Development and preliminary validation of NutriNumerics: assessment of nutrition-related quantitative literacy and numeracy in dietetics

Objective To develop and conduct preliminary validation of NutriNumerics, an assessment of nutrition-related quantitative literacy and numeracy among registered dietitian nutritionists (RDNs) and dietetics trainees. Methods This multi-phase instrument development study included item generation, content and face validity assessment, and preliminary psychometric evaluation. Expert RDNs participated in content validation, while dietetics students, graduates, and practicing RDNs participated in face-validity and reliability testing. Content and face validity indices, Cronbach’s α , and intraclass correlation coefficients (ICC) were examined. Results Expert review of 39 preliminary items yielded an S-CVI/Ave of 0.96 (SD = 0.07). Following face-validity review, 38 items were identified for retention (S-FVI = 0.93, SD = 0.09). Test–retest reliability across 38 common items yielded an ICC of 0.88 (95% CI [0.53, 0.98]). Further refinement yielded a 29-item instrument comprising 12 quantitative-literacy items ( α = 0.60) and 17 numeracy items ( α = 0.63). Exploratory analyses of the 29 retained items yielded an S-CVI/Ave of 0.96 (SD = 0.07), an S-FVI of 0.92 (SD = 0.09), and an ICC of 0.90 (95% CI [0.56, 0.98]). Conclusion NutriNumerics demonstrated favorable preliminary content and face validity measurements and exploratory evidence suggesting temporal stability. Modest internal consistency and restricted score variability indicate that larger, more heterogeneous samples are needed to further evaluate reliability, dimensional structure, and construct validity.

Authors

Institutions

Publication Details

Journal
Frontiers in Nutrition
Published
2026-11-11
DOI
https://doi.org/10.3389/fnut.2026.2010455
Primary Topic
Health Education and Validation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Development and preliminary validation of NutriNumerics: assessment of nutrition-related quantitative literacy and numeracy in dietetics

Luc R LaBonte, Jessica Faucette, Sarah Elliott
Frontiers in Nutrition
Health Education and Validation
article

Development and preliminary validation of NutriNumerics: assessment of nutrition-related quantitative literacy and numeracy in dietetics

Luc R LaBonte, Jessica Faucette, Sarah Elliott
article en

Abstract

Objective To develop and conduct preliminary validation of NutriNumerics, an assessment of nutrition-related quantitative literacy and numeracy among registered dietitian nutritionists (RDNs) and dietetics trainees. Methods This multi-phase instrument development study included item generation, content and face validity assessment, and preliminary psychometric evaluation. Expert RDNs participated in content validation, while dietetics students, graduates, and practicing RDNs participated in face-validity and reliability testing. Content and face validity indices, Cronbach’s α , and intraclass correlation coefficients (ICC) were examined. Results Expert review of 39 preliminary items yielded an S-CVI/Ave of 0.96 (SD = 0.07). Following face-validity review, 38 items were identified for retention (S-FVI = 0.93, SD = 0.09). Test–retest reliability across 38 common items yielded an ICC of 0.88 (95% CI [0.53, 0.98]). Further refinement yielded a 29-item instrument comprising 12 quantitative-literacy items ( α = 0.60) and 17 numeracy items ( α = 0.63). Exploratory analyses of the 29 retained items yielded an S-CVI/Ave of 0.96 (SD = 0.07), an S-FVI of 0.92 (SD = 0.09), and an ICC of 0.90 (95% CI [0.56, 0.98]). Conclusion NutriNumerics demonstrated favorable preliminary content and face validity measurements and exploratory evidence suggesting temporal stability. Modest internal consistency and restricted score variability indicate that larger, more heterogeneous samples are needed to further evaluate reliability, dimensional structure, and construct validity.

Frontiers in NutritionVol. 13
East Carolina University (US)
Health Education and Validation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon