A note on the manova model with double linear restrictions

In this note we show how one may construct goodness-of-fit tests to test hypotheses for the restricted MANOVA and GMANOVA models using the multivariate seemingly unrelated regression (MSUR) model.     

Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form

In this paper we model the Gaussian errors in the standard Gaussian linear state space model as stochastic volatility processes. We show that conventional MCMC algorithms for this class of models are ineffective, but that the problem can be alleviated by reparameterizing the model. Instead of sampling the unobserved variance series directly, we sample in the space of the disturbances, which proves to lower correlation in the sampler and thus increases the quality of the Markov chain.

Using our reparameterized MCMC sampler, it is possible to estimate an unobserved factor model for exchange rates between a group of n countries. The underlying n + 1 country-specific currency strength factors and the n + 1 currency volatility factors can be extracted using the new methodology. With the factors, a more detailed image of the events around the 1992 EMS crisis is obtained.

We assess the fit of competitive models on the panels of exchange rates with an effective particle filter and find that indeed the factor model is strongly preferred by the data.     

Child outcome measures in the study of child care quality

This article assesses whether there are methodological problems with child outcome measures that may contribute to the small associations between child care quality and child outcomes found in the literature. Outcome measures used in 65 studies of child care quality published between 1979 and December 2005 were examined, taking the previous review by Vandell and Wolfe (2000) as the starting point. Serious methodological problems were not pervasive for child outcome measures. However, methodological concerns were most prevalent among measures of socioemotional development. Furthermore, psychometric information on outcome measures was often missing from published reports, and health outcomes and approaches to learning were infrequently studied. Future research should address alignment issues between aspects of quality and the specific child outcomes chosen for study.     

A Fuzzy‐Based Risk Assessment Framework for Autonomous Underwater Vehicle Under‐Ice Missions

The use of autonomous underwater vehicles (AUVs) for various scientific, commercial, and military applications has become more common with maturing technology and improved accessibility. One relatively new development lies in the use of AUVs for under‐ice marine science research in the Antarctic. The extreme environment, ice cover, and inaccessibility as compared to open‐water missions can result in a higher risk of loss. Therefore, having an effective assessment of risks before undertaking any Antarctic under‐ice missions is crucial to ensure an AUV's survival. Existing risk assessment approaches predominantly focused on the use of historical fault log data of an AUV and elicitation of experts’ opinions for probabilistic quantification. However, an AUV program in its early phases lacks historical data and any assessment of risk may be vague and ambiguous. In this article, a fuzzy‐based risk assessment framework is proposed for quantifying the risk of AUV loss under ice. The framework uses the knowledge, prior experience of available subject matter experts, and the widely used semiquantitative risk assessment matrix, albeit in a new form. A well‐developed example based on an upcoming mission by an ISE‐explorer class AUV is presented to demonstrate the application and effectiveness of the proposed framework. The example demonstrates that the proposed fuzzy‐based risk assessment framework is pragmatically useful for future under‐ice AUV deployments. Sensitivity analysis demonstrates the validity of the proposed method.     

Likelihood Inference for Discretely Observed Nonlinear Diffusions

This paper is concerned with the Bayesian estimation of nonlinear stochastic differential equations when observations are discretely sampled. The estimation framework relies on the introduction of latent auxiliary data to complete the missing diffusion between each pair of measurements. Tuned Markov chain Monte Carlo (MCMC) methods based on the Metropolis‐Hastings algorithm, in conjunction with the Euler‐Maruyama discretization scheme, are used to sample the posterior distribution of the latent data and the model parameters. Techniques for computing the likelihood function, the marginal likelihood, and diagnostic measures (all based on the MCMC output) are developed. Examples using simulated and real data are presented and discussed in detail.     

An examination of gender difference in advancement and salary for Marriage and Family Therapy faculty members working in public universities

Evidence for inequitable advancement and salary disparity for women in academia is compelling, but only a marginal amount of research has explored this in the field of Marriage and Family Therapy (MFT) specifically. Current research provides preliminary evidence that women remain underrepresented at the Full Professor rank and are paid less than men MFT faculty. This study collected publicly available data for MFT faculty in public universities to explore gender differences in advancement between ranks, salary disparity, and the representation of women and men in the highest and lowest paying niches of MFT academia. Results showed that, despite being 60.15% of MFTs in public universities, women were paid an average of $5596.25 less than men. Men were 1.40 times more likely than women to be promoted to Full Professor on time—within 13 years of their terminal degree. Implications for addressing inequitable advancement and salary disparity for women MFT faculty are discussed.     

‘We can play tag with a stick’. Children's knowledge,experiences, feelings and creative thinking during the COVID-19 pandemic

Using a relational approach, we draw on repeated interviews with a group of 30 diverse children from Ontario to share and reflect on their knowledge, experiences and feelings early in the COVID-19 pandemic. Prioritising relational interdependence and relational agency, this paper illustrates our participants' embedded engagements with the pandemic and their contribution to the co-production of knowledge. We emphasise their thoughtful responses to the pandemic; their creative, self-reflexive strategies for managing a difficult time; and their advice to others. We thus prioritise children's viewpoints and emphasise their relational interconnections with others during a time that was marked by social isolation.     

Individual differences and dyadic processes in conversations with peers in middle childhood

The goal of the current study was to investigate the contribution of both trait-like individual differences and dyadic processes to the content of children's conversations. Fifty-two groups typically consisting of four same-sex unfamiliar nine-year-old children (N = 202) interacted in all possible dyads, resulting in six dyads per group. Each dyad completed a 5-min frustration task and a 5-min planning task. Observers coded children's verbalizations into 10 categories and further summed these categories into prosocial (suggest, agree, solicit input, ask, encourage, state personal) and antisocial (command, disagree, discourage, aggress) verbalizations, resulting in 24 variables (12 per task). Across both tasks, Social Relations Model analyses provided evidence of the role of both individual differences [significant effects for actor variance (15 of 24 variables), actor-actor correlations, and intrapersonal correlations] and dyadic processes [significant effects for partner variance (4 of 24 variables), relationship variance (18 of 24 variables), dyadic reciprocity correlations (10 of 24 variables), and interpersonal correlations] in children's conversations with peers.     

Peer effects and intentional entrepreneurial behaviour: A systematic literature review and research agenda

What role peers play in individuals’ decisions to become entrepreneurs and to what extent peer effects play a role in influencing behaviours at the various stages of business venturing are important questions for scholars and policymakers. This systematic review takes stock of the recent additions to the literature around the phenomenon of peer influence in entrepreneurship. The review identified 2894 documents which were then narrowed down through three consecutive filtering stages. We thematically analysed the final sample of 27 empirical studies that shed light on how individual peers influence the process and outcomes of these individuals’ entrepreneurial intentions and behaviour, allowing for critical analysis. We propose a conceptual schema of social influence that occurs in interactions among entrepreneurial individuals within business venturing and across the three stages of pre-formation, formation and growth. Our framework reconciles the conceptual classification around discovering, evaluating and exploiting entrepreneurial opportunities with the mechanisms of social influence affecting entrepreneurial behaviours. Grounded in the findings of the literature review, this framework synthesizes peer influence in entrepreneurship with the tripartite distinction of the behavioural motives recognized in contemporary theories of social influence. We suggest promising directions for further research on how interactions with peers might affect individuals’ entrepreneurial behaviours.