Search

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

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.

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.

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.

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.

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.

The Meaningful Learning in the Laboratory Instrument (MLLI) was designed to measure students' expectations before and after their laboratory courses and experiences. Although the MLLI has been used in various studies and laboratory environments to investigate students' cognitive and affective laboratory expectations, the authors of the instrument reported a discrepancy between the intended factor structure of the MLLI and the factor structure suggested by the data collected in preliminary studies. Therefore, the aim of this study was to investigate the validity and reliability evidence related to data collected with the MLLI, especially that related to structural validity. Evidence to support structural validity would provide greater meaning for the reporting and interpretation of MLLI scores. In this study, two possible "a priori" models for the factor structure of data collected from multiple institutions with the MLLI were investigated using confirmatory factory analysis (CFA). This initial investigation found poor data-model fit for each of the two tested models. Cognitive interviews and free response items were then used to inform modifications to the two "a priori structures," and a third alternative structure, which included a negative method factor, was also investigated. Once a best fitting model was identified, further model revisions were informed by a combination of modification indices and qualitative data. Evidence of adequate-to-good data model fit was found for the final revised version of the MLLI, deemed the MLLIv2. Additionally, evidence of both internal structure validity and single administration reliability were found for each of the MLLIv2 factors. The structure of the data from these items leads to scale scores that likely represent student expectations that contribute to meaningful learning and student expectations that detract from meaningful learning. As the results of this study provide the first psychometrically supported scales for MLLI data, they have implications on the future reporting and analyses of MLLI scores.

The Eco-Generativity Scale (EGS) is a recently developed 28-item scale derived from a 4-factor higher-order model (ecological generativity, social generativity, environmental identity, and agency/pathways). The aim of this study was to develop a short-scale version of the EGS to facilitate its use with university students (N = 779) who will determine the future of our world's ecosystem. Data analyses included removing misfitting items and assessing the psychometric properties of the EGS short form. The Eco-Generativity Scale-Short Form (EGS-SF) showed a good fit for a higher-order model composed of four factors and sixteen items (four items for each factor).

We test children's distributive and collective sentence interpretations and the variables that predict them. In our first experiment, we establish that adult English collective sentences with "the" or "some" in the subject are categorically collective in their interpretations. We further demonstrate that children's collective and distributive interpretations are predicted by an independent measure of lexical growth, consistent with the lexical refraction hypothesis, and that their collective interpretations are predicted by their distributive interpretations, consistent with the pragmatic scale hypothesis. Furthermore, the distributive interpretations produce complete mediation between lexicon and collective interpretations in a mediation analysis. In our second experiment, we take independent measures of Spanish-speaking children's knowledge of the Approximate Number System, inhibition, lexicon and phrasal syntax. We then fit a Piecewise Structural Equation Model with these variables, with high statistical power and showing high goodness-of-fit. We consider the contrast between this model of collective interpretations, which are putatively conversational implicatures, and quantity implicature models from previous studies.

Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter--a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its own terms as a measure of absolute model fit and allows for comparing different models fit to the same data. By estimating the degree of misspecification simultaneously with structural parameters, the uncertainty about structural parameters reflects the degree of model misspecification. This results in a model that produces more reliable inference than extant Bayesian structural equation modeling. In addition, the approach estimates the residual covariance matrix that can be the basis for diagnosing misspecifications and updating a hypothesized model. These features are confirmed using simulation studies. Demonstrations with a variety of real-world examples show additional properties of the approach.

In an attempt to measure the dynamic features of foreign language enjoyment (FLE), we adopted a longitudinal confirmatory factor analysis-curve of factors model (LCFA-CFM) approach to substantiate the validity of the original FLE scale (Dewaele and MacIntyre [(2014. "The Two Faces of Janus? Anxiety and Enjoyment in the Foreign Language Classroom." "Studies in Second Language Learning and Teaching" 4 (2): 237-274]) longitudinally and trace changes in this construct at different points of time in an English as a foreign language (EFL) course. This approach ensures measurement invariance over time and deals with second order latent variables. The data were collected from 437 adult EFL learners in four time phases using FLES and were analyzed by Mplus with LCFA-CFM. The model fit was accepted and the invariance of the latent factor was confirmed over time. The negative covariance between slope and intercept (second order latent variables) suggested that learners with lower initial FLE experienced a faster increase in FLE over time, as the mean of slope was larger than that of the intercept, which can be influenced by learner's motivation, changing attitude to L2 learning and the supportive role of the teacher. The key strength of this research lies in the longitudinal confirmatory factor analysis performed to capture the temporal growth of FLE and how it evolved through the L2 course.

