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This paper explores the measurement capabilities of the Trends in International Mathematics and Science Study (TIMSS) and the Program for International Student Assessment (PISA) in assessing school factors that influence student performance. We specifically focus on the 2015 assessments of the science performance of eighth graders in Sweden and Norway. This was the latest year when the two assessments were conducted in the same year and science was the major subject area in the PISA assessment, which was essential for maximizing the assessments' comparability. Using multilevel models, the study identifies common and unique factors across the assessments and investigates the factors that influence student performance at different proficiency levels. The findings highlight the importance of school-level factors, which are significant in both assessments. Moreover, both assessments provide information on overlapping sets of factors that have varying influence on the performance of students with different proficiency levels. Overall, there are limited common factors between TIMSS and PISA. School factors vary between low-performing and high-performing schools, with differing significance in Norway and Sweden. The results indicate that TIMSS and PISA assessments offer complementary information, particularly for low-performing schools. Our findings suggest that different school types may benefit or suffer from distinct school factors. The findings are relevant for both educational professionals and policy-makers. [ABSTRACT FROM AUTHOR]

The overall aim of this study is to examine the association between Swedish students' attitudes towards mathematics, mathematics achievement as measured by the Trends in Mathematics and Science Study (TIMSS), socioeconomic status (SES), and educational background variables. A further aim is to investigate whether students' attitudes towards mathematics have a mediating role between their mathematics achievement and their background. Several indicators of students' SES and background, taken from both the TIMSS 2015 database and from Swedish official registers, were used. The overall results show that there were differences in attitudes towards mathematics in relation to the different SES and educational background measures. There are also associations between students' SES and both TIMSS mathematics achievement and their attitudes towards mathematics. The students' attitudes towards mathematics only had a small mediation role between the students' backgrounds and TIMSS mathematics achievement. Finally, although the mediation models had a better fit when including other information, the mediation effect was lower. Practical implications of the obtained results are discussed. [ABSTRACT FROM AUTHOR]

Multilevel modeling is an important tool for analyzing large-scale assessment data. However, the standard multilevel modeling will typically give biased results for such complex survey data. This bias can be eliminated by introducing design weights which must be used carefully as they can affect the results. The aim of this paper is to examine different approaches and to give recommendations concerning handling design weights in multilevel models when analyzing large-scale assessments such as TIMSS (The Trends in International Mathematics and Science Study). To achieve the goal of the paper, we examined real data from two countries and included a simulation study. The analyses in the empirical study showed that using no weights or only level 1 weights sometimes could lead to misleading conclusions. The simulation study only showed small differences in estimation of the weighted and unweighted models when informative design weights were used. The use of unscaled or not rescaled weights however caused significant differences in some parameter estimates. [ABSTRACT FROM AUTHOR]

Plausible values are typically used in large-scale assessment studies, in particular, in the Trends in International Mathematics and Science Study and the Programme for International Student Assessment. Despite its large spread, there are still some questions regarding the use of plausible values and how such use affects statistical analyses. The aim of this paper is to demonstrate the role of plausible values in large-scale assessment surveys when multilevel modeling is used. Different user strategies concerning plausible values for multilevel models as well as means and variances are examined. The results show that some commonly used user strategies give incorrect results while others give reasonable estimates but incorrect standard errors. These findings are important for anyone wishing to make secondary analyses of large-scale assessment data, especially those interested in using multilevel models to analyze the data. [ABSTRACT FROM PUBLISHER]