Item Response Research Articles

The mental state inferences in healthcare professionals scale: a psychometric study.

Empathizing with patients is an essential component of effective clinical care. Yet, a debate persists regarding how healthcare professionals' emotions and performance are impacted when they engage in empathetic behaviors and attempt to discern patients' mental states during clinical interactions. To approach this issue, this study explores the psychometric properties of the Mental State Inferences in Healthcare Professionals Scale (MSIHPS), a novel eight-item scale to evaluate healthcare professionals' perceptions of their own disposition to infer patients' mental states during clinical interactions. The study was conducted across various units within a regional hospital and primary care units affiliated with the Canarian Public Health Service in Spain. Data collection took place over the course of 2022, spanning from February to November. The psychometric properties of the scale were analyzed, including an exploratory and a confirmatory factor analysis, to test reliability and validity. Additionally, an item response model was run to test potentially biased items. The study collected data from a sample of 585 healthcare professionals. Overall, the results indicate that the psychometric properties of the tool are adequate. Furthermore, the unidimensionality of the scale was confirmed using the item response model, wherein the eight-items significantly contribute to predicting the latent construct. The MSIHPS offers the opportunity to explore the role of mentalizing in a diversity of healthcare settings. This measure can be useful to explore the relationship between healthcare professionals' disposition to infer patients' mental states and other relevant variables in clinical interactions, such as empathy and clinical performance.

Investigating heterogeneity in IRTree models for multiple response processes with score-based partitioning.

Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of interest and the use of response categories, they maintain the basic assumption that the nature and weighting of latent response processes are homogeneous across the entire population of respondents. In the present research, we therefore propose a novel approach for detecting heterogeneity in the parameters of IRTree models across subgroups that engage in different response behavior. The approach uses score-based tests to reveal violations of parameter heterogeneity along extraneous person covariates, and it can be employed as a model-based partitioning algorithm to identify sources of differences in the strength of trait-based responding or other response processes. Simulation studies demonstrate generally accurate Type I error rates and sufficient power for metric, ordinal, and categorical person covariates and for different types of test statistics, with the potential to differentiate between different types of parameter heterogeneity. An empirical application illustrates the use of score-based partitioning in the analysis of latent response processes with real data.

Instructed Response Item Attention Checks in Mixed-mode Questionnaires: Evidence from a Probability-based Mail and Online Panel

Mostly in web surveys, attention checks have been proposed to identify inattentive respondents in self-administered surveys as previous research has argued that low-quality answers may introduce severe biases in data analyses. The increasing popularity of mixing survey modes for conducting probability-based surveys amplifies the need for systematic research on the implementation of attention checks in these survey designs. Our study investigates whether an instructed response item (IRI) can be an adequate predictor of response quality in a self-administered mixed-mode panel survey with a probability-based German general population sample. By doing so, we compare the results of the attention check with those of other response quality indicators. However, while the results for the online respondents were somewhat promising, the mail respondents result suggests that this response quality indicator seems reliable, so that we cannot recommend using IRIs in general population mixed-mode surveys, which include a mail option.

Detecting latent subpopulations in international large-scale assessments by fitting MixIRT models using NUTS

The focus of this study is to use the mixture item response theory (MixIRT) model while implementing the no-U-turn sampler as a technique for investigating the presence of latent classes (i.e., subpopulations) among eighth-grade students who were administered TIMSS 2019 mathematics subtest in paper format from the gulf cooperation council (GCC) countries. One-, two-, and constrained three-parameter logistic MixIRT models with one to four classes were used to fit to the data, where the model data fit was assessed using Bayesian fit indices. The results indicate that multiple latent classes or subpopulations can better reflect the mathematical proficiency of eighth graders from the four GCC countries, and specifically the two-class constrained three-parameter MixIRT model provides a relatively better fit to the data. The results also indicate that when a mixture of several latent classes present, the conventional unidimensional IRT model is limited in providing information for multiple latent classes and shall be avoided. In addition to adding to the existing literature on MixIRT models for international large-scale assessments such as TIMSS on its heterogenous subpopulations from a fully Bayesian approach, this study sheds light on the limitation of conventional unidimensional IRT models and subsequently directs attention to the use of the more complex MixIRT model for such assessments.

