Macro and Micro Goal Setting: In Search of Coherence

In this article we discuss macro and micro goal setting approaches, focusing on the relations between goals at different organisational levels. Translating strategic goals in a top-down manner throughout the organisation is a rather complex process. In organisations, at least one intermediate level between macro (i.e. strategic) and micro (i.e. operational) goals can be distinguished: the level of meso (i.e. tactical) goal setting. The process of a strategic dialogue is introduced to arrive at organisational goal coherence, which is defined as consensus on goal priorities within single constituencies and between multiple constituencies at different organisational levels. This process is illustrated in a case study, in which the CATPCA method has been applied to empirically operationalise degrees of consensus. Effects of the strategic dialogue on organisational goal coherence are demonstrated.     

Points of view analysis revisited: Fitting multidimensional structures to optimal distance components with cluster restrictions on the variables

Points of view analysis (PVA), proposed by Tucker and Messick in 1963, was one of the first methods to deal explicitly with individual differences in multidimensional scaling, but at some point was apparently superceded by the weighted Euclidean model, well-known as the Carroll and Chang INDSCAL model. This paper argues that the idea behind points of view analysis deserves new attention, especially as a technique to analyze group differences. A procedure is proposed that can be viewed as a streamlined, integrated version of the Tucker and Messick Process, which consisted of a number of separate steps. At the same time, our procedure can be regarded as a particularly constrained weighted Euclidean model. While fitting the model, two types of nonlinear data transformations are feasible, either for given dissimilarities, or for variables from which the dissimilarities are derived. Various applications are discussed, where the two types of transformation can be mixed in the same analysis; a quadratic assignment framework is used to evaluate the results.The research of the first author was supported by the Royal Netherlands Academy of Arts and Sciences (KNAW); the research of the second author by the Netherlands Organization for Scientific Research (NWO Grant 560-267-029). An earlier version of this paper was presented at the European Meeting of the Psychometric Society, Leuven, 1989. We wish to thank Willem J. Heiser for his stimulating comments to earlier versions of this paper, and we are grateful to the Editor and anonymous referees for their helpful suggestions.     

Meta‐CART: A tool to identify interactions between moderators in meta‐analysis

In the framework of meta‐analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta‐regression has difficulties in identifying interactions between them. To overcome this problem, meta‐CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The previous version of meta‐CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta‐CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta‐CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta‐regression trees with random‐effects weights and a 0.5‐standard‐error pruning rule perform best. The required sample size for meta‐CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.     

The regression trunk approach to discover treatment covariate interaction

The regression trunk approach (RTA) is an integration of regression trees and multiple linear regression analysis. In this paper RTA is used to discover treatment covariate interactions, in the regression of one continuous variable on a treatment variable withmultiple covariates. The performance of RTA is compared to the classical method of forward stepwise regression. The results of two simulation studies, in which the true interactions are modeled as threshold interactions, show that RTA detects the interactions in a higher number of cases (82.3% in the first simulation study, and 52.3% in the second) than stepwise regression (56.5% and 20.5%). In a real data example the final RTA model has a higher cross-validated variance-accounted-for (29.8%) than the stepwise regression model (12.5%). All of these results indicate that RTA is a promising alternative method for demonstrating differential effectiveness of treatments. Supported by The Netherlands Organization for Scientific Research (NWO), grant 030-56403 to Jacqueline Meulman for the Pioneer project “Subject-Oriented Multivariate Analysis,” and grant 451-02-058 to Elise Dusseldorp for the Veni project “Modeling interaction effects as small trees in regression and classification.” We thank Bram Bakker for making the data of his doctoral dissertation available, and David Hand, Jerome Friedman, Bart Jan van Os, Philip Spinhoven, and the anonymous reviewers for their helpful suggestions. Special thanks are due to Lawrence Hubert.     

Vagueness as closeness

This paper presents and defends a definition of vagueness, compares it favourably with alternative definitions, and draws out some consequences of accepting this definition for the project of offering a substantive theory of vagueness. The definition is roughly this: a predicate ‘F’ is vague just in case for any objects a and b, if a and b are very close in respects relevant to the possession of F, then ‘Fa’ and ‘Fb’ are very close in respect of truth. The definition is extended to cover vagueness of many-place predicates, of properties and relations, and of objects. Some of the most important advantages of the definition are that it captures the intuitions which motivate the thought that vague predicates are tolerant, without leading to contradiction, and that it yields a clear understanding of the relationships between higher-order vagueness, sorites susceptibility, blurred boundaries, and borderline cases. The most notable consequence of the definition is that the correct theory of vagueness must countenance degrees of truth.     

Statistical Significance of the Contribution of Variables to the PCA solution: An Alternative Permutation Strategy

In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix independently and concurrently, thus destroying the entire correlational structure of the data. This strategy is considered appropriate for assessing the significance of the PCA solution as a whole, but is not suitable for assessing the significance of the contribution of single variables. Alternatively, we propose a strategy involving permutation of one variable at a time, while keeping the other variables fixed. We compare the two approaches in a simulation study, considering proportions of Type I and Type II error. We use two corrections for multiple testing: the Bonferroni correction and controlling the False Discovery Rate (FDR). To assess the significance of the variance accounted for by the variables, permuting one variable at a time, combined with FDR correction, yields the most favorable results. This optimal strategy is applied to an empirical data set, and results are compared with bootstrap confidence intervals.     

Sizeism in Therapy: Fat Shaming in Supervision

Abstract

The author, a doctoral student therapist, discusses her experience with a clinical supervisor who fat-shamed a client during supervision. This incident is examined within a shame framework and addresses the experience of shame, the effects of shame and shame-proneness on mental health outcomes, and why discussions about shame are often avoided in therapy by both clients and therapists. Some of the real-world considerations that can impact treatment for fat individuals, including chair size and comfort of the client, implicit bias within therapy, and goals for treatment, are also discussed.     

A meta-analysis of psychoeduational programs for coronary heart disease patients.

In a meta-analysis of 37 studies, the effects of psychoeducational (health education and stress management) programs for coronary heart disease patients were examined. The results suggest that these programs yielded a 34% reduction in cardiac mortality; a 29% reduction in recurrence of myocardial infarction (MI); and significant (p < .025) positive effects on blood pressure, cholesterol, body weight, smoking behavior, physical exercise, and eating habits. No effects of psychoeducational programs were found in regard to coronary bypass surgery, anxiety, or depression. The results also suggest that cardiac rehabilitation programs that were successful on proximal targets (systolic blood pressure, smoking behavior, physical exercise, emotional distress) were more effective on distal targets (cardiac mortality and MI recurrences) than programs without success on proximal targets.