题库结构对原始题在线属性标定准确性之影响研究

目前已有研究证明可达阵在认知诊断测验编制中起重要作用,但迄今为止并没有引起普遍注意。本文主要讨论当题库缺少某些可达阵对应的项目类,对原始题的属性向量在线标定的准确性的影响。本文对含6个属性的独立型结构进行了模拟试验,结果显示：如果题库不充要,原始题的属性标定准确性受到影响,题库中非可达阵中项目对标定有一定的弥补作用。间接印证了可达阵在认知诊断题库起到非常重要的作用。     

A case of extreme simplicity of the core matrix in three-mode principal components analysis

In three-mode Principal Components Analysis, theP ×Q ×R core matrixG can be transformed to simple structure before it is interpreted. It is well-known that, whenP=QR,G can be transformed to the identity matrix, which implies that all elements become equal to values specified a priori. In the present paper it is shown that, whenP=QR − 1,G can be transformed to have nearly all elements equal to values spectified a priori. A cllsed-form solution for this transformation is offered. Theoretical and practical implications of this simple structure transformation ofG are discussed. Constructive comments from anonymous reviewers are gratefully acknowledged.     

MSA for a special spearman matrix

Kaiser's measure of sampling adequacy is applied to a special Spearman matrix and a specialq-cluster generalization. The result supports the contention that the measure should be no less than .5 for data to be appropriate for factor analysis.The research reported in this note was supported in part by the Program in Applied Mathematics and Statistics, National Science Foundation, and by the National Institute of Education, Department of Health, Education, and Welfare.     

Sentence comprehension and production in children with cochlear implants: Errorless procedures and equivalence-based instruction

Research supports equivalence-based instruction (EBI) and matrix training for increased listening and speaking skills in children with cochlear implants (CI). We incorporated errorless procedures to optimize the EBI and evaluated the effects on the auditory comprehension and verbal responding at-sentences level in six CI children who were readers and showed inaccurate tacting. Subject-verb-object sentences were arranged in two matrices; diagonal combinations were trained and evaluated non-diagonal combinations. EBI included stimulus fading and exclusion to directly teach the dictated sentence-picture matching; written-sentences construction upon dictation also was taught. Probes assessed the derived stimulus-stimulus and stimulus-response relations. All participants learned the auditory-visual discriminations and the exclusion resulted in fewer errors than on a stimulus fading procedure. Five participants formed equivalence classes and increased verbal responding to equivalent stimuli, especially pictures tacting. They also showed recombinative generalization for both matrices. Professionals can incorporate errorless procedures to EBI for improving sentence comprehension, tacting, and productivity in CI children.     

Compact integer-programming models for extracting subsets of stimuli from confusion matrices

This paper presents an integer linear programming formulation for the problem of extracting a subset of stimuli from a confusion matrix. The objective is to select stimuli such that total confusion among the stimuli is minimized for a particular subset size. This formulation provides a drastic reduction in the number of variables and constraints relative to a previously proposed formulation for the same problem. An extension of the formulation is provided for a biobjective problem that considers both confusion and recognition in the objective function. Demonstrations using an empirical interletter confusion matrix from the psychological literature revealed that a commercial branch-and-bound integer programming code was always able to identify optimal solutions for both the single-objective and biobjective formulations within a matter of seconds. A further extension and demonstration of the model is provided for the extraction of multiple subsets of stimuli, wherein the objectives are to maximize similarity within subsets and minimize similarity between subsets.     

An evaluation of matrix training to teach college students piano notes and rhythms

Twelve college students learned to tact the names of notes and rhythms and play them when presented with compound stimuli (visuals of notes and rhythms on a musical staff). In Experiment 1, we assessed generalization by presenting novel notes, rhythms, and compound stimuli not previously paired together. In the second experiment, we added a metronome that played at 60 beats per minute in all conditions for 3 out of 6 participants to ensure consistent tempo. Across both experiments, participants passed almost all posttests with the exception of tacting and playing in the presence of sound clips. Our data suggest that matrix training is an effective procedure to teach music skills to college students.     

