenceplot.nomo_network
Plot model-based reliability evidenceplot.nomo_reliability
Plot a nomo_screen auditplot.nomo_screen
Plot convergent or discriminant validity evidenceplot.nomo_validity
Print factor-retention evidenceprint.nomo_factors
Print a nomo_screen objectprint.nomo_screen
Print a factor-retention summaryprint.summary_nomo_factors
Print a summary_nomo_screen objectprint.summary_nomo_screen
Summarize a guided confirmatory factor analysissummary.nomo_cfa
Summarize a measurement-model comparisonsummary.nomo_compare
Summarize a guided exploratory factor analysissummary.nomo_efa
Summarize factor-retention evidencesummary.nomo_factors
Summarize model-based reliability evidencesummary.nomo_reliability
Summarize a nomo_screen auditsummary.nomo_screen
Summarize convergent and discriminant measurement evidencesummary.nomo_validity