Individual random effects model for differences in trait distribution among respondents.

The homogeneity hypothesis is a common assumption in classic measurement. However, the item response theory model assumes that different respondents with same ability have the same option probabilities, which may not hold. The aim of this study is to propose a new individual random effect model that accounts for the differences in option probabilities among respondents with same latent traits by using within-person variance. The performance of the new model is evaluated through simulation studies and real data using the PRESUPP scale of PISA. The model parameters are estimated by the MCMC method. The results show that the individual random effect model can provide more accurate parameter estimates and obtain a scale parameter to describe the distribution of respondents' abilities, under different within-person variances. The new model has lower RMSE and better model fit than the classic IRT model.

Analyzing the psychometric properties of the PHQ-9 using item response theory in a Chinese adolescent population.

BACKGROUND: People are more likely to fall victim to depression during adolescence since it is a period of rapid biopsychosocial transformation. Despite this, most depression research has concentrated on clinical issues, and evaluating depressive symptoms in teenagers is not as widespread. This study used item response theory (IRT) to examine the psychometric properties of the Patient Health Report scale (PHQ-9) in Chinese adolescents. Meanwhile, item function difference tests were used to check whether there were differences in depression symptoms in this group based on education and gender. METHODS: In this research, the PHQ-9 was employed as a measurement tool, and 5958 valid data points were obtained from 12 secondary schools in China (Mage = 13.484; SDage = 1.627; range 11-19 years; 52.17% boys). RESULTS: IRT shows that all items of the PHQ-9 satisfy monotonicity, unidimensionality and local independence and that they have good psychometric properties. Furthermore, DIF analysis revealed gender and educational disparities in adolescent depressive symptoms. CONCLUSION: The study indicates that the PHQ-9 possesses favourable psychometric properties for use in Chinese adolescents. As a result, it serves as a valuable tool for effectively screening depressive symptoms in adolescents. It provides a foundation for prioritizing the development of secondary school students' physical and mental health.

A portable household detection system based on the combination of bidirectional LSTM and residual block for automatical arrhythmia detection.

OBJECTIVES: Arrhythmia is an important component of cardiovascular disease, and electrocardiogram (ECG) is a method to detect arrhythmia. Arrhythmia detection is often paroxysmal, and ECG signal analysis is time-consuming and expensive. We propose a model and device for convenient monitoring of arrhythmia at any time. METHODS: This work proposes a model combining residual block and bidirectional long-term short-term memory network (BiLSTM) to detect and classify ECG signals. Residual blocks can extract deep features and avoid performance degradation caused by convolutional networks. Combined with the feature of BiLSTM to strengthen the connection relationship of the local window, it can achieve a better classification and prediction effect. RESULTS: Model optimization experiments were performed on the MIT-BIH Atrial Fibrillation Database (AFDB) and MIT-BIH Arrhythmia Database (MITDB). The accuracy simulation results on both long and short signal was higher than 99 %. To further demonstrate the applicability of the model, validation experiments were conducted on MIT-BIH Normal Sinus Rhythm Database (NSRDB) and the Long-Term AF Database (LTAFDB) datasets, and the related recognition accuracy were 99.830 and 91.252 %, respectively. Additionally, we proposed a portable household detection system including an ECG and a blood pressure detection module. The detection accuracy was higher than 98 % using the collected data as testing set. CONCLUSIONS: Hence, we thought our system can be used for practical application.

Applying Item Response Theory to the Student Adaptation to College Questionnaire: Examining Psychometric Characteristics and Developing Computerized Adaptive Testing Version.

