ESTABLISHMENT OF THE FACTUAL CAUSATION IN TORT

The article examines the moral basis and significance of causation from the standpoint of corrective justice; the division of factual and legal causation, as well as the theory of conditio sine qua non and NESS test, are critically analyzed; the problems of the former are discussed, while the preference of the latter is justified, with special attention to the torts committed by omission; the relation of factual causation as a matter of substantive law to the procedural form of its reflection is established through the discussion of issues of allocation of burden of proof and standards of proof, as well as admissibility of scientific and statistical evidence of factual causation.

On Two Measure-Theoretic Aspects of the Full Bayesian Significance Test for Precise Bayesian Hypothesis Testing †

The Full Bayesian Significance Test (FBST) has been proposed as a convenient method to replace frequentist p-values for testing a precise hypothesis. Although the FBST enjoys various appealing properties, the purpose of this paper is to investigate two aspects of the FBST which are sometimes observed as measure-theoretic inconsistencies of the procedure and have not been discussed rigorously in the literature. First, the FBST uses the posterior density as a reference for judging the Bayesian statistical evidence against a precise hypothesis. However, under absolutely continuous prior distributions, the posterior density is defined only up to Lebesgue null sets which renders the reference criterion arbitrary. Second, the FBST statistical evidence seems to have no valid prior probability. It is shown that the former aspect can be circumvented by fixing a version of the posterior density before using the FBST, and the latter aspect is based on its measure-theoretic premises. An illustrative example demonstrates the two aspects and their solution. Together, the results in this paper show that both of the two aspects which are sometimes observed as measure-theoretic inconsistencies of the FBST are not tenable. The FBST thus provides a measure-theoretically coherent Bayesian alternative for testing a precise hypothesis.

Examining the Theoretical Relationship between Constructs in the Person-Centred Practice Framework: A Structural Equation Model

Research relating to person-centred practice continues to expand and currently there is a dearth of statistical evidence that tests the validity of an accepted model for person-centred practice. The Person-centred Practice Framework is a midrange theory that is used globally, across a range of diverse settings. The aim of this study was to statistically examine the relationships within the Person-centred Practice Framework. A cross sectional survey design using a standardized tool was used to assess a purposive sample (n = 1283, 31.8%) of multi-disciplinary health professionals across Ireland. Survey construct scores were included in a structural model to examine the theoretical model of person-centred practice. The results were drawn from a multi-disciplinary sample, and represented a broad range of clinical settings. The model explains 60.5% of the total variance. Factor loadings on the second order latent construct, along with fit statistics, confirm the acceptability of the measurement model. Statistically significant factor loadings were also acceptable. A positive, statistically significant relationship was observed between components of the Person-centred Practice Framework confirming it’s theoretical propositions. The study provides statistical evidence to support the Person-centred Practice Framework, with a multidisciplinary sample. The findings help confirm the effectiveness of the Person-Centred Practice Index for-Staff as an instrument that is theoretically aligned to an internationally recognised model for person-centred practice.

Statistical Evidence and the Problem of Specification

Philosophical debates over statistical evidence have long been framed and dominated by L. Jonathan Cohen's Paradox of the Gatecrasher and a related hypothetical example commonly called Prison Yard. These examples, however, raise an issue not discussed in the large and growing literature on statistical evidence – the question of what statistical evidence is supposed to be evidence of. In actual practice, the legal system does not start with a defendant and then attempt to determine if that defendant has committed some unspecified or under-specified act, as these examples appear to suppose. Rather, both criminal and civil litigation start with a sufficiently specified act and then attempt to determine if the defendant has committed it. And when we start with a more fully specified act, the statistics look very different, and these prominent examples no longer present the paradox they are claimed to support. Examining the issue of specification, however, does more than simply undercut the prominent examples in a long and extensive literature. The examination also raises normative issues challenging the legal system's traditional reluctance to base liability on the conjunction of probabilities.

