School-based,randomised controlled trial of an evidence-based condom promotion leaflet

A condom use promotion leaflet was designed for use with older teenagers in schools. The text targeted a series of cognitive and behavioural antecedents of condom use identified in the literature. Given previous evidence that motivational incentives can enhance the effectiveness of health promotion leaflets, the leaflet was presented in conjunction with a quiz and prize draw. Students were randomly assigned to either the intervention condition or a (no leaflet or incentive) control condition. Measures were taken immediately, pre-intervention and 4 weeks later from 404 students. The 20-min intervention successfully promoted six of the eight measured cognitions, namely (1) attitude towards using condoms with a new partner (2) attitude towards using condoms with a steady partner (3) normative beliefs in relation to preparatory actions (4) self-efficacy in relation to both preparatory actions and (5) condom use (6) intention to use condoms, as well as three measured preparatory actions, that is, purchasing condoms, carrying condoms and discussing condom use. The intervention did not increase condom use with steady or new partners but power to test intervention impact on condom use was curtailed.     

Hail the impossible: p-values, evidence, and likelihood

Significance testing based on p-values is standard in psychological research and teaching. Typically, research articles and textbooks present and use p as a measure of statistical evidence against the null hypothesis (the Fisherian interpretation), although using concepts and tools based on a completely different usage of p as a tool for controlling long-term decision errors (the Neyman-Pearson interpretation). There are four major problems with using p as a measure of evidence and these problems are often overlooked in the domain of psychology. First, p is uniformly distributed under the null hypothesis and can therefore never indicate evidence for the null. Second, p is conditioned solely on the null hypothesis and is therefore unsuited to quantify evidence, because evidence is always relative in the sense of being evidence for or against a hypothesis relative to another hypothesis. Third, p designates probability of obtaining evidence (given the null), rather than strength of evidence. Fourth, p depends on unobserved data and subjective intentions and therefore implies, given the evidential interpretation, that the evidential strength of observed data depends on things that did not happen and subjective intentions. In sum, using p in the Fisherian sense as a measure of statistical evidence is deeply problematic, both statistically and conceptually, while the Neyman-Pearson interpretation is not about evidence at all. In contrast, the likelihood ratio escapes the above problems and is recommended as a tool for psychologists to represent the statistical evidence conveyed by obtained data relative to two hypotheses.     

In Search of a Reconciliation Between Semiotics,Thermodynamics and Metasystem Transition Theory

The disciplines of cybernetics, semiotics and thermodynamics investigate evolutionary processes quite independently from each other. The aim of this paper is to draw the parallels and point out the possibility and necessity of a reconciliation between these disciplines. The concept of metasystem transition has been proposed by Turchin as a quantum of evolution from a cybernetic point of view. Semiotic processes are of prime importance for the realisation of metasystem transitions in the course of evolution. From a thermodynamic point of view, the emergence of more complex, self-producing agents depends on the emergence of more advanced forms of semiosis. As an evolutionary consequence, more symbolic forms of semiosis that allow higher competence for abstraction and anticipation emerge.     

Maximum Entropy Inference with Quantified Knowledge

We investigate uncertain reasoning with quantified sentencesof the predicate calculus treated as the limiting case of maximumentropy inference applied to finite domains.     

On Association Coefficients for 2×2 Tables and Properties That Do Not Depend on the Marginal Distributions

We discuss properties that association coefficients may have in general, e.g., zero value under statistical independence, and we examine coefficients for 2×2 tables with respect to these properties. Furthermore, we study a family of coefficients that are linear transformations of the observed proportion of agreement given the marginal probabilities. This family includes the phi coefficient and Cohen’s kappa. The main result is that the linear transformations that set the value under independence at zero and the maximum value at unity, transform all coefficients in this family into the same underlying coefficient. This coefficient happens to be Loevinger’s H.     

Combining standardized mean differences using the method of maximum likelihood

A maximum likelihood procedure for combining standardized mean differences based on a noncentratt-distribution is proposed. With a proper data augmentation technique, an EM-algorithm is developed. Information and likelihood ratio statistics are discussed in detail for reliable inference. Simulation results favor the proposed procedure over both the existing normal theory maximum likelihood procedure and the commonly used generalized least squares procedure.     

Deriving ultrametric tree structures from proximity data confounded by differential stimulus familiarity

This paper presents a new procedure called TREEFAM for estimating ultrametric tree structures from proximity data confounded by differential stimulus familiarity. The objective of the proposed TREEFAM procedure is to quantitatively filter out the effects of stimulus unfamiliarity in the estimation of an ultrametric tree. A conditional, alternating maximum likelihood procedure is formulated to simultaneously estimate an ultrametric tree, under the unobserved condition of complete stimulus familiarity, and subject-specific parameters capturing the adjustments due to differential unfamiliarity. We demonstrate the performance of the TREEFAM procedure under a variety of alternative conditions via a modest Monte Carlo experimental study. An empirical application provides evidence that the TREEFAM outperforms traditional models that ignore the effects of unfamiliarity in terms of superior tree recovery and overall goodness-of-fit.