Incoming students have many difficulties adjusting to college, and selecting appropriate measures to effectively screen them is indispensable, especially in China, where there is insufficient research in this area. To enrich domestic research, this study seeks to examine psychometric characteristics and develop a computerized adaptive version of the Student Adaptation to College Questionnaire (SACQ-CAT) based on a sample of Chinese students. Under the framework of item response theory, the item bank of student adaptation to college was formulated after uni-dimensionality testing, model comparison, item fit testing, and local independence testing. Subsequently, a CAT simulation, including three termination rules, was performed using real data to evaluate and verify the SACQ-CAT. The results showed reliability values exceeding 0.90 when participants' latent traits were between -4 and 3, covering majority of the subjects. The SACQ-CAT administered an average of fewer than 10 items to participants compared to 67 items on the original scale. The correlation coefficient between latency estimated by the SACQ-CAT and the SACQ is greater than .85, whereas the correlation coefficient with the Symptom Checklist 90 (SCL-90) scores ranges from -.33 to -.55 (p < .001). The SACQ-CAT largely reduced the number of items administered to the participants and avoided losing measurement precision.

An Optimal Choice of Cognitive Diagnostic Model for Second Language Listening Comprehension Test.

Cognitive diagnostic models (CDMs) show great promise in language assessment for providing rich diagnostic information. The lack of a full understanding of second language (L2) listening subskills made model selection difficult. In search of optimal CDM(s) that could provide a better understanding of L2 listening subskills and facilitate accurate classification, this study carried a two-layer model selection. At the test level, A-CDM, LLM, and R-RUM had an acceptable and comparable model fit, suggesting mixed inter-attribute relationships of L2 listening subskills. At the item level, Mixed-CDMs were selected and confirmed the existence of mixed relationships. Mixed-CDMs had better model and person fit than G-DNIA. In addition to statistical approaches, the content analysis provided theoretical evidence to confirm and amend the item-level CDMs. It was found that semantic completeness pertaining to the attributes and item features may influence the attribute relationships. Inexplicable attribute conflicts could be a signal of suboptimal model choice. Sample size and the number of multi-attribute items should be taken into account in L2 listening cognitive diagnostic modeling studies. This study provides useful insights into the model selection and the underlying cognitive process for L2 listening tests.

Automated Test Assembly for Multistage Testing With Cognitive Diagnosis.

Computer multistage adaptive test (MST) combines the advantages of paper and pencil-based test (P&P) and computer-adaptive test (CAT). As CAT, MST is adaptive based on modules; as P&P, MST can meet the need of test developers to manage test forms and keep test forms parallel. Cognitive diagnosis (CD) can accurately measure students' knowledge states (KSs) and provide diagnostic information, which is conducive to student's self-learning and teacher's targeted teaching. Although MST and CD have a lot of advantages, many factors prevent MST from applying to CD. In this study, we first attempt to employ automated test assembly (ATA) to achieve the objectives of MST in the application of CD (called CD-MST) via heuristic algorithms. The mean correct response probability of all KSs for each item is used to describe the item difficulty of CD. The attribute reliability in CD is defined as the test quantitative target. A simulation study with the G-DINA model (generalized deterministic input noisy "and" gate model) was carried out to investigate the proposed CD-MST, and the results showed that the assembled panels of CD-MST satisfied the statistical and the non-statistical constraints.

Development of a New Instrument for Depression With Cognitive Diagnosis Models.

Most existing instruments for depression are developed based on classical test theory, factor analysis, or sometimes, item response theory, and focus on the accurate measurement of the severity of depressive disorder. Nevertheless, they tend to be less useful in supporting the decision based on ICD-10 or DSM-5 because of the lack of detailed information for symptoms. To gain rich and valid information at the symptom level, this article developed a depression test under the framework of cognitive diagnosis models (CDMs), referred to as CDMs-D. A total of 1,181 individuals were finally recruited and their responses were used to examine the psychometric properties of CDMs-D. After excluding poor items for statistical reasons (e.g., low discrimination, poor model-fit or having DIF), 56 items were included in the CDMs-D. The CDMs-D measures all ten symptom criteria for depression defined in ICD-10 and covers five domains of depression defined by Gibbons et al. (2012). Comparing with the existing self-report measures (such as PHQ-9, SDS, CES-D and so on), a distinguishing feature of the CDMs-D is that it can provide both overall information about the severity of depressive disorder and the assessment information about specific symptoms, which could be useful for diagnostic and interventional purposes.

Development of a Computerized Adaptive Testing for Internet Addiction.

