Multidimensional Test Research Articles

Dual redox centers in MnCo2O4 nanorod cathode for highly efficient capacitive deionization

Capacitive deionization (CDI) technology via faradic electrodes with active redox pairs has achieved tremendous success in desalination. However, the inner collaborative mechanisms of multi-redox centers in electrodes are rarely deep-analyzed. Thus, this work intensively investigated the inner enhanced mechanisms of multi-redox centers, starting from the dual-redox-based MnCo2O4 nanorod (MCO-NR) cathode in capacitive desalination. The electrochemical tests indicated a surface capacitive ratio of MCO-NR as high as 86 % when the scan rate was 100 mV s−1. Multi-dimensional CDI tests demonstrated that the highest capacity of MCO-NR could reach 61.78 mg g−1, with four theory models elaborating the electrosorption kinetics and maximum desalination capability. The refined ex-situ XPS measurements, carefully designed comparative experiments, and DFT calculations were used to analyze the Na+ (de)intercalation characteristics, the inner enhanced electrosorption mechanisms of dual redox centers, and the effects that the Mn redox center exerts on the MnCo2O4 structure. These results showed the superiority of dual- or multi-redox-center-based faradic capacitive desalination, providing a new perspective for designing high-property faradic electrode materials.

The Impact of Digital Inclusive Finance on the Sustainable Growth of Income of Herdsmen in China’s Pastoral Areas

The sustainable growth of income for farming households in pastoral areas represents a crucial challenge not only for China but also for other developing nations in the process of transition to ecologically friendly models. The scarcity of assets available for collateral, along with vulnerable production conditions and developmental shortcomings, pose significant barriers to the consistent expansion of herdsmen’s income. This paper analyzes the impact of digital financial inclusion on the income of herdsmen in ethnic minority regions using panel data models from nine major pastoral provinces in China from 2011 to 2022. Through a fixed-effects model approach, the analysis delves into the influence of digital financial inclusion and subjects it to multi-dimensional and robustness tests to ensure the reliability of the findings. Furthermore, the research explores the underlying mechanisms through which digital financial inclusion contributes to income enhancement for herdsmen in these areas, employing mediation effect techniques to provide deeper insights into the relationship between digital financial services and economic upliftment in remote ethnic communities. The research findings indicate that digital financial inclusion has a positive impact on the income of herdsmen in ethnic minority regions. The breadth of coverage, depth of usage, and level of digitalization of digital financial inclusion all play a significant role in enhancing the income levels of herdsmen. In terms of the degree of influence, the impact of digital finance on the income of ethnic minorities follows this order: depth of usage > degree of digitalization > breadth of coverage. Notably, through financial backing that catalyzes a shift in herdsmen’s production methodologies and propels industrial upgrading, there is a profound potential to increase herdsmen’s output efficiency. This, in turn, alleviates the ecological strain on China’s environmentally delicate zones. Moreover, financial backing can condense the breeding periods for herdsmen, thereby ameliorating the ecological degradation associated with excessive grazing in pastoral regions. A mediating-effect analysis reveals that digital financial inclusion can boost economic development in ethnic minority regions by increasing wage income, raising per capita GDP levels, and promoting industrial structure upgrading. This, in turn, leads to an improvement in the income of herdsmen in these regions. This is particularly pertinent in China, where the eco-fragility of pastoral regions coincides with the modest income of herdsmen, underscoring the critical importance of herdsmen’s finance. To foster sustainable progress in these regions, financial backing is imperative to elevate herdsmen’s income and to induce a transformative shift in production approaches and industrial layout, paving the way for the sustainable development of these pastoral regions.

Interpretation Evidence for the Multidimensional Test Anxiety Scale: A Brief Report

Standardized testing is an integral part of the English and American education systems. However, the use of high-stakes testing has unintended consequences, one of which is test anxiety. Over the last 50 years, increased attention has been directed to developing tools to identify students experiencing test anxiety. However, many test anxiety instruments have been created for research purposes rather than use within school decision-making and lack evidence for interpretation. The purpose of the current study was to support the use of the Multidimensional Test Anxiety Scale (MTAS) in applied settings by using a latent profile analysis to identify respondent groups to support score interpretation. Participants included 918 secondary students in England.

Multidimensional hierarchical tests of general relativity with gravitational waves

Multidimensional hierarchical tests of general relativity with gravitational waves

Bayesian Adaptive Lasso for Detecting Item-Trait Relationship and Differential Item Functioning in Multidimensional Item Response Theory Models.

In multidimensional tests, the identification of latent traits measured by each item is crucial. In addition to item-trait relationship, differential item functioning (DIF) is routinely evaluated to ensure valid comparison among different groups. The two problems are investigated separately in the literature. This paper uses a unified framework for detecting item-trait relationship and DIF in multidimensional item response theory (MIRT) models. By incorporating DIF effects in MIRT models, these problems can be considered as variable selection for latent/observed variables and their interactions. A Bayesian adaptive Lasso procedure is developed for variable selection, in which item-trait relationship and DIF effects can be obtained simultaneously. Simulation studies show the performance of our method for parameter estimation, the recovery of item-trait relationship and the detection of DIF effects. An application is presented using data from the Eysenck Personality Questionnaire.

Positioning the Quality Excellence Competitiveness of Integrated School Education Using the Multidimensional Scaling Model

This study was conducted by developing a performance analysis model of the competitiveness of Integrated Islamic Middle Schools (SMPIT). As a result of acquiring new students, SMPIT is experiencing increased student interest. The study aims to build a marketing strategy model based on competitive quality performance excellence and to determine the position of excellence of each SMPIT in Klaten City. The quantitative approach was used through a multivariate multidimensional scaling statistical test. The sampling technique used was purposive sampling, which included 112 respondents. The quantitative test of the model consists of the validity and reliability test of the instrument and the quantitative multidimensional scaling test of SMPIT competitiveness using the SPSS 21 program. This study produces attributes in the questionnaire including market orientation, program innovation, environmental adaptability and the influence of competitive advantage. The results of the quantitative test show that the instrument items are valid and reliable. SMPIT Ibnu Abbas and SMPIT Hidayah have the highest competitive advantage points in the competitiveness map of superior schools, both quantitative and qualitative, in Klaten Regency, namely in Quadrant I. The final stage of the research has formed a map of each SMPIT so that the competitiveness of superior Islamic high schools is created to welcome the era of demographic bonus and economic growth towards the golden generation of Indonesia in 2045. The novelty of this research is that it combines the concept of positioning the competitiveness of superior Islamic schools based on multidimensional scaling with the object of observation of secondary Islamic education. This study combines the idea of competitive advantage of the quality performance of Islamic educational institutions through the positioning of multidimensional scaling models.

Goldfit Soccer: A Multidimensional Model for Talent Identification of Young Soccer Players

ABSTRACT Studies have provided empirical evidence on the prognostic relevance of test batteries and the “coach’s eye” for talent identification. The aims were 1) to compare objective and subjective assessments as well as a combined soccer-specific potential index between future selected and non-selected players and 2) to evaluate the prognostic validity of a multidimensional model as a tool for talent identification in soccer. The sample was composed by 132 male players (14,5 ± 1,4 years; regional competitive level) from U13 to U17 age groups of a Brazilian soccer talent development program. Athletes completed a multidimensional test battery and were subjectively rated by their coaches for sporting potential. Players’ success was evaluated five years later and was operationalized by achieving under-20 category of the Brazilian Championship or adult professional level (success rate, 15.9%). Confirming univariate prognostic validity, future selected outperformed non-selected players regarding 20-m sprint (p = .009), agility (p = .04), countermovement jump (p = .04), sit-and-reach (p = .001), Yo-Yo IR1 (p = .001), dribbling (p < .001), perceived competence (p = .007), peaking under pressure (p = .01), confidence/motivation (p = .03), coping skills (p = .03), intangibles (p < .001) and player potential (p < .001). A combined index (objective tests, athlete’s assessments and coach’s eye) named Gold Score Soccer (GSS) showed high prognostic validity (p < .001). A binary logistic regression estimated the probability of success (yes/not) with GSS, ambidextrous and predicted age at peak height velocity as predictors. This multidimensional model named GoldFit Soccer showed high prognostic validity (sensitivity = 85.7%; specificity = 83.8%; accuracy = 84.1%; area under the ROC curve = .93 [.87–.98]). Thus, GoldFit Soccer is a valid multidimensional scientific model for talent identification in soccer.