A Comparison of Inverse-Wishart Prior Specifications for Covariance Matrices in Multilevel Autoregressive Models

Multilevel autoregressive models are especially suited for modeling between-person differences in within-person processes. Fitting these models with Bayesian techniques requires the specification of prior distributions for all parameters. Often it is desirable to specify prior distributions that have negligible effects on the resulting parameter estimates. However, the conjugate prior distribution for covariance matrices—the Inverse-Wishart distribution—tends to be informative when variances are close to zero. This is problematic for multilevel autoregressive models, because autoregressive parameters are usually small for each individual, so that the variance of these parameters will be small. We performed a simulation study to compare the performance of three Inverse-Wishart prior specifications suggested in the literature, when one or more variances for the random effects in the multilevel autoregressive model are small. Our results show that the prior specification that uses plug-in ML estimates of the variances performs best. We advise to always include a sensitivity analysis for the prior specification for covariance matrices of random parameters, especially in autoregressive models, and to include a data-based prior specification in this analysis. We illustrate such an analysis by means of an empirical application on repeated measures data on worrying and positive affect.     

The Symptom as Ally,not Enemy

The practice of the Western medicine often identifies the symptom with the disease itself, but a current of thought and medical practice considers it as the important message of an organic imbalance. In fact, in standard therapies symptoms are usually suppressed, thus interrupting a normal physiological process and risking severe reactions due to the organic imbalance. Dr. Hahnemann, the father of homeopathy, founded his diagnostic and therapeutic model on the interpretation of the symptoms and maintained that symptoms are an expression of altered physiology. The same concept is to be found in Dr. Reckeweg's “reactivity” and homotoxicology; he believed that diseases are the expression of the struggle of the body against toxins. Reckeweg's contribution was particularly important in considering the inflammation process as a biologic process through which the body restores its health. Also PNEI (psycho-neuro-endocrino-immunology) proposes a model where the symptom is interpreted as information and as the result of an imbalance. Several other medical approaches address particular attention to the meaning of symptoms. The Bach Flower Therapy, for instance, is guided exclusively by the negative moods, which can become the cause of functional and organic diseases; balance is restored thanks to superior harmonic energetic vibrations conveyed by the superior energy living in some flowers. This interpretation of the nature of symptoms is becoming a more and more relevant issue among both the specialistic and the general public.     

On initial item selection in cognitive diagnostic computerized adaptive testing

There has recently been much interest in computerized adaptive testing (CAT) for cognitive diagnosis. While there exist various item selection criteria and different asymptotically optimal designs, these are mostly constructed based on the asymptotic theory assuming the test length goes to infinity. In practice, with limited test lengths, the desired asymptotic optimality may not always apply, and there are few studies in the literature concerning the optimal design of finite items. Related questions, such as how many items we need in order to be able to identify the attribute pattern of an examinee and what types of initial items provide the optimal classification results, are still open. This paper aims to answer these questions by providing non‐asymptotic theory of the optimal selection of initial items in cognitive diagnostic CAT. In particular, for the optimal design, we provide necessary and sufficient conditions for the Q ‐matrix structure of the initial items. The theoretical development is suitable for a general family of cognitive diagnostic models. The results not only provide a guideline for the design of optimal item selection procedures, but also may be applied to guide item bank construction.     

Q矩阵包含错误的诊断测验分类准确性比较

Q矩阵是认知诊断测验的重要组成部分之一,围绕Q矩阵构建的诊断模型对Q矩阵中包含的错误较敏感。贝叶斯网分类模型是基于网络结点之间的关系构建的模型,将朴素贝叶斯网作为诊断模型,与DINA模型进行比较。模拟实验结果表明：Q矩阵中是否包含可达矩阵和错误界定的项目数量对DINA模型影响较大,对贝叶斯网模型影响较小;项目数量对DINA和贝叶斯网模型影响都较大;样本大小对贝叶斯网模型影响较大,对DINA模型影响较小。模拟研究结果显示,当Q矩阵中不包含可达阵、包含5个以上错误项目或样本数较大时,贝叶斯网分类模型优于DINA模型;而当Q矩阵中包含可达阵和5个(以下)错误项目时,DINA模型优于贝叶斯分类模型。