Controlling lexical contributions to the reading of pseudohomophones

Reynolds and Besner (2005) presented a computational account of six effects that emerge when readers are asked to pronounce pseudohomophones (nonwords—e.g., brane—that sound like words when pronounced). In the dual route cascaded (DRC) model of reading, they varied a parameter controlling the rate of inhibition from letter units to the orthographic lexicon to mimic strategic control over the extent of specific lexical processing. In this article, we provide an account in which the effects are simulated by varying the DRC’s reading-aloud criterion—a parameter that sets the minimal level of phonemic activation required to pronounce a letter string. We show that varying this parameter provides another means of controlling lexical contributions to reading aloud.     

Ignoring Clustering in Confirmatory Factor Analysis: Some Consequences for Model Fit and Standardized Parameter Estimates

In many situations, researchers collect multilevel (clustered or nested) data yet analyze the data either ignoring the clustering (disaggregation) or averaging the micro-level units within each cluster and analyzing the aggregated data at the macro level (aggregation). In this study we investigate the effects of ignoring the nested nature of data in confirmatory factor analysis (CFA). The bias incurred by ignoring clustering is examined in terms of model fit and standardized parameter estimates, which are usually of interest to researchers who use CFA. We find that the disaggregation approach increases model misfit, especially when the intraclass correlation (ICC) is high, whereas the aggregation approach results in accurate detection of model misfit in the macro level. Standardized parameter estimates from the disaggregation and aggregation approaches are deviated toward the values of the macro- and micro-level standardized parameter estimates, respectively. The degree of deviation depends on ICC and cluster size, particularly for the aggregation method. The standard errors of standardized parameter estimates from the disaggregation approach depend on the macro-level item communalities. Those from the aggregation approach underestimate the standard errors in multilevel CFA (MCFA), especially when ICC is low. Thus, we conclude that MCFA or an alternative approach should be used if possible.     

基于GPCM的计算机自适应测验选题策略比较

选题策略是计算机自适应测验（Computerized Adaptive Testing , CAT）研究的一项重要内容,它的好坏直接关系到考试的信度、效度及考试的安全性。CAT的许多研究与应用,都建立在0-1二级评分模型基础上,对多级评分CAT的选题策略的研究很少报导。目前国内虽已开展了基于GRM的CAT研究,但基于GPCM的CAT的研究尚未见有关报道。本文通过计算机模拟程序,对基于拓广分部评分模型(Generalized Partial Credit Model, GPCM)下的CAT的四种选题策略在多种情况下进行了比较研究。研究结果表明：被试能力呈正态分布时,选题策略的使用效果与项目步骤参数分布有很大的关系。（1）项目步骤参数均服从正态分布时,采用能力与项目步骤参数匹配选题策略效果最佳;（2）项目步骤参数均服从均匀分布时,能力与项目步骤参数平均数匹配选题策略效果最佳     

多维项目反应理论补偿性模型参数估计:基于广义回归神经网络集合

运用广义回归神经网络(GRNN)方法对小样本多维项目反应理论(MIRT)补偿性模型的项目参数进行估计,尝试解决传统参数估计方法样本数量要求较大的问题。MIRT双参数Logistic补偿模型被设置为二级计分的二维模型。首先,模拟二维能力参数、项目参数值与考生作答矩阵。其次,把通过主成分分析得到的前两个因子在每个题目上的载荷作为区分度的初始值以及题目通过率作为难度的初始值,这两个指标的初始值作为神经网络的输入。集成100个神经网络,其输出值的均值作为MIRT的项目参数估计值。最后,设置2×2种(能力相关水平:0.3和0.7; 两种估计方法:GRNN和MCMC方法)实验处理,对GRNN和MCMC估计方法的返真性进行比较。结果表明,小样本的情况下,基于GRNN集成方法的参数估计结果优于MCMC方法。     

层次图(混合Horn)公式的部分极大可满足性

本文为解决一类混合Horn公式([13,14]),又称为层次图公式([15])的MAXSAT问题进行了基于随机局部搜索过程的经验研究。具体地,我们首先在随机CNF公式的MAX2SAT及MAX3SAT问题上进行WalkSAT和Tabu-Sat(及其变种)的比较,其次,我们在层次图公式上比较了上述过程的改进版本,这些公式编码了随机生成层次图的最小化跨边问题。本文所引入的Tabu-Sat过程,当在搜索空间中检测到一个圈的时候,动态地改变Tabu长度参数。另一个被称为Vector-Tabu-Sat的过程,对所有的布尔变元进行独立的Tabu长度参数管理。一些数值实验的结果显示,我们改进的Tabu-Sat变种在子句个数增长的时候优于Walksat.     