Research on the impact of digital economy on green total factor productivity: theoretical mechanism and multidimensional empirical analysis

The digital economy (DE) is emerging as a crucial driver of economic growth and an effective tool for alleviating resource and environmental pressures, thereby evolving into a significant force in facilitating green transformation. This study elaborates on the theoretical mechanism of the impact of DE on green total factor productivity (GTFP), and conducts multidimensional empirical tests using panel data from 284 cities in China. The main findings are as follows: (1) DE exerts significant positive direct, indirect, and spatial spillover effects on GTFP, signifying its growing role as a robust driver of GTFP. Notably, technological innovation emerges as a key mediator of DE’s impact on GTFP. (2) The impact of DE on GTFP exhibits a distinct pattern: initially pronounced, gradually diminishing, and then rebounding as DE progresses. (3) DE tends to exacerbate, rather than alleviate, the development divide and resource curse, especially in underdeveloped and resource-rich cities where its benefits are constrained. (4) Government behavior is pivotal in influencing DE’s impact on GTFP. Supportive policies and strict environmental regulations are critical in harnessing DE’s positive contributions to GTFP. This study lays a scientific foundation for leveraging the “green attributes” of DE and offers insights into bridging the developmental disparities among cities.

Optimal Calibration of Items for Multidimensional Achievement Tests

AbstractMultidimensional achievement tests are recently gaining more importance in educational and psychological measurements. For example, multidimensional diagnostic tests can help students to determine which particular domain of knowledge they need to improve for better performance. To estimate the characteristics of candidate items (calibration) for future multidimensional achievement tests, we use optimal design theory. We generalize a previously developed exchange algorithm for optimal design computation to the multidimensional setting. We also develop an asymptotic theorem saying which item should be calibrated by examinees with extreme abilities. For several examples, we compute the optimal design numerically with the exchange algorithm. We see clear structures in these results and explain them using the asymptotic theorem. Moreover, we investigate the performance of the optimal design in a simulation study.

Proof of Reliability Convergence to 1 at Rate of Spearman–Brown Formula for Random Test Forms and Irrespective of Item Pool Dimensionality

It is shown that the psychometric test reliability, based on any true-score model with randomly sampled items and uncorrelated errors, converges to 1 as the test length goes to infinity, with probability 1, assuming some general regularity conditions. The asymptotic rate of convergence is given by the Spearman–Brown formula, and for this it is not needed that the items are parallel, or latent unidimensional, or even finite dimensional. Simulations with the 2-parameter logistic item response theory model reveal that the reliability of short multidimensional tests can be positively biased, meaning that applying the Spearman–Brown formula in these cases would lead to overprediction of the reliability that results from lengthening a test. However, test constructors of short tests generally aim for short tests that measure just one attribute, so that the bias problem may have little practical relevance. For short unidimensional tests under the 2-parameter logistic model reliability is almost unbiased, meaning that application of the Spearman–Brown formula in these cases of greater practical utility leads to predictions that are approximately unbiased.

Linking Methods for Multidimensional Forced Choice Tests Using the Multi-Unidimensional Pairwise Preference Model.