Internet addiction disorder has become one of the most popular forms of addiction in psychological and behavioral areas, and measuring it is growing increasingly important in practice. This study aimed to develop a computerized adaptive testing to measure and assess internet addiction (CAT-IA) efficiently. Four standardized scales were used to build the original item bank. A total of 59 polytomously scored items were finally chosen after excluding 42 items for failing the psychometric evaluation. For the final 59-item bank of CAT-IA, two simulation studies were conducted to investigate the psychometric properties, efficiency, reliability, concurrent validity, and predictive validity of CAT-IA under different stopping rules. The results showed that (1) the final 59 items met IRT assumptions, had high discrimination, showed good item-model fit, and were without DIF; and (2) the CAT-IA not only had high measurement accuracy in psychometric properties but also sufficient efficiency, reliability, concurrent validity, and predictive validity. The impact and limitations of CAT-IA were discussed, and several suggestions for future research were provided.

Item Selection Methods in Multidimensional Computerized Adaptive Testing With Polytomously Scored Items.

Multidimensional computerized adaptive testing (MCAT) has been developed over the past decades, and most of them can only deal with dichotomously scored items. However, polytomously scored items have been broadly used in a variety of tests for their advantages of providing more information and testing complicated abilities and skills. The purpose of this study is to discuss the item selection algorithms used in MCAT with polytomously scored items (PMCAT). Several promising item selection algorithms used in MCAT are extended to PMCAT, and two new item selection methods are proposed to improve the existing selection strategies. Two simulation studies are conducted to demonstrate the feasibility of the extended and proposed methods. The simulation results show that most of the extended item selection methods for PMCAT are feasible and the new proposed item selection methods perform well. Combined with the security of the pool, when two dimensions are considered (Study 1), the proposed modified continuous entropy method (MCEM) is the ideal of all in that it gains the lowest item exposure rate and has a relatively high accuracy. As for high dimensions (Study 2), results show that mutual information (MUI) and MCEM keep relatively high estimation accuracy, and the item exposure rates decrease as the correlation increases.

A New Measurement of Internet Addiction Using Diagnostic Classification Models.

To obtain accurate, valid, and rich information from the questionnaires for internet addiction, a diagnostic classification test for internet addiction (the DCT-IA) was developed using diagnostic classification models (DCMs), a cutting-edge psychometric theory, based on DSM-5. A calibration sample and a validation sample were recruited in this study to calibrate the item parameters of the DCT-IA and to examine the sensitivity and specificity. The DCT-IA had high reliability and validity based on both CTT and DCMs, and had a sensitivity of 0.935 and a specificity of 0.817 with AUC = 0.919. More important, different from traditional questionnaires, the DCT-IA can simultaneously provide general-level diagnostic information and the detailed symptom criteria-level information about the posterior probability of satisfying each symptom criterion in DMS-5 for each patient, which gives insight into tailoring individual-specific treatments for internet addiction.

Suppression of methane/air explosion by ultrafine water mist containing sodium chloride additive.

The suppression effect of ultrafine mists on methane/air explosions with methane concentrations of 6.5%, 8%, 9.5%, 11%, and 13.5% were experimentally studied in a closed visual vessel. Ultrafine water/NaCl solution mist as well as pure water mist was adopted and the droplet sizes of mists were measured by phase doppler particle analyzer (PDPA). A high speed camera was used to record the flame evolution processes. In contrast to pure water mist, the flame propagation speed, the maximum explosion overpressure (ΔP(max)), and the maximum pressure rising rate ((dP/dt)max) decreased significantly, with the "tulip" flame disappearing and the flame getting brighter. The results show that the suppressing effect on methane explosion by ultrafine water/NaCl solution mist is influenced by the mist amount and methane concentration. With the increase of the mist amount, the pressure, and the flame speed both descended significantly. And when the mist amount reached 74.08 g/m(3) and 37.04 g/m(3), the flames of 6.5% and 13.5% methane explosions can be absolutely suppressed, respectively. All of results indicate that addition of NaCl can improve the suppression effect of ultrafine pure water mist on the methane explosions, and the suppression effect is considered due to the combination effect of physical and chemical inhibitions.