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.

Trifactor Models for Multiple-Ratings Data.

In this study we extend and assess the trifactor model for multiple-ratings data in which two different raters give independent scores for the same responses (e.g., in the GRE essay or to subset of PISA constructed-responses). The trifactor model was extended to incorporate a cross-classified data structure (e.g., items and raters) instead of a strictly hierarchical structure. we present a set of simulations to reflect the incompleteness and imbalance in real-world assessments. The effects of the rate of missingness in the data and of ignoring differences among raters are investigated using two sets of simulations. The use of the trifactor model is also illustrated with empirical data analysis using a well-known international large-scale assessment.

Identifying the top predictors of student well-being across cultures using machine learning and conventional statistics.

Alongside academic learning, there is increasing recognition that educational systems must also cater to students' well-being. This study examines the key factors that predict adolescent students' subjective well-being, indexed by life satisfaction, positive affect, and negative affect. Data from 522,836 secondary school students from 71 countries/regions across eight different cultural contexts were analyzed. Underpinned by Bronfenbrenner's bioecological theory, both machine learning (i.e., light gradient-boosting machine) and conventional statistics (i.e., hierarchical linear modeling) were used to examine the roles of person, process, and context factors. Among the multiple predictors examined, school belonging and sense of meaning emerged as the common predictors of the various well-being dimensions. Different well-being dimensions also had distinct predictors. Life satisfaction was best predicted by a sense of meaning, school belonging, parental support, fear of failure, and GDP per capita. Positive affect was most strongly predicted by resilience, sense of meaning, school belonging, parental support, and GDP per capita. Negative affect was most strongly predicted by fear of failure, gender, being bullied, school belonging, and sense of meaning. There was a remarkable level of cross-cultural similarity in terms of the top predictors of well-being across the globe. Theoretical and practical implications are discussed.

The moderating role of socioeconomic status on the relationship between teacher social support and sense of belonging to school.

BACKGROUND: Teacher social support (TSS) has been identified as one of the most important factors of success and well-being for students. Yet, there is a gap in the literature regarding the impact of students' socioeconomic status (SES) on their perceptions of TSS, and whether SES may impact the strength of the relationship between teacher social support and students' sense of belonging to school (SBS). AIMS: In this preregistered study, we aimed at filling this gap by testing the moderating role of SES on the TSS-SBS link, along with the direct associations between these variables. SAMPLE: We used data from the French sample of the Programme for International Student Assessment 2018 (PISA; N = 6308). METHODS: TSS was primarily assessed as a latent construct based on three indicators provided by PISA: teacher support, teacher emotional support and teacher feedback. Regarding SES, we primarily focused on family wealth possessions and parents' highest level of education. RESULTS: Using structural equation modelling, findings confirmed that the TSS-SBS link was stronger for high-SES than low-SES students. We also found a negative association between teacher support and SES. Importantly, preregistered additional analyses highlight that findings depend on the SES and TSS indicators considered. CONCLUSIONS: Findings support the importance of SES effect on students' perceptions of their interactions with teachers and the extent to which they perceive they belong to the school. The implications and limitations of this research are discussed.

Meta-analyzing individual participant data from studies with complex survey designs: A tutorial on using the two-stage approach for data from educational large-scale assessments.

Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences. Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational large-scale assessments (ELSAs) or social, health, and economic survey and panel studies. The meta-analytic integration of these results offers unique and novel research opportunities to provide strong empirical evidence of the consistency and generalizability of important phenomena and trends. Using ELSAs as an example, this tutorial offers methodological guidance on how to use the two-stage approach to IPD meta-analysis to account for the statistical challenges of complex survey designs (e.g., sampling weights, clustered and missing IPD), first, to conduct descriptive analyses (Stage 1), and second, to integrate results with three-level meta-analytic and meta-regression models to take into account dependencies among effect sizes (Stage 2). The two-stage approach is illustrated with IPD on reading achievement from the Programme for International Student Assessment (PISA). We demonstrate how to analyze and integrate standardized mean differences (e.g., gender differences), correlations (e.g., with students' socioeconomic status [SES]), and interactions between individual characteristics at the participant level (e.g., the interaction between gender and SES) across several PISA cycles. All the datafiles and R scripts we used are available online. Because complex social, health, or economic survey and panel studies share many methodological features with ELSAs, the guidance offered in this tutorial is also helpful for synthesizing research evidence from these studies.

A New Approach to Desirable Responding: Multidimensional Item Response Model of Overclaiming Data.

A variety of approaches have been presented for assessing desirable responding in self-report measures. Among them, the overclaiming technique asks respondents to rate their familiarity with a large set of real and nonexistent items (foils). The application of signal detection formulas to the endorsement rates of real items and foils yields indices of (a) knowledge accuracy and (b) knowledge bias. This overclaiming technique reflects both cognitive ability and personality. Here, we develop an alternative measurement model based on multidimensional item response theory (MIRT). We report three studies demonstrating this new model's capacity to analyze overclaiming data. First, a simulation study illustrates that MIRT and signal detection theory yield comparable indices of accuracy and bias-although MIRT provides important additional information. Two empirical examples-one based on mathematical terms and one based on Chinese idioms-are then elaborated. Together, they demonstrate the utility of this new approach for group comparisons and item selection. The implications of this research are illustrated and discussed.

