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Incorporating ethnoscience into lessons through the ethnoscience-students' worksheet (ESW) is one method to improve student interest in science learning while introducing them to the local culture. However, no research was reported the effects of ESW on students' responses and the factors that influence ESW implementation in science learning. In order to better understand how students learn through ESW, this study investigated the relationship between ethnoscience context, science learning, and the implementation of students' worksheets. Seventy-two students participated in the survey after they studied ethnoscience learning through ESW. Students' responses are more influenced by science learning. In addition, the ethnoscience-integrated students' worksheets (SW) variable indirectly affects students' responses. Additionally, ESW affects students' responses more significantly than science learning and ethnoscience. This research provides insightful implications for educators on planning, designing, and practicing ESW to enhance students' problem-solving motivation and academic achievement. Furthermore, to contribute significantly to future researchers, further research employed the structural equation model through covariance analysis, also known as confirmatory factor analysis (CFA).

To overcome the difficulty and unprecedented suspensions due to the COVID-19 pandemic, every organization is required to consider strategic steps to sustain, one of which is by implementing digital transformation and developing leadership capability. This study aims to determine the impact of digital transformation and leadership capability in maintaining the organization, particularly in distance education. This study used questionnaires distributed to 402 students to obtain their opinions on digital transformation, leadership capability, and organizational resilience in Universitas Terbuka, a founder of distance education institutions in Indonesia. Data were analyzed using descriptive and Higher-Order Structural Equation Modeling analysis. Based on the modelling, it is shown that the modelling had a good value of the Goodness of Fit Indicator through a standardized loading factor (SLF) more than the tolerable loading factor limit (>0.50). The Variance Extracted (VE) and Construct Reliability (CR) values also showed that the questions on every indicator used in this study were sufficiently measurable and reliable. The results of hypothesis testing indicated that there was a positive and significant impact of digital transformation and leadership capability on the organizational resilience of Universitas Terbuka.

This study focuses on developing a five-tier chemical diagnostic test based on a computer-based test with 11 assessment categories with an assessment score from 0 to 10. A total of 20 items produced were validated by education experts, material experts, measurement experts, and media experts, and an average index of the Aiken test > 0.70 was obtained. The validation results were tested on 580 respondents and analyzed using the Generalized Partial Credit Model (GPCM) Item Response Theory (IRT) type. The results of the analysis show that all of the items meet the requirements to be said to be valid for the model; the evidence of the value this: RMSEA < 0.08, CFI > 0.87, SRMR < 0.10, GFI > 0.90, NFI > 0.90, NNFI > 0.90, IFI > 0.90, TLI > 0.90, and RFI > 0.90, and all items were obtained has a p.S_X2 value greater than 0.05 which indicates that all items developed are fit and by the GPCM model. The construct reliability (CR) value is 0.99, which suggests the construct is reliable. The most challenging item is item 9, and the most accessible item is item 4. [Note: The page range (91-105) shown on the PDF citation is incorrect. The correct page range is 92-106.]

A questionnaire for measuring Context University Skills and Workplace-Recognized Skills (LCUS-WRS) requires a validated and reliable instrument. The purpose of this study was to develop and validate a questionnaire to measure learning of context university skills and workplace-recognized skills, in order to carry out an advanced psychometric properties analysis of Work-Integrated Education (WIE) students and the skills of LCUS-WRS, including internal and external reliability. Methods: We conducted multi-stage random sampling, with a sample consisting of 728 students WIE from 20 rural colleges in Thailand. Exploratory Factor Analysis (EFA) was performed on a subset of the sample (n = 279), and Confirmatory Factor Analysis (CFA) was conducted on another subset (n = 449), both in different subgroups. Additionally, the researcher analyzed the internal consistency and temporal reliability of the scale in the instrument. Results: Using exploratory factor analysis with 39 items 6 factors and accounted for 55.881% of variance was identified of the variance for the CFA model. The model achieved a goodness-of-fit of chi-square (X[superscript 2]) = 582.907, df = 308, p = 0.000, X[superscript 2]/df = 1.90, GFI = 0.979, AGFI = 0.985, RMSEA = 0.030, SRMR = 0.030. Conclusion: LCUS-WRS scale has solid construct of validity and excellent internal consistency which to analyze psychometric properties It has optimum temporal reliability. Therefore, the indicated that the dimensions of measuring of LCUS-WRS guaranteed its use in higher education institutions. Keywords: learning of context university skills, workplace-recognized

This study aims to compare item and student parameters of dichotomously scored multidimensional constructs estimated based on unidimensional and multidimensional Item Response Theory (IRT) under different conditions of sample size, interdimensional correlation and number of dimensions. This research, conducted with simulations, is of a basic nature. The standard errors of the item and student parameters estimated according to both models were evaluated by the mean of the error squares. The results of the study indicate that there is no significant difference between the errors of the item parameters obtained from the unidimensional and multidimensional IRT in the cases of two-dimensional data structures and high interdimensional correlations. However, when the data structure are three and five dimensional, the item parameter errors resulting from the unidimensional IRT increase. Standard errors for item parameters decreased as the sample size increased. When the standard errors arising from ability parameters were analysIRTed, it was observed that multidimensional IRT estimated with lower errors for all conditions. As a result of the study, it is established that multidimensional IRT provides more accurate results in the analysis of multidimensional constructs, especially in the estimation of student parameters and in making decisions about students. Therefore, it is recommended that multidimensional models should be used for student ability estimation in national and international large-scale examinations.

