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Low-, Medium-, and High-performing Schools in the Nordic Countries: student Performance at PISA Mathematics 2003-2012
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0001-7282-5384
Umeå University, Faculty of Social Sciences, Department of applied educational science, Departement of Educational Measurement.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Decreasing performance among students in Sweden on international comparative studies and increasing segregation of schools, has led to a debate concerning strategies for improving student performance. The aim of this study is to analyse the between school variance and to identify factors associated with student performance in PISA in mathematics at different performance levels in the Nordic countries. In order to separate the effect of school-level variables, from the effect of student’s background factors and to take care of the multistage sampling design used in PISA, multilevel analysis was used. The results show that no evidence regarding the relationship between the average student performance in mathematics and the between-school variance was found which, is in contrast to previous studies conducted on science performance in PISA. Regarding school-level factors, our results overall have shown that few school-level factors (having a positive or a negative effect) seemed to be associated with performance. School-level factors associated with performance have mainly been identified among low- and medium-performing schools, and to a less extent among students at high-performing schools (only in Sweden and Denmark). This is a result which is in line with other studies showing the educational system’s incapacity to provide support for high-performing students and to enhance their learning.

Keyword [en]
Performance levels, Between-school variance, Multilevel modelling, School-level factors, Large-scale comparative studies, School-effectiveness
National Category
Probability Theory and Statistics
Research subject
Education
Identifiers
URN: urn:nbn:se:umu:diva-128617OAI: oai:DiVA.org:umu-128617DiVA: diva2:1054848
Projects
What can we learn from large-scale assessments about promoting student success?
Funder
Swedish Research Council, 2015-02160
Available from: 2016-12-09 Created: 2016-12-09 Last updated: 2016-12-13
In thesis
1. Statistical modeling in international large-scale assessments
Open this publication in new window or tab >>Statistical modeling in international large-scale assessments
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Statistisk modellering i internationella komparativa mätningar
Abstract [en]

This thesis contributes to the area of research based on large-scale educational assessments, focusing on the application of multilevel models. The role of sampling weights, plausible values (response variable imputed multiple times) and imputation methods are demonstrated by simulations and applications to TIMSS (Trends in International Mathematics and Science Study) and PISA (Programme for International Student Assessment) data.

The large-scale assessments use multistage sampling design, which means that the units such as schools, classrooms, or students at some or all stages are selected with unequal probabilities. In order to make valid estimates and inferences sampling weights should be used. Thus, in the first paper, we examine different approaches and give recommendations concerning handling sampling weights in multilevel models when analyzing large-scale assessments.

Due to limitations in time and the number of students, the complex surveys use matrix sampling of items. This means that a response variable, i.e. students' performance, contains a large amount of information that is missing by design. Therefore, in order to estimate students' proficiency, TIMSS and PISA use the plausible values approach, which results in a set of five plausible values – proficiencies, computed for each student. In the second paper, different user strategies concerning plausible values for multilevel models as well as means and variances are examined with both real and simulated data. Missing information that is present because of the matrix sampling design for instance like the one used in PISA, can be arranged into a non-monotone missing data pattern, where all variables are incomplete and highly positively correlated. In the third paper, we compare a few imputation methods: a single imputation from a conditional distribution (with and without weights) and multiple imputation, for data with a non-monotone missing pattern (with no complete variables) and high positive correlation between variables.

In several of the recent international large-scale assessments, students in Sweden demonstrate a decreasing performance. Some previous research has shown that changes in performance depend on students’ performance levels. In the fourth paper, we studied the relationship between student performance and the between-school variance and tried to identify factors associated with student performance in mathematics in PISA in low-, medium-, and high- performing schools in the Nordic countries.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2016. 18 p.
Series
Statistical studies, ISSN 1100-8989 ; 51
Keyword
multilevel model, plausible values, sampling weights, missing information, multiple imputation, non-monotone missing pattern, TIMSS, PISA
National Category
Probability Theory and Statistics
Research subject
Statistics; Education
Identifiers
urn:nbn:se:umu:diva-128618 (URN)978-91-7601-612-1 (ISBN)
Public defence
2017-01-12, Hörsal E, Humanisthuset, Umeå Universitet, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2016-12-16 Created: 2016-12-09 Last updated: 2017-01-24Bibliographically approved

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