Analysis of Scientific Studies on Item Response Theory by Bibliometric Analysis Method
Bibliographic record
Abstract
The purpose of this study is to analyze the studies, which include Item Response Theory among the keywords, available in the Web of Science database between 1980-2018 through bibliometric analysis method. A total of 1,367 academic works has been analyzed. The authors, journals and countries having the highest number of studies in the field and their interrelations on the network in terms of collaboration have been determined through common citation analysis performed using Citespace II software. In addition, a word analysis was also conducted to determine most frequently used concepts in the field. As a result of the study it was found that the authors that have made the biggest contribution to the field are De Ayala, Embretson, Reckase, Reise and Chalmers; in addition, the countries making the biggest contribution are respectively US, Netherland, Canada, Spain and China. The number of citations that US got, which is the country that received the highest number of citations with 687 citations, is 7 times higher than Netherland, which is the second most cited country. Moreover, it was found that the journals that were mostly cited are respectively Psychometrika, Appl Psych Measurement, Item Response Theory, J Edu Measurement and Educ Psychol Measurement. As a result of the word analysis based on most repeated words, which was performed for the purpose of determining most popular subjects on the field, it was found that most frequently used words are item response theory, classical test theory, model, validation, reliability, validity and Rasch model
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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 | 0.093 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.090 | 0.107 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".