scholarly journals Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study

2021 ◽  
Vol 18 (4) ◽  
pp. 1994
Author(s):  
Lin-Yen Wang ◽  
Tsair-Wei Chien ◽  
Willy Chou
Keyword(s):  
Item Response ◽  
Theory Model ◽  
Forest Plot ◽  
Cumulative Number ◽  
Observation Study ◽  
Irt Model ◽  
Inflection Points ◽  
The Difference ◽  
The Impact ◽  
Absolute Advantage

Coronavirus disease 2019 (COVID-19) occurred in Wuhan and rapidly spread around the world. Assessing the impact of COVID-19 is the first and foremost concern. The inflection point (IP) and the corresponding cumulative number of infected cases (CNICs) are the two viewpoints that should be jointly considered to differentiate the impact of struggling to fight against COVID-19 (SACOVID). The CNIC data were downloaded from the GitHub website on 23 November 2020. The item response theory model (IRT) was proposed to draw the ogive curve for every province/metropolitan city/area in China. The ipcase-index was determined by multiplying the IP days with the corresponding CNICs. The IRT model was parameterized, and the IP days were determined using the absolute advantage coefficient (AAC). The difference in SACOVID was compared using a forest plot. In the observation study, the top three regions hit severely by COVID-19 were Hong Kong, Shanghai, and Hubei, with IPcase indices of 1744, 723, and 698, respectively, and the top three areas with the most aberrant patterns were Yunnan, Sichuan, and Tianjin, with IP days of 5, 51, and 119, respectively. The difference in IP days was determined (χ2 = 5065666, df = 32, p < 0.001) among areas in China. The IRT model with the AAC is recommended to determine the IP days during the COVID-19 pandemic.

scholarly journals Analisis Kualitas Tes Buatan Guru Melalui Pendekatan Item Response Theory (IRT) Model Rasch

2020 ◽  
Vol 7 (1) ◽  
pp. 61-70
Author(s):  
Dinar Pratama
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Theory Model ◽  
Item Response Theory Model ◽  
Response Theory ◽  
Irt Model ◽  
Item Fit

Tujuan utama penelitian ini dilakukan adalah untuk menganalisis dan mendeskripsikan karakteristik khusus tes buatan guru Akidah Akhlak melalui pendekatan Item Response Theory (IRT) model Rasch. Jenis penelitian ini termasuk penelitian  kuantitatif deskriptif. Subjek pada penelitian ini berjumlah 67 pola respon siswa terhadap tes dengan lima alternatif jawaban. Perangkat tes buatan guru ini diambil dari hasil pelaksanaan Ujian Akhir Semester tahun pelajaran 2018/2019 melalui teknik dokumentasi. Analisis data kuantitatif dilakukan melalui pendekatan IRT model Rasch dengan bantuan software QUEST. Berdasarkan hasil analisis, dari 30 item terdapat 28 item fit dengan model Rasch dengan nilai OUTFIT t ≤ 2.00. Ditinjau dari tingkat kesulitan item, terdapat 7 item atau sebesar 25% dengan kategori sangat sulit. Item dengan kategori sulit sebanyak 6 item atau 21.4%, kategori item sedang sebanyak 2 item atau sebesar 7.14%, kategori mudah sebanyak 13 item atau sebesar 46.4%, dan 0% untuk kategori item soal sangat mudah. Rentang nilai tingkat kesukaran berkisar antara -2.94 sampai 4.18. Nilai reliability of item estimate sebesar 0.94 dengan kategori baik sekali dan nilai reliability of case estimate sebesar 0.38 dengan kategori lemah. Berdasarkan nilai reliability of case estimate, tes ini perlu dilakukan revisi agar sesuai dengan kemampuan peserta tes. Kata Kunci: Tes, Item Response Theory, Model Rasch

scholarly journals An Extension of Testlet-Based Equating to the Polytomous Testlet Response Theory Model

2022 ◽  
Vol 12 ◽  
Author(s):  
Feifei Huang ◽  
Zhe Li ◽  
Ying Liu ◽  
Jingan Su ◽  
Li Yin ◽  
...  
Keyword(s):  
Item Response Theory ◽  
Sample Size ◽  
Item Response ◽  
Theory Model ◽  
Response Theory ◽  
Polytomous Items ◽  
Testlet Response Theory ◽  
Accurate Performance ◽  
The Impact ◽  
Item Response Theory Models

