How do people react to negative procedures? On the moderating role of authority’s biased attitudes

The authors focus on the effects an authority’s apparent inconsistency between persons on judgments of relational treatment and procedural justice following negative procedures (i.e., procedures that people commonly regard as unfair). In Experiment 1, participants responded most negatively following a procedure that denied them, but granted another participant, an opportunity to voice an opinion when the intergroup context raised suspicions of bias (i.e., when both the experimenter and another participant were outgroup members). In Experiment 2, participants responded most negatively when the experimenter had expressed biased attitudes in favor of another participant, but this effect occurred only following procedures that denied participants a voice opportunity. We conclude that authority’s biased attitudes help people to make sense of negative procedure information.     

Properties of and Algorithms for Fitting Three-Way Component Models with Offset Terms

Prior to a three-way component analysis of a three-way data set, it is customary to preprocess the data by centering and/or rescaling them. Harshman and Lundy (1984) considered that three-way data actually consist of a three-way model part, which in fact pertains to ratio scale measurements, as well as additive “offset” terms that turn the ratio scale measurements into interval scale measurements. They mentioned that such offset terms might be estimated by incorporating additional components in the model, but discarded this idea in favor of an approach to remove such terms from the model by means of centering. Then estimates for the three-way component model parameters are obtained by analyzing the centered data. In the present paper, the possibility of actually estimating the offset terms is taken up again. First, it is mentioned in which cases such offset terms can be estimated uniquely. Next, procedures are offered for estimating model parameters and offset parameters simultaneously, as well as successively (i.e., providing offset term estimates after the three-way model parameters have been estimated in the traditional way on the basis of the centered data). These procedures are provided for both the CANDECOMP/PARAFAC model and the Tucker3 model extended with offset terms. The successive and the simultaneous approaches for estimating model and offset parameters have been compared on the basis of simulated data. It was found that both procedures perform well when the fitted model captures at least all offset terms actually underlying the data. The simultaneous procedures performed slightly better than the successive procedures. If fewer offset terms are fitted than there are underlying the model, the results are considerably poorer, but in these cases the successive procedures performed better than the simultaneous ones. All in all, it can be concluded that the traditional approach for estimating model parameters can hardly be improved upon, and that offset terms can sufficiently well be estimated by the proposed successive approach, which is a simple extension of the traditional approach. The author is obliged to Jos M.F. ten Berge and Marieke Timmerman for helpful comments on an earlier version of this paper. The author is obliged to Iven van Mechelen for making available the data set used in Section 6.     

On growth curves and mixture models

The multilevel model of change and the latent growth model are flexible means to describe all sorts of population heterogeneity with respect to growth and development, including the presence of sub‐populations. The growth mixture model is a natural extension of these models. It comes at hand when information about sub‐populations is missing and researchers nevertheless want to retrieve developmental trajectories from sub‐populations. We argue that researchers have to make rather strong assumptions about the sub‐populations or latent trajectory classes in order to retrieve existing population differences. A simulated example is discussed, showing that a sample of repeated measures drawn from two sub‐populations easily leads to the mistaken inference of three sub‐populations, when assumptions are not met. The merits of methodological advises on this issue are discussed. It is concluded that growth mixture models should be used with understanding, and offer no free way to growth patterns in unknown sub‐populations. Copyright © 2006 John Wiley & Sons, Ltd.     

Maintenance of semantic information in capacity-limited item short-term memory

We report a semantic effect in immediate free recall, which is localized at recency and is preserved under articulatory suppression but is highly reduced when recall is delayed after an intervening distractor task. These results are explained by a neurocomputational model based on a limited-capacity short-term memory (STM) store, consisting ofactivated long-term memory representations. The model makes additional predictions about serial position functions in semantically cued recall, indicating capacity limitations caused by a displacement type mechanism, which are confirmed in a second experiment. This suggests that in addition to the phonological component in verbal STM, there is an activation/ item-limited component with semantically sensitive representations.     

Bootstrap confidence intervals for principal covariates regression

Principal covariate regression (PCOVR) is a method for regressing a set of criterion variables with respect to a set of predictor variables when the latter are many in number and/or collinear. This is done by extracting a limited number of components that simultaneously synthesize the predictor variables and predict the criterion ones. So far, no procedure has been offered for estimating statistical uncertainties of the obtained PCOVR parameter estimates. The present paper shows how this goal can be achieved, conditionally on the model specification, by means of the bootstrap approach. Four strategies for estimating bootstrap confidence intervals are derived and their statistical behaviour in terms of coverage is assessed by means of a simulation experiment. Such strategies are distinguished by the use of the varimax and quartimin procedures and by the use of Procrustes rotations of bootstrap solutions towards the sample solution. In general, the four strategies showed appropriate statistical behaviour, with coverage tending to the desired level for increasing sample sizes. The main exception involved strategies based on the quartimin procedure in cases characterized by complex underlying structures of the components. The appropriateness of the statistical behaviour was higher when the proper number of components were extracted.     

On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Investigating sources of within- and between-group differences and measurement invariance (MI) across groups is fundamental to any meaningful group comparison based on observed test scores. It is shown that by placing certain restrictions on the multigroup confirmatory factor model, it is possible to investigate the hypothesis that within- and between-group differences are due to the same factors. Moreover, the modeling approach clarifies that absence of measurement bias implies common sources of within- and between-group variation. It is shown how the influence of background variables can be incorporated in the model. The advantages of the modeling approach as compared with other commonly used methods for group comparisons is discussed and illustrated by means of an analysis of empirical data.     

Frequency effects in the processing of lexicalized and novel nominal compounds

The processing of lexicalized and novel noun-noun compounds of high interpretability was investigated. In Experiment 1, the familiarity of the lexicalized compounds had a significant effect on lexical decision times, but no frequency effects were observed for the constituent nouns. In Experiment 2, a frequency effect was found for the first noun of novel compounds, but not for the second noun. This result was replicated in Experiment 3 with different types of nonwords. A negative word frequency effect for the first noun was found in Experiment 4 in which the novel compounds functioned as nonwords. A frequency effect for the first noun was also observed in Experiment 5, in which a semantic classification task was used. Results point to a decomposition second model according to which access is initially based on the whole compound. If this is unsuccessful the compound will be decomposed and the constituent nouns will be processed separately. It is argued that in novel compounds, nouns are processed sequentially, and different orders of processing are discussed.     

Majorization as a tool for optimizing a class of matrix functions

The problem of minimizing a general matrix, trace function, possibly subject to certain constraints, is approached by means of majorizing this function by one having a simple quadratic shape and whose minimum is easily found. It is shown that the parameter set that minimizes the majorizing function also decreases the matrix trace function, which in turn provides a monotonically convergent algorithm for minimizing the matrix trace function iteratively. Three algorithms based on majorization for solving certain least squares problems are shown to be special cases. In addition, by means of several examples, it is noted how algorithms may be provided for a wide class of statistical optimization tasks for which no satisfactory algorithms seem available.The Netherlands organization for scientific research(NWO) is gratefully acknowledged for funding this project. This research was conducted while the author was supported by a PSYCHON-grant (560-267-011) from this organization. The author is obliged to Jos ten Berge, Willem Heiser, and Wim Krijnen for helpful comments on an earlier version of this paper.