Efficient Hessian computation using sparse matrix derivatives in RAM notation

This article proposes a new, more efficient method to compute the minus two log likelihood, its gradient, and the Hessian for structural equation models (SEMs) in reticular action model (RAM) notation. The method exploits the beneficial aspect of RAM notation that the matrix derivatives used in RAM are sparse. For an SEM with K variables, P parameters, and P′ entries in the symmetrical or asymmetrical matrix of the RAM notation filled with parameters, the asymptotical run time of the algorithm is O(P?′?K ²?+?P ² K ²?+?K ³). The naive implementation and numerical implementations are both O(P ² K ³), so that for typical applications of SEM, the proposed algorithm is asymptotically K times faster than the best previously known algorithm. A simulation comparison with a numerical algorithm shows that the asymptotical efficiency is transferred to an applied computational advantage that is crucial for the application of maximum likelihood estimation, even in small, but especially in moderate or large, SEMs.     

Bringing Back the Person into Behavioural Personality Science Using Big Data

Behaviour and the individual person are important but widely neglected topics of personality psychology. We argue that new technologies to collect and new methods to analyse Big (Behavioural) Data have the potential to bring back both more behaviour and the individual person into personality science. The call for studying the individual person in the history of personality science, the related idiographic/nomothetic divide, as well as attempts to reconcile these two approaches are briefly reviewed. Furthermore, different meanings of the term idiographic and some unique selling points that emphasize the importance of idiographic research are highlighted. A nonexhaustive literature review shows that a wealth of behaviours are considered in extant personality studies using such Big Data but only in a nomothetic way. Against this background, we demonstrate the potential of Big Data collection and analysis with regard to four idiographic research topics: (i) unique manifestations of common traits and the resurgence of personal dispositions, (ii) idiographic prediction, (iii) intraindividual consistency versus variability of behaviour and (iv) intraindividual personality trait change through intervention. Methodological, ethical and legal pitfalls of doing Big Data research with individual persons as well as potential countermeasures are considered.     

The effect of multiple indicators on the power to detect inter‐individual differences in change

Hertzog et al. evaluated the statistical power of linear latent growth curve models (LGCMs) to detect individual differences in change, i.e., variances of latent slopes, as a function of sample size, number of longitudinal measurement occasions, and growth curve reliability. We extend this work by investigating the effect of the number of indicators per measurement occasion on power. We analytically demonstrate that the positive effect of multiple indicators on statistical power is inversely related to the relative magnitude of occasion‐specific latent residual variance and is independent of the specific model that constitutes the observed variables, in particular of other parameters in the LGCM. When designing a study, researchers have to consider trade‐offs of costs and benefits of different design features. We demonstrate how knowledge about power equivalent transformations between indicator measurement designs allows researchers to identify the most cost‐efficient research design for detecting parameters of interest. Finally, we integrate different formal results to exhibit the trade‐off between the number of measurement occasions and number of indicators per occasion for constant power in LGCMs.     

Comparing memory skill maintenance across the life span: preservation in adults, increase in children

The authors examined life-span differences in the maintenance of skilled episodic memory performance by assessing 100 individuals (10 -11, 12-13, 21-26, and 66-79 years old) 11 months after termination of an intensive multisession mnemonic training program (Y. Brehmer, S.-C. Li, V. Müller, T. von Oertzen, & U. Lindenberger, 2007). Skill maintenance was tested in 2 follow-up sessions, the first without and the second with mnemonic reinstruction. Younger and older adults' average performance levels were stable across time. In contrast, both younger and older children's memory performance improved beyond originally attained levels. Older adults' performance improved from the first to the second follow-up session, presumably profiting from instruction-induced skill reactivation. Results suggest that (a) skill maintenance is largely intact in healthy older adults, (b) older adults need environmental support to fully reactivate their former skill levels (cf. F. I. M. Craik, 1983), and (c) children adapt a skill learned 11 months ago to their increasing cognitive capabilities.     

