A short note on the estimation of the asymptotic covariance matrix for polychoric correlations

By using a Taylor expansion of the equations that define the two step estimator for polychoric correlations, the asymptotic covariance matrix for the estimated correlations can be derived in a simple and straightforward way.     

Fisher transformation based confidence intervals of correlations in fixed- and random-effects meta-analysis

Meta-analyses of correlation coefficients are an important technique to integrate results from many cross-sectional and longitudinal research designs. Uncertainty in pooled estimates is typically assessed with the help of confidence intervals, which can double as hypothesis tests for two-sided hypotheses about the underlying correlation. A standard approach to construct confidence intervals for the main effect is the Hedges-Olkin-Vevea Fisher-z (HOVz) approach, which is based on the Fisher-z transformation. Results from previous studies (Field, 2005, Psychol. Meth., 10, 444; Hafdahl and Williams, 2009, Psychol. Meth., 14, 24), however, indicate that in random-effects models the performance of the HOVz confidence interval can be unsatisfactory. To this end, we propose improvements of the HOVz approach, which are based on enhanced variance estimators for the main effect estimate. In order to study the coverage of the new confidence intervals in both fixed- and random-effects meta-analysis models, we perform an extensive simulation study, comparing them to established approaches. Data were generated via a truncated normal and beta distribution model. The results show that our newly proposed confidence intervals based on a Knapp-Hartung-type variance estimator or robust heteroscedasticity consistent sandwich estimators in combination with the integral z-to-r transformation (Hafdahl, 2009, Br. J. Math. Stat. Psychol., 62, 233) provide more accurate coverage than existing approaches in most scenarios, especially in the more appropriate beta distribution simulation model.     

Parental Emotional Response to Children’s Traumas

To better understand how parents react to their child’s trauma exposure and evaluate whether different reactions are related to different types of traumas, 120 parents (79.2% mothers, 18.3% fathers, 2.5% other caregivers) were asked about their emotional reactions related to their child’s self-reported worst trauma. Emotional reactions were assessed with the Parental Emotional Reactions Questionnaire (PERQ). Parents reported high levels of distress and guilt. Furthermore, there was a significant relationship between type of trauma and parents’ overall emotional reactions. Parental distress was equally endorsed among the different trauma groups. Parents of children who experienced intrafamilial violence and extrafamilial sexual abuse reported the highest levels of guilt, and child exposure to intrafamilial violence was associated with higher levels of parental shame.     

Learning,Motor Skill,and Long-Range Correlations

Long-range correlations have been evidenced in a number of experiments, generally using overlearned and overpracticed tasks. The authors hypothesized that long-range correlation could represent the byproduct of learning. They analyzed the series of periods produced by a group of expert and a group of novices during prolonged trials on a ski simulator. Results showed a very low variability in expert's series, as compared to novices. Fractal analyses showed that fluctuations were significantly more structured and correlated in experts. These results suggest that learning could be conceived as the progressive installation of complexity in the system.     

Bayesian Estimation of Circumplex Models Subject to Prior Theory Constraints and Scale-Usage Bias

This paper presents a hierarchical Bayes circumplex model for ordinal ratings data. The circumplex model was proposed to represent the circular ordering of items in psychological testing by imposing inequalities on the correlations of the items. We provide a specification of the circumplex, propose identifying constraints and conjugate priors for the angular parameters, and accommodate theory-driven constraints in the form of inequalities. We investigate the performance of the proposed MCMC algorithm and apply the model to the analysis of value priorities data obtained from a representative sample of Dutch citizens. We wish to thank Michael Browne and two anonymous reviewers for their comments. The data for this study were collected as part of the project AIR2-CT94-1066, sponsored by the European Commission.     

Evaluating feedback time delay during perturbed and unperturbed balance in handstand

Feedback delays in balance are often assessed using muscle activity onset latencies in response to discrete perturbations. The purpose of the study was to calculate EMG latencies in perturbed handstand, and determine if delays are different to unperturbed handstand. Twelve national level gymnasts completed 12 perturbed and 10 unperturbed (five eyes open and five closed) handstands. Forearm EMG latencies during perturbed handstands were assessed against delay estimates calculated via: cross correlations of wrist torque and COM displacement, a proportional and derivative model of wrist torque and COM displacement and velocity (PD model), and a PD model incorporating a passive stiffness component (PS-PD model). Delays from the PD model (161 ± 14 ms) and PS-PD model (188 ± 14 ms) were in agreement with EMG latencies (165 ± 14 ms). Cross correlations of COM displacement and wrist torque provided unrealistically low estimates (5 ± 9 ms). Delays were significantly lower during perturbed (188 ± 14 ms) compared to unperturbed handstand (eyes open: 207 ± 12 ms; eyes closed: 220 ± 19 ms). Significant differences in delays and model parameters between perturbed and unperturbed handstand support the view that balance measures in perturbed testing should not be generalised to unperturbed balance.     

Contributions to Estimation of Polychoric Correlations

This research concerns the estimation of polychoric correlations in the context of fitting structural equation models to observed ordinal variables by multistage estimation. The first main contribution of this research is to propose and evaluate a Monte Carlo estimator for the asymptotic covariance matrix (ACM) of the polychoric correlation estimates. In multistage estimation, the ACM plays a prominent role, as overall test statistics, derived fit indices, and parameter standard errors all depend on this quantity. The ACM, however, must itself be estimated. Established approaches to estimating the ACM use a sample-based version, which can yield poor estimates with small samples. A simulation study demonstrates that the proposed Monte Carlo estimator can be more efficient than its sample-based counterpart. This leads to better calibration for established test statistics, in particular with small samples. The second main contribution of this research is a further exploration of the consequences of violating the normality assumption for the underlying response variables. We show the consequences depend on the type of nonnormality, and the number and location of thresholds. The simulation study also demonstrates that overall test statistics have little power to detect the studied forms of nonnormality, regardless of the ACM estimator.     

On infinite EPR-like correlations

The paper investigates, in the framework of branching space–times, whether an infinite EPR-like correlation which does not involve finite EPR-like correlations is possible. The authors read earlier versions of this paper at the seminar ‘Chaos and Quantum Information’ at the Jagiellonian University in Kraków on April 16, 2007 and at the seminar ‘On Determinism’ at the University of Bonn on April 20, 2007.     

Gene–environment interdependence

The modern understanding of genetic influences, of environmental effects, of mental disorder, and of heritabilities is noted. The practical utility of finding susceptibility genes with a very small effect is questioned. The empirical findings and implications of developmental perturbations, epigenetics, gene–environment correlations and interactions are then discussed. It is noted that the genes involved in gene–environment interactions may be concerned with susceptibility to all environments and not just adverse ones.