Evaluating DETECT Classification Accuracy and Consistency When Data Display Complex Structure

DETECT, the acronym for Dimensionality Evaluation To Enumerate Contributing Traits, is an innovative and relatively new nonparametric dimensionality assessment procedure used to identify mutually exclusive, dimensionally homogeneous clusters of items using a genetic algorithm ( Zhang & Stout, 1999 ). Because the clusters of items are mutually exclusive, this procedure is most useful when the data display approximate simple structure. In many testing situations, however, data display a complex multidimensional structure. The purpose of the current study was to evaluate DETECT item classification accuracy and consistency when the data display different degrees of complex structure using both simulated and real data. Three variables were manipulated in the simulation study: The percentage of items displaying complex structure (10%, 30%, and 50%), the correlation between dimensions (.00, .30, .60, .75, and .90), and the sample size (500, 1,000, and 1,500). The results from the simulation study reveal that DETECT can accurately and consistently cluster items according to their true underlying dimension when as many as 30% of the items display complex structure, if the correlation between dimensions is less than or equal to .75 and the sample size is at least 1,000 examinees. If 50% of the items display complex structure, then the correlation between dimensions should be less than or equal to .60 and the sample size be, at least, 1,000 examinees. When the correlation between dimensions is .90, DETECT does not work well with any complex dimensional structure or sample size. Implications for practice and directions for future research are discussed.     

Classification Consistency and Accuracy for Complex Assessments Using Item Response Theory

In this article, procedures are described for estimating single-administration classification consistency and accuracy indices for complex assessments using item response theory (IRT). This IRT approach was applied to real test data comprising dichotomous and polytomous items. Several different IRT model combinations were considered. Comparisons were also made between the IRT approach and two non-IRT approaches including the Livingston-Lewis and compound multinomial procedures. Results for various IRT model combinations were not substantially different. The estimated classification consistency and accuracy indices for the non-IRT procedures were almost always lower than those for the IRT procedures.     

电解质溶液中微粒浓度关系的正确比较

电解质溶液是中学化学的一个重要组成部分,是高考的热点,电解质溶液中微粒浓度关系的正确比较与判断,常常是很多学生学习的难点和盲点。正确理解电解质溶液中微粒浓度的关系,既能加深对水解和电离知识的理解,更能提高综合分析事物的能力。     

大气中微子vμ振荡几率估算

据高能中微子的"长基线”实验计设参数,用数值实验常数相关联的唯象方法算出中微子质量数值,估算了中微子振荡几率,判断了μ中微子vμ与τ型中微子vτ最可能发生振荡.     

论陈独秀大革命失败后对中国革命形势的正确估计

192 7年 7月中国大革命失败后 ,中国革命形势进入低潮时期 ,但当时中国共产党内部出现了“左”倾情绪 ,因而普遍地认为革命形势不是低潮而是继续高潮。陈独秀则明确认定是处于低潮时期 ,陈独秀的低潮论是对中国革命形势的正确估计。     

随机右截断下半线性模型中估计的相合性

考虑一个随机右截断下的线性模型，在王启华＾「１」研究的参数β的核型估计β＾ｎ一致性的基础上，继续并且改进了王启华＾「１」的有关工作。     

以频率估计概率的误差估计及其应用

阐述了以频率估计概率的误差估计公式,并举例说明公式在实际问题中的有关应用。     

The Efficacy of Using Diagrams When Solving Probability Word Problems in College

Previous experiments have shown a deleterious effect of visual representations on college students’ ability to solve total- and joint-probability word problems. The present experiments used conditional-probability problems, known to be more difficult than total- and joint-probability problems. The diagram group was instructed in how to use tree diagrams to solve conditional probability problems; the equation-only group was instructed only in the formulation of equations. Results indicated that the diagram group outperformed the equation-only group on a posttest of 10 near- and far-transfer problems given immediately and as long as 1 week later (Experiment 2). In addition, when rating their experience with the training phase the equation-only group reported significantly higher levels of frustration and lower levels of perceived success than the diagram group.     

Improving the Predictive Validity of Reading Comprehension Using Response Times of Correct Item Responses

Response times have often been used as ancillary information to improve parameter estimation. Under the dual processing theory, assuming reading comprehension requires an automatic process, a fast, correct response is an indicator of effective automatic processing. A skilled, automatic comprehender should be high in response accuracy and low in response times. Following this argument, several questions were addressed in this study. First, individuals with higher ability endorsed a correct response more quickly in the reading comprehension assessment, suggesting correct response times provide useful information in discriminating individuals of different abilities. Second, in terms of predicting an external criterion of reading proficiency, the increment of predictive validity of categorized correct response times was larger than the incremental validity of continuous response times based on both correct and incorrect response times. An index reflecting both accuracy and response time yielded higher incremental validity than indices reflecting response time only. Results support response time as manifesting a dimension of interest in its own right, over its inclusion as an ancillary dimension in a multidimensional model.     