On the power of multivariate latent growth curve models to detect correlated change

We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris (1985) were used to evaluate the power to detect slope covariances. Even with large samples (N = 500) and several longitudinal occasions (4 or 5), statistical power to detect covariance of slopes was moderate to low unless growth curve reliability at study onset was above .90. Studies using LGCMs may fail to detect slope correlations because of low power rather than a lack of relationship of change between variables. The present findings allow researchers to make more informed design decisions when planning a longitudinal study and aid in interpreting LGCM results regarding correlated interindividual differences in rates of development.     

Kasuistik einer adoleszenten Frau mit NMDA-Rezeptor-antikörperpositiven Enzephalitis

Paraneoplastic encephalitis, an immune-mediated disease, has been known for a long time. In the last decade, several specific antibodies against cell membrane-located antigens causing limbic or diffuse encephalitis have been identified. Most of these cases are in fact non-paraneoplastic. These antibodies are likely primarily involved in the pathogenesis, in contrast to antineuronal antibodies against cytoplasmatic antigens (e.g., anti-Hu, anti-Yo, and anti-Ma2), which are more likely an epiphenomenon. The case reported here is an adolescent woman presenting with NMDAR (N-methyl D-aspartate receptor) antibody encephalitis, finally diagnosed during a relapse. The diagnostic and therapeutic approach to NMDAR antibody and limbic encephalitis is discussed.     

Power equivalence in structural equation modelling

Implementing large‐scale empirical studies can be very expensive. Therefore, it is useful to optimize study designs without losing statistical power. In this paper, we show how study designs can be improved without changing statistical power by defining power equivalence, a relation between structural equation models (SEMs) that holds true if two SEMs have the same power on a likelihood ratio test to detect a given effect. We show systematic operations of SEMs that maintain power, and give an algorithm that efficiently reduces SEMs to power‐equivalent models with a minimal number of observed parameters. In this way, optimal study designs can be found without reducing statistical power. Furthermore, the algorithm can be used to drastically increase the speed of power computations when using Monte Carlo simulations or approximation methods.     

Time Delay Embedding Increases Estimation Precision of Models of Intraindividual Variability

This paper investigates the precision of parameters estimated from local samples of time dependent functions. We find that time delay embedding, i.e., structuring data prior to analysis by constructing a data matrix of overlapping samples, increases the precision of parameter estimates and in turn statistical power compared to standard independent rows of panel data. We show that the reason for this effect is that the sign of estimation bias depends on the position of a misplaced data point if there is no a priori knowledge about initial conditions of the time dependent function. Hence, we reason that the advantage of time delayed embedding is likely to hold true for a wide variety of functions. We support these conclusions both by mathematical analysis and two simulations.     

Epilepsie – eine potenziell chirurgisch behandelbare Erkrankung

Epilepsy is one of the most frequent neurological diseases in adults. First line therapy consists of antiepileptic drugs. However, up to 50?% of patients with focal epilepsy do not adequately respond to medical therapy. After failure of two first-line drugs, patients should be referred to a comprehensive epilepsy center for evaluation of the possibility for epilepsy surgery. The noninvasive phase 1 includes history with eye witness report, clinical examination, structural and functional imaging techniques, simultaneous video-EEG monitoring as well as a neuropsychological examination. An invasive phase 2 can be added in cases with incongruent results. Subdural strip and grid electrodes as well as stereotactically implanted depth electrodes give more detailed information on seizure origin and spread. Detailed planning of the surgical approach is of utmost importance. In patients with temporal lobe epilepsy, seizure freedom can be achieved in 60–80?% of cases after epilepsy surgery.     

Dyadic drumming across the lifespan reveals a zone of proximal development in children

Many social interactions require the synchronization--be it automatically or intentionally--of one's own behavior with that of others. Using a dyadic drumming paradigm, the authors delineate lifespan differences in interpersonal action synchronization (IAS). Younger children, older children, younger adults, and older adults in same- and mixed-age dyads were instructed to drum in synchrony with their interaction partner at a constant, self-chosen tempo. Adult-only dyads showed the highest and children-only the lowest levels of IAS accuracy. It is important to note that children improved reliably in IAS accuracy when paired with older partners. The observed age-related differences in IAS accuracy remained reliable after statistically controlling for individual differences in the ability to synchronize to a metronome and for between-dyad differences in tempo. The authors conclude that IAS improves from middle childhood to adulthood and that adult interaction partners may facilitate its development.