The split-ballot multitrait-multimethod approach: implementation and problems
Saris, Satorra, and Coenders (2004) proposed a new approach to estimate the quality of survey questions, combining the advantages of 2 existing approaches: the multitrait–multimethod (MTMM) and the split-ballot (SB) ones. Implemented in practice, this new approach led to frequent problems of nonconv...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universitat Pompeu Fabra |
| Repositorio: | Repositorio Digital de la UPF |
| OAI Identifier: | oai:repositori.upf.edu:10230/46006 |
| Acceso en línea: | http://hdl.handle.net/10230/46006 http://dx.doi.org/10.1080/10705511.2013.742379 |
| Access Level: | acceso abierto |
| Palabra clave: | Convergence Heywood cases Monte Carlo cimulations Quality of survey questions Split-ballot multitrait–multimethod approach |
| Sumario: | Saris, Satorra, and Coenders (2004) proposed a new approach to estimate the quality of survey questions, combining the advantages of 2 existing approaches: the multitrait–multimethod (MTMM) and the split-ballot (SB) ones. Implemented in practice, this new approach led to frequent problems of nonconvergence and improper solutions. This article uses Monte Carlo simulations to understand why the SB-MTMM is working well in some cases but not in others. The number of SB groups is a crucial element: The 3-group design is performing better. However, the 2-group design can also perform well: The analyses suggest that the interaction between the absolute values of the correlations between the traits and the relative values of the different correlations between traits plays an important role. |
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