Applications of multidimensional forced choice (MFC) testing have increased considerably over the last 20years. Yet there has been little, if any, research on methods for linking the parameter estimates from different samples. This research addressed that important need by extending four widely used methods for unidimensional linking and comparing the efficacy of new estimation algorithms for MFC linking coefficients based on the Multi-Unidimensional Pairwise Preference model (MUPP). More specifically, we compared the efficacy of multidimensional test characteristic curve (TCC), item characteristic curve (ICC; Haebara, 1980), mean/mean (M/M), and mean/sigma (M/S) methods in a Monte Carlo study that also manipulated test length, test dimensionality, sample size, percentage of anchor items, and linking scenarios. Results indicated that the ICC method outperformed the M/M method, which was better than the M/S method, with the TCC method being the least effective. However, as the number of items "per dimension" and the percentage of anchor items increased, the differences between the ICC, M/M, and M/S methods decreased. Study implications and practical recommendations for MUPP linking, as well as limitations, are discussed.

IRT for voting advice applications: a multi-dimensional test that is adaptive and interpretable

Voting advice applications rely on user input to match user preferences to political parties or candidates. Providing the input can be time-consuming, which may have a negative effect on participation. For individuals who are under time constraints or who are affected by survey fatigue, the participation threshold may be lowered if there is an option to conclude the test without answering all question items. The test result should ideally be close to the result that the participant would have gotten after answering the full battery of questions. We propose a method that allows respondents to conclude a VAA early and still get results with sufficient accuracy.The method proposed here extends the Graded Response Model and the Maximum Information Criterion, used in Item Response Theory. The aim of the method is to allow the user to control the length of the test. Furthermore, we want a simpler interpretation of multidimensional parameter estimates than we get from traditional MIRT. To achieve this, we propose an algorithm for adaptive IRT capable of selecting from a pool of items that belong to separate unidimensional scales. Using both simulated data and response data from a voting advice application project, we evaluate the accuracy of shorter tests implemented with our adaptive method. When only a few test items are answered, our proposed method outperforms a static-order IRT test of equal length in identifying the best match. We expect that implementation of the method can increase participation and engagement in voting advice applications.

Development and Factor Structure of Problematic Multidimensional Smartphone Use Scale

AbstractLiving in an intensifying technological and digital environment makes people more engaged with smartphones and related internet platforms. Alongside its highly debated advantages and disadvantages, modern smartphone use is a global phenomenon which has been mainly debated within the context of cultural and technology relationships which also influence cross-cultural activities. Focusing on one of the modern factors (i.e., smartphone use) that impact human behavior, this article presents the development of a measure of the Multidimensional Smartphone Use Scale (MSUS) and tests its factor structure to offer a scale that covers smartphone-related problems from a broader perspective. Using an online survey, a total of 514 participants took part in the current study. The scale was developed to measure a multidimensional conception of smartphone use-related problems and behavioral disorders including nomophobia, ringxiety, textiety, and phubbing which were supported with exploratory and confirmatory factors analyses. The MSUS indicated good internal consistency reliability ranging from α = .75 to α = .89. This article presents the preliminary evidence regarding the reliability and validity of the MSUS which can be used in a wide range of settings to measure maladaptive modes of smartphone use and related behaviors.

Efficiency of PROMIS MCAT Assessments for Orthopaedic Care

ABSTRACT Administration of patient-reported outcome measures (PROs), using multidimensional computer adaptive tests (MCATs) has the potential to reduce patient burden, but the efficiency of MCAT depends on the degree to which an individual’s responses fit the psychometric properties of the assessment. Assessing patients’ symptom burden through the administration of MCATs is gaining popularity in clinical settings, especially in orthopeadics where patients reporting low physical functioning and high pain interference may not always report high levels of depression. Are MCATs more efficient and precise than unidimensional CATs in a patient profile where the person-item fit varies between symptoms? Results depend on the item selection algorithm used in the assessments. Constraining item selection algorithm can substantially reduce test lengths where person-item fit is poor. This study demonstrates how MCAT can reduce patient burden in settings where collecting PROs is becoming part of routine care. Abbreviations: PRO: Patient-reported outcome; PROMIS®: Patient-Reported Outcomes Measurement Information System; CAT: Computer Adaptive Test; IRT: item response theory; EHR: electronic health record; UCAT: unidimensional computer adaptive tests; MCAT: multidimensional computer adaptive tests; HRQOL: health-related quality of life; MEPV: minimum expected posterior variance; SE: standard error; RMSE: root mean squared error; EAP: Expected a priori; MPEV-P: minimum expected posterior variance with domain preference; KL: Kullback-Leibler.

