Using partial least squares in archival accounting research: an application to earnings quality measuring
Despite the advantages of Structural Equation Modelling (SEM) over regression models that have contributed to its popularisation in several fields of research in social sciences, it has not been broadly applied in archival accounting research. In this paper, we present a guidance for the application...
| Autor: | |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/1161 |
| Acceso en línea: | https://hdl.handle.net/10953/1161 |
| Access Level: | acceso abierto |
| Palabra clave: | Structural equation models (SEM) partial least squares (PLS) earnings dimensions earnings quality |
| Sumario: | Despite the advantages of Structural Equation Modelling (SEM) over regression models that have contributed to its popularisation in several fields of research in social sciences, it has not been broadly applied in archival accounting research. In this paper, we present a guidance for the application of SEM – and, particularly, the Partial Least Squares (PLS) method – to the (arguably) most recurrent topic on empirical archival accounting research: earnings quality. We highlight several problems that arise in earnings quality measuring, indicating how PLS can help to overcome them. We also run a simulation process whose results show that PLS method outperforms the other approaches even in situations of limited information. |
|---|