Search

In the research reported here, we propose a new method for scale alignment and test scoring in the context of supporting students with disabilities. In educational assessment, students from these special populations take modified tests because of a demonstrated disability that requires more assistance than standard testing accommodation. Updated federal education legislation and guidance require that these students be assessed and included in state education accountability systems, and their achievement reported with respect to the same rigorous content and achievement standards that the state adopted. Routine item calibration and linking methods are not feasible because the size of these special populations tends to be small. We develop a unified cross-classified random effects model that utilizes item response data from the general population as well as judge-provided data from subject matter experts in order to obtain revised item parameter estimates for use in scoring modified tests. We extend the Metropolis-Hastings Robbins-Monro algorithm to estimate the parameters of this model. The proposed method is applied to Braille test forms in a large operational multistate English language proficiency assessment program. Our work not only allows a broader range of modifications that is routinely considered in large-scale educational assessments but also directly incorporates the input from subject matter experts who work directly with the students needing support. Their structured and informed feedback deserves more attention from the psychometric community.

The multiple imputation two-stage (MI2S) approach holds promise for evaluating the model fit of structural equation models for ordinal variables with multiply imputed data. However, previous studies only examined the performance of MI2S-based residual-based test statistics. This study extends previous research by examining the performance of two alternative test statistics: the mean-adjusted test statistic (T M ) and the mean- and variance-adjusted test statistic (T MV ). Our results showed that the MI2S-based T MV generally outperformed other test statistics examined in a wide range of conditions. The MI2S-based root mean square error of approximation also exhibited good performance. This article demonstrates the MI2S approach with an empirical data set and provides Mplus and R code for its implementation. [ABSTRACT FROM AUTHOR]

Advanced modeling of item response data through the item response theory (IRT) or item factor analysis frameworks is becoming increasingly popular. In the social and behavioral sciences, the underlying structure of tests/assessments is often multidimensional (i.e., more than 1 latent variable/construct is represented in the items). This review provides an introduction and overview of flexMIRT 3.5, a commercially available statistical software program for the analysis of item response data that was developed specifically for fitting such complex models. Three models are used as motivating examples: (a) a bifactor model, (b) a multidimensional IRT (MIRT) model with correlated factors and (c) a diagnostic classification model with testlet structure. Discussion regarding flexMIRT input syntax and output files is provided and the topics of model fit evaluation, limited-information fit statistics in particular, and specifics of more recently introduced estimation methods and models are also covered.

Background: Alcohol withdrawal is widely recognized as a trigger for acute symptomatic seizures among individuals with chronic alcohol consumption. While most alcohol withdrawal seizures occur shortly after cessation, chronic alcohol consumption can be associated with the development of epilepsy, necessitating anti‐epileptic drug (AED) therapy. This study aimed to investigate the clinical characteristics, seizure recurrence, and epilepsy development in patients with alcohol‐related seizures and to identify prognostic factors for epilepsy. Methods: In a retrospective analysis at Ewha Womans University Mokdong Hospital, 206 patients with alcohol‐related seizures were examined and 15 were excluded due to preexisting epilepsy. Demographic and clinical data, including alcohol withdrawal duration, seizure recurrence, types, and comorbidities, were investigated. Logistic regression models were used to analyze the risk factors for seizure recurrence and epilepsy development. The performance of the final models was evaluated based on the area under the receiver operating characteristic curve (AUC) and validated using calibration plots and leave‐one‐out cross‐validation. Results: Of the 191 patients (146 males; mean age 48.3 ± 12.1 years) with alcohol‐related seizures, 99 patients (51.8%) experienced seizure recurrence and 79 patients (41.4%) developed epilepsy. Factors associated with seizure recurrence included alcohol consumption levels, occurrence of focal impaired awareness seizure, anxiety, and headache. The number of recurrent seizures, semiology, status epilepticus, electroencephalogram findings, and brain imaging findings was associated with epilepsy development. The predictive models showed strong diagnostic performance, with AUCs of 0.833 for seizure recurrence and 0.939 for epilepsy development. Conclusion: High alcohol consumption and specific clinical and diagnostic features are significant predictors of seizure recurrence and the development of epilepsy among patients with alcohol‐related seizures. These findings underscore the importance of early identification and intervention to prevent seizure recurrence and the onset of epilepsy, emphasizing the importance of AED treatment in managing these conditions. [ABSTRACT FROM AUTHOR]

In this study, we introduced a cross‐classified multidimensional nominal response model (CC‐MNRM) to account for various response styles (RS) in the presence of cross‐classified data. The proposed model allows slopes to vary across items and can explore impacts of observed covariates on latent constructs. We applied a recently developed variant of the Metropolis‐Hastings Robbins‐Monro (MH‐RM) algorithm to address the computational challenge of estimating the proposed model. To demonstrate our new approach, we analyzed empirical student evaluation of teaching (SET) data collected from a large public university with three models: a CC‐MNRM with RS, a CC‐MNRM with no RS, and a multilevel MNRM with RS. Results indicated that the three models led to different inferences regarding the observed covariates. Additionally, in the example, ignoring/incorporating RS led to changes in student substantive scores, while the instructor substantive scores were less impacted. Misspecifying the cross‐classified data structure resulted in apparent changes on instructor scores. To further evaluate the proposed modeling approach, we conducted a preliminary simulation study and observed good parameter and score recovery. We concluded this study with discussions of limitations and future research directions. [ABSTRACT FROM AUTHOR]