Evaluation of child-friendly school (CFS) policies is essential to determine the achievements of school efforts in reducing violence cases. This research aims to proving the reliability and validity of CFS policy evaluation instruments in elementary schools with different locations. This investigation uses the Context Input Process Product (CIPP) evaluation model to confirm the factor structure or dimensions of several observed variables (items) to evaluate the reliability and validity of the CFS policy evaluation tool in elementary schools. The validity and reliability of previously created instruments are evaluated quantitatively, but this study utilises different subjects and study sites. There were 320 respondents, with 145 school principals and 175 teachers taken randomly. Confirmatory factors analysis (CFA) results show goodness of fit (GOF), and the model is acceptable. The CFS evaluation instrument can be accepted after eliminating several question items and modifying them. All items of the teal instrument meet the goodness of fit criteria in terms of chi-square and root mean square error of approximation (RMSEA). The instrument for evaluating CFS policies in primary schools using the CIPP model has met a valid and reliable psychometric property test so that it can be applied.

In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that occurred a long time ago--a phenomenon known as "memory decay". Previous studies either predefined a short-run and long-run memory or fixed a parametric exponential memory decay using a predefined half-life period. In real-life relational event networks, however, it is generally unknown how the influence of past events fades as time goes by. For this reason, it is not recommendable to fix memory decay in an ad-hoc manner, but instead we should learn the shape of memory decay from the observed data. In this paper, a novel semi-parametric approach based on Bayesian Model Averaging is proposed for learning the shape of the memory decay without requiring any parametric assumptions. The method is applied to relational event history data among socio-political actors in India and a comparison with other relational event models based on predefined memory decays is provided.

This study examined the psychometric properties of the Career Adapt-Abilities Scale in a Lebanese sample. The study includes 236 Lebanese citizens (54.2% women; M[subscript age] = 30.14). Confirmatory factor analyses indicated that a hierarchical model yielded a good fit, with the CAAS measuring four distinct dimensions that can be combined in a higher-order career adaptability factor in a Lebanese sample. Results showed that the overall CAAS and sub-dimensions were highly reliable. Pearson correlation coefficients demonstrated that the CAAS' scores were related to those of basic values and life satisfaction. The CAAS constitutes a promising measure to be used in Lebanese career counselling.

Descriptive fit indices that do not require a formal statistical basis and do not specifically depend on a given estimation criterion are useful as auxiliary devices for judging the appropriateness of unrestricted or exploratory factor analytical (UFA) solutions, when the problem is to decide the most appropriate number of common factors. While overall indices of this type are well known in UFA applications, especially those intended for item analysis, difference indices are much more scarce. Recently, Raykov and collaborators proposed a family of effect-size-type descriptive difference indices that are promising for UFA applications. As a starting point, we considered the simplest measure of this family, which (a) can be viewed as absolute and (b) from which only tentative cutoffs and reference values have been provided so far. In this situation, this article has three aims. The first is to propose a relative version of Raykov's effect-size measure, intended to be used as a complement of the original measure, in which the increase in explained common variance is related to the overall prior estimated amount of common factor variance. The second is to establish reference values for both indices in item-analysis scenarios using simulation. And the third aim (instrumental) is to implement the proposal in both R language and a well-known non-commercial factor analysis program. The functioning and usefulness of the proposal is illustrated using an existing empirical dataset.

Academic mismatch, the incompatibility between applicants'/students' aptitude and their desired/current academic program, is considered a key predictor of degree attainment. Evaluations of this link tend to be cross-sectional, however, focusing on specific stages of the college pipeline and ignoring mismatch at prior or later stages and their potential outcomes. We developed and tested a longitudinal and multidimensional framework that classifies mismatches along the college pipeline by direction (match, overmatch, undermatch) and stage (application, admission, enrollment). We combined them into match pathways and evaluated how these configurations shape graduation outcomes. Analyses of administrative data on all applicants and students at universities in Israel between 1998 and 2003 demonstrate the added value of this framework. We show that academic mismatch is substantially more prevalent and complex than previously depicted, with only a third of all students fully matched at all stages. Mismatch at each stage affects graduation chances, but the effect is also path-dependent. Thus, it is important to study the entire match pathway to understand how academic mismatch shapes inequality in graduation outcomes. Our findings have important implications for policies designed to increase degree attainment and diversity.

CDKL5 deficiency disorder (CDD) results in early-onset epilepsy and lifelong cognitive and motor impairments. With no validated measure for communication in CDD, this study evaluated the psychometric properties of the Communication and Symbolic Behavior Scales-Developmental Profile Infant Toddler Checklist (CSBS-DP ITC). Caregivers (n = 150; affected individuals aged 1-29 years) completed the CSBS-DP ITC. Distribution of scores indicated a floor effect. There was poor divergent validity for the three-factor model but goodness of fit and convergent validity data were satisfactory for the one-factor model. Individuals with poorer overall functional abilities scored lower on the CSBS-DP ITC. Test-retest reliability was excellent. The floor effect could explain the very high reliability, suggesting problems as a sensitive outcome measure in clinical trials for CDD.

