Hierarchical Multinomial Processing Tree Models: A Latent-Class Approach

Multinomial processing tree models are widely used in many areas of psychology. Their application relies on the assumption of parameter homogeneity, that is, on the assumption that participants do not differ in their parameter values. Tests for parameter homogeneity are proposed that can be routinely used as part of multinomial model analyses to defend the assumption. If parameter homogeneity is found to be violated, a new family of models, termed latent-class multinomial processing tree models, can be applied that accommodates parameter heterogeneity and correlated parameters, yet preserves most of the advantages of the traditional multinomial method. Estimation, goodness-of-fit tests, and tests of other hypotheses of interest are considered for the new family of models. The author thanks Bill Batchelder, Edgar Erdfelder, Thorsten Meiser, and Christoph Stahl for helpful comments on a previous version of this paper. The author is also grateful to Edgar Erdfelder for making available the data set analyzed in this paper.     

‘Automatic’ evaluation? Strategic effects on affective priming

Two studies examined strategic effects on affective priming. Extending prior research by Klauer and Teige-Mocigemba [Klauer, K. C., & Teige-Mocigemba, S. (2007). Controllability and resource dependence in automatic evaluations. Journal of Experimental Social Psychology, 43, 648-655], the influence of different control strategies on a priming measure of prejudice was assessed. In both studies, a short stimulus onset asynchrony between prime and target (275 ms) was implemented along with considerable time pressure. In Study 1, participants could strategically eliminate priming effects with attitudinal prime categories (Arabs and liked celebrities) represented by several exemplars per category while priming effects for control categories remained intact. In Study 2, two strategies (payoff and faking) were induced to motivate participants to respond particularly fast and accurately to incongruent targets. Both strategies were successful in counteracting the usual priming effects, while leaving priming effects for non-targeted primes intact. We consider the role of so-called implementation intentions in accounting for the present findings.     

Crossed categorization and stereotyping: Structural analyses, effect patterns, and dissociative effects of context relevance

Based on the “Who said what?” paradigm, a new method is proposed for investigating social categorization and non-evaluative stereotype application in crossed categorization. The method is applied in 5 studies that manipulated relative context relevance of crossed age and gender categories. Social categorization is characterized by 2 indices: Relative subgroup memory assesses the amount of subgroup formation represented in memory, and relative category dominance the relative weight of each dimension of categorization. Both indices were affected by context relevance. There was strong evidence for social categorization at the subgroup level, whereas stereotype application followed a simple pattern of category dominance, in which only the context-relevant dimension exerted an effect. The results bear on current models of category-based impression formation and intergroup perception, and on category-activation cum stereotype-inhibition models.     

Sozialmedizinische Begutachtung der beruflichen Leistungsfähigkeit

The ?Interview for the Evaluation of Psychosocial Dimensions of Performance Capability“ represents a potentially suitable instrument for the assessment of performance capability in various contexts. Regarding time expenditure, it is very viable and can be applied without special training. Test statistics for subscales derived from factor analysis are satisfying. The modified scales should be subject to further studies of validity and reliability. However, the instrument is already suited as a tool for the assessment of performance capability, represents an algorithm for decision-making and is related to the concepts of the International Classification of Functioning (ICF).     

Exact and best confidence intervals for the ability parameter of the Rasch model

A commonly used method to evaluate the accuracy of a measurement is to provide a confidence interval that contains the parameter of interest with a given high probability. Smallest exact confidence intervals for the ability parameter of the Rasch model are derived and compared to the traditional, asymptotically valid intervals based on the Fisher information. Tables of the exact confidence intervals, termed Clopper-Pearson intervals, can be routinely drawn up by applying a computer program designed by and obtainable from the author. These tables are particularly useful for tests of only moderate lengths where the asymptotic method does not provide valid confidence intervals.     

Visuelle und räumliche Interferenzen beim Schlußfolgern

The present paper provides evidence for a differential involvement of spatial and visual mental resources in propositional and spatial reasoning. Two experiments consider the load on visuospatial working memory during reasoning. Subjects solve propositional and spatial reasoning tasks either alone or in combination with visual tracking. In the first experiment, subjects resource allocation strategy is manipulated. Independently of the allocation of mental resources to the reasoning tasks or the tracking task, tracking is found to interfere much more with spatial than with propositional reasoning. In the second experiment, propositional reasoning after a comprehension training is disrupted by a simultaneous secondary spatial task, but not by the visual tracking task.     

Ordinal network representation: Representing proximities by graphs

Ordinal network representations are graph-theoretic representations of proximity data. They seek to provide parsimonious representations of the ordinal (nonmetric) information in observed proximity data by means of the minimum-path-length distance of a connected and weighted graph. In contrast to traditional tree-based graph-theoretic approaches, ordinal network representation is not limited to but includes the representation by trees. Asymmetry in the proximity data and violations of zero-minimality are allowed for. The paper explores fundamental representation and uniqueness results and discusses a method of constructing ordinal network representations. A simple strategy for handling the problem of errors in the data is described and illustrated.This work was supported by grant Fe 75/20-2 of the Deutsche Forschungsgemeinschaft. The author is indebted to Hubert Feger for many inspiring discussions.     

On the controllability of evaluative-priming effects: Some limits that are none

Two experiments examined recent claims of uncontrollability of the evaluative-priming effect. According to these claims, imposing an adaptive 600 ms response deadline prevents successful faking (Degner, 2009). Furthermore, strategic control attempts have been argued not to reduce the priming measure's sensitivity to spontaneous evaluations so that correlations of evaluative-priming effects with external criteria are not affected by attempts to fake (Bar-Anan, 2010). Here, we show that faking is possible even with an adaptive 600 ms response deadline when faking instructions do not conflict with speed pressures imposed thereby (Experiments 1 and 2). In addition, suitable faking instructions substantially affect the predictive validity of priming effects in terms of their correlations with (non-faked) self-report measures and the Implicit Association Test (Experiment 2). The previous claims about the uncontrollability of the evaluative-priming effect may thus have been premature.     

Toward a complete decision model of item and source recognition: A discrete-state approach

In source-monitoring experiments, participants study items from two sources (A and B). At test, they are presented Source A items, Source B items, and new items. They are asked to decide whether a test item is old or new (item memory) and whether it is a Source A or a Source B item (source memory). Hautus, Macmillan, and Rotello (2008) developed models, couched in a bivariate signal detection framework, that account for item and source memory across several data sets collected in a confidence-rating response format. The present article enlarges the set of candidate models with a discrete-state model. The model is a straightforward extension of Bayen, Murnane, and Erdfelder's (1996) multinomial model of source discrimination to confidence ratings. On the basis of the evaluation criteria adopted by Hautus et al., it provides a better account of the data than do Hautus et al.'s models.