RESUMO
Background: This article explores the suitability of a proposed Dual model, in which both people and items are sources of measurement error, by assessing how the test scores are expected to behave in terms of marginal reliability and external validity when the model holds. Method: Analytical derivations are produced for predicting: (a) the impact of person and item errors in the amount of marginal reliability and external validity, as well as the occurrence of ceiling effects; (b) the changes in test reliability across groups with different average amounts of person error, and (c) the phenomenon of differential predictability. Two empirical studies are also used both as an illustration and as a check of the predicted results. Results: Results show that the model-based predictions agree with existing evidence as well as with basic principles in classical test theory. However, the additional inclusion of individuals as a source of error leads to new explanations and predictions. Conclusions: The proposal and results provide new sources of information in personality assessment as well as of evidence of model suitability. They also help to explain some disappointing recurrent results. (AU)
Antecedentes: se explora la adecuación de un modelo Dual en el que ítems y personas son fuente de error de medida, evaluando como se espera que se comporten las puntuaciones en un test en términos de fiabilidad y validez cuando el modelo se cumple. Método: se derivan analíticamente predicciones respecto a: (a) el impacto del error en personas y en ítems en las estimaciones de fiabilidad y validez externa, así como en efectos techo esperados, (b) cambios en la fiabilidad marginal en grupos con diferente magnitud media de error individual, y (c) el fenómeno de la predictibilidad diferencial. Se incluyen dos estudios empíricos a efectos de ilustración y verificación empírica. Resultados: las predicciones concuerdan con la evidencia acumulada y con los principios de la teoría clásica del test. Sin embargo, la inclusión del parámetro de error individual permite llegar a nuevas explicaciones y predicciones. Conclusiones: la propuesta y resultados proporcionan nuevas fuentes de información en la medida de la personalidad, así como evidencia de la adecuación del modelo. También explican algunos resultados decepcionantes y recurrentes. (AU)
Assuntos
Humanos , Testes de Personalidade/estatística & dados numéricos , Reprodutibilidade dos Testes , Valor Preditivo dos TestesRESUMO
Many psychometric measures yield data that are compatible with (a) an essentially unidimensional factor analysis solution and (b) a correlated-factor solution. Deciding which of these structures is the most appropriate and useful is of considerable importance, and various procedures have been proposed to help in this decision. The only fully developed procedures available to date, however, are internal, and they use only the information contained in the item scores. In contrast, this article proposes an external auxiliary procedure in which primary factor scores and general factor scores are related to relevant external variables. Our proposal consists of two groups of procedures. The procedures in the first group (differential validity procedures) assess the extent to which the primary factor scores relate differentially to the external variables. Procedures in the second group (incremental validity procedures) assess the extent to which the primary factor scores yield predictive validity increments with respect to the single general factor scores. Both groups of procedures are based on a second-order structural model with latent variables from which new methodological results are obtained. The functioning of the proposal is assessed by means of a simulation study, and its usefulness is illustrated with a real-data example in the personality domain.
RESUMO
Measures initially designed to be single-trait often yield data that are compatible with both an essentially unidimensional factor-analysis (FA) solution and a correlated-factors solution. For these cases, this article proposes an approach aimed at providing information for deciding which of the two solutions is the most appropriate and useful. The procedures we propose are an FA extension of the "added-value" procedures initially proposed for subscale scores in educational testing. The basic principle is that the multiple FA solution is defensible when the factor score estimates of the primary factors are better measures of these factors than score estimates derived from a unidimensional or second-order solution. Methodologically, new results are obtained, and relations with factor indeterminacy measures and second-order FA are discussed. The procedures have been implemented in a noncommercial and widely known program for exploratory FA. The functioning of the proposal is assessed by means of a simulation study, and its usefulness is illustrated with a real-data example in the personality domain.
