Causal inferences on the effectiveness of complex social programs: Navigating assumptions,sources of complexity and evaluation design challenges

This paper explores avenues for navigating evaluation design challenges posed by complex social programs (CSPs) and their environments when conducting studies that call for generalizable, causal inferences on the intervention’s effectiveness. A definition is provided of a CSP drawing on examples from different fields, and an evaluation case is analyzed in depth to derive seven (7) major sources of complexity that typify CSPs, threatening assumptions of textbook-recommended experimental designs for performing impact evaluations. Theoretically-supported, alternative methodological strategies are discussed to navigate assumptions and counter the design challenges posed by the complex configurations and ecology of CSPs. Specific recommendations include: sequential refinement of the evaluation design through systems thinking, systems-informed logic modeling; and use of extended term, mixed methods (ETMM) approaches with exploratory and confirmatory phases of the evaluation. In the proposed approach, logic models are refined through direct induction and interactions with stakeholders. To better guide assumption evaluation, question-framing, and selection of appropriate methodological strategies, a multiphase evaluation design is recommended.     

Bayesian autoregressive spectral analysis

The article describes an operational Bayesian approach to making inferences for the spectral density function for univariate autoregressive processes and for the AR operator of multivariate autoregressive processes. The derivation of the approach is described. Numerical examples, including the Wolfer Sunspot numbers, are used to demonstrate the practical usefulness of the approach.     

All pairwise comparisons of exponential location parameters under type i censoring — equal scale parameter case

In this paper, procedures for all pairwise comparisons of location parameters of negative exponential populations are developed when the common scale parameter is known or unknown using large sample distributional approximations of the relevant random variables. The small sample performance of these procedures are then examined using Monte Carlo simulation.     

系统化逻辑的基本概念

逻辑是对于推理的结构特性的研究。本文着重讨论和辨析了现代逻辑系统的一些基本概念，如演绎有效性，真与可靠性，逻辑系统的句法，逻辑系统的语义，逻辑系统的元逻辑，等等。     

自寻烦恼的逻辑原因

人生活在世界上,免不了会遇到很多的烦恼,导致我们的生理、心理的负担,更可能导致行为的失误或失败。但是并非所有的烦恼都是必然的,事实上有些烦恼是客观存在的,而有些烦恼是主观的、是自寻的。自寻烦恼有很多的原因,大部分是认识发生偏差的结果,尤其是进行了错误的推理更容易导致自寻烦恼。本文对错误推理导致自寻烦恼进行了一定的分析。     

英语阅读中的错误推断与理解空白

1In trodu ctionForm any students,read ing is by far the mostimportant ofthe four sk ills in a second language(Carrell,1989).Forstudents in Ch ina,where English is learned as a fore ign languageand face-to-facecommun ication w ith native speakers of Eng-li…     

形式规则还是心理模型--演绎推理过程中两种判断标准的比较研究

演绎推理是人类独有的高级认知活动.然而心理学家对于人们是如何进行推理的一直存在两种不同意见有人认为推理是依赖于形式规则进行的;而另一些人则认为推理是依赖于心理模型的语义过程.对这两种判断标准理论进行分析和研究,心理模型理论在如下三个方面优于形式规则理论一是心理模型理论比形式规则理论适用的范围更广;二是心理模型理论对无效判断更为关注;三是心理模型理论关注系统错误.     

双关语的语用解释与翻译

双关语是中外文学作品中常用的一种修辞手段,对双关语的解释和翻译是解析文学作品的难点。从语用认知及关联理论分析了双关语的理解过程,并从语用翻译的角度探讨了双关语的三元翻译理论,提出双关语的翻译是一个复杂的双重推理活动,是译者对双关语原话语的理解和把握以及对译语文化和译语读者期待的一个估计过程。     

Benchmark Analysis: Shopping with Proper Confidence

We discuss the issue of using benchmark doses for quantifying (excess) risk associated with exposure to environmental hazards. The paradigm of low-dose risk estimation in dose-response modeling is used as the primary application scenario. Emphasis is placed on making simultaneous inferences on benchmark doses when data are in the form of proportions, although the concepts translate easily to other forms of outcome data.     

Comparing volatility forecasting models during the global financial crisis

Volatility estimation in financial markets has always been a challenge especially in time of crisis. Once asset prices and investment decisions are highly sensitive to such variable, many different models have been proposed in literature. This article estimates the volatility from a new family of stochastic volatility models called non-Gaussian State Space Models, a subclass of state space models where it is possible to compute exact likelihood. Volatilities of important Asian and Oceanian stock market indexes have been estimated and compared to APARCH model estimates. Results showed that non-Gaussian State Space Models outperformed significantly in both in-sample and forecasting cases.