Executive function (EF) is an important predictor of numerous developmental outcomes, such as academic achievement and behavioral adjustment. Although a plethora of measurement instruments exists to assess executive function in children, only few of these are suitable for toddlers, and even fewer have undergone psychometric evaluation. The present study evaluates the psychometric properties and validity of an assessment battery for measuring EF in two-year-olds. A sample of 2437 children were administered the assessment battery at a mean age of 2;4 years (SD = 0;3 years) in a large-scale field study. Measures of both hot EF (snack and gift delay tasks) and cool EF (six boxes, memory for location, and visual search task) were included. Confirmatory Factor Analyses showed that a two-factor hot and cool EF model fitted the data better than a one-factor model. Measurement invariance was supported across groups differing in age, gender, socioeconomic status (SES), home language, and test setting. Criterion and convergent validity were evaluated by examining relationships between EF and age, gender, SES, home language, and parent and teacher reports of children's attention and inhibitory control. Predictive validity of the test battery was investigated by regressing children's pre-academic skills and behavioral problems at age three on the latent hot and cool EF factors at age 2 years. The test battery showed satisfactory psychometric quality and criterion, convergent, and predictive validity. Whereas cool EF predicted both pre-academic skills and behavior problems 1 year later, hot EF predicted behavior problems only. These results show that EF can be assessed with psychometrically sound instruments in children as young as 2 years, and that EF tasks can be reliably applied in large scale field research. The current instruments offer new opportunities for investigating EF in early childhood, and for evaluating interventions targeted at improving EF from a young age.

Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples (N≥ 1,000), using cutoff values for the lower (<.05) and upper limit (<.08) as guideline. An empirical illustration further shows the advantage of the BRMSEA in large sample Bayesian CFA models. In conclusion, it can be stated that the BRMSEA is well suited to evaluate model fit in large sample Bayesian CFA models by taking sample size and model complexity into account.

Insight into the reciprocal association between both directions of work-family conflict and depressive complaints is lacking. This study shows that there is a cross-lagged association between home-work interference and depressive complaints over time, suggesting a partly reciprocal association. This finding suggests that prevention of home-work interference and depressive complaints is important since the two may aggravate each other over time.Affiliation:

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