A Monte Carlo simulation study was conducted to compare fit indices used for detecting the correct latent class in three dichotomous mixture item response theory (IRT) models. Ten indices were considered: Akaike's information criterion (AIC), the corrected AIC (AICc), Bayesian information criterion (BIC), consistent AIC (CAIC), Draper's information criterion (DIC), sample size adjusted BIC (SABIC), relative entropy, the integrated classification likelihood criterion (ICL-BIC), the adjusted Lo-Mendell-Rubin (LMR), and Vuong-Lo-Mendell-Rubin (VLMR). The accuracy of the fit indices was assessed for correct detection of the number of latent classes for different simulation conditions including sample size (2,500 and 5,000), test length (15, 30, and 45), mixture proportions (equal and unequal), number of latent classes (2, 3, and 4), and latent class separation (no-separation and small separation). Simulation study results indicated that as the number of examinees or number of items increased, correct identification rates also increased for most of the indices. Correct identification rates by the different fit indices, however, decreased as the number of estimated latent classes or parameters (i.e., model complexity) increased. Results were good for BIC, CAIC, DIC, SABIC, ICL-BIC, LMR, and VLMR, and the relative entropy index tended to select correct models most of the time. Consistent with previous studies, AIC and AICc showed poor performance. Most of these indices had limited utility for three-class and four-class mixture 3PL model conditions.

In recent years, some models for binary and graded format responses have been proposed to assess unipolar variables or "quasi-traits." These studies have mainly focused on clinical variables that have traditionally been treated as bipolar traits. In the present study, we have made a proposal for unipolar traits measured with continuous response items. The proposed log-logistic continuous unipolar model (LL-C) is remarkably simple and is more similar to the original binary formulation than the graded extensions, which is an advantage. Furthermore, considering that irrational, extreme, or polarizing beliefs could be another domain of unipolar variables, we have applied this proposal to an empirical example of superstitious beliefs. The results suggest that, in certain cases, the standard linear model can be a good approximation to the LL-C model in terms of parameter estimation and goodness of fit, but not trait estimates and their accuracy. The results also show the importance of considering the unipolar nature of this kind of trait when predicting criterion variables, since the validity results were clearly different.

The present study administered six test instruments to 13- to 14-year-old learners of English in Austria and Sweden (N = 213), countries offering settings with more explicit and implicit learning environments, respectively. Confirmatory Factor Analyses for Austria yielded a factor comprising timed grammaticality judgment tests, an oral narrative test, and elicited imitation, labelled in this study Automatized and/or Implicit Knowledge, and a factor including an untimed grammaticality judgment test and a metalinguistic knowledge test, named in this study Explicit Knowledge. In the Swedish context, goodness-of-fit indices provided some evidence that a single-factor model shows a better fit, although a comparison of this model with two-factor models did not reach statistical significance. The findings point to the potential importance of considering the specificities of a learning environment in interpreting learner achievement on measures of the implicit versus explicit knowledge spectrum.

The Expectancy-Value model has been extensively used to understand students' achievement motivation. However, recent studies propose the inclusion of cost as a separate construct from values, leading to the development of the Expectancy-Value-Cost model. This study aimed to adapt Kosovich et al.'s ("The Journal of Early Adolescence", 35, 5-6, 2015) instrument for assessing achievement motivation in science and math among Spanish-speaking students aged 9-12. The psychometric evaluation encompassed examining validity, reliability, and measurement invariance for gender. The findings support the applicability of the expectancy-value-cost model in elementary school students, providing a valid and reliable tool for assessing achievement motivation in the science and math domain. However, the findings for the measurement invariance for gender are less conclusive and warrant future psychometric evaluations. This study opens avenues for future research on achievement motivation in Spanish-speaking contexts and populations.

The influence of Michigan leadership behaviors in organizational variables has been conducted in the Western context. However, limited studies have been conducted in the Vietnam context, particularly in the primary school sector. This study aims to examine the impact of Michigan leadership behaviors on organizational citizenship behaviors (OCBs) in Vietnamese primary schools. In addition, this research aims to challenge the mediating role of organizational justice (OJ) and person-organization fit (POF) in the relationship between Michigan leadership behaviors and OCBs. A total of 282 responses were collected from various primary schools in Vietnam. Exploratory factor analysis, confirmatory factor analysis, and linear regression analysis were used to answer the research question. Findings partially supported our hypotheses as two Michigan leadership dimensions significantly influence OCBs, and OJ mediates the relationship between relationship-oriented leadership behavior (ROL) and OCBs. This study opens the main gaps in the current leadership studies, including the influence of Michigan leadership behaviors on OCB, its role in promoting OJ, and the underlying mechanism through which Michigan leadership behaviors contribute to OCB with the mediation of OJ.

In this study, a valid and reliable scale (TSSSSS) was aimed to develop to measure secondary school students' perceptions of teacher support. The research was carried out on a total of 773 students studying in 6 different secondary schools in Ankara's Yenimahalle district in the Spring Term of the 2021-2022 Academic Year. The students who participated in the research voluntarily were divided into four study groups. The first study group consisted of 7th and 8 th grade students for the pre-application of the scale. The second study group and the third study group consisted of 5th, 6th, 7th, and 8 th grade students to determine the structure of the scale and verify the structure of the scale. The fourth study group consisted of students studying in the 7th and 8th grades of the school where the pre-application was made for the reliability study of the scale. The application form of the scale, which was created based on the literature review, was submitted to expert opinion for content and face validity and rearranged in line with the recommendations of the experts. As a result of the pre-application, some statements in the scale were changed based on the students' opinions. A structure with 36 items and four factors was gained with EFA, which explained 66.56% of the total variance. The factors of the scale were categorized under the names of emotional support (10 items), instructional support (5 items), guidance and orientation support (12 items), and problem-solving support (9 items). The structure of the scale was confirmed by CFA. Reliability coefficients obtained by Cronbach Alpha, composite reliability, and the test-retest method were examined for the reliability of the measurements related to the dimensions of the TSSSSS, and it was found that the reliability was quite high. The analyses carried out to determine the distinctiveness of the items on the scale revealed that all of the items were distinctive. In addition, as a result of examining the Pearson product-moment correlation coefficients calculated for the sub-dimensions of the TSSSSS after the item analysis, it was determined that the compatibility and the correlation between the dimensions of the scale were high. Based on these findings, it can be said that TSSSSS is a valid and reliable scale that can be used to measure secondary school students' perceptions of teacher support.

