Two contrasting views toward the evaluation of multiple tests of constraints and control of Type I errors in structural equation modeling are presented. (a) Exploring data helps researchers make decisions about inclusion of relevant model parameters and control of Type I errors hinders this process. (b) Exploring data is not likely to yield meaningful models unless we can limit the process on the basis of methods and theory, and controlling Type I errors is a useful device to force us to limit our searches. Also, in evaluating multiple tests of constraints for applications other than exploratory analyses, we should control for Type I errors as we do in testing multiple comparisons in analysis of variance. We argue for the second perspective and present examples to illustrate methods for controlling Type I errors when making model comparisons.
ASJC Scopus subject areas
- Statistics and Probability
- Experimental and Cognitive Psychology
- Arts and Humanities (miscellaneous)