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1.
This study explores classification consistency and accuracy for mixed-format tests using real and simulated data. In particular, the current study compares six methods of estimating classification consistency and accuracy for seven mixed-format tests. The relative performance of the estimation methods is evaluated using simulated data. Study results from real data analysis showed that the procedures exhibited similar patterns across various exams, but some tended to produce lower estimates of classification consistency and accuracy than others. As data became more multidimensional, unidimensional and multidimensional item response theory (IRT) methods tended to produce different results, with the unidimensional approach yielding lower estimates than the multidimensional approach. Results from simulated data analysis demonstrated smaller estimation error for the multidimensional IRT methods than for the unidimensional IRT method. The unidimensional approach yielded larger error as tests became more multidimensional, whereas a reverse relationship was observed for the multidimensional IRT approach. Among the non-IRT approaches, the normal approximation and Livingston-Lewis methods performed well, whereas the compound multinomial method tended to produce relatively larger error. 相似文献
2.
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. 相似文献
3.
When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA and CC nonparametrically by replacing the role of the parametric IRT model in Lee's classification indices with a modified version of Ramsay's kernel‐smoothed item response functions. The performance of the nonparametric CA and CC indices are tested in simulation studies in various conditions with different generating IRT models, test lengths, and ability distributions. The nonparametric approach to CA often outperforms Lee's method and Livingston and Lewis's method, showing robustness to nonnormality in the simulated ability. The nonparametric CC index performs similarly to Lee's method and outperforms Livingston and Lewis's method when the ability distributions are nonnormal. 相似文献
4.
Won-Chan Lee Stella Y. Kim Jiwon Choi Yujin Kang 《Journal of Educational Measurement》2020,57(2):230-254
This article considers psychometric properties of composite raw scores and transformed scale scores on mixed-format tests that consist of a mixture of multiple-choice and free-response items. Test scores on several mixed-format tests are evaluated with respect to conditional and overall standard errors of measurement, score reliability, and classification consistency and accuracy under three item response theory (IRT) frameworks: unidimensional IRT (UIRT), simple structure multidimensional IRT (SS-MIRT), and bifactor multidimensional IRT (BF-MIRT) models. Illustrative examples are presented using data from three mixed-format exams with various levels of format effects. In general, the two MIRT models produced similar results, while the UIRT model resulted in consistently lower estimates of reliability and classification consistency/accuracy indices compared to the MIRT models. 相似文献
5.
Tianyou Wang Michael J. Kolen Deborah J. Harris 《Journal of Educational Measurement》2000,37(2):141-162
With a focus on performance assessments, this paper describes procedures for calculating conditional standard error of measurement (CSEM) and reliability of scale scores and classification consistency of performance levels. Scale scores that are transformations of total raw scores are the focus of these procedures, although other types of raw scores are considered as well. Polytomous IRT models provide the psychometric foundation for the procedures that are described. The procedures are applied using test data from ACT's Work Keys Writing Assessment to demonstrate their usefulness. Two polytomous IRT models were compared, as were two different procedures for calculating scores. One simulation study was done using one of the models to evaluate the accuracy of the proposed procedures. The results suggest that the procedures provide quite stable estimates and have the potential to be useful in a variety of performance assessment situations. 相似文献
6.
Attribute‐Level and Pattern‐Level Classification Consistency and Accuracy Indices for Cognitive Diagnostic Assessment 
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下载免费PDF全文 Wenyi Wang Lihong Song Ping Chen Yaru Meng Shuliang Ding 《Journal of Educational Measurement》2015,52(4):457-476
Classification consistency and accuracy are viewed as important indicators for evaluating the reliability and validity of classification results in cognitive diagnostic assessment (CDA). Pattern‐level classification consistency and accuracy indices were introduced by Cui, Gierl, and Chang. However, the indices at the attribute level have not yet been constructed. This study puts forward a simple approach to estimating the indices at both the attribute and the pattern level through one single test administration. Detailed elaboration is made on how the upper and lower bounds for the attribute‐level accuracy can be derived from the variance of error of the attribute mastery probability estimate. In addition, based on Cui's pattern‐level indices, an alternative approach to estimating the attribute‐level indices is also proposed. Comparative analysis of simulation results indicate that the new indices are very desirable for evaluating test‐retest consistency and correct classification rate. 相似文献
7.
The current study aims to evaluate the performance of three non-IRT procedures (i.e., normal approximation, Livingston-Lewis, and compound multinomial) for estimating classification indices when the observed score distribution shows atypical patterns: (a) bimodality, (b) structural (i.e., systematic) bumpiness, or (c) structural zeros (i.e., no frequencies). Under a bimodal distribution, the normal approximation procedure produced substantially large bias. For a distribution with structural bumpiness, the compound multinomial procedure tended to introduce larger bias. Under a distribution with structural zeroes, the relative performance of selected estimation procedures depended on cut score location and the sample-size conditions. In general, the differences in estimation errors among the three procedures were not substantially large. 相似文献
8.