Unidimensionality is an important assumption of measurement but it is violated very often. Most of the time, tests are deliberately constructed to be multidimensional to cover all aspects of the intended construct. In such situations, the application of unidimensional item response theory (IRT) models is not justifieddue to poor model fit and misleading results. Multidimensional IRT (MIRT) models can handle several dimensions simultaneously and yield person ability parameters on several dimensions which is helpful for diagnostic purposes too. Furthermore, MIRT models use the correlation between the dimensions to enhance the precision of the measurement. In this study, a reading comprehension test is modeled with the multidimensional Rasch model. The findings showed that a correlated 2-dimensional model has the best fit to the data. The bifactor model revealed some interesting information about the structure of reading comprehension and the reading curriculum. Implications of the study for the testing and teaching of reading comprehension are discussed.

Cognitive diagnostic models (CDMs) have received sustained attention in educational settings because they can be used to operationalize formative assessment to provide diagnostic feedback and inform instruction. A large number of CDMs have been developed over the past few years. An important component of all CDMs is a Q-matrix that specifies a particular hypothesis about the relationship between each test item and its required attributes. The purpose of this study was to construct and validate a Q-matrix for the reading comprehension section of the Islamic Azad University English Proficiency Test (IAUEPT), as an advanced English placement test designed to measure the language ability of Ph.D. candidates who tend to pursue their studies in the IAU. To achieve this, using item responses of 1152 candidates to twenty items of the reading section of the test, an initial Q-matrix was constructed based on theories and models of second/foreign language (L2) reading comprehension, previous applications of CDMs on L2 reading comprehension, and brainstorming and consensus of five content experts. Then, the initial Q-matrix was empirically validated using the method proposed by de la Torre and Chiu (2016) and checking mesa plots, and heatmap plot. Five attributes were derived for the reading comprehension section: vocabulary, grammar, making an inference, understanding specific information, and identifying explicit information. Finally, the analysis of the Generalized Deterministic Inputs, Noisy "and" Gate (GDINA) regarding absolute fit at the item- and test-level as well as three residual-based statistics showed the accuracy of the Q-matrix and a perfect model-data fit.

As the theme of the 2022 annual meeting of the American Education Research Association, cultivating equitable education systems has gained renewed attention amid an increasingly diverse society. However, systemic inequalities persist for traditionally underserved student populations. As a way to better address diverse students' needs, it is of critical importance to understand different subgroups' performances. In the educational measurement field, evaluating the differences among multiple groups is an important consideration in addressing fairness issues for diverse groups of students. This article offers one technique to do so. It demonstrates how commonly-used multiple regression analysis can be applied to evaluate the equivalence of predictive structure across multiple groups in place of the factor analytic approach that requires a relatively large sample size per subgroup and strong assumptions. The technique is utilized in examining the relationship between English language proficiency and academic performance of English learners in one state when the subgroups are categorized by home language. The results showed statistically significant group differences between the reference group (Spanish-speaking ELs) and other focal groups (different home-language ELs) in various levels of comparisons (model fit, model structure, and individual predictor weights). The strengths and limitations of a proposed multiple group regression (MGR) approach are discussed in the educational research context.

Statistical process control (SPC) charts have been widely used in the field of educational measurement. The cumulative sum (CUSUM) is an established SPC method to detect aberrant responses for educational assessments. There are many studies that investigated the performance of CUSUM in different test settings. This paper describes the CUSUM procedure and shows how it can be used to monitor the testtaking process and detect aberrant responses. It aims to provide an accessible guide for the CUSUM control chart.

This study aimed to construct a path model on the factors influencing the mathematics achievement of Senior High School students. In particular, this study focuses on five subject components: Junior High School (JHS) math experience, JHS grade point average, attitudes toward mathematics, self-efficacy, and anxiety. The causal relationships and relative strengths of the direct and indirect relationships were explored for the sets of exogenous and endogenous variables using path analysis. The results unveiled an acceptable fit for the path model of the mathematics achievement in Senior High School. It indicated that students' mathematics achievement was influenced directly by JHS math general point average and mathematics anxiety; and indirectly by JHS math experience, attitudes, and self-efficacy. Moreover, the path model revealed that the JHS math grade point average exhibited the strongest influence on students' mathematics achievement. This suggests that a greater emphasis on developing basic mathematics skills and building a solid foundation at the Junior High School level leads to a better comprehension of higher mathematics.

Knowledge-based economy is an economic model students need to be prepared for a future economic model that uses knowledge as its main resource. Therefore, this study developed and validated instruments for constructing knowledge-based economy readiness among undergraduate students. This study used an online questionnaire with 120 respondents of economic education students in educational universities in East Java, Indonesia, for exploratory factor analysis and 417 respondents for confirmatory factor analysis. Then, statistical analysis was conducted using exploratory factor analysis in SPSS and confirmatory factor analysis in AMOS. This study first developed five factors of knowledge of economics, readiness for economic challenges, readiness for education, readiness for infrastructure, and readiness for innovation, consisting of 27 items. However, one item was removed because the loading factor was below 0.50. Consequently, 26 items were retained because the loading factor was significantly greater than 0.50. The Cronbach's alpha value for each item of the knowledge-based economy readiness construct was >0.60 and met all goodness of fit index criteria, which means that it meets the requirements and can measure the construct of knowledge-based economy readiness. Since this study meets the validity and reliability requirements of the constructs leading to knowledge-based economy readiness, these results will help students prepare for the current and future knowledge-based economy. They can be used in developing economic education curricula in higher education.