Development and validation of a method for deriving MMPI-3 scores from MMPI-2/MMPI-2-RF item responses.

The Minnesota Multiphasic Personality Inventory-3 (MMPI-3; Ben-Porath & Tellegen, 2020a) was developed to expand the content coverage of the MMPI-2-Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008/2011) and to update the test's norms to better represent the U.S. population. Because most MMPI-3 items were carried over from the MMPI-2-RF, most MMPI-3 scales can be either fully scored or prorated from the MMPI-2/MMPI-2-RF item pool. We describe development of a procedure for deriving MMPI-3 scores from MMPI-2/MMPI-2-RF protocols and examine the psychometric properties of prorated MMPI-3 scores across various samples. Following Tarescavage et al. (2016), who developed a method for prorating most MMPI-2-RF scores from the original MMPI, prorated MMPI-3 scales had to meet three criteria: (a) sufficient representation in the MMPI-2-RF item pool, (b) association with the full-length corresponding MMPI-3 scale at r ≥ .90 in multiple samples, and (c) comparable T score means and standard deviations in multiple samples. These criteria resulted in the exclusion of the three MMPI-3 non-content-based Validity Scales, as well as nine Specific Problems Scales, yielding 16 MMPI-3 scales that can be fully scored and 24 for which psychometrically comparable prorated score estimates can be derived from responses to the MMPI-2 or MMPI-2-RF item pools. Evidence of the psychometric functioning of the resulting scale scores and their comparability with the full versions of these scales across samples is presented and discussed. This procedure provides a means to study 40 of the 52 MMPI-3 scales using archival data obtained with older versions of the inventory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

DKVMN&MRI: A new deep knowledge tracing model based on DKVMN incorporating multi-relational information.

Knowledge tracing is a technology that models students' changing knowledge state over learning time based on their historical answer records, thus predicting their learning ability. It is the core module that supports the intelligent education system. To address the problems of sparse input data, lack of interpretability and weak capacity to capture the relationship between exercises in the existing models, this paper build a deep knowledge tracing model DKVMN&MRI based on the Dynamic Key-Value Memory Network (DKVMN) that incorporates multiple relationship information including exercise-knowledge point relations, exercise-exercise relations, and learning-forgetting relations. In the model, firstly, the Q-matrix is utilized to map the link between knowledge points and exercises to the input layer; secondly, improved DKVMN and LSTM are used to model the learning process of learners, then the Ebbinghaus forgetting curve function is introduced to simulate the process of memory forgetting in learners, and finally, the prediction strategies of Item Response Theory (IRT) and attention mechanism are used to combine the similarity relationship between learners' knowledge state and exercises to calculate the probability that learners would correctly respond during the subsequent time step. Through extensive experiments on three real-world datasets, we demonstrate that DKVMN&MRI has significant improvements in both AUC and ACC metrics contrast with the latest models. Furthermore, the study provides explanations at both the exercise level and learner knowledge state level, demonstrating the interpretability and efficacy of the proposed model.

Translating and validating the Ghosting Questionnaire into Arabic: results from classical test theory and item response theory analyses

