Combining mixture distribution and multidimensional IRTree models for the measurement of extreme response styles

Personality constructs, attitudes and other non-cognitive variables are often measured using rating or Likert-type scales, which does not come without problems. Especially in low-stakes assessments, respondents may produce biased responses due to response styles (RS) that reduce the validity and comparability of the measurement. Detecting and correcting RS is not always straightforward because not all respondents show RS and the ones who do may not do so to the same extent or in the same direction. The present study proposes the combination of a multidimensional IRTree model with a mixture distribution item response theory model and illustrates the application of the approach using data from the Programme for the International Assessment of Adult Competencies (PIAAC). This joint approach allows for the differentiation between different latent classes of respondents who show different RS behaviours and respondents who show RS versus respondents who give (largely) unbiased responses. We illustrate the application of the approach by examining extreme RS and show how the resulting latent classes can be further examined using external variables and process data from computer-based assessments to develop a better understanding of response behaviour and RS.     

视觉运动Simon效应和认知Simon效应的影响因素及机制

Simon效应是指与反应要求无关的刺激位置和反应位置在同侧时, 个体反应更快更准确的现象。对于Simon效应的产生机制, 大多数研究者认为, 在不同实验情境中获得的Simon效应有共同的产生机制。但是, 越来越多的证据显示, 在刺激形式、排列方式、刺激—反应规则以及反应方式等因素的影响下, 存在两种不同性质的Simon效应, 即视觉运动Simon效应和认知Simon效应。视觉运动Simon效应源于刺激位置自动激活其同侧反应所产生的影响, 认知Simon效应源于转译生成的编码间的相互干扰, 两者分别与背侧通路和腹侧通路的加工有关。     

Testing for selectivity in the dependence of random variables on external factors

Random variables A and B, whose joint distribution depends on factors (x,y), are selectively influenced by x and y, respectively, if A and B can be represented as functions of, respectively, (x,S_A,C) and (y,S_B,C), where S_A,S_B,C are stochastically independent and do not depend on (x,y). Selective influence implies selective dependence of marginal distributions on the respective factors: thus no parameter of A may depend on y. But parameters characterizing stochastic interdependence of A and B, such as their mixed moments, are generally functions of both x and y. We derive two simple necessary conditions for selective dependence of (A,B) on (x,y), which can be used to conduct a potential infinity of selectiveness tests. One condition is that, for any factor values x,x^′ and y,y^′,

s_xy≤s_xy^′+s_x^′y^′+s_x^′y,     

The mechanism underlying inhibition of saccadic return

Human observers take longer to re-direct gaze to a previously fixated location. Although there has been some exploration of the characteristics of inhibition of saccadic return (ISR), the exact mechanisms by which ISR operates are currently unknown. In the framework of accumulation models of response times, in which evidence is integrated over time to a response threshold, ISR could reflect a reduction in the rate of accumulation for saccades to return locations or an increase in the effective criterion for response. In two experiments, participants generated sequences of three saccades, in response to a peripheral or a central cue. ISR occurred across these manipulations: saccade latency was consistently increased for movements to the immediately previously fixated location. Latency distributions from individual observers were fit with a Linear Ballistic Accumulator model. ISR was best accounted for as a change in the accumulation rate. We suggest this parameter represents the overall desirability of a particular course of action, the evidence for which may be derived from a variety of sensory and non-sensory sources.     

