The Reading the Mind in the Eyes Test is a popular measure of individual differences in Theory of Mind that is often applied in the assessment of particular clinical populations (primarily, individuals on the autism spectrum). However, little is known about the test's psychometric properties, including factor structure, internal consistency, and convergent validity evidence. We present a psychometric analysis of the test followed by an evaluation of other empirically proposed and statistically identified structures. We identified, and cross-validated in a second sample, an adequate short-form solution that is homogeneous with adequate internal consistency, and is moderately related to Cognitive Empathy, Emotion Perception, and strongly related to Vocabulary. We recommend the use of this short-form solution in normal adults as a more precise measure over the original version. Future revisions of the test should seek to reduce the test's reliance on one's vocabulary and evaluate the short-form structure in clinical populations.

Measurement in personality development faces many psychometric problems. First, theory-based measurement models do not fit the empirical data in terms of traditional confirmatory factor analysis. Second, measurement invariance across age, which is necessary for a meaningful interpretation of age-associated personality differences, is rarely accomplished. Finally, continuous moderator variables, such as age, are often artificially categorized. This categorization leads to bias when interpreting differences in personality across age. In this tutorial, we introduce methods to remedy these problems. We illustrate how Ant Colony Optimization can be used to sample indicators that meet prespecified demands such as model fit. Further, we use Local Structural Equation Modeling to resample and weight subjects to study differences in the measurement model across age as a continuous moderator variable. We also provide a detailed illustration for both tools with the Neuroticism scale of the openly available International Personality Item Pool -NEO inventory using data from the UK sample (N = 15 827). Combined, both tools can remedy persistent problems in research on personality and its development. In addition to a step-by-step illustration, we provide commented syntax for both tools.

The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.

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