This article introduces procedures for the computation and asymptotic statistical inference for classification consistency and accuracy indices specifically designed for cognitive diagnostic assessments. The new classification indices can be used as important indicators of the reliability and validity of classification results produced by cognitive diagnostic assessments. For tests with known or previously calibrated item parameters, the sampling distributions of the two new indices are shown to be asymptotically normal. To illustrate the computations of the new indices, we apply them to the real diagnostic data from a fraction subtraction test (Tatsuoka). We also use simulated data to evaluate their performances and distributional properties. 相似文献
9.
This paper describes four procedures previously developed for estimating conditional standard errors of measurement for scale scores: the IRT procedure (Kolen, Zeng, & Hanson. 1996), the binomial procedure (Brennan & Lee, 1999), the compound binomial procedure (Brennan & Lee, 1999), and the Feldt-Qualls procedure (1998). These four procedures are based on different underlying assumptions. The IRT procedure is based on the unidimensional IRT model assumptions. The binomial and compound binomial procedures employ, as the distribution of errors, the binomial model and compound binomial model, respectively. By contrast, the Feldt-Qualls procedure does not depend on a particular psychometric model, and it simply translates any estimated conditional raw-score SEM to a conditional scale-score SEM. These procedures are compared in a simulation study, which involves two-dimensional data sets. The presence of two category dimensions reflects a violation of the IRT unidimensionality assumption. The relative accuracy of these procedures for estimating conditional scale-score standard errors of measurement is evaluated under various circumstances. The effects of three different types of transformations of raw scores are investigated including developmental standard scores, grade equivalents, and percentile ranks. All the procedures discussed appear viable. A general recommendation is made that test users select a procedure based on various factors such as the type of scale score of concern, characteristics of the test, assumptions involved in the estimation procedure, and feasibility and practicability of the estimation procedure. 相似文献
10.
Lawrence T. DeCarlo 《Journal of Educational Measurement》2005,42(1):53-76
An approach to essay grading based on signal detection theory (SDT) is presented. SDT offers a basis for understanding rater behavior with respect to the scoring of construct responses, in that it provides a theory of psychological processes underlying the raters' behavior. The approach also provides measures of the precision of the raters and the accuracy of classifications. An application of latent class SDT to essay grading is detailed, and similarities to and differences from item response theory (IRT) are noted. The validity and utility of classifications obtained from the SDT model and scores obtained from IRT models are compared. Validity coefficients were found to be about equal in magnitude across SDT and IRT models. Results from a simulation study of a 5-class SDT model with eight raters are also presented. 相似文献
11.
Because parameter estimates from different calibration runs under the IRT model are linearly related, a linear equation can
convert IRT parameter estimates onto another scale metric without changing the probability of a correct response (Kolen &
Brennan, 1995, 2004). This study was designed to explore a new approach to finding a linear equation by fixing C-parameters
for anchor items in IRT equating. A rationale for fixing C-parameters for anchor items in IRT equating can be established
from the fact that the C-parameters are not affected by any linear transformation. This new approach can avoid the difficulty
in getting accurate C-parameters for anchor items embedded in the application of the IRT model. Based upon our findings in
this study, we would recommend using the new approach to fix C-parameters for anchor items in IRT equating.
This work was supported by a Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research 相似文献
12.
The usefulness of item response theory (IRT) models depends, in large part, on the accuracy of item and person parameter estimates. For the standard 3 parameter logistic model, for example, these parameters include the item parameters of difficulty, discrimination, and pseudo-chance, as well as the person ability parameter. Several factors impact traditional marginal maximum likelihood (ML) estimation of IRT model parameters, including sample size, with smaller samples generally being associated with lower parameter estimation accuracy, and inflated standard errors for the estimates. Given this deleterious impact of small samples on IRT model performance, use of these techniques with low-incidence populations, where it might prove to be particularly useful, estimation becomes difficult, especially with more complex models. Recently, a Pairwise estimation method for Rasch model parameters has been suggested for use with missing data, and may also hold promise for parameter estimation with small samples. This simulation study compared item difficulty parameter estimation accuracy of ML with the Pairwise approach to ascertain the benefits of this latter method. The results support the use of the Pairwise method with small samples, particularly for obtaining item location estimates. 相似文献
13.
