Infant and parental pathways to preschool cognitive competence

In a longitudinal study, 62 parent‐child dyads were seen during the second year of life and at 4 years of age. At 12 months, measures included parental sensitive responsiveness during free play, knowledge of cognitive‐communicative development in infancy, and level of exploration and disinhibitedness of the infant. At 16 and at 20 months, parental responsiveness and directiveness and infant task mastery behaviour were assessed in constructive play. Quality of verbal guidance of the parent was assessed in a joint attention situation. At 48 months of age, the McCarthy Scales of Children's Abilities were administered at home, together with dyadic tasks. A path analysis revealed a model in which both verbal abilities and perceptual performance outcome measures were well predicted by the quality of parental verbal guidance in the second year. The latter measure was shown to be independent of the socioeconomic status of parents in the group, but was significantly related with knowledge of infant cognitive‐communicative development. Of the measures at the outset of the second year, only socioeconomic status remained as having a direct path at pre‐school age. The consistency of the model with other empirical findings underscores parental verbal scaffolding as an important shaper of cognitive development

Finding Exogenous Variables in Data with Many More Variables than Observations

Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations (p<n, p: the number of variables and n: the number of observations). However, modern datasets including gene expression data need high-dimensional causal modeling in challenging situations with orders of magnitude more variables than observations (p>>n). In this paper, we propose a method to find exogenous variables in a linear non-Gaussian causal model, which requires much smaller sample sizes than conventional methods and works even when p>>n. The key idea is to identify which variables are exogenous based on non-Gaussianity instead of estimating the entire structure of the model. Exogenous variables work as triggers that activate a causal chain in the model, and their identification leads to more efficient experimental designs and better understanding of the causal mechanism. We present experiments with artificial data and real-world gene expression data to evaluate the method.Comment: A revised version of this was published in Proc. ICANN201

Specifying the directionality of fault propagation paths using transfer entropy

July 4-7 200

A quantitative analysis of the impact of a computerised information system on nurses' clinical practice using a realistic evaluation framework

Objective: To explore nurses' perceptions of the impact on clinical practice of the use of a computerised hospital information system. Design: A realistic evaluation design based on Pawson and Tilley's work has been used across all the phases of the study. This is a theory-driven approach and focuses evaluation on the study of what works, for whom and in what circumstances. These relationships are constructed as context-mechanisms-outcomes (CMO) configurations. Measurements: A questionnaire was distributed to all nurses working in in-patient units of a university hospital in Spain (n = 227). Quantitative data were analysed using SPSS 13.0. Descriptive statistics were used for an overall overview of nurses' perception. Inferential analysis, including both bivariate and multivariate methods (path analysis), was used for cross-tabulation of variables searching for CMO relationships. Results: Nurses (n = 179) participated in the study (78.8% response rate). Overall satisfaction with the IT system was positive. Comparisons with context variables show how nursing units' context had greater influence on perceptions than users' characteristics. Path analysis illustrated that the influence of unit context variables are on outcomes and not on mechanisms. Conclusion: Results from the study looking at subtle variations in users and units provide insight into how important professional culture and working practices could be in IT (information technology) implementation. The socio-technical approach on IT systems evaluation suggested in the recent literature appears to be an adequate theoretical underpinning for IT evaluation research. Realistic evaluation has proven to be an adequate method for IT evaluation. (C) 2009 Elsevier Ireland Ltd. All rights reserved

Peeling Back the Onion Competitive Advantage Through People: Test of a Causal Model

Proponents of the resource-based view (RBV) of the firm have identified human resource management (HRM) and human capital as organizational resources that can contribute to sustainable competitive success. A number of empirical studies have documented the relationship between systems of human resource policies and practices and firm performance. The mechanisms by which HRM leads to firm performance, however, remain largely unexplored. In this study, we explore the pathways leading from HRM to firm performance. Specifically, we use structural equation modeling to test a model positing a set of causal relationships between high performance work systems (HPWS), employee retention, workforce productivity and firm market value. Within a set of manufacturing firms, results indicate the primary impact of HPWS on productivity and market value is through its influence on employee retention