Despite the numerous endeavours made to develop questionnaires to assess learners' strategic behaviour in general and learning/use strategies across different language areas and skills, one can surprisingly find no inventories to address learners' grammar learning /use strategies. This study aims to validate a measure of additional language learners' grammar strategies. The validation of the instrument, grammar learning/use strategy questionnaire, involved three phases. To generate an item pool as the first phase, the researcher drew on Oxford's instructional modes of L2 learning, Oxford's strategic self-regulated model of learning, Larsen-Freeman's three-dimension model of grammar, grammar strategies in the inventories of language skills and Brigg's classroom task-based grammar learning strategies. The second phase of the study involved submitting the instrument to extensive piloting in an adequate sample, followed by the third phase, which comprised evaluating the psychometric properties of the revised instrument through exploratory and confirmatory factor analysis. The results indicate that the developed instrument has satisfactory psychometric characteristics and that the hypothesized theoretical model has a great fit with the data. The article closes with some tentative pedagogical recommendations and implications as well as guidelines on how grammar learning strategies could be utilized and investigated.

This study aims to analyze the identity components of graduates from the Faculty of Forestry at Kasetsart University using exploratory factor analysis and confirmatory factor analysis. Two sample groups were involved: Group 1 (400 individuals) for exploratory factor analysis and Group 2 (710 individuals) for confirmatory factor analysis. Both groups responded to a 5-point Likert scale questionnaire. The exploratory factor analysis results showed a strong Kaiser-Meyer-Olkin measure of .930 and Bartlett's Test of Sphericity with a chi-square value of 10437.275, 1770 degrees of freedom, and a significance level of .0001. Nine identity components were identified through factor rotation using the Varimax method: teamwork, commitment to task completion, application of knowledge, integrity towards oneself and others, adaptability to the environment, humility, willingness to help and share, non-egoism, and perseverance. The confirmatory factor analysis confirmed a good fit of the model to the observed data, indicated by indices such as p = 0.148, X[superscript 2]/df = 1.314, RMSEA = 0.02, GFI = 0.991, AGFI = 0.98, CFI = 0.998, and RMR = 0.097. Notably, "teamwork" had the highest standardized weight of 0.825, followed by "willingness to help and share" (0.749) and "commitment to task completion" (0.739). The standardized weights ranged from 0.825 to 0.636. All variables had statistically significant p-values, and the coefficients of determination (R[superscript 2]) ranged from 0.680 to 0.405 when measured by Square Multiple Correlation.

Among all, Cronbach's Alpha and McDonald's Omega are commonly used for reliability estimations. The alpha uses inter-item correlations while omega is based on a factor analysis result. This study uses simulated ordinal data sets to test whether the alpha and omega produce different estimates. Their performances were compared according to the sample size, number of items, and deviance from tau equivalence. Based on the result, the alpha and omega had similar results, except for the small sample size, the smaller number of items, and the low factor loading values. When there were 5 or more items in the scale and factor analysis which the omega was calculated from showed fit to the data set, using omega over alpha could be preferred. Also, as the number of items exceeds 5, the alpha and omega differences disappear. Since calculating the alpha is easier compared to the omega (omega requires fitting a factor model first) using alpha over omega can also be suggested. However, when the number of items and the correlations among the items were small, omega performed worse than alpha. Therefore, alpha should be used for the reliability estimations.

The Jefferson Scale of Empathy is one of the most commonly used scales in medical education to measure empathy. It is specific to the field of medical education and geared toward orienting medical students to physician empathy in patient care situations. The scale was transferred to the educational context in teacher education. In doing so, the questionnaire was reduced from the original 20 items to 9 because of content and methodological issues. A CFA showed good model-fit parameters for a three-factor model, and correlations with the German version of the Interpersonal reactivity Index were in line with the magnitudes reported in previous literature. In total, the JST-E scales show evidence for their factorial and convergent validity and their reliability. The JSE-T proves to be a good instrument for measuring empathy in educational contexts and thus closes the gap between trait measurement procedures such as the IRI and concrete-situational judgment tests, so that an economical, multidimensional testing of empathy becomes possible.

There is an expectation that health professions schools respond to priority societal health needs. This expectation is largely based on the underlying assumption that schools are aware of the priority needs in their communities. This paper demonstrates how open-access, pan-national health data can be used to create a reliable health index to assist schools in identifying societal needs and advance social accountability in health professions education. Using open-access data, a psychometric evaluation was conducted to examine the reliability and validity of the Canadian Health Indicators Framework (CHIF) conceptual model. A non-linear confirmatory factor analysis (CFA) on 67 health indicators, at the health-region level (n = 97) was used to assess the model fit of the hypothesized 10-factor model. Reliability analysis using McDonald's Omega were conducted, followed by Pearson's correlation coefficients. Findings from the non-linear CFA rejected the original conceptual model structure of the CHIF. Exploratory post hoc analyses were conducted using modification indices and parameter constraints to improve model fit. A final 5-factor multidimensional model demonstrated superior fit, reducing the number of indicators from 67 to 32. The 5-factors included: Health Conditions (8-indicators); Health Functions (6-indicators); Deaths (5-indicators); Non-Medical Health Determinants (7-indicators); and Community & Health System Characteristics (6-indicators). All factor loadings were statistically significant (p < 0.001) and demonstrated excellent internal consistency ([omega]>0.95). Many schools struggle to identify and measure socially accountable outcomes. The process highlighted in this paper and the indices developed serve as starting points to allow schools to leverage open-access data as an initial step in identifying societal needs.