BackgroundGhosting refers to the sudden cessation of communication in interpersonal relationships. Ghosting has gained attention as a phenomenon commonly encountered in the context of digital communication. Earlier studies on ghosting mostly focused on Western societies while, in Arab societies, research into this practice has yet to be initiated. The current study aimed to address this gap by translating and validating the commonly used Ghosting Questionnaire (GHOST) into Arabic.MethodsThe translation process involved forward and back translation, expert review, and pilot testing to ensure linguistic and cultural equivalence. A convenience sample of 607 participants from Bahrain, Egypt, Jordan, Oman, and Tunisia completed the Arabic version of the GHOST. Statistical analyses, including reliability testing and confirmatory factor analysis, were conducted to assess the psychometric properties of the instrument.ResultsThe Arabic version of the GHOST demonstrated high reliability. The Cronbach’s alpha (α = 0.87) and McDonald’s omega (ω = 0.87) coefficients indicated strong internal consistency. Test-retest reliability coefficients confirmed the stability of the responses over time (ICC 0.89, p < 0.001). CFA supported a single-factor structure in alignment with the conceptual framework of the original English version.ConclusionsThe successful translation and validation of the GHOST into Arabic provide researchers with a reliable tool for investigating ghosting behavior within Arab societies. Future research endeavors can build upon these findings to explore the psychological implications of ghosting. Researchers can now also develop culturally sensitive understanding of online dating and related practices in Arab communities.

Automatic setting optimization for robotic upper-extremity rehabilitation in patients with stroke using ReoGo-J: a cross-sectional clinical trial

Stroke-induced upper-extremity paralysis affects a substantial portion of the population, necessitating effective rehabilitation strategies. This study aimed to develop an automated program, incorporating the item response theory, for rehabilitation in patients with post-stroke upper-extremity paralysis, focusing on the ReoGo-J device, and to identify suitable robot parameters for robotic rehabilitation. ReoGo-J, a training device for upper-extremity disorders including 71 items, was administered to over 300 patients with varying degrees of post-stroke upper-extremity paralysis. Each item was rated on a three-point scale (0, very difficult; 1, adequate; 2, very easy). The results were analyzed using the graded response model, an extension of the two-parameter logistic model within the framework of the item response theory, to grade the training items based on ability of the patients. The relationship between the predicted ability, an indicator of the predicted ability the paralyzed upper-extremity to perform an item in the item response theory analysis (higher numbers indicate higher ability, lower numbers indicate lower ability), and the items in the Fugl–Meyer assessment (FMA), which indicate the degree of paralysis, was analyzed using Pearson’s correlation coefficient. This study included 312 patients with post-stroke upper-extremity paralysis. The predicted ability (θ) of the tasks included in the original ReoGo-J test for forward reaching, reaching for mouth, rotational reaching, radial reaching (2D), abduction reaching, reaching in eight directions, radial reach (3D), and reaching for head ranged from − 2.0 to − 0.8, − 1.3 to − 0.8, − 1.0 to − 0.1, − 0.7 to 0.3, − 0.2 to 0.4, − 0.4 to 0.6, − 0.1 to 0.6, and 0.5 to 0.6, respectively. Significantly high correlations (r = 0.80) were observed between the predicted ability of all patients and the upper-extremity items of shoulder-elbow-forearm in the FMA. We have introduced an automated program based on item response theory and determined the order of difficulty of the 71 training items in ReoGo-J. The strong correlation between the predicted ability and the shoulder-elbow-forearm items in FMA may be used to ameliorate post-stroke upper-extremity paralysis. Notably, the program allows for estimation of appropriate ReoGo-J tasks, enhancing clinical efficiency.Trial registration: https://www.umin.ac.jp/ctr/index-j.htm (UMIN000040127).

Validity and reliability of a physical literacy knowledge, attitudes, self-efficacy and behaviors questionnaire for early childhood educators (PLKASB-ECE): An exploratory factor analysis.

