Adaptive pairwise comparison for educational measurement

Pairwise comparison is becoming increasingly popular as a holistic measurement method in education. Unfortunately, many comparisons are required for reliable measurement. To reduce the number of required comparisons, we developed an Adaptive Selection Algorithm (ASA) that selects the most informative comparisons while taking the uncertainty of the object parameters into account. The results of the simulation study showed that, given the number of comparisons, the ASA resulted in smaller standard errors of object parameters than a random selection algorithm that served as a benchmark. Rank order accuracy and reliability were similar for the two algorithms. Caution is required for interpreting the Scale Separation Reliability when the ASA is used, because this coefficient may overestimate the benchmark reliability.

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Adaptive Pairwise Comparison for Educational Measurement

Journal of Educational and Behavioral Statistics ◽

10.3102/1076998619890589 ◽

2019 ◽

Vol 45 (3) ◽

pp. 316-338

Author(s):

Elise A. V. Crompvoets ◽

Anton A. Béguin ◽

Klaas Sijtsma

Keyword(s):

Rank Order ◽

Pairwise Comparison ◽

Parameter Estimates ◽

Selection Algorithm ◽

Reliable Measurement ◽

Order Accuracy ◽

Scale Separation ◽

Adaptive Selection ◽

Object Parameter ◽

Object Parameters

Pairwise comparison is becoming increasingly popular as a holistic measurement method in education. Unfortunately, many comparisons are required for reliable measurement. To reduce the number of required comparisons, we developed an adaptive selection algorithm (ASA) that selects the most informative comparisons while taking the uncertainty of the object parameters into account. The results of the simulation study showed that, given the number of comparisons, the ASA resulted in smaller standard errors of object parameter estimates than a random selection algorithm that served as a benchmark. Rank order accuracy and reliability were similar for the two algorithms. Because the scale separation reliability (SSR) may overestimate the benchmark reliability when the ASA is used, caution is required when interpreting the SSR.

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Pairwise comparison using a Bayesian selection algorithm: Efficient holistic measurement

10.31234/osf.io/32nhp ◽

2021 ◽

Author(s):

Elise Anne Victoire Crompvoets ◽

Anton A. Béguin ◽

Klaas Sijtsma

Keyword(s):

Simulation Study ◽

Pairwise Comparison ◽

Random Selection ◽

Standard Errors ◽

Parameter Estimates ◽

Pairwise Comparisons ◽

Selection Algorithm ◽

Reliable Measurement ◽

Scale Separation ◽

Wide Range

The method of pairwise comparison has been used in a wide range of contexts. In educational measurement, many pairwise comparisons are required for reliable measurement when the comparisons to be performed are selected using the commonly-used semi-random selection algorithm (SSA). We proposed a Bayesian selection algorithm (BSA) to obtain smaller standard errors of parameter estimates and higher reliability compared with the SSA, and we evaluated the performance of these algorithms in a simulation study. We conclude that 1) the BSA should be preferred to the SSA, 2) the number of comparisons required for reliable measurement depends on the object variance, and 3) the Scale Separation Reliability (SSR) may systematically overestimate reliability even when the SSA is used.

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Developing a robust, fast and reliable measurement method for the analysis of methylarginine derivatives and related metabolites

Journal of Mass Spectrometry and Advances in the Clinical Lab ◽

10.1016/j.jmsacl.2021.02.002 ◽

2021 ◽

Vol 19 ◽

pp. 34-45

Author(s):

Duygu Eryavuz Onmaz ◽

Sedat Abusoglu ◽

Havva Yaglioglu ◽

Gulsum Abusoglu ◽

Ali Unlu

Keyword(s):

Measurement Method ◽

Reliable Measurement

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Radiographic goniometry of the PIP-joint: A reliable measurement method

Journal of Hand Therapy ◽

10.1016/j.jht.2020.08.002 ◽

2020 ◽

Author(s):

Pauline D.H.M. Verhaegen ◽

Lynn T. Kemper ◽

Sander M. Brink ◽

Tjeerd R. de Jong

Keyword(s):

Measurement Method ◽

Reliable Measurement ◽

Pip Joint

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Pulmonary Lesion Subtypes Recognition of COVID-19 From Radiomics Data With Three Dimensional Texture Characterization in CT Images

10.21203/rs.3.rs-532131/v1 ◽

2021 ◽

Author(s):

Wei Li ◽

Yangyong Cao ◽

Kun Yu ◽

Yibo Cai ◽

Feng Huang ◽

...

