A Test of Order-Constrained Hypotheses for Circular Data With Applications to Human Movement Science

Researchers studying the movements of the human body often encounter data measured in angles (e.g., angular displacements of joints). The evaluation of these circular data requires special statistical methods. The authors introduce a new test for the analysis of order-constrained hypotheses for circular data. Through this test, researchers can evaluate their expectations regarding the outcome of an experiment directly by representing their ideas in the form of a hypothesis containing inequality constraints. The resulting data analysis is generally more powerful than one using standard null hypothesis testing. Two examples of circular data from human movement science are presented to illustrate the use of the test. Results from a simulation study show that the test performs well.     

Strategy instruction for writing composition at school and at home

Abstract

The objectives of this study are 1) to analyze the effectiveness of a writing composition intervention program aimed to increase children's writing performance and other personal variables; 2) to analyze whether implementation in the environment (school and home) or responsible figures (parents and teachers) produced different effects in program effectiveness; and 3) to check whether increasing parental involvement in doing homework was sufficient to improve children's outcomes or whether prior family training is required to enable them to be efficient. The sample consisted of 112 students (5th-6th grade) and their families. 26 of them made up the EFP group (mothers implementing a writing program), 25 of them were assigned to the PAD group (mothers increasing help with writing homework), 35 to the PRO group (teachers applying a writing program) and 26 to the OC group (ordinary curriculum). We analyze the results and present and discuss our conclusions.     

Psychobiography in psychology: Past,present, and future

The present article reviews the history and emerging trends in the field of psychobiography. Five historical periods are highlighted: hagiography and pre-19th century study of lives; Freud and applied psychoanalysis; psychodynamic extensions and personology; modern multi-theoretical models; and psychobiography as interdisciplinary science. The author advocates for a science of psychobiography manifested in expanded and empirically validated theoretical models anchoring research. Further, attention is drawn to the need for more rigorous historiographic research methods weighting first-person sources and incorporating mixed methods designs. Increased attention to ethical and legal issues in the conduct and reporting of psychobiographical studies is also highlighted. Finally, emerging trends in psychobiography related to research production, academic training, and organisational initiatives are presented.     

Multicriteria Classification with Unknown Categories: A Clustering–Sorting Approach and an Application to Conflict Management

This work proposes an approach to cluster and sort a set of alternatives considering multi‐criteria categories with a partial order structure. It can be considered a heuristic approach because it does not attempt to derive an optimal partial order among all conceivable clusters of alternatives. Rather than this, it intends to be a simple approach that is transparent to the Decision Maker (DM) whose assistance is sought to help shaping the results. The approach proposed arises from the conjugation of traditional Clustering analysis and Multi‐criteria sorting tools. At the outset, the number of categories and their characteristics is unknown. First, we need to detect only the clusters themselves on the basis of a similarity measure independent of the preferences of the DM. Next, we detect potential partial order relations that might exist between them, according to the subjective preferences of the DM. Such preferences are elicited only after the DM has examined the clusters detected and deemed that these categories made sense. The new approach performs very well in a real‐world problem of management of intragroup conflicts and conflict handling strategies. Copyright © 2012 John Wiley & Sons, Ltd.     

Heterogeneous factor analysis models: A bayesian approach

Multilevel factor analysis models are widely used in the social sciences to account for heterogeneity in mean structures. In this paper we extend previous work on multilevel models to account for general forms of heterogeneity in confirmatory factor analysis models. We specify various models of mean and covariance heterogeneity in confirmatory factor analysis and develop Markov Chain Monte Carlo (MCMC) procedures to perform Bayesian inference, model checking, and model comparison.We test our methodology using synthetic data and data from a consumption emotion study. The results from synthetic data show that our Bayesian model perform well in recovering the true parameters and selecting the appropriate model. More importantly, the results clearly illustrate the consequences of ignoring heterogeneity. Specifically, we find that ignoring heterogeneity can lead to sign reversals of the factor covariances, inflation of factor variances and underappreciation of uncertainty in parameter estimates. The results from the emotion study show that subjects vary both in means and covariances. Thus traditional psychometric methods cannot fully capture the heterogeneity in our data.     

Algorithmic Proof Methods and Cut Elimination for Implicational Logics Part I: Modal Implication

In this work we develop goal-directed deduction methods for the implicational fragment of several modal logics. We give sound and complete procedures for strict implication of K, T, K4, S4, K5, K45, KB, KTB, S5, G and for some intuitionistic variants. In order to achieve a uniform and concise presentation, we first develop our methods in the framework of Labelled Deductive Systems [Gabbay 96]. The proof systems we present are strongly analytical and satisfy a basic property of cut admissibility. We then show that for most of the systems under consideration the labelling mechanism can be avoided by choosing an appropriate way of structuring theories. One peculiar feature of our proof systems is the use of restart rules which allow to re-ask the original goal of a deduction. In case of K, K4, S4 and G, we can eliminate such a rule, without loosing completeness. In all the other cases, by dropping such a rule, we get an intuitionistic variant of each system. The present results are part of a larger project of a goal directed proof theory for non-classical logics; the purpose of this project is to show that most implicational logics stem from slight variations of a unique deduction method, and from different ways of structuring theories. Moreover, the proof systems we present follow the logic programming style of deduction and seem promising for proof search [Gabbay and Reyle 84, Miller et al. 91].     

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.     

Shoppers in the Spiritual Supermarket: A Quantitative Study of Visitors to Scandinavia’s Largest Alternative Fair

Presenting data from a quantitative survey (N=362) at ‘The Great Alternative Fair’ in Lillestrøm, Norway, this article explores the demographic and religious profiles of the ‘customers’ in the ‘spiritual marketplace’. Many adhere to ideas that are historically associated with different religious traditions. At the same time, there are strong positive statistical correlations between belief in alternative ideas, such as healing, reincarnation, channelling, and clairvoyance. This indicates that such ideas constitute a clustered and shared set of beliefs. Against this background, the article questions the widely held assumption that people who are oriented towards alternative spiritualities are independent of institutions and authorities and it argues that they are informed to a large extent by a set of collective resources. It is suggested that the marketplace, which constitutes a common framework of interaction, plays a key role in connecting various ideas and actors and in creating a common ideological profile.