Nurturing physical literacy in young children offers a unique opportunity to address global physical inactivity trend. Early childhood education and care (ECEC) environments, with their extensive reach into this age group, and early childhood educators, through their daily interactions with children, are strategically positioned to influence children's physical literacy development. However, enhancing educators' ability to foster physical literacy requires valid and reliable assessment tools to measure holistic physical literacy constructs (i.e., cognitive, affective, behavioral) to assess the impact of physical literacy educational interventions. The purpose of this study was to develop a holistic digital tool to measure physical literacy knowledge, attitudes, self-efficacy, and behaviors of early childhood educators (PLKASB-ECE) for both their professional teaching context as well as the educator's own personal physical literacy behaviours. This study was conducted in two phases. Phase 1 involved item generation and content validation. Phase 2 employed a cross-sectional validation study design to assess the psychometric properties of the PLKASB-ECE tool. Based on a literature review an initial 19-item instrument was developed that subsequently underwent three rounds of expert content validation. Six additional items were added, resulting in a final 25-item self-report measure with a readability score equivalent to an 8th grade reading level (Flesch-Kincaid Grade Level: 8.7). This included 1 global rating item, 2 qualitative response items, 7 knowledge items, and 15 items addressing physical literacy attitudes, self-efficacy and behaviors using a 7-point Likert response scale. The PLKASB-ECE tool was administered to 470 educators in Alberta, Canada between 2019 to 2022. The 15 items were subjected to exploratory factor analysis and resulted in a five-factor scale with one item not loading. The five-factor scale held with the final 14 items with loadings ranging from 0.481 to 0.886, Cronbach's alpha ranging from 0.70 to 0.82, with ordinal omega ranging from 0.72 to 0.82. Usability, as assessed by completion time, was 8.15 minutes. These findings demonstrate good indices of reliability and validity for the PLKASB-ECE tool. This tool will be valuable as a research outcome measure for assessing educational interventions aimed at enhancing educators' understanding, confidence, and strategies for fostering holistic concepts of physical literacy in young children within ECECs.

Investigating Approaches to Controlling Item Position Effects in Computerized Adaptive Tests

AbstractItem position effect (IPE) refers to situations where an item performs differently when it is administered in different positions on a test. The majority of previous research studies have focused on investigating IPE under linear testing. There is a lack of IPE research under adaptive testing. In addition, the existence of IPE might violate Item Response Theory (IRT)’s item parameter invariance assumption, which facilitates applications of IRT in various psychometric tasks such as computerized adaptive testing (CAT). Ignoring IPE might lead to issues such as inaccurate ability estimation in CAT. This article extends research on IPE by proposing and evaluating approaches to controlling position effects under an item‐level computerized adaptive test via a simulation study. The results show that adjusting IPE via a pretesting design (approach 3) or a pool design (approach 4) results in better ability estimation accuracy compared to no adjustment (baseline approach) and item‐level adjustment (approach 2). Practical implications of each approach as well as future research directions are discussed as well.

Handling missing data in variational autoencoder based item response theory.

Recently Variational Autoencoders (VAEs) have been proposed as a method to estimate high dimensional Item Response Theory (IRT) models on large datasets. Although these improve the efficiency of estimation drastically compared to traditional methods, they have no natural way to deal with missing values. In this paper, we adapt three existing methods from the VAE literature to the IRT setting and propose one new method. We compare the performance of the different VAE-based methods to each other and to marginal maximum likelihood estimation for increasing levels of missing data in a simulation study for both three- and ten-dimensional IRT models. Additionally, we demonstrate the use of the VAE-based models on an existing algebra test dataset. Results confirm that VAE-based methods are a time-efficient alternative to marginal maximum likelihood, but that a larger number of importance-weighted samples are needed when the proportion of missing values is large.

Efficiency of multivariate tests in trials in progressive supranuclear palsy

Measuring disease progression in clinical trials for testing novel treatments for multifaceted diseases as progressive supranuclear palsy (PSP), remains challenging. In this study we assess a range of statistical approaches to compare outcomes as measured by the items of the progressive supranuclear palsy rating scale (PSPRS). We consider several statistical approaches, including sum scores, a modified PSPRS rating scale that had been recommended by FDA in a pre-IND meeting, multivariate tests, and analysis approaches based on multiple comparisons of the individual items. In addition, we propose two novel approaches which measure disease status based on Item Response Theory models. We assess the performance of these tests under various scenarios in an extensive simulation study and illustrate their use with a re-analysis of the ABBV-8E12 clinical trial. Furthermore, we discuss the impact of the FDA-recommended scoring of item scores on the power of the statistical tests. We find that classical approaches as the PSPRS sum score demonstrate moderate to high power when treatment effects are consistent across the individual items. The tests based on Item Response Theory (IRT) models yield the highest power when the simulated data are generated from an IRT model. The multiple testing based approaches have a higher power in settings where the treatment effect is limited to certain domains or items. The study demonstrates that there is no one-size-fits-all testing procedure for evaluating treatment effects using PSPRS items; the optimal method varies based on the specific effect size patterns. The efficiency of the PSPRS sum score, while generally robust and straightforward to apply, varies depending on the specific patterns of effect sizes encountered and more powerful alternatives are available in specific settings. These findings can have important implications for the design of future clinical trials in PSP and similar multifaceted diseases.

