| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Social Sciences and Humanities | 1005 | 8 | 15 | 5 |
| Psychology | 1170 | 6 | 7 | 4 |
Journal of Educational Measurement mainly deals with areas of study such as Statistics, Test (assessment), Item response theory, Econometrics and Mathematics education. Journal of Educational Measurement encompasses presentations on Statistics, specifically Equating, Item analysis, Differential item functioning, Sample size determination and Test score. The majority of Equating studies are focused on the issues of Anchor test.
The research on Test (assessment) tackled can also make contributions to studies in the areas of Developmental psychology and Social psychology, Set (psychology). Research on Social psychology addressed in it frequently intersections with the field of Cognitive psychology. Topics in Item response theory were tackled in line with various other fields like Computerized adaptive testing, Rasch model, Psychometrics and Artificial intelligence.
Econometrics research discussed connects with the study of Polytomous Rasch model. The journal explores topics in Mathematics education which can be helpful for research in disciplines like Higher education and Pedagogy.
The journal publications are organized to reinforce research efforts on Statistics, Test (assessment), Item response theory, Econometrics and Social psychology. The Test (assessment) research presented in the most cited publications focuses mostly on Mathematics education and, on occasion, topics in Educational measurement and Higher education. The journal publications explore topics in Item response theory which can be helpful for research in disciplines like Rasch model, Psychometrics, Artificial intelligence and Computerized adaptive testing.
The topics of Artificial intelligence, Machine learning, Statistics, Response time and Algorithm are the focal point of discussions in Journal of Educational Measurement. Issues in Artificial intelligence were discussed, taking into consideration concepts from other disciplines like Test (assessment), Perception and Natural language processing. While Machine learning is the focus of it, it also provided insights into the studies of Rasch model and Adaptation (computer science).
The study on Statistics presented in it intersects with subjects under the field of Variation (linguistics). The journal focused on works that combine foundational knowledge of Response time with components of disciplines like Control theory, Estimation and Detection theory. Some problems in Algorithm that were presented in the journal overlapped with concepts under Simple (abstract algebra) and Consistency (statistics).
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 Journal of Educational Measurement (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 Journal of Educational Measurement (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, 23.53% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 30.77% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.69% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 23.08% of all publications and 38.46% 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.
The Journal of Educational Measurement has not only been a frontrunner in advancing various fields such as statistics, assessment testing, econometrics, and mathematics education, it has also made crucial contributions to areas of social and developmental psychology. These contributions, albeit not direct, entail potential applications in the broader area of mental health and counseling, including marriage and family therapy. The studies highlighted in the journal can provide key insights for professionals working in this area, particularly those seeking to integrate modern research methods and data analysis techniques into their practice.
The branch of social psychology, as presented by the journal, frequently intersects with cognitive psychology. This can offer a fresh lens to marriage and family therapists in New Mexico and help them understand the mental processes that influence individual and group behavior. Furthermore, insights gained from focused studies on item response theory, such as computerized adaptive testing and artificial intelligence, can be instrumental in developing new approaches to psychological assessments.
While the Journal does not explicitly address marriage and family therapy, it indirectly adds to building robust education and practice resources for therapists. For further guidance and to understand the academic requirements for becoming a marriage and family therapist, learners can refer to this Marriage and family therapist education requirements in New article on our website. Expanding interdisciplinary knowledge will equip these professionals to cater to the holistic needs of their clients, bringing together insights from various fields to deliver comprehensive therapy.
Steven L. Wise;Megan R. Kuhfeld
(2021)Esther Kaufmann;David V. Budescu
(2020)Wim J. van der Linden;Seung W. Choi
(2020)Chia‐Wen Chen;Wen‐Chung Wang;Ming Ming Chiu;Sage Ro
(2020)Tyler Strachan;Uk Hyun Cho;Kyung Yong Kim;John T. Willse
(2021)Katherine E. Castellano;Daniel F. McCaffrey
(2020)Daniel M. Bolt;Xiangyi Liao
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