以频率估计概率的误差估计及其应用

阐述了以频率估计概率的误差估计公式，并举例说明公式在实际问题中的有关应用。     

Reliability Estimation When a Test Is Split Into Two Parts of Unknown Effective Length

In some measurement settings internal consistency reliability of a measure must be based on a partition of the instrument into only 2 parts that cannot be further subdivided. Each of these 2 parts yields only a single score. If the functional lengths of the parts appear to be unequal or the parts are scored on different scales, the setting calls for a congeneric coefficient. It is shown that a single-valued estimate of the total score reliability is possible only if an assumption is made about the comparative size of the error variances of the parts. Without such an assumption, a range of reliability estimates is consistent with the part-test variances and covariance. But if the reliability of 1 part can be estimated independent of scores on the 2nd part, then a single-valued congeneric estimate of total score reliability is possible.     

试题信息函数对分数等级分类一致性和准确性的影响分析

在Rasch测量模型框架下,根据Rudner分类方法,对第三批高考综合改革8省份使用的5等级赋分方式进行等级分数分类一致性和准确性研究。研究侧重试题信息函数对分数等级分类一致性和准确性的影响,结果表明:构成测验的试题类型、试题数量、提供的信息量大小、信息函数分布等是影响分类一致性和准确性的重要因素,其中,试题信息函数分布和试题类型对分类一致性和准确性的影响更为显著。     

An Investigation of Classification Consistency Indexes Estimated Under Alternative Strong True Score Models

Using several data sets, the authors examine the relative performance of the beta binomial model and two other more general strong true score models in estimating several indexes of classification consistency. It is shown that the beta binomial model can provide inadequate fits to raw score distributions compared to more general models. This lack of fit is reflected in differences in decision consistency indexes computed using the beta binomial model and the other models. It is recommended that the adequacy of a model in fitting the data be assessed before the model is used to estimate decision consistency indexes. When the beta binomial model does not fit the data, the more general models discussed here may provide an adequate fit and, in such cases, would be more appropriate for computing decision consistency indexes.     

课堂突发事件的正确应对与师生和谐关系的构建

能否正确应对课堂突发事件，科学有效地掌控课堂教学进程和学习气氛，关系到师生和谐关系的构建，关系到当事学生的学习和心理问题。本文提供了三例正确应对课堂突发事件的案例，并对此进行分析，为一线教育工作者提供了有益的信息。     

Estimating Classification Accuracy and Consistency Indices for Multiple Measures with the Simple Structure MIRT Model

Multiple measures, such as multiple content domains or multiple types of performance, are used in various testing programs to classify examinees for screening or selection. Despite the popular usages of multiple measures, there is little research on classification consistency and accuracy of multiple measures. Accordingly, this study introduces an approach to estimate classification consistency and accuracy indices for multiple measures under four possible decision rules: (1) complementary, (2) conjunctive, (3) compensatory, and (4) pairwise combinations of the three. The current study uses the IRT-recursive-based approach with the simple-structure multidimensional IRT model (SS-MIRT) to estimate the classification consistency and accuracy for multiple measures. Theoretical formulations of the four decision rules with a binary decision (Pass/Fail) are presented. The estimation procedures are illustrated using an empirical data example based on SS-MIRT. In addition, this study applies the estimation procedures to the unidimensional IRT (UIRT) context, considering that UIRT is practically used more. This application shows that the proposed procedure of classification consistency and accuracy could be used with a UIRT model for individual measures as an alternative method of SS-MIRT.     

不完全数据下响应变量的精度估计

讨论了在不完全数据下的响应变量的估计精度,当不可忽略缺失下,样本的响应有缺失数据时,其联合分布是不可识别的.现证明了估计的精度与工具变量的相关性,当工具变量独立于目标变量时,目标变量估计的方差趋于无穷大.     

随机变系数线性结构关系EV模型参数估计的强相合性

证明了随机变系数线性结构关系EV模型中参数估计的强相合性.     

Estimation of the All Tests Pass Rate When No Examinee Took All of the Tests

Two models are presented in this article for estimating the proportion of students who would pass all of three or more content area tests given that none have actually been tested in more than two of the content areas. The first model allows one to estimate the proportion of students who would pass all of three or more content area tests from the test results of a study in which no student took more than two of the tests; the second model (which requires an outside estimate of the correlations between the different content area tests) allows one to estimate the proportion of students who would pass all of three or more content area tests from the test results of a study (or field test results) in which students took only one content area test. The models were tested on the Texas End-of-Course test battery (which consists of four content area tests) results of students who took all four content area tests prior to or in the spring of 2001, with at least one of the end-of-course content area tests taken in the spring of 2001. The model test results may have particular application to state assessment programs that must perform standard setting on high-stakes exams before the first live administration of the exams.     

Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model

To better understand the statistical properties of the deterministic inputs, noisy “and” gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the prior distribution matches the latent class structure. However, when the latent classes are of indefinite structure, the empirical Bayes method in conjunction with an unstructured prior distribution provides much better estimates and classification accuracy. Moreover, using empirical Bayes with an unstructured prior does not lead to extremely poor results as other prior-estimation method combinations do. The simulation results also show that increasing the sample size reduces the variability, and to some extent the bias, of item parameter estimates, whereas lower level of guessing and slip parameter is associated with higher quality item parameter estimation and classification accuracy.     

单纯阵的分类及特征值估计

本文给出了单纯阵新的等价条件,从对角形的角度建立起单纯阵的一种分类方法。按照这种分类法,我们研究了某些特殊单纯阵的特征值的估计,推广并改进了[2—6]的有关结果。