The Comparison of PISA 2015-2018 Mathematics Trend Items Based on Item Response Times

This study aims to explore the intricate relationship between students' response times, item characteristics, and the effort invested during the Programme for International Student Assessment (PISA) 2015 and 2018 cycles. Through the analysis of data obtained from 69 mathematics trend items administered in a computer-based format across both cycles, this research investigates the dynamics of students' response times and their implications on effort and item characteristics. Findings reveal a significant increase in students' mean response times in the 2018 cycle compared to 2015, indicating potentially heightened effort and solution behavior. Notably, item formats exerted a substantial influence on response times, with open-ended items consistently eliciting lengthier response times compared to multiple-choice items. Additionally, a correlation between response times and item difficulty emerged, suggesting that more challenging items tend to consume more time, possibly due to the complexity of involved cognitive processes. Item based effort, assessed through Response Time Fidelity (RTF) indices, highlighted that a majority of students exhibited solution behavior across both cycles to the items.. Moreover, a decrease in the proportion of students displaying rapid-guessing behavior was observed in the 2018 cycle, potentially reflecting increased engagement with the assessment. While providing insights into the interplay of response times, item characteristics, and effort, this study emphasizes the need for further exploration into the multifaceted nature of effort in educational assessments. Overall, this research contributes valuable perspectives on nuances surrounding test performance and effort evaluation within PISA mathematics assessments.

Empirically Deriving Cut Scores in the Positive Behavioral Interventions and Supports (PBIS) Tiered Fidelity Inventory (TFI) Through a Bookmarking Process

We aimed to identify empirically valid cut scores on the positive behavioral interventions and supports (PBIS) Tiered Fidelity Inventory (TFI) through an expert panel process known as bookmarking. The TFI is a measurement tool to evaluate the fidelity of implementation of PBIS. In the bookmark method, experts reviewed all TFI items and item scores by difficulty in order estimated by an item response theory (IRT) model of TFI data. Over two sessions involving data review and discussion, panelists individually identified which corresponding cut scores in each TFI tier scale indicated minimally adequate fidelity in the TFI, leading to corresponding scores of 83.3% for TFI Tier 1, 84.6% for TFI Tier 2, and 82.4% for TFI Tier 3. Then, the proposed cut scores were compared with the original cut scores of 70% for percentage of schools meeting fidelity and the association between the cut scores and school discipline outcomes using an extant sample. The results of descriptive statistics and multiple t-tests suggested that the bookmark cut score in TFI Tier 1 was reachable and more strongly associated with improved school discipline outcomes. Findings for TFI Tier 2 and Tier 3 were less conclusive.

Cross-cultural adaptation and validation of an Urdu version of the Vaccine Attitudes Examination (VAX-U) scale.

