Multilevel Analysis of the PISA Data

Multilevel Analysis of the PISA Data PDF

Author: Esther Sui Chu Ho

Publisher: The Chinese University of Hong Kong Press

Published: 2013-12-15

Total Pages: 292

ISBN-13: 9628908448

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Multilevel analysis can help to get deeper insights into factors that may have impact on schooling outcomes assessed in PISA. In this book, multilevel analysis is applied by linking student performance to the structure and processes of both the family and the school, the two major social contexts that exert powerful influence on young people. Essential/important policy issues including parental involvement, school decentralization, and medium of instruction are examined, and the possible relationship between these policies and student's achievement in light of the evidence collected in the first three cycles of the PISA study is explored. Besides, appreciating how researchers have used multilevel analysis in a variety of ways would be an effective path to learn it. The analysis in this book will add significantly to the storehouse of knowledge about the application of multilevel analysis in assessing the quality and equality of education in East Asian societies. The findings thereof would also serve as useful references for researchers, policymakers, school administrators, and teachers.

Multilevel Analysis of the PISA Data

Multilevel Analysis of the PISA Data PDF

Author: Esther Sui-chu Ho

Publisher:

Published: 2013

Total Pages: 277

ISBN-13: 9789628908479

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Multilevel analysis can help to get deeper insights into factors that may have impact on schooling outcomes assessed in PISA. In this book, multilevel analysis is applied by linking student performance to the structure and processes of both the family and the school, the two major social contexts that exert powerful influence on young people. Essential/important policy issues including parental involvement, school decentralization, and medium of instruction are examined, and the possible relationship between these policies and student's achievement in light of the evidence collected in the first three cycles of the PISA study is explored. Besides, appreciating how researchers have used multilevel analysis in a variety of ways would be an effective path to learn it. The analysis in this book will add significantly to the storehouse of knowledge about the application of multilevel analysis in assessing the quality and equality of education in East Asian societies. The findings thereof would also serve as useful references for researchers, policymakers, school administrators, and teachers.

PISA Data Analysis Manual: SAS, Second Edition

PISA Data Analysis Manual: SAS, Second Edition PDF

Author: OECD

Publisher: OECD Publishing

Published: 2009-03-31

Total Pages: 470

ISBN-13: 9264056254

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This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses.

PISA Data Analysis Manual: SPSS, Second Edition

PISA Data Analysis Manual: SPSS, Second Edition PDF

Author: OECD

Publisher: OECD Publishing

Published: 2009-03-31

Total Pages: 478

ISBN-13: 9264056270

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This publication includes detailed information on how to analyse the PISA data, enabling researchers to both reproduce the initial results and to undertake further analyses.

Multilevel Analysis

Multilevel Analysis PDF

Author: Tom A. B. Snijders

Publisher: SAGE

Published: 1999

Total Pages: 282

ISBN-13: 9780761958901

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Multilevel analysis covers all the main methods, techniques and issues for carrying out multilevel modeling and analysis. The approach is applied, and less mathematical than many other textbooks.

Methodology for Multilevel Modeling in Educational Research

Methodology for Multilevel Modeling in Educational Research PDF

Author: Myint Swe Khine

Publisher: Springer Nature

Published: 2022-04-10

Total Pages: 419

ISBN-13: 9811691428

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This edited volume documents attempts to conduct systematic and prodigious research using multilevel analysis in educational settings, and present their findings and identify future research directions. It showcases the versatility of multilevel analysis, and elucidates the unique advantages in examining complex and wide-ranging educational issues. This book brings together leading experts around the world to share their works in the field, highlighting recent advances, creative and unique approaches, and innovative methods using multilevel modeling and theoretical and practical aspects of multilevel analysis in culturally and linguistically-diverse educational contexts.

Improving a Country’s Education

Improving a Country’s Education PDF

Author: Nuno Crato

Publisher: Springer Nature

Published: 2020-11-23

Total Pages: 263

ISBN-13: 3030590313

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This open access book compares and contrasts the results of international student assessments in ten countries. The OECD’s Programme for International Student Assessment (PISA) released the results of its 2018 assessment in December 2019. This book reflects the debates that typically follow the release of these results and focuses on the causes of differences between countries. Such causes include continuous decline in one country, improvement combined with increasing internal inequalities in another country, or rapid improvement in spite of an outdated curriculum in yet another. In addition, the book discusses a number of general questions: Is knowledge outdated? Are computers taking over and replacing teachers? Are schools killing creativity? Are we adequately preparing the next generation? Are schools failing to educate our kids? The book starts out with a summary of PISA’s evolution and PISA results, and an explanation of the major factors that play a role in changes in countries’ results. The next ten chapters are devoted to ten specific countries, offering a summary of data and an explanation of the major drives for changes in education results for each one. Each chapter includes a short description of the country’s educational system as well as the impact of PISA and other ILSA studies on the country’s educational policies. The chapters also include a timeline of policy measures and main hallmarks of the country’s educational evolution, discussing the impact of these measures on its PISA results. A final reference chapter explains what PISA is, what it measures and how. While highlighting the 2018 results, the book also takes into consideration previous results, as well as long-term initiatives. This book gathers the contribution of well-known and respected experts in the field. Specialists such as Eric Hanushek, for the US, Tim Oates, for England, Montse Gomendio, for Spain, Gunda Tire, for Estonia, and all other contributors draw on their vast experience and statistical analysis expertise to draw a set of rich country lessons and recommendations that are invaluable for all of those who care about improving a country’s education system.

Quality Research in Literacy and Science Education

Quality Research in Literacy and Science Education PDF

Author: Mack C. Shelley

Publisher: Springer Science & Business Media

Published: 2008-12-30

Total Pages: 667

ISBN-13: 1402084277

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Statistical models attempt to describe and quantify relationships between variables. In the models presented in this chapter, there is a response variable (sometimes called dependent variable) and at least one predictor variable (sometimes called independent or explanatory variable). When investigating a possible cause-and-effect type of relationship, the response variable is the putative effect and the predictors are the hypothesized causes. Typically, there is a main predictor variable of interest; other predictors in the model are called covariates. Unknown covariates or other independent variables not controlled in an experiment or analysis can affect the dependent or outcome variable and mislead the conclusions made from the inquiry (Bock, Velleman, & De Veaux, 2009). A p value (p) measures the statistical significance of the observed relationship; given the model, p is the probability that a relationship is seen by mere chance. The smaller the p value, the more confident we can be that the pattern seen in the data 2 is not random. In the type of models examined here, the R measures the prop- tion of the variation in the response variable that is explained by the predictors 2 specified in the model; if R is close to 1, then almost all the variation in the response variable has been explained. This measure is also known as the multiple correlation coefficient. Statistical studies can be grouped into two types: experimental and observational.