Today, school bullying is considered an important social problem that causes developmental injuries and health issues in bullies and victims. The present study investigated the mediating role of brain-behavioural systems in relationships between school climate and emotional self-regulation with school bullying in high school students. This study had a cross-sectional correlational design. The sample size was 257, and the participants were selected using cluster sampling. They completed the Olweus Bully/Victim Questionnaire, Organizational Climate Index, Emotion Regulation Questionnaire, and Behavioural/Brain Systems Questionnaire. The data were analysed using the Pearson correlation coefficient and structural equation modelling (SEM). The findings revealed that the school bullying model based on the school climate and emotional self-regulation with the mediating role of brain-behavioural systems had a good fit. They also demonstrated that school climate had a direct negative effect on school bullying, whereas emotional self-regulation had a direct positive effect on it (p < 0.01). The school climate and emotional self-regulation were found to have a positive indirect effect on school bullying through the mediating role of brain-behavioural systems (p < 0.01). Therefore, it is necessary to develop plans to improve school climate, emotional self-regulation, and brain-behavioural systems to reduce school bullying.

Purpose: The purpose of this research was to develop and assess the psychometric properties of the Persian version of the Life Skills Scale for Physical Education (P-LSSPE). Method: During Study 1, which included four translators, eight physical education experts, and 45 physical education students, the LSSPE was translated and adapted into Persian, and its content validity was assessed. Study 2 assessed evidence for the factorial validity and reliability of the scale with a sample of 1,004 students. Study 3, which included 462 students, assessed nomological validity evidence. Results: In Study 1, the content validity analyses indicated that the P-LSSPE items and their dimensions were clear in language, practical in application, and represented the life skills in question. In Study 2, a bifactor confirmatory factor analysis model was the best representation of the data and provided evidence for the construct validity of the scale. In Study 3, evidence for the nomological validity of the P-LSSPE was provided, with the correlation coefficients indicating that teacher autonomy support was associated with students' development of all eight life skills and total life skills. Discussion/Conclusion: Overall, the findings of this research suggest that the P-LSSPE can be used to accurately measure the life skills development of Iranian physical education students.

Simple matrix sampling planned missing (SMS PD) design, introduce missing data patterns that lead to covariances between variables that are not jointly observed, and create difficulties for analyses other than mean and variance estimations. Based on prior research, we adopted a new multigroup confirmatory factor analysis (CFA) approach to handle missing data in such designs, in comparison to a regular CFA with full information maximum likelihood estimator. In Study 1, we tested the two approaches in 36 scenarios (4 sample sizes x 3 inter-item correlations x 3 numbers of x-set items) given a total of 20 items. We found that, the multigroup CFA approach performed with acceptable convergence rates, power to recover population values, acceptable standard errors and model fit in certain scenarios by larger sample size, higher bivariate correlation, and more items in the x-set. We found a few scenarios where regular CFA with FIML performed well. These findings suggested that the approaches can be implemented to handle the special missing data introduced by the SMS PM designs, and, thereby, enhance the utility of SMS PM data. In Study 2, we applied the multigroup CFA approach in real-world data to demonstrate the feasibility and analytic value of this approach.

The global COVID-19 health pandemic caused major interruptions to educational assessment systems, partially due to shifts to remote learning environments, entering the post-COVID educational world into one that is more open to heterogeneity in instructional and assessment modes for secondary students. In addition, in 2020, educational inequities were brought to the forefront of social conscience. The purpose of this study is to empirically explore how contextual (i.e., school-level) race and economic factors may relate to and explain measurement challenges that can arise during shifts to remote learning. We fit a series of multilevel models to explore school-level factors in assessment data alongside psychometric problems of differential item functioning and person fit in classroom assessment measurement models. Our results demonstrate ways in which our project's classroom assessments were impacted by shifts to remote learning, emphasizing the importance of researchers and practitioners evaluating such concerns when seeking validity evidence for interpretation of classroom assessment data.

The Evaluating the Quality of Instruction in Post-secondary Mathematics (EQIPM) is a 14-item instrument that uses videos of teaching to investigate the quality of instruction in college algebra courses taught at community colleges. We hypothesize that quality of instruction can be characterized along three distinct factors, the interactions between: (a) students and the content, (b) instructors and the content, and (c) students and the instructors. We used confirmatory factor analysis to empirically evaluate this hypothesis. Using coding of over 900 segments of video of instruction from 40 intermediate and college algebra teachers from eight community colleges, we performed a factor analysis to identify the structure of the instrument. Our findings suggest that the EQIPM instrument captures the three hypothesized underlying factors of quality of instruction with adequate fit statistics and factor loadings. The availability of this instrument opens the door for further research on aspects of quality that influence student performance and for promising programs of professional development for community college faculty teaching mathematics.

