Conceptions of giftedness and expertise put to the empirical test

Abstract

Recent handbooks of giftedness or expertise propose a plethora of conceptions on the development of excellent performance but, to our knowledge, there are no comparative studies that provide empirical evidence of their validity to guide researchers and practitioners in their adoption of a particular conception. This study sought to close that gap by conducting an empirical comparison of the major approaches to giftedness and expertise currently in use: the IQ model, the performance model, the moderator model, and the systemic model. The four models were tested in a longitudinal study with a sample of N = 350 German students attending university preparatory schools; 25% of the sample had been assigned to special classes for the gifted. The construct and predictive validity of the four models were tested by means of structural equation modeling. Theoretical considerations along with our results indicated a differentiation among the models whereby some could only predict while others could also explain the emergence of excellent performance and thereby yield valuable information for the design of interventions. The empirical comparison of the approaches showed that they were unequally suited for the two challenges. For prediction purposes, the performance approach proved best while, for explanations, the moderator and systemic approaches were the most promising candidates. Even so, the latter did demonstrate conceptual and/or methodological problems. The IQ approach was superseded by the other approaches on both prediction and explanation. Implications and limitations of the findings are discussed.

Keywords:

• conceptions of giftedness
• expertise
• empirical comparison
• intelligence
• performance
• moderators
• systemic

Notes

1. SEM recalculates the empirical covariances found in the data-set from the model one implements with latent variables. As most of the variables of interest in educational research cannot be directly observed, latent variables are supposed to capture the essence, the common variance, of the different observed variables and represent the construct more validly (i.e. the measurement model or the confirmatory factor analyses (CFAs) in the model). Subsequently, regressions and correlations can be implemented between these latent factors (i.e. the structural model) to test the hypothesized influences and relations.