Personality and Learning Behavioral Characteristics as Predictors of Academic Achievement of Medical Students

This study investigates whether personality characteristics and learning behaviors can predict medical students’ academic achievement in Korea, specifically in terms of successfully completing medical school without delays or achieving a high grade point average (GPA) in their final year. In May 2018, 316 medical students took the Multi-Dimensional Learning Strategy Test, 2nd edition, which provided data on their personality and learning behavioral characteristics. Their final year’s GPA and any delays in completing medical school were ascertained by reviewing all electronic academic records of each semester they had been enrolled. The combination of personality and learning behavioral characteristics was significantly associated with completing medical school without delays, even after adjusting for sex and admission path. A multiple logistic regression analysis showed that the adjusted odds ratios and 95% confidence intervals for completing medical school without delays were 1.52 (95% confidence interval [CI], 0.83–2.78) and 3.64 (95% CI, 1.70–7.82) for “others” and “both high” categories, respectively, when compared with the “both low” category. For 235 students who completed medical school without delays, their learning behavioral characteristics (scores) were significantly associated with their final year’s GPA even after adjusting for sex, admission path, and personality characteristics (scores) as determined by the multiple linear regression analysis. This study suggests that individual personality and learning behavior characteristics are predictors of medical students’ academic achievement. Therefore, interventions such as personalized counseling programs should be provided in consideration of such student characteristics.

Does Carbon Trading Lead to Green Technology Innovation: Recent Evidence From Chinese Companies in Resource-Based Industries

The carbon emission trading scheme (ETS) is critical to China&#x0027;s ability to reach the established carbon dioxide (CO₂) emissions reduction goal by 2030 and carbon neutrality by 2060. To determine whether the pilot ETS policy promulgated in China in 2011 has induced green technological innovation, we conducted a multidimensional empirical test to assess the financial and green patent licensing data of Chinese A-share (publicly listed) enterprises in Shanghai and Shenzhen from 2004 to 2019. Using a difference-in-difference model with robustness checks, we did not find that the pilot ETS has had green innovation effects on entire industries or all manufacturing. However, we did identify significant green innovation improvements in some resource-based industrial enterprises, and the promotion effect led to more green utility models than green invention patents. Furthermore, the policy&#x0027;s greatest green-innovation effects are reflected primarily in new patents&#x002F;utility models for low-carbon, energy-saving, and alternative energy technologies, particularly among large and nonstate enterprises. The impact mechanism tests indicate that the pilot policy encourages green innovation primarily through four policy instruments: increasing benefit income for resource-based industrial enterprises, reducing financial constraints, granting subsidies, and increasing incentives for enterprise R&amp;D. Therefore, this article not only confirms the effectiveness of carbon emission trading market policy, but also provides scientific guidance for the realization of green technology innovation.

Cataluminescence sensor based on LaCO3OH microspheres for volatile organic compounds detection and pattern recognition

