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Programme for International Student Assessment 2003 (PISA 2003)

Table of contents

Project description

Blank data sets

Documentation

Further information

Notes on the use of the data

Literature

> Link to application form (Scientific Use Files)

Project description

With the Programme for International Student Assessment (PISA) the OECD has set new standards for international educational comparisons. PISA aims to measure how well students at age 15 are prepared to meet the challenges of today's knowledge societies and lifelong learning. The results obtained in the key competency areas of reading, basic education in mathematics and science, and problem-solving allow conclusions to be drawn about strengths and weaknesses of the education systems of participating countries. As PISA is carried out at three-year intervals, participating countries receive important information allowing them to gauge to what extent the quality of their educational outcomes has changed. The PISA 2003 survey focused on the subject area of mathematical literacy while also assessing student performance in reading comprehension and science on a smaller scale. PISA 2003 consists of several random samples. The PISA study that aimed at comparing Germany's outcomes with those of other participating countries (PISA-International; PISA-I) was supplemented in 2000, 2003 and 2006 by a series of representative surveys at Länder level (PISA-Ergänzung; PISA-E). (IQB)

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Blank data sets

For a first overview of the data set and its variables, dummy data sets containing the variables used and the value labels relating to them are provided for download here.

PISA 2003 E

• PISA 2003 E Students (SPSS)
• PISA 2003 E Schools (SPSS)

PISA 2003 I

• PISA 2003 I Students grade 9 (SPSS)
• PISA 2003 I Schools grade 9 (SPSS)
• PISA 2003 I Teacher grade 9 (SPSS)
• PISA 2003 I Students 15 years (SPSS)
• PISA 2003 I Schools 15 years (SPSS)
• PISA 2003 I Teacher 15 years (SPSS)

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Documentation

Here you can find further documentation:

• PISA 2003 - Scaling Manual: Unfortunately, we do not have permission to publish more than extracts from this documentation of PISA 2006 survey questionnaires on our website.
• List of variables Students
• List of variables Schools

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Further information

• General information, the technical report, and the data of the PISA 2003 international survey can be found on the Organisation for Economic Co-Operation and Development (OECD).
• Information about the PISA E-Data

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Notes on the use of the data

Are the competence estimators of the PISA, IGLU and IQB studies comparable with each other?

In principle, the tests from PISA and the IQB studies correlate highly, but the underlying competency models differ. The IQB tests are based on the educational standards of the The Standing Conference of the Ministers of Education and Cultural Affairs of the Länder in the Federal Republic of Germany and thus more closely aligned with the schools´ curriculum than the PISA tests. Comparability can be tested using IRT methods based on studies in which both PISA and IQB items were used. Some studies for comparison are for example

• an den Ham, Ann-Katrin; Ehmke, Timo; Hahn, Inga; Wagner, Helene; Schöps, Katrin (2016). Mathematische und naturwissenschaftliche Kompetenz in PISA, im IQB-Ländervergleich und in der National Educational Panel Study (NEPS) – Vergleich der Rahmenkonzepte und der dimensionalen Struktur der Testinstrumente. In: Bundesministerium für Bildung und Forschung [Hrsg.]: Forschungsvorhaben in Ankopplung an Large-Scale-Assessments. Berlin, Bundesministerium für Bildungund Forschung, S. 140-160.
• Jude, Nina,Klieme, Eckhard [Hrsg.](2013). PISA 2009 - Impulse für die Schul- und Unterrichtsforschung. Weinheim u.a.: Beltz. (Zeitschrift für Pädagogik, Beiheft 59)
• Hartig, Johannes, Frey, Andreas (2012).Validität des Tests zur Überprüfung des Erreichens der Bildungsstandards in Mathematik Zusammenhänge mit den bei PISA gemessenen Kompetenzen und Varianz zwischen Schulen und Schulformen. Diagnostica 58, S. 3-14.

The extent of comparability must be considered separately for reading and mathematical literacy and for secondary and primary education. Although it can be assumed that federal state differences can be well mapped using both measures, it is unfortunately not possible to analyse absolute trends on a common metric.

The data sets of PISA 2012 and IQB National Assessment Study 2012 studies can be linked with each other using the ID variable [idstud_FDZ]. This allows you to compare correlations between the scaled test values of both studies.

Please note additionally:

1.) In contrast to the PISA surveys, reading and mathematical literacy are only tested together in the IQB study in primary school: Reading literacy was recorded in the IQB National Assessment Study 2009 (lower secondary level) and in the IQB National Assessment Study 2011 (primary school) as well as in the IQB Trends in Student Achievement 2015 (lower secondary level) and in the IQB Trends in Student Achievement 2016 (primary school). Mathematics competencies can be found in the IQB National Assessment Study 2012 (secondary level) and IQB National Assessment Study 2011 (primary level) as well as in the IQB Trends in Student Achievement 2016 (primary level) and the IQB Trends in Student Achievement 2018 (secondary level).