C-Test is a gap-filling test designed to measure first and second language proficiency. Over the past four decades, researchers have shown the fit of C-Test data to parametric item response theory (IRT) models, but no study so far has shown the fit of C-Tests to nonparametric IRT models. The purpose of this study is to contribute to the ongoing C-Test validation project by providing evidence of fit to the Mokken scale analysis as a widely used nonparametric IRT model. A six-passage C-Test battery was analyzed using the monotone homogeneity model and the double monotonicity model of Mokken. Unidimensionality was evaluated using the automatic item selection procedure. Findings showed that the C-Test passages form a strong unidimensional scale, fit well to the monotone homogeneity model, and, after deleting one item, have a sufficient fit to the double monotonicity model. The findings also indicated that the items form a hierarchy, and persons can be located on an ordinal scale using their C-Test sum scores. Implications of the study for C-Test validity and application are discussed.

This study aimed to investigate the reliability and validity of the Childhood Career Development Scale (CCDS) on a sample of 1018 Iranian elementary school students. The Confirmatory factor analysis revealed that the original eight-factor model was of a good fit. Measurement invariance analyses indicated equivalence across gender and grade. A factorial MANOVA was applied to assess gender and grade mean differences in CCDS subscales. Concurrent validity of the CCDS subscales with the measures of Locus of Control and the children's Industry was confirmed. The CCDS Persian version proved to have good psychometric indexes for assessing children's career development.

This study sought to propose the Undesirable Behavior Strategies Questionnaire--Physical Education (UBSQ-PE), based on Doyle, W. (1977). Paradigms for research on teacher effectiveness. "Review of Research in Education," 5(1), 163-198. classroom ecology paradigm, and validate its use within Physical Education. 289 physical educators participated in the study (80 of them in the pilot study) and completed a survey evaluating the strategies they used across the classroom ecology task systems. An exploratory factor analysis proposed a four-factor solution that measured teachers' strategies for "Differentiation of Instruction, Interaction for Students, Classroom Management," and "Interaction with Students" and explained a cumulative 47.45% of the total explained variance (KMO = 0.809, [chi-squared subscript 231] = 1,266, 684, p < 0.001). A confirmatory factor analysis indicated a good fit of the data to the four-factor solution ([chi-squared]/df ratio = 1.52, comparative fit index = 0.906, Tucker Lewis index = 0.890, standardized root mean square residual = 0.079, root mean square error of approximation = 0.05), providing evidence for the instrument's internal consistency. Multiple analyses of variance showed that there were differences in teachers' strategies based on their gender, age, studies, professional knowledge, and school level. The use of UBSQ-PE asserts that teachers use undesirable behavior (UB) strategies according to their personal and professional characteristics. Teacher education programs could use this information for supporting PE teachers to adjust their UB strategies in response to each classroom's interrelated task systems.

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.

The commonly used Vineland Adaptive Behavior Scales (VABS-3) divides adaptive behavior into three domains comprising three subdomains. The validity of this three-factor structure has not been explored in autistic samples, which are often heterogeneous with respect to language and IQ. Furthermore, although there are two comparable forms, Comprehensive Interview Form and Comprehensive Parent/Caregiver Form (a questionnaire), the original validation was based on interview data. Considering the widespread use of the VABS-3 in autism research, and the increased feasibility of online-administered questionnaires, it is necessary to establish the validity of the Parent/Caregiver form in autistic individuals across a range of abilities. This study aimed to investigate the measurement invariance of the VABS-3 Parent/Caregiver form between a minimally verbal group and verbal group of autistic people; however, poor overall fit of the three-factor structure precluded invariance analyses. Subsequent analyses suggested poor fit in both language and age groups, as well as a unidimensional model. The results of the current study suggest that neither the three-factor or unidimensional model fit the VABS-3 Parent/Caregiver Form, thereby cautioning against interpretation of domain or overall adaptive behavior composite scores in autistic individuals and further encouraging careful consideration of administration format.

