The DOI matches its Crossref record.
Paradigmatic Distinctions between Instructionally Relevant Measurement Models
Abstract
In this article, we delineate essential commonalities and distinctions between two approaches to measurement for instructional decision making. Specific subskill mastery measurement is explained using a case study, and salient features of this predominant model are described. Then, a major contrasting approach, the general outcome measurement model, is explained; a curriculum-based measurement case study is provided to illustrate general outcome measurement; and the essential features of this alternative model are reviewed. Finally, we describe how general outcome measurement represents an innovative approach to assessment by bridging traditional and contemporary paradigms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Dynamic Indicators of Basic Early Literacy Skills
(2018) · The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation · doi:10.4135/9781506326139.n214 · Cited by 1,051
Good, R. H., Simmons, D. C., & Kame'enui, E. J. (2001) · Scientific Studies of Reading · doi:10.1207/s1532799xssr0503_4 · Cited by 545
Using Curriculum‐Based Measurement to Improve Student Achievement: Review of Research
Stecker, P. M., Fuchs, L. S., & Fuchs, D. (2005) · Psychology in the Schools · doi:10.1002/pits.20113 · Cited by 506
Self-citationDeno, S. L., Fuchs, L. S., Marston, D., & Shin, J. (2001) · School Psychology Review · doi:10.1080/02796015.2001.12086131 · Cited by 405
Self-citationFuchs, L. S., Fuchs, D., & Compton, D. L. (2004) · Exceptional Children · doi:10.1177/001440290407100101 · Cited by 225
Self-citation
Metadata
- Authors
- Fuchs, L. S., & Deno, S. L.
- Venue
- Exceptional Children
- Publisher
- SAGE Publications
- Published
- 1991-05
- Volume
- 57
- Issue
- 6
- Pages
- 488-500
- ISSN
- 0014-4029 (ISSN-L)
- 2163-5560
- OpenAlex
- W1944577436
- Cited by
- 393
- References
- 0
- FWCI
- 53.43
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Paradigmatic Distinctions between Instructionally Relevant Measurement Models. https://app.cochat.ai/papers/10.1177/001440299105700603
- The paper
- Fuchs, L. S., & Deno, S. L. (1991). Paradigmatic Distinctions between Instructionally Relevant Measurement Models. Exceptional Children. https://doi.org/10.1177/001440299105700603
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.