Martin and Petrie developed the Vaccine Attitudes Examination (VAX) scale with an aim to document general vaccination attitudes. Vaccine acceptance plays an important role in curbing COVID-19 infections. Thus, it was important to assess vaccination attitudes of Pakistani people towards novel COVID-19 vaccines. The VAX scale was not available in Urdu language for Pakistani population. The study aimed to carry out cross-cultural adaptation and validation of an Urdu version of the Vaccine Attitude Examination (VAX) scale in a selected population sample from Pakistan. A cross sectional study was conducted in September 2021 in an outpatient department of a healthcare facility in Karachi, Pakistan. Adult visitors, eligible for COVID-19 vaccine and spoke Urdu as first language were invited. Convenient sampling was used, and sample size was based on an item response ratio of 1:20. An Urdu version of the VAX scale was developed. The reliability was assessed using Cronbach's alpha (α) and intraclass correlation coefficient (ICC). The model fitness was evaluated using confirmatory factor analysis (CFA) and fit indices namely goodness of fit index (GFI), adjusted goodness of fit index (AGFI), Tucker Lewis index (TLI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). A structural equation model (SEM) was also developed. IBM SPSS and AMOS were used to analyse the data. The study had ethical clearance. A total of 211 responses were analysed. The reliability (Cronbach's α) was 0.821. The ICC was 0.831 (95% CI: 0.795-0.863). CFA using a 4-factor model revealed the following values for fit indices; GFI = 0.944, AGFI = 0.909, TLI = 0.953, CFI = 0.966, and RMSEA = 0.051. All values reported were in the acceptable range. The VAX Urdu version is a reliable and valid instrument for use in an Urdu speaking population and will enable clinicians to assess the public's attitude towards general vaccination including COVID-19 vaccination. Clinicians can use the VAX-U to document a person's vaccine hesitancy and tailor their counselling to address the issues in vaccine uptake.

Delineating Care Recipient Burden Constructs: Development and Validation of the CARE-2B Scale for Care Recipient Self-Perceived Burden and Proxy Assessment of Caregiver Burden.

Care Recipient Self-Perceived Burden (CR-SPB) to Caregivers is an important but overlooked aspect within the caregiver-care recipient relationship. This study aimed to 1) develop and validate the CARE-2B (Care Recipient's Two Burden) Scale, assessing both CR-SPB and their proxy assessment of caregiver burden (Proxy-CB); 2) examine whether the CR-SPB and Proxy-CB differ from caregiver burden's own assessment and other health and social care constructs. Data were collected from 504 caregiver-care recipient dyads in the US using an online panel between August 2022 and February 2023. Care recipients completed the CARE-2B Scale, which includes two subscales: their self-perceived burden (CR-SPB) and their proxy assessment of caregiver burden (Proxy-CB). Care recipients also completed measures related to health, well-being: SPB-scale, EQ-5D-5L, and EQ-HWB. Caregivers completed CarerQoL and ASCOT-Carer. Psychometric analysis included exploratory and confirmatory factor analysis, item response theory (IRT), and construct validity. CR-SPB correlated strongly with the SPB-Scale (r=0.73), while Proxy-CB correlated more strongly with caregiver-reported burden (r=0.61). Both CR-SPB and Proxy-CB items demonstrated good discrimination and information coverage in IRT analysis. EFA further supported the distinctiveness of CR-SPB and Proxy-CB, with CR-SPB items loading on a separate factor from caregiver burden and health constructs, while Proxy-CB aligned with caregiver-reported burden. The CARE-2B Scale innovatively assesses both CR-SPB and Proxy-CB from the care recipient's perspective, providing new insights into the caregiving relationship. This dual-perspective measure has implications for enhancing care strategies for individuals with chronic conditions or disabilities and their caregivers.

Cross-national Psychometric Evaluation of the Jefferson Scale of Empathy-Medical Student Version.

This study aimed to examine the factorial structure of the Jefferson Scale of Empathy-Medical student version (JSE-S) and assess items' discriminatory ability at higher and lower empathy levels in medical student populations from different countries and languages. JSE-S datasets were retrieved from previous studies of 4113 first- and/or second-year medical students from Switzerland, Portugal, Italy, Brazil, France, Spain, New Zealand, UK, Ireland, Mexico, and Peru. Parallel principal component analyses and item response theory were applied. Excluding item 18, the Compassionate Care subscale emerged in five samples (Switzerland, Portugal, Italy, France and UK/Ireland), Perspective Taking in three (Switzerland, Portugal and UK/Ireland) and Walking in Patient's Shoes in five (Switzerland, Portugal, Spain, UK/Ireland and Mexico/Peru). All subscales had items providing overlapping information. The JSE-S subscales had reliable high test performance at low empathy levels, while when the JSE-S increased, the standard error also increased. There was no consistent support across countries for the JSE-S three-factorial structure. Items provided redundant information and discriminatory power decreased when aiming to differentiate students at high empathy levels. A JSE-S revision to improve cross-cultural comparability, results' generalizability and validity should focus on suppressing or reformulating items that cannot differentiate medical students' empathy.