Predicting group-level outcome variables: An empirical comparison of analysis strategies

This study provides a review of two methods for analyzing multilevel data with group-level outcome variables and compares them in a simulation study. The analytical methods included an unadjusted ordinary least squares (OLS) analysis of group means and a two-step adjustment of the group means suggested by Croon and van Veldhoven (2007). The Type I error control, power, bias, standard errors, and RMSE in parameter estimates were compared across design conditions that included manipulations of number of predictor variables, level of correlation between predictors, level of intraclass correlation, predictor reliability, effect size, and sample size. The results suggested that an OLS analysis of the group means, with White’s heteroscedasticity adjustment, provided more power for tests of group-level predictors, but less power for tests of individual-level predictors. Furthermore, this simple analysis avoided the extreme bias in parameter estimates and inadmissible solutions that were encountered with other strategies. These results were interpreted in terms of recommended analytical methods for applied researchers.     

Theory of learning mathematics with computer-assisted instruction: Hierarchical and nonhierarchical options

The learning of arithmetic problems is assumed to be a Markov process involving conditioning of a set of k subskills, each consisting of one or more productions. An axiom set is provided, with the choice between two options for one axiom controlling which of two models results. Model 1, which assumes that every subskill is attempted on every occurrence of a given problem, is a nonhierarchical model. Model 2, which assumes that every subskill in the temporal sequence for a problem is attempted until one is failed, is a somewhat unconventional hierarchical model: It is hierarchical in the sense that conditioningor chance success at one level is prerequisite to performance of the next subskill (next level) in the problem. As the value of the guessing parameter, g, declines, Model 2 leads to less efficient learning than Model 1.     

On the shape of the probability weighting function

Empirical studies have shown that decision makers do not usually treat probabilities linearly. Instead, people tend to overweight small probabilities and underweight large probabilities. One way to model such distortions in decision making under risk is through a probability weighting function. We present a nonparametric estimation procedure for assessing the probability weighting function and value function at the level of the individual subject. The evidence in the domain of gains supports a two-parameter weighting function, where each parameter is given a psychological interpretation: one parameter measures how the decision maker discriminates probabilities, and the other parameter measures how attractive the decision maker views gambling. These findings are consistent with a growing body of empirical and theoretical work attempting to establish a psychological rationale for the probability weighting function.     

Optimal and Most Exact Confidence Intervals for Person Parameters in Item Response Theory Models

The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter θ is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given level of significance in many cases; and, therefore, the corresponding intervals are no longer confidence intervals in terms of the actual definition. In the present work, confidence intervals are defined more precisely by utilizing the relationship between confidence intervals and hypothesis testing. Two approaches to confidence interval construction are explored that are optimal with respect to criteria of smallness and consistency with the standard approach.     

Associations of blood glucose control with self-efficacy and rated anxiety/depression in type II diabetes mellitus patients

We investigated the possible relations of hemoglobin A1c (HbA1c) level, a parameter of blood glucose control with scores on anxiety, depression, and self-efficacy for 113 outpatients (55 men and 58 women) with type II diabetes mellitus. The relation between the HbA1c level and the self-efficacy scores as well as with the ratings of anxiety and depression was evaluated. A significant relation was found for the HbA1c level with the scores; however, self-efficacy and anxiety and depression showed no association. These results suggest that HbA1c can be maintained at better levels by increasing self-efficacy of diabetic patients.     

Force variability in isometric responses

In the present study we examined the contribution of different impulse parameters to peak force variability in an isometric task. Five experiments are reported that each held constant a different impulse parameter while allowing the other impulse parameters to vary. The results indicate that change in force level is the parameter that has the greatest effect on peak force variability, although time to peak force and preload also systematically influence response variability. A formula that accommodates the relation between impulse parameters and force variability is proposed. The data suggest that even in isometric tasks, it is the force-time properties of the impulse, rather than discrete parameters such as peak force, that determine the outcome variability.     