The role of exemplar distribution in infants'' differentiation of categories

Three experiments investigated how the frequency of exposure to particular exemplars influenced 10-month-old infants' differentiation of land and sea animals in an object-examining task. In Experiments 1 and 2, one category exemplar was presented more frequently than the others during familiarization (i.e., that exemplar was presented on 6 of 12 familiarization trials, and 3 other exemplars were each presented on 2 familiarization trials). For half of the infants, the frequent exemplar was similar to other category exemplars (e.g., a zebra if the familiarization category was land animals), and for half the frequent exemplar was not similar to many other category exemplars (e.g., a rabbit). Infants who frequently experienced the similar exemplar formed an exclusive category, and differentiated land and sea animals. Infants who frequently experienced a dissimilar exemplar, in contrast, formed an inclusive category, and failed to differentiate between land and sea animals. In Experiment 3, infants received frequent experience with a set of similar or dissimilar exemplars, and the same pattern was observed. Thus, 10-month-old infants are sensitive to the distribution of the exemplars to which they are exposed, and they form different category boundaries depending on that distribution.     

On Holland's Dutch identity conjecture

The manifest probabilities of observed examinee response patterns resulting from marginalization with respect to the latent ability distribution produce the marginal likelihood function in item response theory. Under the conditions that the posterior distribution of examinee ability given some test response pattern is normal and the item logit functions are linear, Holland (1990a) gives a quadratic form for the log-manifest probabilities by using the Dutch Identity. Further, Holland conjectures that this special quadratic form is a limiting one for all smooth unidimensional item response models as test length tends to infinity. The purpose of this paper is to give three counterexamples to demonstrate that Holland's Dutch Identity conjecture does not hold in general. The counterexamples suggest that only under strong assumptions can it be true that the limits of log-manifest probabilities are quadratic. Three propositions giving sets of such strong conditions are given.     

Some implications of a relational principle of reinforcement

A formal statement of a relational principle of reinforcement is developed that makes contact with analyses of choice, interresponse-time distributions, and stimulus control. Some implications for current theoretical and empirical work in the various areas are examined.     

A model for repeat paired comparison preference tests

A model is proposed that describes subject behavior on repeat paired comparison preference tests. The model extends prior work in this area in that it explicitly allows for abstentions and permits the derivation of individual true scores for discrimination ability as well as conditional estimates of proportionate preference. With these results, the properties of a paired comparison test can be thoroughly explored. An empirical example is presented, and test design issues are considered. In particular, repeat paired comparison preference tests are shown to be inherently less efficient discrimination tests than are pick 1 of 2 tests.     

Confidence regions for multidimensional scaling analysis

Techniques are developed for surrounding each of the points in a multidimensional scaling solution with a region which will contain the population point with some level of confidence. Bayesian credibility regions are also discussed. A general theorem is proven which describes the asymptotic distribution of maximum likelihood estimates subject to identifiability constraints. This theorem is applied to a number of models to display asymptotic variance-covariance matrices for coordinate estimates under different rotational constraints. A technique is described for displaying Bayesian conditional credibility regions for any sample size.The research reported here was supported by grant number APA 320 to the author by the National Research Council of Canada.     

Flexible Bayesian modelling in dichotomous item response theory using mixtures of skewed item curves

Most item response theory (IRT) models for dichotomous responses are based on probit or logit link functions which assume a symmetric relationship between the probability of a correct response and the latent traits of individuals taking a test. This assumption restricts the use of those models to the case in which all items behave symmetrically. On the other hand, asymmetric models proposed in the literature impose that all the items in a test behave asymmetrically. This assumption is inappropriate for great majority of tests which are, in general, composed of both symmetric and asymmetric items. Furthermore, a straightforward extension of the existing models in the literature would require a prior selection of the items' symmetry/asymmetry status. This paper proposes a Bayesian IRT model that accounts for symmetric and asymmetric items in a flexible but parsimonious way. That is achieved by assigning a finite mixture prior to the skewness parameter, with one of the mixture components being a point mass at zero. This allows for analyses under both model selection and model averaging approaches. Asymmetric item curves are designed through the centred skew normal distribution, which has a particularly appealing parametrization in terms of parameter interpretation and computational efficiency. An efficient Markov chain Monte Carlo algorithm is proposed to perform Bayesian inference and its performance is investigated in some simulated examples. Finally, the proposed methodology is applied to a data set from a large-scale educational exam in Brazil.