Cultural background can influence cognition, including executive functions (EFs), abilities that encompass skills responsible for self-regulation of thoughts and behavior. The seminal unity and diversity model of EFs proposes the existence, in adulthood, of at least three correlated but separable EF latent (shared variance in more than one task/indicator) domains: inhibition, updating and shifting. However, evidence of the cross-cultural generality of this framework is lacking, especially in adolescence, an age during which these domains become more clearly separable. We tested whether this EF fractionation could be observed in early adolescents (9- to 15-year-olds) from metropolitan areas in Brazil (São Paulo) and Iran (Tehran) (total sample: 739; 407 Iranians; 358 girls). Participants carried out two open-access tasks that are representative of each EF domain and that were adapted to each cultural context. Seven latent model configurations were tested. The three-correlated latent factor structure had adequate fit, and multiple-group confirmatory factor analysis invariance testing showed invariance for country at the level of the latent factor structure (configural), factor loadings (metric), and partial invariance at the intercept (scalar) level. Iranians had higher scores in all domains. Multiple indicators multiple causes invariance testing showed model invariance across age (except for one task) and parental education. Performance in all domains improved with age and only minimally with parental schooling. We conclude that EF fractionation into three domains is present in the first half of adolescence in two samples from underrepresented populations in the literature, suggesting a potential generality of EF latent unity/diversity development at this age.

The purpose of this article is to discuss reliability and precision through the lens of a modern measurement approach, item response theory (IRT). Reliability evidence in the field of counseling is primarily generated using Classical Test Theory (CTT) approaches, although recent studies in the field of counseling have shown the benefits of using IRT approaches to explore measurement precision. We discuss the theoretical foundations and assumptions of CTT and IRT, and examine how modern measurement theory (i.e. IRT) poses advantages for capturing measurement precision. We use an example analysis to demonstrate the indices of measurement precision in IRT approaches (e.g. standard errors, person-fit, item-fit, targeting, residual-based fit statistics). Finally, we discuss practical and clinical implications for counseling researchers for using IRT approaches to measure precision, including insights into precision for persons, items, and measurement invariance as well as the utility of brief and adaptive scales.

Purpose: This study investigated the reading profiles of middle school Spanish-speaking emergent bilinguals (EBs) with significantly below grade level reading comprehension and whether these profiles varied in their reading comprehension performance over time. Method: Latent profile analyses were used to classify Grade 6 and 7 Hispanic EBs (n = 340; 39% female) into subgroups based on their word reading and vocabulary knowledge. Growth models were then fit within each profile to evaluate reading comprehension performance over time. Results: Analyses revealed four latent profiles emerged: (a) very low word reading and low vocabulary (10%), (b) low word reading and low vocabulary (71%), (c) average word reading and low vocabulary (16%), and (d) high word reading and low vocabulary (3%). Subgroups varied in their reading comprehension initially and over one year. Students in the subgroup marked by very low word reading and low vocabulary showed the lowest reading comprehension performance initially; however, they also showed the greatest growth over one year. Conclusion: These findings suggest that there is heterogeneity in the reading skill profiles of Spanish-speaking EBs with reading comprehension difficulties. They also underscore the prevalence of word reading difficulties among these students. These may be important factors to consider when developing interventions to prevent and remediate these difficulties.

Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla-Wagner error-correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla-Wagner rule by using it to explain the behavior of participants across a range of tasks, we focus on testing predictions generated by the model in a controlled natural language learning task and model the data at the level of the individual learner. By adjusting the parameters of the model to fit the trial-by-trial behavioral choices of participants, rather than fitting a one-for-all model using a single set of default parameters, we show that the model accurately captures participants' choices, time latencies, and levels of response agreement. We also show that gender and working memory capacity affect the extent to which the Rescorla-Wagner model captures language learning.

The current study sought out to advance the Social Self-Efficacy and Social Outcome Expectations scale using multiple approaches to scale development. Data from 583 undergraduate students were used in two scale development approaches: Classic Test Theory (CTT) and Item Response Theory (IRT). Confirmatory factor analysis suggested a 2-factor structure that aligns with the theoretically based domains for SEOES items and supports previously proposed models of this scale from CTT and psychometric analyses. The IRT analysis indicated that the SEOES items have greater measurement precision at measuring lower levels of the latent constructs. Future research directions are provided and practice implications are discussed.

Confirmatory factor analyses (CFA) are often used in psychological research when developing measurement models for psychological constructs. Evaluating CFA model fit can be quite challenging, as tests for exact model fit may focus on negligible deviances, while fit indices cannot be interpreted absolutely without specifying thresholds or cutoffs. In this study, we review how model fit in CFA is evaluated in psychological research using fit indices and compare the reported values with established cutoff rules. For this, we collected data on all CFA models in "Psychological Assessment" from the years 2015 to 2020 (N[subscript Studies]=221). In addition, we reevaluate model fit with newly developed methods that derive fit index cutoffs that are tailored to the respective measurement model and the data characteristics at hand. The results of our review indicate that the model fit in many studies has to be seen critically, especially with regard to the usually imposed independent clusters constraints. In addition, many studies do not fully report all results that are necessary to re-evaluate model fit. We discuss these findings against new developments in model fit evaluation and methods for specification search.

Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a factor-analysis model; as such, the accuracy of omega estimates is likely to depend on correct model specification. The current paper presents a simulation study to investigate the performance of omega-unidimensional (based on the parameters of a one-factor model) and omega-hierarchical (based on a bifactor model) under correct and incorrect model misspecification for high and low reliability composites and different scale lengths. Our results show that coefficient omega estimates are unbiased when calculated from the parameter estimates of a properly specified model. However, omega-unidimensional produced positively biased estimates when the population model was characterized by unmodeled error correlations or multidimensionality, whereas omega-hierarchical was only slightly biased when the population model was either a one-factor model with correlated errors or a higher-order model. These biases were higher when population reliability was lower and increased with scale length. Researchers should carefully evaluate the feasibility of a one-factor model before estimating and reporting omega-unidimensional.

