| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Mathematics | 477 | 5 | 10 | 6 |
The objective of British Journal of Mathematical and Statistical Psychology is to combine knowledge in the areas of Statistics, Econometrics, Applied mathematics, Sample size determination and Algorithm. Research on Statistics presented in it focuses, in particular, on Type I and type II errors, Estimator, Statistical hypothesis testing, Test (assessment) and Statistic. It explores issues in Econometrics which can be linked to other research areas like Latent variable model, Latent class model, Latent variable, Covariance and Item response theory.
It addresses concerns in Applied mathematics which are intertwined with other disciplines, such as Covariance matrix and Mathematical optimization.
The journal articles focus largely on the fields of Statistics, Econometrics, Applied mathematics, Algorithm and Covariance. In the Statistics research discussed in the published papers, Estimator, Sample size determination, Type I and type II errors, Statistical hypothesis testing and Statistic are all tackled. In addition to Econometrics research, the most cited articles aim to explore topics under Test (assessment), Multivariate analysis, Standard error, Rasch model and Multivariate statistics.
The journal primarily tackles Statistics, Item response theory, Latent variable, Algorithm and Econometrics. Statistics research featured in it incorporates concerns from various other topics such as Measure (mathematics) and Random effects model. British Journal of Mathematical and Statistical Psychology facilitates discussions on Item response theory that incorporate concepts from other fields like Cognitive psychology, Rating scale, Binary number, Response bias and Anchoring.
Issues in Latent variable were discussed, taking into consideration concepts from other disciplines like Goodness of fit, Estimation theory and Model selection. Topics in Algorithm were tackled in line with various other fields like Gibbs sampling, Monte Carlo method, Conditional probability distribution and Expectation–maximization algorithm. The work on Econometrics tackled in the journal brings together disciplines like Structural equation modeling, Data structure, Piecewise and Nonlinear system.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in British Journal of Mathematical and Statistical Psychology (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in British Journal of Mathematical and Statistical Psychology (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 17.78% were posted by at least one author from the top 10 institutions publishing in the journal. Another 15.56% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.22% of all publications and 44.44% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Xueying Tang;Zhi Wang;Jingchen Liu;Zhiliang Ying
(2021)Ke-Hai Yuan;Ke-Hai Yuan;Brenna Gomer
(2021)Rand R. Wilcox
(2021)Michael J. Brusco;J. Dennis Cradit;Douglas Steinley
(2020)Haochen Xu;Guanhua Fang;Zhiliang Ying
(2020)Lieke Voncken;Lieke Voncken;Thomas Kneib;Casper J. Albers;Nikolaus Umlauf
(2021)Michael Brusco;Patrick Doreian;Patrick Doreian;Douglas Steinley
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