Background: The positive youth development (PYD) framework orients developmental scholarship to focusing on youth's strengths rather than deficits, however, validity evidence on PYD measures among ethnic-minority youth is limited. Objectives: The objectives of the current study were to (a) examine the factor structure of a PYD measure within a Latin American adolescent sample and (b) test associations between PYD constructs and adjustment indicators. Methods: The sample included 288 Latin American students from the Southwest (M[subscript age] = 13.69 years, SD = 0.56; 47.7 female; 86.3% U.S.-born). Participants reported their PYD (i.e., Competence, Confidence, Character, Connection, and Caring) and academic performance. Mothers reported on youth's internalizing and externalizing behaviors. The internal structure of the PYD measure was assessed through confirmatory factor analyses (CFAs) and model comparisons. Criterion evidence was evaluated via regressions between the Five C's, academic performance, internalizing, and externalizing behaviors. Results: The five-correlated factor structure best fit the data with certain modifications. Character was negatively associated with externalizing behavior. All C's were positively related to academic performance. Conclusions: The results provide empirical support for the five-correlated factor model within a sample of Latin American adolescents, but modifications were necessary to maximize model fit. Dropping items within the "values diversity" subscale highlight the need for cultural assets within PYD measurement models for ethnic-minority youth, to best reflect their appraisals of diversity as minoritized individuals. Additionally, our findings suggest that the C's are salient to specific indices of Latin American adolescents' adjustment.

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.

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.

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.

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.

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.

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.

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.

Testing programs have explored the use of technology-enhanced items alongside traditional item types (e.g., multiple-choice and constructed-response items) as measurement evidence of latent constructs modeled with item response theory (IRT). In this report, we discuss considerations in applying IRT models to a particular type of adaptive testlet referred to as a branching item. Under the branching format, all test takers are assigned to a common question, and the assignment of the next question relies on the response to the first question through deterministic rules. In addition, the items at both stages are scored together as one polytomous item. Real and simulated examples are provided to discuss challenges in applying IRT models to branching items. We find that model-data misfit is likely to occur when branching items are scored as polytomous items and modeled with the generalized partial credit model and that the relationship between the discrimination of the routing component and the discriminations of the subsequent components seemed to drive the misfit. We conclude with lessons learned and provide suggested guidelines and considerations for operationalizing the use of branching items in future assessments.

Logistic regression (LoR) is a foundational supervised machine learning algorithm and yet, unlike linear regression, appears rarely taught early on, where analogy and proximity to linear regression would be an advantage. A random sample of 50 syllabi from undergraduate business statistics courses shows only two percent of the courses included LoR. Conceivable reasons for this dearth of LoR content is likely related to topic complexity, time constraints, and varying degrees of tool ease of use and support. We propose that these constraints can be countered by: [1] introducing logistic regression early, [2] informed tool selection prioritizing ease of use with comprehensive output, and [3] using/developing innovative, accessible, and easy to understand concept learning aids. This approach would leverage the proximity to linear regression and probability readily embed distributed practice for student understanding of a foundational technique.

A C-Test is a gap-filling test for measuring language competence in the first and second language. C-Tests are usually analyzed with polytomous Rasch models by considering each passage as a super-item or testlet. This strategy helps overcome the local dependence inherent in C-Test gaps. However, there is little research on the best polytomous Rasch model for C-Tests. In this study, the Rating Scale Model (RSM) and the Partial Credit Model (PCM) for analyzing C-Tests were compared. To this end, a C-Test composed of six passages with both RSM and PCM was analyzed. The models were compared in terms of overall fit, individual item fit, dimensionality, test targeting, and reliability. Findings showed that, although the PCM has a better overall fit compared to the RSM, both models produce similar test statistics. In light of the findings of the study, the choice of the best Rasch model for C-Tests will be discussed.

Globally, online course enrollments have grown, and English is often used as a lingua franca for instruction. The Community of Inquiry (CoI) framework can inform the creation of more supportive, interaction-rich online learning environments. However, the framework and its accompanying validated instrument were created in North America, limiting researchers' ability to use the instrument in courses where participants have varying levels of English language proficiency. We revised the CoI instrument so it could be more easily read and understood by individuals whose native language is not English. Using exploratory and confirmatory factor analyses (EFA and CFA) on data obtained from global online courses and MOOCs, we found the revised instrument had good fit statistics once seven items were removed. This study expands the usability of the CoI instrument beyond the original and translated versions, and provides an example of adapting and validating an existing instrument for global courses.

