A geometrical perspective on parametric psychometric models
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916441910935552 |
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| author | Tuerlinckx, Francis |
| author_facet | Tuerlinckx, Francis |
| contents | Psychometrics and quantitative psychology rely strongly on statistical models to measure psychological processes. As a branch of mathematics, geometry is inherently connected to measurement and focuses on properties such as distance and volume. However, despite the common root of measurement, geometry is currently not used a lot in psychological measurement. In this paper, my aim is to illustrate how ideas from non-Euclidean geometry may be relevant for psychometrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12450 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A geometrical perspective on parametric psychometric models Tuerlinckx, Francis Applications Differential Geometry Other Statistics Psychometrics and quantitative psychology rely strongly on statistical models to measure psychological processes. As a branch of mathematics, geometry is inherently connected to measurement and focuses on properties such as distance and volume. However, despite the common root of measurement, geometry is currently not used a lot in psychological measurement. In this paper, my aim is to illustrate how ideas from non-Euclidean geometry may be relevant for psychometrics. |
| title | A geometrical perspective on parametric psychometric models |
| topic | Applications Differential Geometry Other Statistics |
| url | https://arxiv.org/abs/2410.12450 |