Abstract: Inference and Interval Estimation for Indirect Effects With Latent Variable Models

Models specifying indirect effects (or mediation) and structural equation modeling are both popular in the social sciences. Yet relatively little research has compared methods that test for indirect effects among latent variables and provided precise estimates of the effectiveness of different methods.

This simulation study provides an extensive comparison of methods for constructing confidence intervals and for making inferences about indirect effects with latent variables. We compared the percentile (PC) bootstrap, bias-corrected (BC) bootstrap, bias-corrected accelerated (BC _a ) bootstrap, likelihood-based confidence intervals (Neale & Miller, 1997), partial posterior predictive (Biesanz, Falk, and Savalei, 2010), and joint significance tests based on Wald tests or likelihood ratio tests. All models included three reflective latent variables representing the independent, dependent, and mediating variables. The design included the following fully crossed conditions: (a) sample size: 100, 200, and 500; (b) number of indicators per latent variable: 3 versus 5; (c) reliability per set of indicators: .7 versus .9; (d) and 16 different path combinations for the indirect effect (α = 0, .14, .39, or .59; and β = 0, .14, .39, or .59). Simulations were performed using a WestGrid cluster of 1680 3.06GHz Intel Xeon processors running R and OpenMx.

Results based on 1,000 replications per cell and 2,000 resamples per bootstrap method indicated that the BC and BC _a bootstrap methods have inflated Type I error rates. Likelihood-based confidence intervals and the PC bootstrap emerged as methods that adequately control Type I error and have good coverage rates.     

Alkoholabhängigkeit in der psychiatrischen Klinik

In Germany 2.4?% of the population suffer from alcohol dependency and 3.8?% of adults from alcohol abuse. Despite an extensive number of options available for addiction therapy only a minority of affected persons receive addiction-specific therapy. This results in a chronification of the disease and a reduction of approximately 15 years in life expectancy. Background knowledge of the foundations of alcohol addiction leads to a new understanding of the processual course of the disease as well as to new therapy interventions. Individualized treatment includes a combination of outpatient, partial inpatient or inpatient therapeutic measures and range from medical advice for reduction of alcohol consumption, to qualified withdrawal treatment up to pharmacological relapse prophylaxis and long-term weaning treatment. Under current therapeutic conditions abstinence quotas of 50-70?% over 1 year can be achieved. Treating physicians must have sufficient diagnostic certainty and furthermore must be in command of motivating discourse techniques, knowledge on the therapeutic options of outpatient and inpatient withdrawal treatment, pharmaceutical relapse prophylaxis and mediation in rehabilitation measures.     

Maximum Marginal Likelihood Estimation of a Monotonic Polynomial Generalized Partial Credit Model with Applications to Multiple Group Analysis

We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang’s (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock–Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.     

Using the visual world paradigm to study language processing: a review and critical evaluation

We describe the key features of the visual world paradigm and review the main research areas where it has been used. In our discussion we highlight that the paradigm provides information about the way language users integrate linguistic information with information derived from the visual environment. Therefore the paradigm is well suited to study one of the key issues of current cognitive psychology, namely the interplay between linguistic and visual information processing. However, conclusions about linguistic processing (e.g., about activation, competition, and timing of access of linguistic representations) in the absence of relevant visual information must be drawn with caution.     

Prediction during language processing is a piece of cake-But only for skilled producers

Are there individual differences in children's prediction of upcoming linguistic input and what do these differences reflect? Using a variant of the preferential looking paradigm (Golinkoff, Hirsh-Pasek, Cauley, & Gordon, 1987), we found that, upon hearing a sentence like, "The boy eats a big cake," 2-year-olds fixate edible objects in a visual scene (a cake) soon after they hear the semantically constraining verb eats and prior to hearing the word cake. Importantly, children's prediction skills were significantly correlated with their productive vocabulary size-skilled producers (i.e., children with large production vocabularies) showed evidence of predicting upcoming linguistic input, while low producers did not. Furthermore, we found that children's prediction ability is tied specifically to their production skills and not to their comprehension skills. Prediction is really a piece of cake, but only for skilled producers. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

Neural theory for the perception of causal actions

The efficient prediction of the behavior of others requires the recognition of their actions and an understanding of their action goals. In humans, this process is fast and extremely robust, as demonstrated by classical experiments showing that human observers reliably judge causal relationships and attribute interactive social behavior to strongly simplified stimuli consisting of simple moving geometrical shapes. While psychophysical experiments have identified critical visual features that determine the perception of causality and agency from such stimuli, the underlying detailed neural mechanisms remain largely unclear, and it is an open question why humans developed this advanced visual capability at all. We created pairs of naturalistic and abstract stimuli of hand actions that were exactly matched in terms of their motion parameters. We show that varying critical stimulus parameters for both stimulus types leads to very similar modulations of the perception of causality. However, the additional form information about the hand shape and its relationship with the object supports more fine-grained distinctions for the naturalistic stimuli. Moreover, we show that a physiologically plausible model for the recognition of goal-directed hand actions reproduces the observed dependencies of causality perception on critical stimulus parameters. These results support the hypothesis that selectivity for abstract action stimuli might emerge from the same neural mechanisms that underlie the visual processing of natural goal-directed action stimuli. Furthermore, the model proposes specific detailed neural circuits underlying this visual function, which can be evaluated in future experiments.