This study used a combined person- and variable-centered approach to identify self-regulated online learning latent profiles and examine their relationships with the predicted and earned course grades. College students (N=177) at a Southeastern U.S. university responded to the Online Self-Regulated Learning Questionnaire. Exploratory structural equation modeling revealed four self-regulation factors: goal setting, environment management, peer help-seeking, and task strategies. Latent profile analysis yielded four latent profiles: Below Average Self-Regulation (BASR), Average Self-Regulation (ASR), Above Average Self-Regulation (AASR), and Low Peer Help-Seeking (LPHS). Compared with the AASR group, when students anticipated obtaining a higher course grade, they were less likely to engage in peer help-seeking and task strategies and more likely to adopt the LPHS self-regulation profile. Relating to LPHS, membership to all other groups predicted significantly lower course grades. AASR and LPHS predicted their performance most accurately, with non-significant differences between the predicted and the final course grades.

Social desirability bias (SDB) has been a major concern in educational and psychological assessments when measuring latent variables because it has the potential to introduce measurement error and bias in assessments. Person-fit indices can detect bias in the form of misfitted response vectors. The objective of this study was to compare the performance of 14 person-fit indices to identify SDB in simulated responses. The area under the curve (AUC) of receiver operating characteristic (ROC) curve analysis was computed to evaluate the predictive power of these statistics. The findings showed that the agreement statistic (A) outperformed all other person-fit indices, while the disagreement statistic (D), dependability statistic (E), and the number of Guttman errors (G) also demonstrated high AUCs to detect SDB. Recommendations for practitioners to use these fit indices are provided.

Any time examinees have had access to items and/or answers prior to taking a test, the fairness of the test and validity of test score interpretations are threatened. Therefore, there is a high demand for procedures to detect both compromised items (CI) and examinees with preknowledge (EWP). In this article, we develop a procedure that uses item scores and distractors to simultaneously detect CI and EWP. The false positive rate and true positive rate are evaluated for both items and examinees using detailed simulations. A real data example is also provided using data from an information technology certification exam.

This study aims to develop a standard scale by making the validity and reliability of the globalization survey developed for education faculties in the sample of sports sciences. A total of 878 people, including 81 academicians, 336 physical education and sports teachers, 118 trainers, and 343 sports sciences students, who were reached by a simple random method, participated in the study. The data was collected at 3 different times, Exploratory Factor Analysis (EFA) in the first group (n = 426), Confirmatory Factor Analysis (CFA) in the second group (n = 412), and test-retest analysis in the third group (n = 40) was performed. As a result of EFA, which was conducted to determine the construct validity, a structure consisting of 17 items and 3 factors was obtained, which explains 71.588% of the total variance. These factors are; teaching-learning processes are named as management and technology. The Cronbach Alpha internal consistency coefficient of the scale was calculated as .94. According to the CFA results (X[superscript2] / df: 2.63;; RMSEA: 0.06; PGFI: 0.69; PNFI: 0.79; GFI: 0.92; AGFI: 0.90; IFI: 0.96; TLI: 0.95; NFI: 0.94; CFI: 0.96) the scale has acceptable fit values. In the test-retest analysis, a positive correlation was found 0.83. As a result, it can be said that the globalization scale can be used in sports science research and is a valid and reliable scale.

Readiness is important for the success of the e-learning process. The purpose of this study was twofold: to develop a scale to measure K-12 teachers' e-learning readiness, and to examine their readiness to teach online. The participants were 3,295 K-12 teachers working in Izmir, Turkey. First EFA, then CFA-SEM was performed. Additionally, teachers' e-readiness in terms of gender, years of service, school level, and daily device usage time were examined. Teachers are ready for e-learning considering their overall scores. A significant difference was found in favor of males in the "technical competence" factor and in favor of females in the "colleague, content, and pedagogical and ethical competence factors". The readiness of younger teachers is generally higher. On a factor basis, there is only a significant difference in the factors of computer self-efficacy and student readiness according to educational level. As the use of devices increases, technology-related readiness increases. The readiness of teachers plays an important role in determining future strategies, measures, and interventions that need to be taken to advance e-learning.