民办幼儿园教师专业素养培训方式实验研究

随着现代教育的发展，提升幼儿园教师专业素养已成为促进学前教育发展的必然要求。以探究民办幼儿园教师专业素养为视角，提出通过干预行为提升教师专业素养水平的构想。实验随机选取成都市某民办幼儿园的20名教师为实验组，再选取与其办园环境、办园背景相似的另一所民办幼儿园的20名教师为对照组，对实验园教师的专业理念与师德、专业知识、专业能力3个维度进行干预。实验结果表明：与对照园相比，实验园教师的专业能力提升显著；与实验园实验前后相比，教师在专业知识、专业能力维度及专业素养总体水平方面均有提升。对教师培训的具体建议为：（1）单一与混合相结合的培训方式对提升民办幼儿园教师专业素养总体水平有现实意义；（2）民办幼儿园教师专业知识的提升需理论培训与自主学习相结合；（3）专家"现场诊断-指导式"培训方式对提升民办幼儿园教师专业能力有显著作用。综上，建议民办幼儿园在了解本园教师专业素养水平的基础上，针对教师专业素养的不同方面，分别采取不同的有效培训方式。     

Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.     

Lognormal Approximations of Fault Tree Uncertainty Distributions

Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed‐form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling‐based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed‐form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks’s method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models.     

Analyzing multiple vector autoregressions through matrix-variate normal distribution with two covariance matrices

Abstract

This article proposes a new approach to analyze multiple vector autoregressive (VAR) models that render us a newly constructed matrix autoregressive (MtAR) model based on a matrix-variate normal distribution with two covariance matrices. The MtAR is a generalization of VAR models where the two covariance matrices allow the extension of MtAR to a structural MtAR analysis. The proposed MtAR can also incorporate different lag orders across VAR systems that provide more flexibility to the model. The estimation results from a simulation study and an empirical study on macroeconomic application show favorable performance of our proposed models and method.     

Land markets and migration trends in Tanzania: A qualitative‐quantitative analysis

While migration has been found to enhance welfare across a range of settings, most of the literature focuses on rural‐to‐urban migrant flows. Using a unique dataset from north‐western Tanzania, this article probes an important, yet overlooked, link between land markets and rural‐to‐rural migration. A mixed‐methods approach is used to discern how these two forces are intertwined in village life. Results indicate that household decisions to migrate are particularly influenced by the ease of market‐based land access in their new communities. This suggests that labour mobility may be facilitated through the development of a well‐functioning land market.     

Overcoming Old in Age-Friendliness

In this article, we explore views on an age-friendly space in the Netherlands by analysing the responses of older individuals (N = 54) in focus groups and by examining the perspectives around an age-friendly zone in the Netherlands, Parkstad Limburg. We found that a central issue in the wishes for living at a later age are adjustments to envisioned physical limitations that come with the ageing process; this includes adjustments to ensure safety, accessibility and mobility, in order to facilitate older individuals' efforts to stay engaged with the world around them. In their wishes, the older participants constructed ideal dwelling places that closely resembled a senior home, but at the same time they rejected wishing to live in a place that was identified as a senior home. We explain this paradox by the representation of such a space as being for old people, i.e. needy older individuals, which was not how the older participants wished to be identified. We conclude that the conception of age-friendly environments will have to face the difficult challenge of overcoming the association with old age, while simultaneously taking into account adjustments that signify and relate to the ageing process and that seem inescapably tied to oldness.     

Multiparameter evidence synthesis in epidemiology and medical decision-making: current approaches

Summary.  Alongside the development of meta-analysis as a tool for summarizing research literature, there is renewed interest in broader forms of quantitative synthesis that are aimed at combining evidence from different study designs or evidence on multiple parameters. These have been proposed under various headings: the confidence profile method, cross-design synthesis, hierarchical models and generalized evidence synthesis. Models that are used in health technology assessment are also referred to as representing a synthesis of evidence in a mathematical structure. Here we review alternative approaches to statistical evidence synthesis, and their implications for epidemiology and medical decision-making. The methods include hierarchical models, models informed by evidence on different functions of several parameters and models incorporating both of these features. The need to check for consistency of evidence when using these powerful methods is emphasized. We develop a rationale for evidence synthesis that is based on Bayesian decision modelling and expected value of information theory, which stresses not only the need for a lack of bias in estimates of treatment effects but also a lack of bias in assessments of uncertainty. The increasing reliance of governmental bodies like the UK National Institute for Clinical Excellence on complex evidence synthesis in decision modelling is discussed.     

山东方言研究方法新探

本文从研究方法的角度节选《山东方言研究·概论编》的三部分内容：一是通过山东史前文 化推断山东方言的产生及现代山东方言分区格局的形成，二是结合山东地方历史评述山东方 言在汉语发展中的地位和影响，三是从研究内容和方法总结建国以来山东方言研究的发展道 路。     

On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.     

LOG-PERIODOGRAM ESTIMATION OF LONG MEMORY VOLATILITY DEPENDENCIES WITH CONDITIONALLY HEAVY TAILED RETURNS

Many recent papers have used semiparametric methods, especially the log-periodogram regression, to detect and estimate long memory in the volatility of asset returns. In these papers, the volatility is proxied by measures such as squared, log-squared, and absolute returns. While the evidence for the existence of long memory is strong using any of these measures, the actual long memory parameter estimates can be sensitive to which measure is used. In Monte-Carlo simulations, I find that if the data is conditionally leptokurtic, the log-periodogram regression estimator using squared returns has a large downward bias, which is avoided by using other volatility measures. In United States stock return data, I find that squared returns give much lower estimates of the long memory parameter than the alternative volatility measures, which is consistent with the simulation results. I conclude that researchers should avoid using the squared returns in the semiparametric estimation of long memory volatility dependencies.