Keyword(s):

Data Augmentation ◽

Critical Role ◽

Three Dimensional ◽

Treatment Decision ◽

Ground Glass Opacity ◽

Ct Images ◽

Classification Model ◽

Volume Data ◽

Selection Algorithm ◽

Adaptive Selection

Abstract Background: The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. Method: The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are ground-glass opacity (GGO), cord, solid and subsolid. A computer aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three dimensional texture descriptors are applied on the volume data of lesions as well as shape and First order features. The massive feature data are selected by hybrid adaptive selection algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. Results: There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (0.9306, 0.9684, 0.9958, and 0.9430), the recall is (0.9552, 0.9158, 0.9580 and 0.8075) and the f-score is (0.93.84, 0.92.37, 0.95.47, and 84.42). Conclusion: The method is evaluated in multiple sufficient experiments. The results show that the 3D radiomics features chosen by hybrid adaptive selection algorithm can better express the advanced information of the lesion data. The classification model obtains a good performance and is compared the models of COVID-19 in the stat of art, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

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Research on Adaptive Selection Algorithm for Multi-model Load Forecasting Based on Adaboost

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/610/1/012005 ◽

2020 ◽

Vol 610 ◽

pp. 012005

Author(s):

Kaiwen Zeng ◽

Jianing Liu ◽

Haizhu Wang ◽

Zhengjun Zhao ◽

Chengsheng Wen

Keyword(s):

Load Forecasting ◽

Selection Algorithm ◽

Adaptive Selection

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Structural Modal Parameter Identification of the Bridge Subject to Environment Excitation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.482.136 ◽

2013 ◽

Vol 482 ◽

pp. 136-140

Author(s):

Yan Chen ◽

Can Ying Huang ◽

Shu Yun Zhu

Keyword(s):

Power Spectrum ◽

Measurement Method ◽

Function Analysis ◽

Coherence Function ◽

Measurement Data ◽

Phase Information ◽

Modal Parameter ◽

Reliable Measurement ◽

Modal Parameter Identification ◽

Identification Methods

Experiments were performed on Jundu Mountain Aqueduct Bridge, the article introduces the identification methods and processes which obtain modal parameter of bridge subject to Environment excitation. Layout of points effectively by moving the measurement method can obtain reliable measurement data, and it uses self-power spectrum、cross power spectrum and coherence function analysis , With the use of analytic technology for spectrum, has identified the preceding six natural frequency of the bridge vibration and has analyzed the phase information of he bridge vibration.

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Increased selection response in larger populations. I. Selection for wing-tip height in Drosophila melanogaster at three population sizes.

Genetics ◽

10.1093/genetics/125.3.579 ◽

1990 ◽

Vol 125 (3) ◽

pp. 579-584 ◽

Cited By ~ 5

Author(s):

K E Weber

Keyword(s):

Drosophila Melanogaster ◽

Standard Deviation ◽

Population Size ◽

Rank Order ◽

Selection Response ◽

Standard Errors ◽

Selection Intensity ◽

Population Sizes ◽

Selection For ◽

Wing Tip

Abstract The effect of population size on selection response was investigated with replicated selection lines of 40, 200 and 1000 selected parents, using Drosophila melanogaster homozygous for the mutant raised. Selection for increased wing-tip height was carried out for 55 generations, with an average selection intensity of 0.6 standard deviation. The rank order of responses in the seven individual lines was significantly in order of population size, and the variance of response among lines showed a significant effect of population size. The final mean responses (selected - controls, +/- standard errors) in the three treatments, in order of increasing population size, were 8.6 +/- 1.8 mils (three small lines), 15.1 +/- 1.3 mils (two medium lines), and 19.8 +/- 1.5 mils (two large lines). The differences between treatments seem to have emerged too rapidly to be the result of mutations, and are probably due mainly to the utilization of existing variation with greater efficiency by selection in larger populations.

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