A multitrait-multimethod model with minimal assumptions

A new model of confirmatory factor analysis (CFA) for multitrait-multimethod (MTMM) data sets is presented. It is shown that this model can be defined by only three assumptions in the framework of classical psychometric test theory (CTT). All other properties of the model, particularly the uncorrelated-ness of the trait with the method factors are logical consequences of the definition of the model. In the model proposed there are as many trait factors as different traits considered, but the number of method factors is one fewer than the number of methods included in an MTMM study. The covariance structure implied by this model is derived, and it is shown that this model is identified even under conditions under which other CFA-MTMM models are not. The model is illustrated by two empirical applications. Furthermore, its advantages and limitations are discussed with respect to previously developed CFA models for MTMM data.     

The Failure to Use Gender Information in Parsing: A Comment on van Berkum, Brown, and Hagoort (1999)

We critically review the empirical evidence published by van Berkum, Brown, and Hagoort (1999a, b) against syntax-first models of sentence parsing. According to van Berkum et al., discourse factors and word gender information are used instantaneously to guide the parser. First, we note that the density of the experimental trials (relative to fillers) and the slow presentation rate of the van Berkum et al. design seem likely to have elicited the use of tactics involving rapid reanalysis of the material. Second, we present new data from a questionnaire study showing that the grammatical gender information of a relative pronoun in Dutch is often completely ignored, even during the wrap-up phase at the end of the sentence.     

The modified star graph and the petal diagram: two new visual aids for discrete alternative multicriteria decision making

Many studies have looked into the provisions of visual aids to multicriteria decision making. However, most of them have separated the display of alternative profiles and criteria weight information into two displays. This makes the analysis of the relationship between the criteria and alternatives and the effect of changing the criteria weights on the decision difficult. In this study, displays that can incorporate the display of both alternative profiles and criteria weight information for discrete alternative multicriteria decision-making problems are examined. The simple additive model is the multicriteria analysis method used. The result is two new visual aids for representing data of multicriteria decision-making problems, the modified star graph and the petal diagram. This paper discusses the two displays and compares their strengths and weaknesses. The results of a preliminary test conducted to evaluate the effectiveness of the displays are also included. © 1998 John Wiley & Sons, Ltd.     

Decomposition,Interdependence and Precision in Multiattribute Utility Measurements

Traditionally, parameters of multiattribute utility models, representing a decision maker's preference judgements, are treated deterministically. This may be unrealistic, because assessment of such parameters is potentially fraught with imprecisions and errors. We thus treat such parameters as stochastic and investigate how their associated imprecision/errors are propagated in an additive multiattribute utility function in terms of the aggregate variance. Both a no information and a rank order case regarding the attribute weights are considered, assuming a uniform distribution over the feasible region of attribute weights constrained by the respective information assumption. In general, as the number of attributes increases, the variance of the aggregate utility in both cases decreases and approaches the same limit, which depends only on the variances as well as the correlations among the single-attribute utilities. However, the marginal change in aggregate utility variance decreases rather rapidly and hence decomposition as a variance reduction mechanism is generally useful but becomes relatively ineffective if the number of attributes exceed about 10. Moreover, it was found that utilities which are positively correlated increase the aggregate utility variance, hence every effort should be made to avoid positive correlations between the single-attribute utilities. We also provide guidelines for determining under what condition and to what extent a decision maker should decompose to obtain an aggregate utility variance that is smaller than that of holistic assessments. Extensions of the current model and empirical research to support some of our behavioural assumptions are discussed. © 1997 John Wiley & Sons, Ltd.     

Information matrix estimation procedures for cognitive diagnostic models

Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich-type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross-product information matrix, the sandwich-type covariance matrix and the two approaches proposed by de la Torre (2009, J. Educ. Behav. Stat., 34, 115) are discussed. Simulation results show that, for a correctly specified CDM and Q-matrix or with a slightly misspecified probability model, the observed information matrix and the sandwich-type covariance matrix exhibit good performance with respect to providing consistent standard errors of item parameter estimates. However, with substantial model misspecification only the sandwich-type covariance matrix exhibits robust performance.     

Using multidimensional item response theory to evaluate how response styles impact measurement

Multidimensional item response theory (MIRT) models for response style (e.g., Bolt, Lu, & Kim, 2014, Psychological Methods, 19, 528; Falk & Cai, 2016, Psychological Methods, 21, 328) provide flexibility in accommodating various response styles, but often present difficulty in isolating the effects of response style(s) from the intended substantive trait(s). In the presence of such measurement limitations, we consider several ways in which MIRT models are nevertheless useful in lending insight into how response styles may interfere with measurement for a given test instrument. Such a study can also inform whether alternative design considerations (e.g., anchoring vignettes, self-report items of heterogeneous content) that seek to control for response style effects may be helpful. We illustrate several aspects of an MIRT approach using real and simulated analyses.     

Is imagery better than reality? Performance in imagined dart throwing

We investigated whether deviations from optimal performance are predicted in motor imagery. In Experiment 1, novices and experts imagined and executed dart throws. In imagination, they reported the final position of the dart. Experts performed better than novices in execution and imagination. Distance to the target and bias were smaller in imagination than in execution. In Experiment 2, we dissociated the roles of feedback from proximal and distal action elements for predictions. Three groups of novices estimated the dart’s final position in imagination, in execution without visual feedback, or in execution with delayed visual feedback. Estimates did not differ significantly between groups, indicating that (the lack of) feedback did not influence predictions. Deviations from optimal performance were lower in estimated than in actual performance. In conclusion, although predictive mechanisms may be similar in imagination and execution, the full extent of deviation from optimal performance is not predicted.     

Using Dynamic Panel Models to Study Age‐related Differences and Time‐related Changes in Personality

Along with an increasing interest in the plasticity and role of personality across the adult lifespan comes the need for a diverse set of innovative statistical approaches to study it. With this paper, we set out to illustrate some of the possibilities and challenges in modelling age‐related differences and time‐related changes in personality psychology by means of dynamic panel models. To this end, we first distinguish between the study of age‐related differences and time‐related changes and demonstrate how the treatment of age and time as either discrete or continuous variables implies important modelling choices. Second, we present a selection of four example cases that address the topic of age moderation in diverse matters and with different objectives. Based on our cross‐tabulation of age and time as discrete and continuous variables, the first two example cases represent fairly well‐established models (cases A and B), whereas the remaining cases are used to illustrate current developments in the field (cases C and D). We close the paper with some final remarks on current limitation and future research directions. © 2019 European Association of Personality Psychology     

Linking property crime using offender crime scene behaviour: A comparison of methods

This study compared the ability of seven statistical models to distinguish between linked and unlinked crimes. The seven models utilised geographical, temporal, and modus operandi information relating to residential burglaries (n = 180), commercial robberies, (n = 118), and car thefts (n = 376). Model performance was assessed using receiver operating characteristic analysis and by examining the success with which the seven models could successfully prioritise linked over unlinked crimes. The regression‐based and probabilistic models achieved comparable accuracy and were generally more accurate than the tree‐based models tested in this study. The Logistic algorithm achieved the highest area under the curve (AUC) for residential burglary (AUC = 0.903) and commercial robbery (AUC = 0.830) and the SimpleLogistic algorithm achieving the highest for car theft (AUC = 0.820). The findings also indicated that discrimination accuracy is maximised (in some situations) if behavioural domains are utilised rather than individual crime scene behaviours and that the AUC should not be used as the sole measure of accuracy in behavioural crime linkage research.