The quantitative analysis of career paths. A review of the latest techniques
Quantitative methodology applied to the study of career paths has undergone a rapid boom that goes beyond traditional sequence analysis. This paper reviews the latest statistical techniques that can be or are already being applied to the study of career paths. We also include suggested statistical s...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | INCLIVA |
| Repositorio: | r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA |
| OAI Identifier: | oai:incliva.fundanetsuite.com:p18923 |
| Acceso en línea: | https://incliva.portalinvestigacion.com/publicaciones/18923 |
| Access Level: | acceso abierto |
| Palabra clave: | career paths statistical methods sequence analysis Markov models compartment models |
| Sumario: | Quantitative methodology applied to the study of career paths has undergone a rapid boom that goes beyond traditional sequence analysis. This paper reviews the latest statistical techniques that can be or are already being applied to the study of career paths. We also include suggested statistical software for each of the techniques described. All techniques described here are analysed from a conceptual point of view in order to reach a broader readership who may not have a strong statistical background. It is through this overview that we will show the weaknesses and strengths of each technique, as well as a linking thread that takes us from one to another. The paper starts with a general survey of the study of career paths, before going into greater depth, focusing on a small part of a given career path or even on simple changes of status. The increasing complexity of the models described here will be the subject of a final discussion, looking at the new challenges presented by their application and implementation. It is against this background that we will argue for the need for a statistical profile within the context of research projects, as is already the case in other scientific areas. Finally, we will discuss the usefulness of Bayesian statistics in analysing complex modelling. |
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