A Model for Evaluating Organizational Competencies: An Application in the Context of a Quality Management Initiative*

Despite the important contributions made by the Competency‐Based Perspective (CBP) to strategic thought, certain issues on the operational definition of the theoretical concepts that characterize this approach remain unresolved, thus limiting its empirical application. In addressing this issue, the present study puts forward a procedure for measuring the competencies that can be developed in association with a Quality Management (QM) initiative and analyzes the reliability and validity of the resulting scale. This procedure could be transferred to studies that aim to carry out an empirical analysis based on the theoretical position of the CBP.     

Conceptual, computational and inferential benefits of the missing data perspective in applied and theoretical statistical problems

This article advocates the following perspective: When confronting a scientific problem, the field of statistics enters by viewing the problem as one where the scientific answer could be calculated if some missing data, hypothetical or real, were available. Thus, statistical effort should be devoted to three steps:

Detecting multiple confounders

This paper proposes an approach for detecting multiple confounders which combines the advantages of two causal models, the potential outcome model and the causal diagram. The approach need not use a complete causal diagram as long as it is known that a known covariate set Z$Z$ contains the parent set of the exposure E  . On the other hand, whether a covariate is or not a confounder may depend on its categorization. We introduce uniform non-confounding which implies non-confounding in any subpopulation defined by the interval of a covariate (or any pooled level for a discrete covariate). We show that the conditions in Miettinen and Cook's criteria for non-confounding also imply uniform non-confounding. Further we present an algorithm for deleting non-confounders from the potential confounder set Z$Z$, which extends Greenland et al.'s [1999a. Causal diagrams for epidemiologic research. Epidemiology 10, 37–48] approach by splitting Z$Z$ into a series of potential confounder subsets. We also discuss conditions for non-confounding bias in the subpopulations in which we are interested, where the subpopulations may be defined by non-confounders.     

Analysing direct effects in randomized trials with secondary interventions: an application to human immunodeficiency virus prevention trials

Summary.  The 'Methods for improving reproductive health in Africa' trial is a recently completed randomized trial that investigated the effect of diaphragm and lubricant gel use in reducing infection by the human immunodeficiency virus (HIV) among susceptible women. 5045 women were randomly assigned to either the active treatment arm or not. Additionally, all subjects in both arms received intensive condom counselling and provision, the 'gold standard' HIV prevention barrier method. There was much lower reported use of condoms in the intervention arm than in the control arm, making it difficult to answer important public health questions based solely on the intention-to-treat analysis. We adapt an analysis technique from causal inference to estimate the 'direct effects' of assignment to the diaphragm arm, adjusting for use of condoms in an appropriate sense. Issues raised in the trial apply to other trials of HIV prevention methods, some of which are currently being conducted or designed.     

试析休谟问题的本质

通过梳理休谟在《人类理解研究》中得出所谓＂休谟问题＂的论证过程,可以从问题产生源头分析休谟问题本质。真正首提＂休谟的问题＂一说法者实为康德。以下仅论述认识论意义上的＂休谟问题＂,其本质即为彻底的经验论内部蕴含的、无可回避的逻辑结局。休谟问题的本质要么是＂因果问题＂,要么是＂归纳问题＂,两者必有一者更为根本。从休谟得出问题的原始论证过程看,休谟问题本质应为因果问题。由于因果关系是人类得到＂实际的事情＂方面知识的重要依据,休谟试图为因果关系寻找必然性基础。     

Bayesian Causality

Abstract

Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework. In this approach, causality statements are viewed as hypotheses, or models, about the world and the fundamental object to be computed is the posterior distribution of the causal hypotheses, given the data and the background knowledge. Computation of the posterior, illustrated here in simple examples, may involve complex probabilistic modeling but this is no different than in any other Bayesian modeling situation. The main advantage of the approach is its connection to the axiomatic foundations of the Bayesian framework, and the general uniformity with which it can be applied to a variety of causality settings, ranging from specific to general cases, or from causes of effects to effects of causes.     

A Bayesian nonparametric causal model

Typically, in the practice of causal inference from observational studies, a parametric model is assumed for the joint population density of potential outcomes and treatment assignments, and possibly this is accompanied by the assumption of no hidden bias. However, both assumptions are questionable for real data, the accuracy of causal inference is compromised when the data violates either assumption, and the parametric assumption precludes capturing a more general range of density shapes (e.g., heavier tail behavior and possible multi-modalities). We introduce a flexible, Bayesian nonparametric causal model to provide more accurate causal inferences. The model makes use of a stick-breaking prior, which has the flexibility to capture any multi-modalities, skewness and heavier tail behavior in this joint population density, while accounting for hidden bias. We prove the asymptotic consistency of the posterior distribution of the model, and illustrate our causal model through the analysis of small and large observational data sets.     

The problem with knowledge ambiguity

This paper proposes a new conceptualisation of the construct of knowledge ambiguity. This new conceptualisation is essential because (1) past researchers have tended to narrowly define and operationalise knowledge ambiguity in terms of causal ambiguity or tacitness and (2) the prevalent non-comprehensive conceptualisation constrains our ability to overcome the problem of knowledge ambiguity. Knowledge ambiguity has been identified as a major obstacle to effective knowledge transfer and to the implementation of overall knowledge management systems. The new conceptualisation proposes that knowledge ambiguity is composed of two types of ambiguity: component ambiguity and causal ambiguity. Component ambiguity is uncertainty about knowledge content, whereas causal ambiguity is uncertainty about how to use the knowledge. This re-conceptualisation is supported by previous studies on knowledge characteristics, absorptive capacity and cognitive learning. In this paper, theoretical propositions are developed to demonstrate the compatibility of the new conceptualisation with the current understanding of these concepts. The present paper not only advances our understanding of knowledge ambiguity, it also points towards solutions for overcoming the problems associated with knowledge ambiguity. Different measures are required to overcome problems created by component ambiguity vs. causal ambiguity. This paper’s re-conceptualisation of knowledge ambiguity makes it easier to theorise about and operationalise the concept. It aligns the definition of knowledge ambiguity with current definitions of related constructs such as absorptive ambiguity and cognitive learning that are used in the broader knowledge transfer and knowledge management literatures.     

Criteria for confounders in epidemiological studies

The paper addresses a formal definition of a confounder based on the qualitative definition that is commonly used in standard epidemiology text-books. To derive the criterion for a factor to be a confounder given by Miettinen and Cook and to clarify inconsistency between various criteria for a confounder, we introduce the concepts of an irrelevant factor, an occasional confounder and a uniformly irrelevant factor. We discuss criteria for checking these and show that Miettinen and Cook's criterion can also be applied to occasional confounders. Moreover, we consider situations with multiple potential confounders, and we obtain two necessary conditions that are satisfied by each confounder set. None of the definitions and results presented in this paper require the ignorability and sufficient control confounding assumptions which are commonly employed in observational and epidemiological studies.