Educational assessments tests are often constructed using testlets because of the flexibility to test various aspects of the cognitive activities and broad content sampling. However, the violation of the local item independence assumption is inevitable when tests are built using testlet items. In this study, simulations are conducted to evaluate the performance of item response theory models and testlet response theory models for both the dichotomous and polytomous items in the context of equating tests composed of testlets. We also examine the impact of testlet effect, length of testlet items, and sample size on estimating item and person parameters. The results show that more accurate performance of testlet response theory models over item response theory models was consistently observed across the studies, which supports the benefits of using the testlet response theory models in equating for tests composed of testlets. Further, results of the study indicate that when sample size is large, item response theory models performed similarly to testlet response theory models across all studies.

scholarly journals A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats

2019 ◽  
Vol 80 (3) ◽  
pp. 578-603
Author(s):  
HyeSun Lee ◽  
Weldon Z. Smith
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Model Performance ◽  
Theory Model ◽  
Forced Choice ◽  
Simultaneous Estimation ◽  
Response Theory ◽  
Irt Model ◽  
Measurement Models ◽  
Random Block

Based on the framework of testlet models, the current study suggests the Bayesian random block item response theory (BRB IRT) model to fit forced-choice formats where an item block is composed of three or more items. To account for local dependence among items within a block, the BRB IRT model incorporated a random block effect into the response function and used a Markov Chain Monte Carlo procedure for simultaneous estimation of item and trait parameters. The simulation results demonstrated that the BRB IRT model performed well for the estimation of item and trait parameters and for screening those with relatively low scores on target traits. As found in the literature, the composition of item blocks was crucial for model performance; negatively keyed items were required for item blocks. The empirical application showed the performance of the BRB IRT model was equivalent to that of the Thurstonian IRT model. The potential advantage of the BRB IRT model as a base for more complex measurement models was also demonstrated by incorporating gender as a covariate into the BRB IRT model to explain response probabilities. Recommendations for the adoption of forced-choice formats were provided along with the discussion about using negatively keyed items.

scholarly journals The Bayesian Multilevel Trifactor Item Response Theory Model

2018 ◽  
Vol 79 (3) ◽  
pp. 462-494 ◽  
Author(s):  
Ken A. Fujimoto
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Latent Trait ◽  
Theory Model ◽  
Response Theory ◽  
Irt Model ◽  
Response Data ◽  
Proposed Model ◽  
Method Effects ◽  
Bayesian Irt

Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the aspects of another source (i.e., a nested method–source interaction). For this study, then, a Bayesian IRT model is proposed, one that accounts for such interaction among method sources while controlling for the clustering of individuals within the sample. The proposed model accomplishes these tasks by specifying a multilevel trifactor structure for the latent trait space. Details of simulations are also reported. These simulations demonstrate that this model can identify when item response data represent a multilevel trifactor structure, and it does so in data from samples as small as 250 cases nested within 50 clusters. Additionally, the simulations show that misleading estimates for the item discriminations could arise when the trifactor structure reflected in the data is not correctly accounted for. The utility of the model is also illustrated through the analysis of empirical data.

The Four-Parameter Logistic Item Response Theory Model As a Robust Method of Estimating Ability Despite Aberrant Responses

2012 ◽  
Vol 40 (10) ◽  
pp. 1679-1694 ◽  
Author(s):  
Wen-Wei Liao ◽  
Rong-Guey Ho ◽  
Yung-Chin Yen ◽  
Hsu-Chen Cheng
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Computerized Adaptive Testing ◽  
Theory Model ◽  
Ability Estimation ◽  
Response Theory ◽  
Test Items ◽  
Irt Model ◽  
Efficiency Measures ◽  
3Pl Model

In computerized adaptive testing (CAT), aberrant responses such as careless errors and lucky guesses may cause significant ability estimation biases in the dynamic administration of test items. We investigated the robustness of the 4-parameter logistic item response theory (4PL IRT; Barton & Lord, 1981) model in comparison with the 3-parameter logistic (3PL) IRT model (Birnbaum, 1968). We applied additional precision and efficiency measures to evaluate the 4PL IRT model. We measured the precision of CAT with respect to the estimation bias and mean absolute differences (MAD) between estimated and actual abilities. An improvement in administrative efficiency is reflected in fewer items being required for satisfying the stopping rule. Our results indicate that the 4PL IRT model provides a more efficient and robust ability estimation than the 3PL model.

