The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
A tutorial on how to do a Mokken scale analysis on your test and questionnaire data
Open accessAbstract
Over the past decade, Mokken scale analysis ( MSA ) has rapidly grown in popularity among researchers from many different research areas. This tutorial provides researchers with a set of techniques and a procedure for their application, such that the construction of scales that have superior measurement properties is further optimized, taking full advantage of the properties of MSA . First, we define the conceptual context of MSA , discuss the two item response theory ( IRT ) models that constitute the basis of MSA , and discuss how these models differ from other IRT models. Second, we discuss dos and don'ts for MSA ; the don'ts include misunderstandings we have frequently encountered with researchers in our three decades of experience with real‐data MSA . Third, we discuss a methodology for MSA on real data that consist of a sample of persons who have provided scores on a set of items that, depending on the composition of the item set, constitute the basis for one or more scales, and we use the methodology to analyse an example real‐data set.
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
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Coefficient Alpha and the Internal Structure of Tests
Cronbach, L. J. (1951) · Psychometrika · doi:10.1007/bf02310555 · Cited by 43,931
Differential Item Functioning.
Gudgeon, A. C., Holland, P. W., & Wainer, H. (1994) · The Statistician · doi:10.2307/2348590 · Cited by 2,091
DS14: Standard Assessment of Negative Affectivity, Social Inhibition, and Type D Personality
Denollet, J. (2005) · Psychosomatic Medicine · doi:10.1097/01.psy.0000149256.81953.49 · Cited by 1,549
A Basis for Analyzing Test-Retest Reliability
Guttman, L. (1945) · Psychometrika · doi:10.1007/bf02288892 · Cited by 1,122
A Theory and Procedure of Scale Analysis: With Applications in Political Research
Mokken, R. J. (1971) · doi:10.1515/9783110813203 · Cited by 1,120
An adjusted boxplot for skewed distributions
Hubert, M., & Vandervieren, E. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.11.008 · Cited by 834
Introduction to Nonparametric Item Response Theory
Sijtsma, K., & Molenaar, I. (2002) · Sage eBooks · doi:10.4135/9781412984676 · Cited by 761
Self-citationDesign, Evaluation, and Analysis of Questionnaires for Survey Research
Saris, W. E., & Gallhofer, I. N. (2007) · Wiley eBooks · doi:10.1002/9780470165195 · Cited by 643
Ark, L. A. V. D. (2007) · Journal of Statistical Software · doi:10.18637/jss.v020.i11 · Cited by 467
Cited by
The German Version of the Mobile App Rating Scale (MARS-G): Development and Validation Study
Messner, E.-M., Terhorst, Y., Barke, A., Baumeister, H., Stoyanov, S., Hides, L., Kavanagh, D., Pryss, R., Sander, L., & Probst, T. (2020) · JMIR mHealth and uHealth · doi:10.2196/14479 · Cited by 156
Kerstan, S., Bienefeld, N., & Grote, G. (2024) · Risk Analysis · doi:10.1111/risa.14216 · Cited by 55
Item Response Theory: A Statistical Framework for Educational and Psychological Measurement
Chen, Y., Li, X., Liu, J., & Ying, Z. (2025) · Statistical Science · doi:10.1214/23-sts896 · Cited by 30
The Eco-Generativity Scale (EGS): A New Resource to Protect the Environment and Promote Health
Di Fabio, A., & Svicher, A. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20156474 · Cited by 21
Metadata
- Authors
- Sijtsma, K., & van der Ark, L. A.
- Venue
- British Journal of Mathematical and Statistical Psychology
- Publisher
- Wiley
- Published
- 2016-12-13
- Volume
- 70
- Issue
- 1
- Pages
- 137-158
- ISSN
- 0007-1102 (ISSN-L)
- 2044-8317
- OpenAlex
- W2565010544
- Cited by
- 258
- References
- 51
- FWCI
- 12.04
- Type
- journal article
- Language
- en
- Open access
- bronze
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: A tutorial on how to do a Mokken scale analysis on your test and questionnaire data. https://app.cochat.ai/papers/10.1111/bmsp.12078
- The paper
- Sijtsma, K., & van der Ark, L. A. (2017). A tutorial on how to do a Mokken scale analysis on your test and questionnaire data. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12078
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.