A Note on Equivalent and Nonequivalent Parametrizations of the Two-Parameter Logistic Item Response Model

The two-parameter logistic (2PL) item response model is typically estimated using an unbounded distribution for the trait θ. In this article, alternative specifications of the 2PL models are investigated that consider a bounded or a positively valued θ distribution. It is highlighted that these 2PL specifications correspond to the partial membership mastery model and the Ramsay quotient model, respectively. A simulation study revealed that model selection regarding alternative ranges of the θ distribution can be successfully applied. Different 2PL specifications were additionally compared for six publicly available datasets.

Application of the Contemporary Psychometrics for Assessing Economic Literacy

Currently, new skills and various types of "new literacies" relevant to the modern world are becoming issues of growing importance. One of them is economic literacy; however, there are only few assessment instruments that fulfil the academic requirements for its assessment among university students. One of such internationally established instruments is the Test of Understanding in College Economics (TUCE), which is a popular tool in empirical studies of economic literacy in many countries around the world. Despite its advantages, the currently available version of the TUCE designed for American colleges back in 2006, is prone to cheating and provides limited opportunities for formative feedback. The purpose of this paper is to present the Updated Test of Understanding in College Economics (U-TUCE). In developing the U-TUCE, we utilized the capabilities of contemporary psychometrics, which offer sufficient advances in overcoming all limitations of the original TUCE mentioned before. First, we present a revised theoretical framework of the U-TUCE, highlighting that the test measures different types of mastery of economic literacy. Second, we describe the approaches used for modifying the TUCE items and developing new items. A half of the original test items have been replaced or redesigned to reflect the economic context that has changed since 2006. Third, we utilize the logic of automatic item generation algorithms to increaseg the level of test protection against cheating. We made all changes in such a way as to maintain comparability with the previous versions of the TUCE test if necessary. Finally, the use of the Item Response Theory (IRT) is paired up with that of Cognitive Diagnostic Modeling (CDM) to ensure the quality of the U-TUCE and enhance its formative value. We show that IRT can be used to estimate the construct as a whole (which is of interest to researchers, administrators, and policy makers), while CDM provides information relating to each of the construct components, which are of interest to educational practitioners and students themselves. The results of the data analyses show that the test can be used for both purposes simultaneously.

Using Item Response Theory to Develop a Shortened Version of the Penn State Worry Questionnaire for Children.

The PSWQ-C is a widely used international scale to measure worry traits in children and adolescents. It has been translated into several versions for use in different countries. The purpose of this study was to examine the psychometric characteristics of the Penn State Worry Questionnaire for Children (PSWQ-C) particularly three reverse-scored items, and to develop a shorter version of the PSWQ-C based on Item Response Theory (IRT) methods. 903 children and adolescents of different ages from China participated in the study. This study used the Graded Response Model (GRM) to fit the data and examined the IRT parameters of each item, Item Characteristic Curve, Item Information Functions, and Differential Item Functioning. Eight items with undesirable functioning were removed, while six items with good functioning were retained, resulting in a simplified version of the PSWQ-C. The abbreviated version of the PSWQ-C was subsequently validated for its reliability and validity. The results confirmed that the abbreviated scale is reliable and effective, with a Cronbach's alpha coefficient of 0.84, indicating good reliability. Regarding validity, the abbreviated version of the PSWQ-C demonstrated significant correlations with IUS-12 and MASC, both exceeding 0.60, which closely resembles the correlation of the original PSWQ-C with these two scales. Furthermore, the correlation between the two versions was 0.96, indicating that the abbreviated PSWQ-C can effectively replace the original version and has a broader range of applications.