What Fosters School Connectedness? The Roles of Classroom Interactions and Parental Support.

Although research has identified the impact of school connectedness on a variety of outcomes for adolescents, much less work has focused on identifying its precursors. This study examined the relative influences of classroom interactions and parental support on elements of school connectedness among a sample of 4838 students (Mage = 15.84, SD = 0.29; 49.1% female) in the United States from the Programme for International Student Assessment (PISA) 2018 data. The results showed that three domains of classroom interactions (i.e., classroom management, instructional support, and emotional support) and parental support played unique roles in predicting school connectedness (i.e., teacher support and school belonging). Specifically, classroom management positively predicted both teacher support and school belonging; instructional support, especially directed instruction, positively predicted teacher support; emotional support was unrelated to teacher support and school belonging. Parental support positively predicted school belonging, but not teacher support. Overall, these findings highlight the roles of both teachers and parents in providing developmentally appropriate support to facilitate school connectedness.

Measurement invariance of the Mental Health Continuum-Short Form across US military veteran and civilian adults.

OBJECTIVE: Recent research has suggested that mental health is not only the absence of mental illness but includes aspects of well-being. One common psychological assessment used to assess dimensions of well-being is the Mental Health Continuum-Short Form (MHC-SF). The MHC-SF is a 14-item measure that assesses emotional, psychological, and social well-being. The purpose of this study was to assess measurement invariance of the MHC-SF across US military veterans and civilians. METHOD: First, we examined the factor structure of the MHC-SF separately for veterans (n = 418) and civilians (n = 411). We then conducted multiple group confirmatory factor analysis (MG-CFA) to assess measurement invariance for the two groups. RESULTS: Findings suggested there were three latent factors representing emotional, social, and psychological well-being. Results from MG-CFA suggested that the MHC-SF is invariant across veterans and civilians. CONCLUSION: Researchers and practitioners can administer the MHC-SF with both groups.

Comparison of parameter estimation approaches for multi-unidimensional pairwise preference tests.

Multidimensional forced-choice (MFC) testing has been proposed as a way of reducing response biases in noncognitive measurement. Although early item response theory (IRT) research focused on illustrating that person parameter estimates with normative properties could be obtained using various MFC models and formats, more recent attention has been devoted to exploring the processes involved in test construction and how that influences MFC scores. This research compared two approaches for estimating multi-unidimensional pairwise preference model (MUPP; Stark et al., 2005) parameters based on the generalized graded unfolding model (GGUM; Roberts et al., 2000). More specifically, we compared the efficacy of statement and person parameter estimation based on a "two-step" process, developed by Stark et al. (2005), with a more recently developed "direct" estimation approach (Lee et al., 2019) in a Monte Carlo study that also manipulated test length, test dimensionality, sample size, and the correlations between generating person parameters for each dimension. Results indicated that the two approaches had similar scoring accuracy, although the two-step approach had better statement parameter recovery than the direct approach. Limitations, implications for MFC test construction and scoring, and recommendations for future MFC research and practice are discussed.

Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments.

To obtain more accurate and robust feedback information from the students' assessment outcomes and to communicate it to students and optimize teaching and learning strategies, educational researchers and practitioners must critically reflect on whether the existing methods of data analytics are capable of retrieving the information provided in the database. This study compared and contrasted the prediction performance of an item response theory method, particularly the use of an explanatory item response model (EIRM), and six supervised machine learning (ML) methods for predicting students' item responses in educational assessments, considering student- and item-related background information. Each of seven prediction methods was evaluated through cross-validation approaches under three prediction scenarios: (a) unrealized responses of new students to existing items, (b) unrealized responses of existing students to new items, and (c) missing responses of existing students to existing items. The results of a simulation study and two real-life assessment data examples showed that employing student- and item-related background information in addition to the item response data substantially increases the prediction accuracy for new students or items. We also found that the EIRM is as competitive as the best performing ML methods in predicting the student performance outcomes for the educational assessment datasets.

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

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

Examining the Efficacy of Culturally Responsive Interventions for Autistic Children and Their Families: A Meta-Analysis.

Culturally responsive interventions for autistic children and their families have been developed and implemented to address issues related to limited representation, inequities, and disparities in access to care of minoritized families in research. Currently available reviews are relatively limited in scope or do not synthesize interventions specifically. Therefore, we conducted a meta-analysis to synthesize autism intervention literature that specifically targeted autistic individuals and their family members from minoritized backgrounds, such as immigrant families. We used four databases to identify studies that used culturally responsive interventions with minoritized autistic children and their families. An article was included if it included empirical intervention data using an experimental design. A total of 354 studies were initially screened, and 24 studies were included. Effect sizes of these studies were extracted across two levels (i.e., child and family levels). Data from group design studies were extracted manually, and data from single-case design studies were extracted using a web-based tool. We used design-comparable standardized effect sizes to compare across both designs. The analysis revealed a large, positive, and significant overall effect size across culturally responsive interventions. Specifically, social-communication and mental health outcomes yielded significant effects at the child level. Additionally, parents' mental health and fidelity of strategy implementation also yielded significant results. Our results suggest that culturally responsive interventions yield comparable outcomes to unadapted, original interventions. Future research should examine the distinction between the effect of cultural adaptation and the efficacy of the intervention itself.

Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing.

The key assumption of conditional independence of item responses given latent ability in item response theory (IRT) models is addressed for multistage adaptive testing (MST) designs. Routing decisions in MST designs can cause patterns in the data that are not accounted for by the IRT model. This phenomenon relates to quasi-independence in log-linear models for incomplete contingency tables and impacts certain types of statistical inference based on assumptions on observed and missing data. We demonstrate that generalized residuals for item pair frequencies under IRT models as discussed by Haberman and Sinharay (J Am Stat Assoc 108:1435-1444, 2013. https://doi.org/10.1080/01621459.2013.835660 ) are inappropriate for MST data without adjustments. The adjustments are dependent on the MST design, and can quickly become nontrivial as the complexity of the routing increases. However, the adjusted residuals are found to have satisfactory Type I errors in a simulation and illustrated by an application to real MST data from the Programme for International Student Assessment (PISA). Implications and suggestions for statistical inference with MST designs are discussed.