Anchor Point Selection: Scale Alignment Based on an Inequality Criterion

For detecting differential item functioning (DIF) between two or more groups of test takers in the Rasch model, their item parameters need to be placed on the same scale. Typically this is done by means of choosing a set of so-called anchor items based on statistical tests or heuristics. Here the authors suggest an alternative strategy: By means of an inequality criterion from economics, the Gini Index, the item parameters are shifted to an optimal position where the item parameter estimates of the groups best overlap. Several toy examples, extensive simulation studies, and two empirical application examples are presented to illustrate the properties of the Gini Index as an anchor point selection criterion and compare its properties to those of the criterion used in the alignment approach of Asparouhov and Muthén. In particular, the authors show that—in addition to the globally optimal position for the anchor point—the criterion plot contains valuable additional information and may help discover unaccounted DIF-inducing multidimensionality. They further provide mathematical results that enable an efficient sparse grid optimization and make it feasible to extend the approach, for example, to multiple group scenarios.     

Memory plasticity across the life span: uncovering children's latent potential

Memory plasticity, or the ability to improve one's memory performance through instruction and training, is known to decline during adulthood. However, direct comparisons among middle childhood, adulthood, and old age are lacking. The authors examined memory plasticity in an age-comparative multisession training study. One hundred and eight participants ages 9-10, 11-12, 20-25, and 65-78 years learned and practiced an imagery-based mnemonic technique to encode and retrieve words by location cues. Individuals of all ages were able to acquire and optimize use of the technique. Older adults and children showed similar baseline performance and improvement through mnemonic instruction. However, in line with tenets from life-span psychology (P. B. Baltes, 1987), children profited more from mnemonic practice and reached higher levels of final performance than did older adults.     

Bayesian power equivalence in latent growth curve models

Longitudinal studies are the gold standard for research on time-dependent phenomena in the social sciences. However, they often entail high costs due to multiple measurement occasions and a long overall study duration. It is therefore useful to optimize these design factors while maintaining a high informativeness of the design. Von Oertzen and Brandmaier (2013,Psychology and Aging, 28, 414) applied power equivalence to show that Latent Growth Curve Models (LGCMs) with different design factors can have the same power for likelihood-ratio tests on the latent structure. In this paper, we show that the notion of power equivalence can be extended to Bayesian hypothesis tests of the latent structure constants. Specifically, we show that the results of a Bayes factor design analysis (BFDA; Schönbrodt & Wagenmakers (2018,Psychonomic Bulletin and Review, 25, 128) of two power equivalent LGCMs are equivalent. This will be useful for researchers who aim to plan for compelling evidence instead of frequentist power and provides a contribution towards more efficient procedures for BFDA.     

Influence of dialect use on early reading and spelling acquisition in German-speaking children in Grade 1

During literacy acquisition, children learn to match written and spoken language. Little is known about how this is achieved by children who grow up speaking a dialect. The present study examined literacy-related skills before school in 71 children (mean_age: 7.61y) with a differing degree of exposure to Swiss-German (SwissG) dialect and tested their reading and spelling skills at the end of Grade 1. No differences in Grade 1 reading and spelling were found between groups of children with different SwissG exposure. Structural Equation Modelling (SEM) revealed that SwissG exposure was negatively associated with Grade 1 spelling and reading, when statistically controlling for early literacy-related-skills. At the same time, SwissG exposure was positively associated with early literacy-related skills that drive reading and spelling development. Thus, literacy acquisition in children speaking a dialect is characterised by disadvantages due to a linguistic mismatch, but also by compensatory advantages of higher metalinguistic skills.     

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE)

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participant’s personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual’s data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies.     

Behandlung von Epilepsie bei Menschen mit Intelligenzminderung

People with intellectual disability frequently suffer from epilepsy. Management of epilepsy in this population is particularly challenging because of the different ways of communicating, cognitive impairment, changes in behavior which can be disruptive, the severity of the epilepsy syndrome, the seizure types, and the seizure frequency. Treatment of epilepsy is complicated due to the interaction of the intellectual disability and the behavior as well as behavioral changes caused by the antiepileptic drugs or the change of seizure types or frequency. This article provides the reader with a guidance for managing epilepsy in this particular population and raises important open questions.     

Cross-sectional age variance extraction: what's change got to do with it?

In cross-sectional age variance extraction (CAVE), age, the indicator of a hypothesized developmental mechanism, and a developmental outcome are specified as independent, mediator, and target variables, respectively, to test hypotheses about behavioral development. We show that: (a) longitudinal change in a mediator variable accounting for substantial cross-sectional age-related variance in the target variable need not correlate with the target variable's longitudinal change; and, conversely, (b) longitudinal change in a mediator not sharing cross-sectional age-related variance with the target variable may nevertheless correlate highly with that variable's longitudinal change. We discourage use of CAVE for testing multivariate hypotheses about behavioral development.