Researcher capacity estimation based on the Q model
A generalized linear mixed model perspective
Bibliographic Data
| ID | 21443284 |
|---|---|
| Authors | Boris Forthmann (0000-0001-9755-7304, University of Münster, corresponding author) |
| Year | 2023 |
| Volume | 128 |
| Issue | 8 |
| Pages | 4753-4764 |
| Publication date | 2023-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-023-04756-9 |
| OpenAlex | W4379470274 |
| Language | EN |
| Citations received | 2 |
| References cited | 23 |
Chance models of scientific creative productivity allow estimation of researcher capacity. One prominent such model is the Q model in which the impact of a scholarly work is modeled as a multiplicative function of researcher capacity and a potential impact (i.e., luck) parameter. Previous work estimated researcher capacity based on an approximation of the Q parameter. In this work, however, I outline how the Q model can be estimated within the framework of generalized linear mixed models. This way estimates of researcher capacity (and all other parameters of the Q model) are readily available and obtained by standard statistical software packages. Usage of such software further allows comparing different distributional assumptions and calculation of reliability of the Q parameter (i.e., researcher capacity). This is illustrated for a large dataset of multidisciplinary scientists ( N = 20,296). The Poisson Q model was found to have negligibly better predictive accuracy than the original normal Q model. Reliability estimates revealed excellent reliability of Q estimates with conditional reliability being mostly in acceptable ranges. Reliability of Q parameter estimates further depended heavily on the number of publications of a scientist with reliability increasing with the number of papers. The future and limitations of the Q model in the context of researcher capacity estimation are thoroughly discussed
Context (archaeology) · Econometrics · Economics · Estimation · Estimation theory · Function (biology) · Generalized linear model · Mathematical analysis · Multiplicative function · Physics · Poisson distribution · Reliability (semiconductor) · Statistics · Applied Mathematics · Computer Science · Decision-Making and Behavioral Economics · Mathematics · Meta-analysis and systematic reviews
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| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |