A BIBLIOMETRIC ANALYSIS OF STUDIES ON DIMENSIONALITY IN EDUCATION AND PSYCHOLOGY
Bibliographic record
Abstract
The main purpose of this study was to establish a theoretical framework for researchers concerning studies on dimensionality and reveal trends in studies on dimensionality. Therefore, the bibliometric features of publications on dimensionality in the Web of Science (WOS) database were analyzed using the Biblioshiny interface in R programming language. The study determined which studies on dimensionality were effective in education and psychology, which concepts were associated with these concepts throughout the years, and which authors and countries contributed to the progress of studies on this concept. A total of 1726 authors’ studies on dimensionality in education and psychology, published between 1970 and May 2023, were reached. The number of citations was 26.49 on average. Out of 1726 authors, 132 single-author articles were published. The number of articles written on dimensionality started to increase from 2003 on, with fluctuations in the rate of increase until 2012. However, since 2012, there has been a rapid increase in the number of articles on dimensionality. The study found that the average annual citation count increased from 6.7 in 1977 to 79.23 in 2011. The journal with the highest number of published articles on dimensionality was Psychological Reports, which has published 38 articles. Another finding indicated that Schumm Wr, Longan Cj, and Marsh Hw had the most publications on dimensionality. The most commonly used keyword regarding dimensionality was model. Turkey had cooperation on dimensionality research with several countries, such as the United States, Romania, Canada, Brazil, and China. Studies on dimensionality clustered around topics, such as multidimensionality, unidimensionality, validity studies, and measurement models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.149 | 0.202 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".