- 行動計量学 (ISSN:03855481)
- vol.29, no.2, pp.182-197, 2002-12-25
In this rejoinder, special attentions are paid to error covariances and specific factors in the comparison between SEM and traditional methods. When a factor analysis model receives a poor fit, it does not make sense to simply remove important variables although inconsistent with the factor analysis model, as pointed out by the discussants. It is to be emphasized that a better way than removing the variables is to allow for error covariances, in order to overcome the inconsistency problem. The model with error covariances guarantees the invariance of estimation results over item selection. The discussants pointed out that an important difference between a scale score (sum of items) and a measurement model by effect indicators in SEM is that a scale score includes specific factors whereas a measurement model excludes them. Practitioners could use scale scores when they are interested in effects of specific factors as well as a common factor. It is argued, however, that appropriate use of error terms and a common factor in SEM can make better inference than the use of unidimensional scale scores, because the error terms of effect indicators contain information on specific factors and they can individually evaluate the effects of the common factor and the specific factors in SEM. Other related topics are also discussed.