Multiple imputation (MI) is one of the recommended techniques for handling missing data in ordinal factor analysis models. However, methods for computing MI-based fit indices under ordinal factor analysis models have yet to be developed. In this short note, we introduced the methods of using the standardized root mean squared residual (SRMR) and the root mean square error of approximation (RMSEA) to assess the fit of ordinal factor analysis models with multiply imputed data. Specifically, we described the procedure for computing the MI-based sample estimates and constructing the confidence intervals. Simulation results showed that the proposed methods could yield sufficiently accurate point and interval estimates for both SRMR and RMSEA, especially in conditions with larger sample sizes, less missing data, more response categories, and higher degrees of misfit. Based on the findings, implications and recommendations were discussed.

Cognitive diagnostic assessment (CDA) intends to identify learners' strengths and weaknesses in latent cognitive attributes to provide personalized remedial instructions. Previous CDA studies on English as a Foreign Language (EFL)/English as a Second Language (ESL) writing have adopted dichotomous cognitive diagnostic models (CDMs) to analyze data from checklists using simple yes/no judgments. Compared to descriptors with multiple levels, descriptors with only yes/no judgments were considered too absolute, potentially resulting in misjudgment of learners' writing ability. However, few studies have used polytomous CDMs to analyze graded response data from rating scales to diagnose writing ability. This study applied polytomous CDMs to diagnose 1166 EFL learners' writing performance scored with a three-level rating scale. The sG-DINA model was selected after comparing model-data fit statistics of multiple polytomous CDMs. The results of classification accuracy indices and item discrimination indices further demonstrated that sG-DINA had good performance on identifying learners' strengths and weaknesses. The generated diagnostic information at group and individual levels was further synthesized into a personalized diagnostic report, although its usefulness still requires further investigation. The findings provided evidence for the feasibility of applying polytomous CDM in EFL writing assessment.

This study sought to develop and validate an instrument to measure teacher professional identity of student teachers. Through a literature review, three indicators, namely teacher self-efficacy, commitment to teaching and professional orientation, were chosen to form the construct for validation. The factor structure was first tested through confirmatory factor analysis using a sample of 185 student teachers and cross-validated using a new sample of 412. Evidence from goodness-of-fit statistics, parameter estimates and internal consistency indicated that the three indicators are valid factors of the construct. The instrument can provide information to better meet the developmental needs of student teachers.

Applications of polytomous IRT models in applied fields (e.g., health, education, psychology) are abound. However, little is known about the impact of the number of categories and sample size requirements for precise parameter recovery. In a simulation study, we investigated the impact of the number of response categories and required sample size for successful parameter recovery and also examined model fit. Our results suggested that sample size requirements of approximately 250 or larger are typically sufficient for reasonable recovery of either person or item parameters (in particular discrimination). Further, we found that in many cases, the conditions of 10 items yielded more favorable results than those conditions when only 5 items were considered. With respect to the number of response categories, results were inconsistent, suggesting more complex recommendations in selecting the number of categories when developing scales. We discuss limitations of the current study, identify future directions, and provide recommendations for applied researchers who engage in scale development.

Studies have demonstrated that task-evoked pupillary responses (TEPRs) can be adopted to measure the examinee's cognitive load. This study compared three approaches for the measurement of TEPRs, mean pupil diameter, mean pupil dilation, and mean percentage of pupil dilation, to determine the best-fit measuring method. The valid participants of this study were eight sixth-grade elementary students. The experimental materials used were two mathematics questions with differing difficulty. The generalized estimating equation (GEE) was employed to compare the goodness of fit of each approach. The results revealed that the measurement of TEPRs based on the mean percentage of pupil dilation measured every 4 s provided the best fit.

With the development of artificial intelligence (AI) applications, it has become critical for scholars, educators and practitioners to understand an individual's perceived self-efficacy regarding the use of AI technologies/products. Understanding users' subsequent behaviors toward the advancement of AI technology is also critical. Despite the growing focus on AI, a suitable scale for measuring AI self-efficacy (AISE) has yet to be developed. Current scales for measuring AISE (i.e., technology self-efficacy scales) are considered inapplicable because they neglect to evaluate perceptions of specific AI characteristics (e.g., AI-based configuration or anthropomorphic design). Given the limitations of existing self-evaluation and diagnostic instruments, the aim of this research is to investigate the construct of AISE, and develop and validate an AISE scale (AISES) for measuring an individual's perceived self-efficacy in regard to the use of AI technologies/products, in accordance with established exploratory and confirmatory scale development procedures. Specifically, a literature review is employed to generate initial items. An exploratory factor analysis is then performed for item purification purposes. At this stage, potential elements of AISE are extracted. Subsequently, factor extraction and confirmatory factor analysis are used to verify the construct structure of AISE. An analysis of 314 responses indicates that the AISE construct contains four factors: assistance, anthropomorphic interaction, comfort with AI, and technological skills. The scale is comprised of 22 items, and is found to have good fit, reliability, convergent validity, discriminant validity, content validity, and criterion-related validity. Moreover, nomological validity is built by the positive correlation between the AISE construct and motivated learning behaviors. This paper is the pioneer in developing and validating a scale to measure AISE. The findings extend existing knowledge of AISE and can help scholars further develop AISE theories. Our findings will also help educators and practitioners assess individuals' AISE and explore related behaviors.