Detecting DIF in Multidimensional Forced Choice Measures Using the Thurstonian Item Response Theory Model

2020 ◽  
pp. 109442812095982
Author(s):  
Philseok Lee ◽  
Seang-Hwane Joo ◽  
Stephen Stark
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Test Construction ◽  
Theory Model ◽  
Forced Choice ◽  
Practical Significance ◽  
Large Sample Size ◽  
Response Theory ◽  
Study Results ◽  
The Impact

Although modern item response theory (IRT) methods of test construction and scoring have overcome ipsativity problems historically associated with multidimensional forced choice (MFC) formats, there has been little research on MFC differential item functioning (DIF) detection, where item refers to a block, or group, of statements presented for an examinee’s consideration. This research investigated DIF detection with three-alternative MFC items based on the Thurstonian IRT (TIRT) model, using omnibus Wald tests on loadings and thresholds. We examined constrained and free baseline model comparisons strategies with different types and magnitudes of DIF, latent trait correlations, sample sizes, and levels of impact in an extensive Monte Carlo study. Results indicated the free baseline strategy was highly effective in detecting DIF, with power approaching 1.0 in the large sample size and large magnitude of DIF conditions, and similar effectiveness in the impact and no-impact conditions. This research also included an empirical example to demonstrate the viability of the best performing method with real examinees and showed how a DIF and a DTF effect size measure can be used to assess the practical significance of MFC DIF findings.

scholarly journals Transmissibility of COVID-19 and its association with temperature and humidity

2020 ◽  
Author(s):  
Xiao-Jing Guo ◽  
Hui Zhang ◽  
Yi-Ping Zeng
Keyword(s):  
Infectious Disease ◽  
Hubei Province ◽  
Cumulative Number ◽  
Actual Number ◽  
Seir Model ◽  
Inflection Points ◽  
Temperature And Humidity ◽  
Calculation Results ◽  
Reported Data ◽  
The Impact

Abstract Background: The new coronavirus disease COVID-19 outbroke in Wuhan, Hubei Province, China in December 2019, and has spread by human-to-human transmission to other areas. This study evaluated the transmissibility of the infectious disease and analyzed its association with temperature and humidity, in order to put forward suggestions on how to suppress the transmission. Methods: In this study, we revised the reported data in Wuhan to estimate the actual number of confirmed cases. Then we used the equation derived from the Susceptible–Exposed–Infectious–Recovered (SEIR) model to calculate R0 from January 24, 2020 to February 13, 2020 in 11 major cities in China for comparison. With the calculation results, we conducted correlation analysis and regression analysis between R0 and temperature and humidity to see the impact of weather on the transmissibility of COVID-19. Results: It was estimated that the cumulative number of confirmed cases had exceeded 45,000 by February 13, 2020 in Wuhan. The average R0 in Wuhan was 2.7011, significantly higher than those in other cities ranging from 1.7762 to 2.3700. The inflection points in the cities outside Hubei Province were between January 30, 2020 and February 3, 2020, while there had not been an obvious downward trend of R0 in Wuhan. R0 negatively correlated with both temperature and humidity, which was significant at the 0.01 level. Conclusions: The transmissibility of COVID-19 was strong and importance should be attached to the intervention of its transmission especially in Wuhan. According to the correlation between R0 and weather, the spread of disease will be suppressed as the weather warms.

Assessing Dimensionality of the Ideal Point Item Response Theory Model Using Posterior Predictive Model Checking

2021 ◽  
pp. 109442812110506
Author(s):  
Seang-Hwane Joo ◽  
Philseok Lee ◽  
Jung Yeon Park ◽  
Stephen Stark
Keyword(s):  
Item Response Theory ◽  
Model Checking ◽  
Predictive Model ◽  
Item Response ◽  
Ideal Point ◽  
Theory Model ◽  
Type I ◽  
Response Theory ◽  
Posterior Predictive Model Checking ◽  
Irt Model

Although the use of ideal point item response theory (IRT) models for organizational research has increased over the last decade, the assessment of construct dimensionality of ideal point scales has been overlooked in previous research. In this study, we developed and evaluated dimensionality assessment methods for an ideal point IRT model under the Bayesian framework. We applied the posterior predictive model checking (PPMC) approach to the most widely used ideal point IRT model, the generalized graded unfolding model (GGUM). We conducted a Monte Carlo simulation to compare the performance of item pair discrepancy statistics and to evaluate the Type I error and power rates of the methods. The simulation results indicated that the Bayesian dimensionality detection method controlled Type I errors reasonably well across the conditions. In addition, the proposed method showed better performance than existing methods, yielding acceptable power when 20% of the items were generated from the secondary dimension. Organizational implications and limitations of the study are further discussed.

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