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Large-scale surveys such as the Programme for International Student Assessment (PISA), the Teaching and Learning International Survey (TALIS), and the Programme for the International Assessment of Adult Competences (PIAAC) use advanced statistical models to estimate scores of latent traits from multiple observed responses. The comparison of such estimated scores across different groups of respondents is valid to the extent that the same set of estimated parameters holds in each group surveyed. This issue of invariance of parameter estimates is addressed in model fit indices which gauge the likelihood that one set of parameters can be used across all groups. Therefore, the problem of scale invariance across groups of respondents can typically be framed as the question of how well a single model fits the responses of all groups. However, the procedures used to evaluate the fit of these models pose a series of theoretical and practical problems. The most commonly applied procedures to establish invariance of cognitive and non-cognitive scales across countries in large-scale surveys are developed within the framework of confirmatory factor analysis and item response theory. The criteria that are commonly applied to evaluate the fit of such models, such as the decrement of the Comparative Fit Index in confirmatory factor analysis, work normally well in the comparison of a small number of countries or groups, but can perform poorly in large-scale surveys featuring a large number of countries. More specifically, the common criteria often result in the non-rejection of metric invariance; however, the step from metric invariance (i.e. identical factor loadings across countries) to scalar invariance (i.e. identical intercepts, in addition to identical factor loadings) appears to set overly restrictive standards for scalar invariance (i.e. identical intercepts). This report sets out to identify and apply novel procedures to evaluate model fit across a large number of groups, o

Expert elicitation—an approach to systematically consult experts and quantify their insights—has been succesfully applied in fields as risk assessment, health and environmental research. Unfortunately, it has never been used within the Educational sciences, while it offers ample opportunities for educational practice, especially when used to foster the accuracy of teacher judgments; generally referred to as their “diagnostic competence.” The current paper is the first to explore expert elicitation in an educational context and has two major goals. The first goal is to develop a digital expert elicitation method suitable to be used by elementary school teachers for self-reflection purposes. The second goal is to extensively test the expert elicitation method, using a test panel of 24 primary school teachers for 503 pupils in total. Results regarding the development of the elicitation method and its reliability, construct validity, face validity and feasibility are discussed as well as ideas how this elicitation method can be a valuable self-reflection instrument for teachers. The results are promising: all measures of reliability, feasibility, face validity and construct validity show positive results and teachers are enthusiastic about the possibilities of the method.