Self-Determination Theory: Statistical Correlations Between Motivational Regulations and Basic Psychological Needs

Self-determination theory has been predominantly used as a psychological framework to describe human motivation. It classifies three types of motivation with various forms of regulations, ranging from amotivation, extrinsic motivation (i.e., extrinsic regulation, introjected regulation, identified regulation, and integrated regulation), to intrinsic motivation. In addition, it points out three basic psychological needs (i.e., autonomy, relatedness, competence) which are essential components to enhance the process of internalisation, meaning motivations driven by identified, integrated and intrinsic regulations. This study aims to provide statistical evidence of statistical correlations among these factors and regulations. To achieve this, an online questionnaire with 9 close-ended statements based on 5-Likert scale was distributed to high-school students and school leavers in Thailand. A total of 206 responses were received. Overall, a correlational analysis showed a positive correlation between internalisation and the basic psychological needs (r = 0.4). Furthermore, there was a moderate positive correlation between integrated correlation and intrinsic motivation (r = 0.6) which support the described theoretical framework. In addition, there was a moderate positive correlation between autonomy and competence (r = 0.6) in the perspective of psychological needs. However, the statistical evidence shows a moderate relationship between identified regulation and external regulation (r = 0.6) which adds an insightful finding to our current understanding. It is suggested that in order to increase the level of internalisation, it is essential to provide a learning environment where students can feel the sense of success (competence), constructive relationship with others (relatedness), and a fair degree of self-regulated learning (autonomy).

The Month-of-the-Year Effect in the European, American, Australian and Asian Markets

This paper examines the existence of the month-of-the-year effects in four different continents, namely Europe, Asia, America, and Oceania. Nine indexes were analyzed in order to verify differences between monthly returns from January 1990 to December 2013, followed by an examination of the January effect, Halloween effect, and the October effect, testing for statistical significance using an OLS linear regression in order to verify whether those effects offer consistent opportunities for investors. Investors with globally diversified portfolios benefit from the Halloween effect, with a 1.2% average monthly excess return in winter and spring, while the pre-dotcom-bubble period had a better performance than the post-dotcom-bubble period. In the global post-dotcom-bubble period, there is statistical evidence for 1.60% and 1% lower average monthly returns in January (the January effect) and in months other than October (the October effect), respectively, contradicting the literature. The dotcom bubble seems to be responsible for the January effect differing from what might otherwise have been expected in the later period. There is no consistent and clear impact on continental incidence. The Halloween effect is revealed to be a fruitful strategy in the FTSE, DAX, Dow Jones, BOVESPA, and N225 indexes taken one-by-one. The January effect excess average return was only statistically significative for the pre-dotcom-bubble period for globally diversified portfolios. This paper contributes to a wider global and comparable view upon month-of-the-year effect.

Crime After Conflict

Overwhelming statistical evidence shows a correlation between conflict and crime rates, both at a structural and individual level. This is assumed by many to mean that conflict is responsible for rises in crime. This article describes an alternative approach: that conflict is conducive to organised criminality, but does not necessarily cause it. By demonstrating examples from post-conflict societies, it is shown that the causal nexus of conflict and crime is actually security, development and governance. This effect is particularly pronounced where violent crime is concerned, but the inconsistent and often contextual nature of such crime renders any attempt to draw conclusions difficult. By framing peacebuilding efforts around conflict, and prioritising the neoliberal democratic model of the Global North as a cure to security and development shortcomings, crime is actually further enabled as the symptoms of criminality are not addressed. By returning the focus to security, development and governance, critical discussion may be able to cut through the noise and provide practical solutions to the crime epidemics characteristic of post-conflict environments.

Bayesianism: Objections and Rebuttals

While the laws of probability are rarely disputed, the question of how we should interpret probability judgments is less straightforward. Broadly, there are two ways to conceive of probability—either as an objective feature of the world, or as a subjective measure of our uncertainty. Both notions have their place in science, but it is the latter subjective notion (the Bayesian approach) that is crucial in legal reasoning. This chapter explains the advantages of using Bayesian networks in adjudicative factfinding. It addresses a number of common objections to the Bayesian approach, such as “There is no such thing as a probability of a single specified event”; “The Bayesian approach only works with statistical evidence”; “The Bayesian approach is too difficult for legal factfinders to comprehend”; and “A Bayesian network can never capture the full complexity of a legal case.” Fenton and Lagnado offer rebuttals to each of these objections.