How Schools Affect Student Well-Being: A Cross-Cultural Approach in 35 OECD Countries.

A common approach for measuring the effectiveness of an education system or a school is the estimation of the impact that school interventions have on students' academic performance. However, the latest trends aim to extend the focus beyond students' acquisition of knowledge and skills, and to consider aspects such as well-being in the academic context. For this reason, the 2015 edition of the international assessment system Programme for International Student Assessment (PISA) incorporated a new tool aimed at evaluating the socio-emotional variables related to the well-being of students. It is based on a definition focused on the five dimensions proposed in the PISA theoretical framework: cognitive, psychological, social, physical, and material. The main purpose of this study is to identify the well-being components that significantly affect student academic performance and to estimate the magnitude of school effects on the well-being of students in OECD countries, the school effect being understood as the ability of schools to increase subjective student well-being. To achieve this goal, we analyzed the responses of 248,620 students from 35 OECD countries to PISA 2015 questionnaires. Specifically, we considered non-cognitive variables in the questionnaires and student performance in science. The results indicated that the cognitive well-being dimension, composed of enjoyment of science, self-efficacy, and instrumental motivation, as well as test anxiety all had a consistent relationship with student performance across countries. In addition, the school effect, estimated through a two-level hierarchical linear model, in terms of student well-being was systematically low. While the school effect accounted for approximately 25% of the variance in the results for the cognitive dimension, only 5-9% of variance in well-being indicators was attributable to it. This suggests that the influence of school on student welfare is weak, and the effect is similar across countries. The present study contributes to the general discussion currently underway about the definition of well-being and the connection between well-being and achievement. The results highlighted two complementary concerns: there is a clear need to promote socio-emotional education in schools, and it is important to develop a rigorous framework for well-being assessment. The implications of the results and proposals for future studies are discussed.

Monitoring Countries in a Changing World: A New Look at DIF in International Surveys.

This paper discusses the issue of differential item functioning (DIF) in international surveys. DIF is likely to occur in international surveys. What is needed is a statistical approach that takes DIF into account, while at the same time allowing for meaningful comparisons between countries. Some existing approaches are discussed and an alternative is provided. The core of this alternative approach is to define the construct as a large set of items, and to report in terms of summary statistics. Since the data are incomplete, measurement models are used to complete the incomplete data. For that purpose, different models can be used across countries. The method is illustrated with PISA's reading literacy data. The results indicate that this approach fits the data better than the current PISA methodology; however, the league tables are nearly identical. The implications for monitoring changes over time are discussed.

Malaysian and Singaporean students' affective characteristics and mathematics performance: evidence from PISA 2012.

This paper attempts to identify the extent to which the affective characteristics of Malaysian and Singaporean students' attainment compared to the OECD average in Programme for International Student Assessment (PISA) 2012, and examine the influence of students' affective characteristics, gender, and their socioeconomic status on mathematics performance at both student and school levels. Sample consisted of 5197 and 5546 15-year-old Malaysian and Singaporean students. Data were analysed using hierarchical linear modelling approach with HLM 7.0 software. Results showed that the Index of economic, social, and cultural status (ESCS), mathematics self-efficacy, and mathematics anxiety have significant effects on mathematics performance in Malaysia and Singapore at the student level. Proportion of boys at the school level has no significant effects on mathematics performance for both Malaysian and Singaporean students. ESCS mean at the school level has positive and significant effects on mathematics performance in Malaysia, but not in Singapore. Limitations, implications, and future studies were discussed.

On the relevance of assumptions associated with classical factor analytic approaches.

A personal trait, for example a person's cognitive ability, represents a theoretical concept postulated to explain behavior. Interesting constructs are latent, that is, they cannot be observed. Latent variable modeling constitutes a methodology to deal with hypothetical constructs. Constructs are modeled as random variables and become components of a statistical model. As random variables, they possess a probability distribution in the population of reference. In applications, this distribution is typically assumed to be the normal distribution. The normality assumption may be reasonable in many cases, but there are situations where it cannot be justified. For example, this is true for criterion-referenced tests or for background characteristics of students in large scale assessment studies. Nevertheless, the normal procedures in combination with the classical factor analytic methods are frequently pursued, despite the effects of violating this "implicit" assumption are not clear in general. In a simulation study, we investigate whether classical factor analytic approaches can be instrumental in estimating the factorial structure and properties of the population distribution of a latent personal trait from educational test data, when violations of classical assumptions as the aforementioned are present. The results indicate that having a latent non-normal distribution clearly affects the estimation of the distribution of the factor scores and properties thereof. Thus, when the population distribution of a personal trait is assumed to be non-symmetric, we recommend avoiding those factor analytic approaches for estimation of a person's factor score, even though the number of extracted factors and the estimated loading matrix may not be strongly affected. An application to the Progress in International Reading Literacy Study (PIRLS) is given. Comments on possible implications for the Programme for International Student Assessment (PISA) complete the presentation.