This study investigates a sequence of item response theory (IRT) true score equatings based on various scale transformation approaches and evaluates equating accuracy and consistency over time. The results show that the biases and sample variances for the IRT true score equating (both direct and indirect) are quite small (except for the mean/sigma method). The biases and sample variances for the equating functions based on the characteristic curve methods and concurrent calibrations for adjacent forms are smaller than the biases and variances for the equating functions based on the moment methods. In addition, the IRT true score equating is also compared to the chained equipercentile equating, and we observe that the sample variances for the chained equipercentile equating are much smaller than the variances for the IRT true score equating with an exception at the low scores. 相似文献
14.
A common suggestion made in the psychometric literature for fixed‐length classification tests is that one should design tests so that they have maximum information at the cut score. Designing tests in this way is believed to maximize the classification accuracy and consistency of the assessment. This article uses simulated examples to illustrate that one can obtain higher classification accuracy and consistency by designing tests that have maximum test information at locations other than at the cut score. We show that the location where one should maximize the test information is dependent on the length of the test, the mean of the ability distribution in comparison to the cut score, and, to a lesser degree, whether or not one wants to optimize classification accuracy or consistency. Analyses also suggested that the differences in classification performance between designing tests optimally versus maximizing information at the cut score tended to be greatest when tests were short and the mean of ability distribution was further away from the cut score. Larger differences were also found in the simulated examples that used the 3PL model compared to the examples that used the Rasch model. 相似文献
15.
Tom Bramley 《Educational research; a review for teachers and all concerned with progress in education》2013,55(3):325-330
Background:?A recent article published in Educational Research on the reliability of results in National Curriculum testing in England (Newton, The reliability of results from national curriculum testing in England, Educational Research 51, no. 2: 181–212, 2009) suggested that: (1) classification accuracy can be calculated from classification consistency; and (2) classification accuracy on a single test administration is higher than classification consistency across two tests. Purpose:?This article shows that it is not possible to calculate classification accuracy from classification consistency. It then shows that, given reasonable assumptions about the distribution of measurement error, the expected classification accuracy on a single test administration is higher than the expected classification consistency across two tests only in the case of a pass–fail test, but not necessarily for tests that classify test-takers into more than two categories. Main argument and conclusion:?Classification accuracy is defined in terms of a ‘true score’ specified in a psychometric model. Three things must be known or hypothesised in order to derive a value for classification accuracy: (1) a psychometric model relating observed scores to true scores; (2) the location of the cut-scores on the score scale; and (3) the distribution of true scores in the group of test-takers. 相似文献
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17.
《Educational Assessment》2013,18(2):125-146
States are implementing statewide assessment programs that classify students into proficiency levels that reflect state-defined performance standards. In an effort to provide support for score interpretations, this study examined the consistency of classifications based on competing item response theory (IRT) models for data from a state assessment program. Classification of students into proficiency levels was compared based on a 1-parameter vs. a 3-parameter IRT model. Despite an overall high level of agreement between classifications based on the 2 models, systematic differences were observed. Under the 1-parameter model, proficiency was underestimated for low proficiency classifications but overestimated for upper proficiency classifications. This resulted in higher "Below Basic" and "Advanced" classifications under 1-parameter vs. 3-parameter IRT applications. Implications of these differences are discussed. 相似文献
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A potential concern for individuals interested in using item response theory (IRT) with achievement test data is that such tests have been specifically designed to measure content areas related to course curriculum and students taking the tests at different points in their coursework may not constitute samples from the same population. In this study, data were obtained from three administrations of two forms of a Biology achievement test. Data from the newer of the two forms were collected at a spring administration, made up of high school sophomores just completing the Biology course, and at a fall administration, made up mostly of seniors who completed their instruction in the course from 6–18 months prior to the test administration. Data from the older form, already on scale, were collected at only a fall administration, where the sample was comparable to the newer form fall sample. IRT and conventional item difficulty parameter estimates for the common items across the two forms were compared for each of the two form/sample combinations. In addition, conventional and IRT score equatings were performed between the new and old forms for each o f the form sample combinations. Widely disparate results were obtained between the equatings based on the two form/sample combinations. Conclusions are drawn about the use o f both classical test theory and IRT in situations such as that studied, and implications o f the results for achievement test validity are also discussed 相似文献
20.
回顾国内外有关小样本情况下估计试题的Logistic IRT参数的研究,可以总结出六种参数估计方法,分别是:修改IRT模型法、提供先验信息法、人工神经网络法、非参数估计法、经典测验理论标准化法以及使用数据增强技术。后续研究应加强对已有参数估计方法的改进,使用包括标准误在内的多种误差指标,在250人以内的样本水平上,采用模拟数据与真实数据相结合的模拟实验法开展更加严谨的模拟研究。 相似文献