When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling?
| Autor: | Rob R. Meijer, Wes Bonifay, Steven P. Reise, Richard Scheines |
|---|---|
| Přispěvatelé: | Psychometrics and Statistics |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Index (economics)
Sociology and Political Science Group factor dimensionality assessment Psychology and Cognitive Sciences Multidimensional data Commerce General Decision Sciences Bioengineering Degree (music) structural equation modeling Structural equation modeling Mathematical Sciences bifactor model Management Data set Specification Modeling and Simulation Statistics Econometrics Benchmark (computing) Social Sciences Methods Tourism and Services Psychology General Economics Econometrics and Finance |
| Zdroj: | Structural Equation Modeling A Multidisciplinary Journal, vol 22, iss 4 Structural Equation Modeling: A Multidisciplinary Journal, 22(4), 504-516 Structural Equation Modeling, vol 22, iss 4 Bonifay, WE; Reise, SP; Scheines, R; & Meijer, RR. (2015). When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling? An Evaluation of the DETECT Multidimensionality Index. Structural Equation Modeling. doi: 10.1080/10705511.2014.938596. UCLA: Retrieved from: http://www.escholarship.org/uc/item/1p020503 |
| ISSN: | 1532-8007 |
| Popis: | © 2015, Routledge. All rights reserved. In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates. This issue is commonly addressed through testing the fit of a unidimensional model specification, a strategy previously determined to be problematic. As an alternative to the use of fit indexes, we considered the utility of a statistical tool that was expressly designed to assess the degree of departure from unidimensionality in a data set. Specifically, we evaluated the ability of the DETECT “essential unidimensionality” index to predict the bias in parameter estimates that results from misspecifying a unidimensional model when the data are multidimensional. We generated multidimensional data from bifactor structures that varied in general factor strength, number of group factors, and items per group factor; a unidimensional measurement model was then fit and parameter bias recorded. Although DETECT index values were generally predictive of parameter bias, in many cases, the degree of bias was small even though DETECT indicated significant multidimensionality. Thus we do not recommend the stand-alone use of DETECT benchmark values to either accept or reject a unidimensional measurement model. However, when DETECT was used in combination with additional indexes of general factor strength and group factor structure, parameter bias was highly predictable. Recommendations for judging the severity of potential model misspecifications in practice are provided. |
| Databáze: | OpenAIRE |
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