The cataluminescence (CTL) reaction detects the presence and concentration of toxic volatile organic compounds by detecting the luminescence intensity of the different components of the gas mixture. In our study, a cataluminescence online sensor system based on LaCO3OH microspheres was developed for the low concentration detection of volatile organic compounds. Multi-dimensional CTL test was carried out to analyse the correlation characteristics of heterogeneous analytes among the catalytic activity, selectivity, sensitivity, response/recover time, stability, and photoelectric signals by changing the parameters of wavelength, flow rate, temperature, and response time under the optimal conditions (280 mL/min, 180ºC, and 480 nm). Due to noise interference received by the CTL signal, the weak CTL signal was filtered based on the time-frequency characteristics of the CTL pattern signal power frequency interference and background noise. Next, this study also studied the mechanism action of the cataluminescence sensor based on LaCO3OH microspheres for gas detection, including ethyl ether, acetone, chloroform, and formaldehyde. The principal component analysis (PCA) algorithm was adopted to analyse the CTL patterns, after threshold filtering, the total variance was improved from 89.23% to 98.78% and each vapour gave its unique CTL patterns. Moreover, clustering and identification are discussed to analyse the results, including Hierarchical Cluster Analysis (HCA) and Radial Basis Function Neural Networks (RBFNN), was also discussed. The excellent cluster and recognition ability indicate the feasibility of this CTL sensor for volatile compound determination.

Can innovative urban design optimize the business environment? Quasi-natural experimental evidence from China

Improving the business environment (BE) is crucial for sustainable regional development. Innovative city design characterized by innovation driven and knowledge intensive is an important way to shape and optimize urban BE. This study constructs a quasi-natural experiment based on China’s Innovative city pilot (ICP) policy. The beneficial contribution of the ICP policy to the improvement of urban BE is empirically examined using asymptotic double-difference models and prefecture-level city panel data from 2004 to 2019. The results find that innovative urban design can effectively improve urban BE, and this policy effect still holds after a multidimensional robustness test. The mechanism test shows that optimizing the allocation of innovation factors is the influence mechanism of ICP policy to optimize urban BE. This mechanism includes four paths: technological innovation effect, government efficiency improvement effect, policy-leading effect, and human capital aggregation effect. Further analysis reveals that this policy effect is more pronounced in central and western regions and regions with higher administrative levels but does not differ significantly depending on the market potential. Furthermore, the ICP policy has a spatial spillover effect, which can improve BE levels in neighboring cities. The findings provide strong evidence for optimizing urban BE and promoting economic transformation and development through regional innovation policies.

Is ESG a New Trick for the Chinese Stock Market? An Empirical Analysis of the Relationship Between Corporate ESG and Financial Performance

As an essential indicator to measure corporate financial performance and social responsibility, ESG has been widely used in theory and practice. This paper uses the data of listed companies combined with the least square regression test, a multidimensional empirical test of the impact of ESG on corporate financial performance. The results demonstrate that ESG has a differentiated impact on different financial indicators such as ROA (returns on assets), ROE (returns on equity), and Tobin's Q index, implying that the impact of ESG is relatively complex. Based on this, this paper puts forward some suggestions hoping to provide some references for enterprises to improve their financial performance and promote sustainable development.

Disability and morbidity among US birth cohorts, 1998–2018: A multidimensional test of dynamic equilibrium theory

A substantial body of prior research has explored patterns of disability-free and morbidity-free life expectancy among older populations. However, these distinct facets of later-life health are almost always studied in isolation, even though they are very likely to be related. Using data from the US Health and Retirement Study and a multistate life table approach, this paper explores the interactions between disability, morbidity, and mortality by sex and education among four successive US birth cohorts, born from 1914 to 1923 to 1944–1953 and compared in the periods 1998–2008 and 2008–2018. We find little compression of disability but a marked expansion of morbidity across cohorts. However, disability-free life expectancy (DFLE) among those living with chronic morbidities has increased, even though at the population-level DFLE is largely unchanged. Broadly, these patterns suggest that successive cohorts of older populations in the US are experiencing a dynamic equilibrium, where the link between chronic morbidities and disability has weakened over successive cohorts. Investigating patterns by educational attainment, we find marked disparities where the least educated individuals not only live significantly fewer years free of disabilities or chronic morbidities but also have experienced an expansion in morbidity and disability. Our findings suggest that the future trajectory of disability-free life expectancy in the US is increasingly contingent on efforts to improve disease management and control the severe consequences of chronic morbidities.