The article focuses on Minnesota most divorce records are public documents, accessible to anyone with an internet connection. It mentions advent of Minnesota Court Records Online (MCRO), court files are now accessible in everyone's living room and Supreme Court has repeatedly disagreed, recognizing the private and personal nature of marriage and family relationships. It also mentions public interest in disclosure is outweighed by a legitimate or overriding interest in confidentiality.

Nonlinear mixed-effects models (NLMEMs) allow researchers to model curvilinear patterns of growth, but there is ambiguity as to what functional form the data follow. Often, researchers fit multiple nonlinear functions to data and use model selection criteria to decide which functional form fits the data "best." Frequently used model selection criteria only account for the number of parameters in a model but overlook the complexity of intrinsically nonlinear functional forms. This can lead to overfitting and hinder the generalizability and reproducibility of results. The primary goal of this study was to evaluate the performance of eight model selection criteria via a Monte Carlo simulation study and assess under what conditions these criteria are sensitive to model overfitting as it relates to functional form complexity. Results highlighted criteria with the potential to capture overfitting for intrinsically nonlinear functional forms for NLMEMs. Information criteria and the stochastic information complexity criterion recovered the true model more often than the average or conditional concordance correlation. Results also suggest that the amount of residual variance and sample size have an impact on model selection for NLMEMs. Implications for future research and recommendations for application are also provided. [ABSTRACT FROM AUTHOR]

Social science research often utilizes measurement instruments that generate ordinal data (e.g., Likert scales). Many empirical studies also face the challenge of missing data, which can be addressed by performing multiple imputation followed by analyses of the imputed datasets. However, when missing data exist on ordinal variables, there has been limited research on how to evaluate model fit of structural equation models for ordinal variables. Recent studies suggest that two multiple-imputation-based approaches show great promise: The D2 procedure, and the Multiple Imputation Two-step (MI2S) approach, though the two have not been systematically compared. This study extends previous research by comparing the D2 with the MI2S fit statistics in a wider range of conditions than previous studies. Our findings revealed a number of factors that can influence the performance of these test statistics. [ABSTRACT FROM AUTHOR]

Objectives: a) To evaluate the item and scale properties of the Oral Health Literacy Adults Questionnaire (OHL‐AQ) in an adult general population. b) To determine precision or accuracy of the respondents' estimated scores along the Oral Health Literacy (OHL) spectrum using item response theory (IRT) modeling. Methods: Survey data were collected from a convenience sample of 405 adult attendees of the 2014 Minnesota State Fair. We used the two‐parameter logistic (2PL) model for the item response theory (IRT) analyses of OHL‐AQ data and calibrated items to estimate model‐based item difficulty and discrimination parameters. Item and scale properties were also assessed by plotting and interpreting item characteristic curves (ICCs), test characteristic curve (TCC), and test information function (TIF). Results: Based on interpretation of model coefficients, statistical testing, and model fit criteria, we deemed the 2PL model superior and selected this model to examine item and scale properties. Scale reliability was shown to be good through the test information function (TIF). TIF from our analysis showed that higher levels of OHL were measured less precisely than lower levels of OHL. Conclusion: We demonstrated OHL‐AQ as a whole has promising psychometric properties. However, for equiprecise measurement across the scale range, the scale needs more items for measuring higher levels of OHL. [ABSTRACT FROM AUTHOR]

Objectives: To investigate differential item functioning across the Spanish and English versions of the Orofacial Esthetic Scale (OES). Methods: OES data were collected from a consecutive sample of a total of 622 adult dental patients (311 English‐ and 311 Spanish‐speaking adults) from HealthPartners dental clinics in Minnesota. We inspected boundary characteristic curves (BCCs) based on item response theory (IRT) in English speakers and Spanish speakers to examine differential item functioning (DIF). Using the Samejima's graded response model, we used the item response theory log‐likelihood ratio (IRTLR) approach to test whether DIF was statistically significant. Results: Inspection of the BCCs did not reveal substantial differences in item difficulty and discrimination between English speakers and Spanish speakers. When IRTLR tests were performed for the seven OES items, no item was flagged with significant DIF after P‐values were adjusted for multiple comparisons. Conclusions: Pooling of Orofacial Esthetic Scale (OES) data from English and Spanish‐speaking dental patients for analysis and interpretation is supported by the absence of differential item functioning (DIF) across the two language groups. [ABSTRACT FROM AUTHOR]