Predicting group-level outcome variables from variables measured at the individual level: a latent variable multilevel model

In multilevel modeling, one often distinguishes between macro-micro and micro-macro situations. In a macro-micro multilevel situation, a dependent variable measured at the lower level is predicted or explained by variables measured at that lower or a higher level. In a micro-macro multilevel situation, a dependent variable defined at the higher group level is predicted or explained on the basis of independent variables measured at the lower individual level. Up until now, multilevel methodology has mainly focused on macro-micro multilevel situations. In this article, a latent variable model is proposed for analyzing data from micro-macro situations. It is shown that regression analyses carried out at the aggregated level result in biased parameter estimates. A method that uses the best linear unbiased predictors of the group means is shown to yield unbiased estimates of the parameters.     

An exact and optimal standardized person test for assessing consistency with the rasch model

The Rasch model predicts that an individual's ability level is invariant over subtests of the total test, and thus, all subtests measure the same latent trait. A person test of this invariance hypothesis is discussed that is uniformly most powerful and standardized in the sense that the conditional distribution of the test statistic, given a particular level of ability, does not depend on the absolute value of the examinee's ability parameter. The test can be routinely performed by applying a computer program designed by and obtainable from the author. Finally, a suboptimal test is derived that is extremely easy to use, and an overall group test of the invariance hypothesis discussed. All tests considered do not rely on asymptotic approximations; hence, they may be applied when the test is of only moderate length and the group of examinees is small.     

Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes

We examine emotion self-regulation and coregulation in romantic couples using daily self-reports of positive and negative affect. We fit these data using a damped linear oscillator model specified as a latent differential equation to investigate affect dynamics at the individual level and coupled influences for the 2 partners in each couple. Results indicate an absence of damping of relationship-specific affect within individuals in the sample. When both positive and negative affect are modeled at the individual level, the influence of positive affect is greater than that of negative affect. At the dyad level, the findings indicate coupled influences in both positive and negative affect between partners. With regard to positive affect, females are sensitive to their partners' overall displacement from average as well as their rate of change; males are sensitive only to their partners' displacement from average. For negative affect both partners are sensitive to each other's displacement from average, yet there are no coupled influences for rates of change in this dimension. We interpret the influence of the parameters on the system by examining the expected behavior of the system as a function of varying parameter values.     

Identification of the 1PL Model with Guessing Parameter: Parametric and Semi-parametric Results

In this paper, we study the identification of a particular case of the 3PL model, namely when the discrimination parameters are all constant and equal to 1. We term this model, 1PL-G model. The identification analysis is performed under three different specifications. The first specification considers the abilities as unknown parameters. It is proved that the item parameters and the abilities are identified if a difficulty parameter and a guessing parameter are fixed at zero. The second specification assumes that the abilities are mutually independent and identically distributed according to a distribution known up to the scale parameter. It is shown that the item parameters and the scale parameter are identified if a guessing parameter is fixed at zero. The third specification corresponds to a semi-parametric 1PL-G model, where the distribution G generating the abilities is a parameter of interest. It is not only shown that, after fixing a difficulty parameter and a guessing parameter at zero, the item parameters are identified, but also that under those restrictions the distribution G is not identified. It is finally shown that, after introducing two identification restrictions, either on the distribution G or on the item parameters, the distribution G and the item parameters are identified provided an infinite quantity of items is available.     

Decomposing the reaction time distribution: Pure insertion and selective influence revisited

This paper investigates the consequences of extending the assumptions of pure insertion and selective influence (popular in RT theorizing) to the level of the distribution. In the case of pure insertion and under the additional assumption that the additive random variable is exponentially distributed, a solution is obtained which not only allows estimation of the exponential-rate parameter but also provides a test of the assumptions. The result is shown to be applicable not only when processing is serial but also for certain parallel models. In addition, discrimination between self-terminating and exhaustive search strategies is provided, and in the case of either, both parameter estimation and tests of the model are possible. Extensions to nonexponential models are investigated and a general method of moments solution is outlined. In the case of selective influence a general nonparametric alternative to Sternberg's additive factor method is developed. The problem of empirical estimation and application is then considered. Simulations which place bounds on the type I and II error are reported. Finally the first theorem is provided an illustrative application with data from a memory scanning experiment. The results provide some support for the double assumption of pure insertion and that the additive random variable is distributed exponentially.     

Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data

This article devises a Bayesian multivariate formulation for analysis of ordinal data that records teacher classroom performance along multiple dimensions to assess aspects characterizing good instruction. Study designs for scoring teachers seek to measure instructional performance over multiple classroom measurement event sessions at varied occasions using disjoint intervals within each session and employment of multiple ratings on intervals scored by different raters; a design which instantiates a nesting structure with each level contributing a source of variation in recorded scores. We generally possess little a priori knowledge of the existence or form of a sparse generating structure for the multivariate dimensions at any level in the nesting that would permit collapsing over dimensions as is done under univariate modeling. Our approach composes a Bayesian data augmentation scheme that introduces a latent continuous multivariate response linked to the observed ordinal scores with the latent response mean constructed as an additive multivariate decomposition of nested level means that permits the extraction of de-noised continuous teacher-level scores and the associated correlation matrix. A semi-parametric extension facilitates inference for teacher-level dependence among the dimensions of classroom performance under multi-modality induced by sub-groupings of rater perspectives. We next replace an inverse Wishart prior specified for the teacher covariance matrix over dimensions of instruction with a factor analytic structure to allow the simultaneous assessment of an underlying sparse generating structure. Our formulation for Bayesian factor analysis employs parameter expansion with an accompanying post-processing sign re-labeling step of factor loadings that together reduce posterior correlations among sampled parameters to improve parameter mixing in our Markov chain Monte Carlo (MCMC) scheme. We evaluate the performance of our formulation on simulated data and make an application for the assessment of the teacher covariance structure with a dataset derived from a study of middle and high school algebra teachers.     

Film Analysis and Psychophysiology Effects of Moments of Impact and Protagonists

An interdisciplinary empirical approach that connects film analysis and psychophysiological measurement is used to investigate the effects of narrative structures and their combinations (moments of impact), and the effects of narrative structures associated with protagonists. The film The Piano by Jane Campion is employed in the assessment. The features of the film that are relevant for the reception process are determined on the basis of a film-analytic model. For 60 participants changes of heart rate as an indicator for attention processes were examined by adapting time-series strategies. An analysis of the individual processes demonstrates that narrative structures associated with protagonists cause significant reactions, whereas only few parameter estimations for reactions to moments of impact are significant. The parameter estimations differ to a high degree from person to person. The results of the analysis of the common process of all participants support the findings. The supported relevance of narrative structures perceived on a more subconscious level opens empirical access to a still unexplored level of media communication. Perspectives for future research using physiological measurement and time series analysis are discussed.     

Voluntary timing and brain function: an information processing approach.

This article takes an information processing perspective to review current understanding of brain mechanisms of human voluntary timing. Theoretical accounts of timing of the production of isochronous tapping and rhythms and of bimanual responding repetitive responding are reviewed. The mapping of higher level temporal parameter setting and memory processes and of lower level motor implementation process onto cortical and subcortical brain structures is discussed in relation to evidence from selective lesions in a range of neurological motor disorders. Brain activation studies that have helped identify key brain structures involved in the control of timing are reviewed.     

The time course of attentional zooming: A comparison of voluntary and involuntary allocation of attention to the levels of compound stimuli

Summary The time course of attentional zooming between the levels of hierarchically structured compound stimuli (level-specific covert orienting of attention) is explored experimentally. The experiment compares the RTSOA functions of voluntarily and involuntarily initiated level-specific reorienting within a cost-benefit experiment using level-specific cues. With respect to the two modes of initiation, the results reveal no functional differences between attention shift and attentional zooming. Both can be initiated either voluntarily or involuntarily, the latter mode dominating the former; for both, involuntary initiation produces faster reorienting of attention than does voluntary initiation, and for both, involuntary initiation is more effective than voluntary initiation. However, the time needed to complete attentional orienting is about twice as long for zooming than for shifts. This quantitative difference suggests that there is a functional difference between level-specific and horizontal covert orienting. The difference is explained by postulating that zooming and attention shifts differ in the number of parameter sets that have to be adjusted in the reorienting of attention. The experiment also reveals that attentional zooming to the local level needs more time than does zooming to the global level. This result gives some support to the hypothesis that the RT difference between global and local identifications (Navon's global-dominance phenomenon) is due to an additional step in the course of reorienting attention away from the global level (which is usually attended to first) to the local level, when this level is to be identified.