Impact Factor
2.94
Journal Citation Impact
Estimated influence, citation density, publishing volume, and citation integrity from the analytics table.
Citation rate
2.24
171 total citations
h5-index
16
h5 depth signal
Cited share
61.9%
52 of 84
Total citations
171
Citation footprint
Metric signal
2.70
External metric anchor
Self-citation
8.2%
14 self-citations
Citations Trend
Monthly Publications Cadence
Citing Geographic Demographics
United States
174
China
68
Netherlands
57
Germany
50
Italy
19
Citing Fields of Research
Statistical Methods
228
Psychiatry & Psychology
192
AI & Machine Learning
87
Education & Pedagogy
34
Neurosciences
17
Publishing Activity & Reach
Volume, recency, access pattern, and citing-community signals from the journal analytics record.
All-time article footprint
1,722
Articles tracked across the journal lifetime
Indexed publication set
84
52 cited publications
Last 12 months
65
5.4 publications/month
Open access share
60.0%
39 OA publications in the last year
Leading citing country
United States
174 citing publications
Leading citing field
Statistical Methods
228 citing publications
Latest tracked publication
19 October 2026
10 recent records available
Coverage & Indexing History
v19n1,May 1966-
v19n1,May 1966-
Active Coverage
Active Coverage
Research Focus & Classification
Indexed Subjects
Psychiatry & Psychology
Statistical Methods
Issuing & Sponsoring Bodies
Contributor
British Psychological Society
Broad Categories
Psychology
Statistics as Topic
Psychiatry & Psychology
Statistical Methods
MeSH Descriptors
Psychology
Latest Publications
Recent article records with DOI-first linking and condensed abstracts.
01
DOI 10.1111/bmsp.70081
Open DOI
Issue Information
02
DOI 10.1111/bmsp.70080
Open DOI
An uncomplicated introduction to representational measurement theory
Measurement in psychology is often associated with psychometrics. Despite the theoretical insights afforded by psychometric methods, they represent only a restricted range of possible measurement approaches in the social sciences. Repres...
03
DOI 10.1111/bmsp.70074
Open DOI
A taxonomy of reliability indices for cluster‐level constructs measured by multiple individual‐level indicators
Generalizability theory (GT) provides a variety of intraclass correlation coefficients (ICCs) to quantify reliability across different conditions of measurement—for example, scale and interrater reliability quantify generalization across...
04
DOI 10.1111/bmsp.70078
Open DOI
Model fit and parameter uncertainty in amortized variational inference based item response theory
Amortized variational inference (AVI) has become a popular, computationally efficient alternative to marginal maximum likelihood estimation (MML) for estimating high‐dimensional item response theory models on large datasets. However, cur...
05
DOI 10.1111/bmsp.70077
Open DOI
Reliability revisited: Special issue editorial
06
DOI 10.1111/bmsp.70076
Open DOI
A regularised latent‐class item response model for detecting measurement non‐invariance in ordinal response scales
Measurement non‐invariance arises when the psychometric properties of a scale differ across subgroups, undermining the validity of group comparisons. At the item level, this manifests as differential item functioning (DIF), where item re...
07
DOI 10.1111/bmsp.70075
Open DOI
Kurtosis‐robust estimation of the fixed effects eta‐squared: An impossibility theorem for F ‐based estimators and a practical correction
Eta‐squared ( η 2 ) is the most widely reported proportion‐of‐variance effect size in ANOVA, yet its estimators are biased and non‐normality is rarely studied. We prove that, for balanced one‐way fixed effects ANOVA, no function of the u...
08
DOI 10.1111/bmsp.70073
Open DOI
Detecting differential item functioning with unknown subgroups and anchor items in cognitive diagnostic assessment
Differential item functioning (DIF) analysis is essential for evaluating measurement invariance in educational and psychological assessments. In cognitive diagnostic assessment, however, most existing methods require prespecified compari...
09
DOI 10.1111/bmsp.70072
Open DOI
Sensitivity analysis for unmeasured pretreatment confounders in causal mediation analysis with debiased machine learning
Sensitivity analysis for unmeasured confounders is essential for assessing the robustness of causal mediation conclusions. Most existing methods rely on parametric assumptions, which are ill‐suited for machine learning‐based estimators t...
10
DOI 10.1111/bmsp.70071
Open DOI
Geometric standardized mean difference and its application to meta‐analysis
The standardized mean difference (SMD) is a widely used measure of effect size, particularly common in psychology, clinical trials and meta‐analysis involving continuous outcomes. Traditionally, under the equal variance assumption, the S...
Bibliographical & Catalog Notes
Also issued online.
Issued by the British Psychological Society.
Vol. 39, no. 1 (1986)19920824 Notice not published Printed on acid-free paper