2.) If you wish to conduct analyses that include unpublished, novel comparisons between single federal states, our Rules of Procedure state that an extended application procedure with a review process applies.

Is it possible to record the age of students (to the day) in the IQB National Assessment Studies/IQB Trends in Student Achievement Studies and in PISA?

Information on the year and age of birth of students is collected as standard in the IQB National Assessment Studies and PISA studies and is available for re- and secondary analyses of the data. For reasons of data protection, however, the exact date of birth was not recorded and is not available in the data sets. The exact test date is also not included in most data sets (in PISA 2009 this information is available). Frequently, however, the data sets contain an age variable that was formed using the year and month of birth in relation to the test date (e.g. in the IQB National Assessment Studies 2011, 2012 and in PISA 2012, 2009, 2006).

Which PISA data can be linked to which IQB National Assessment Studies/IQB Trends in Student Achievement Studies?

The PISA 2012 data sets can be combined with the IQB National Assessment Study 2012. The students IDs have already been recoded in the data sets available at the FDZ at IQB in such a way that a linkage of both data sources is possible. Unfortunately, it is not possible to link the other PISA waves with the data from the IQB IQB National Assessment Studies /IQB Trends in Student Achievement Studies because the ID variables cannot be recoded uniformly.

At what levels was the Germany PISA data collected?

In the German PISA studies, information is only available at the federal state level. Please note that special conditions of use must be observed when analysing data from the federal states. You can read them here:

• Rules of Procedure as of January 2019
• Rules of Procedure - innovative state comparisons as of January 2019

What is the number of classes drawn per school in PISA surveys?

Information on sampling in the studies can be found in the results reports or scale manuals.

Here is a brief summary of sampling in PISA:

PISA 2000:
random selection of 28 15-year-olds and 10 non-15-year-old ninth graders per school; thus, full classes were not drawn, analyses can only be done at the school level

PISA 2003:
random selection of 15-year-olds per school; in addition, two complete 9th graders were drawn per school for the national expansion; in the PISA-E data, however, no class-based sampling was realised.

PISA 2006:
school-based sampling, then random selection of 15-year-olds per school; at the schools in the international sample (PISA_I), students from two complete 9th grades were additionally tested

PISA 2009:
school-based sampling, additionally students from two complete 9th grades per school were tested

PISA 2012:
school-based sampling, additionally students from two complete 9th grades per school were tested

PISA 2015:
school-based sampling, additionally random selection of 15 ninth graders per school

PISA 2018:
school-based sampling, additionally random selection of 15 ninth graders per school

How many students in special and vocational schools are included in the PISA data?

Special needs and vocational students were covered separately in the above PISA surveys. The sample sizes for these subgroups are given below. They are based on the data in the German PISA Extended Samples (PISA-E) in the student and school management data sets. Where appropriate, there may be slight differences from the reported sample sizes in the results reports.

PISA 2000 E:
- 9th grade: n= 11 students in vocational schools, n= 22 students in special schools out of a total of n= 34,754 students
- 15-year-olds: n= 241 students at vocational schools, n= 799 students at special schools out of a total of n= 35,584 students
- School data set: n= 18 vocational schools, n= 4 special schools out of a total of n= 1,342 schools

PISA 2003 E (here no differentiation between data sets for 9th grade & 15-year-olds possible):
- 9th grade: n= 654 students at vocational schools, n= 1,712 students at special schools out of a total of n= 46,185 students
- School data set: n= 43 vocational schools, no special schools out of a total of n= 1,411 schools

PISA 2006 E:
- 9th grade: no students at vocational schools or special schools in the data set
- 15-year-olds: n= 625 students at vocational schools, n= 2,560 students at special schools out of a total of n= 39,573 students
- School data set: n= 42 vocational schools, no special schools out of a total of n= 1,496 schools

PISA 2009 E:
- 9th grade: no students at vocational schools or special schools in the data set out of a total of n= 9,461 students
- School data set: n= 9 vocational schools, n= 13 special schools out of a total of n= 226 schools

PISA 2012 E:
- 9th grade: no students at vocational schools, n= 153 at special schools out of a total of n= 9,998 students
- 15-year-olds: n= 99 students at vocational schools, n= 139 at special schools out of a total of n= 5,001 students
- School data set: n= 7 vocational schools, n= 12 special schools out of a total of n= 230 schools

PISA 2015 E:
- 9th grade:no students at vocational schools, n= 165 at special schools out of a total of n= 4,149 students
- 15-year-olds: n= 160 students at vocational schools, n= 134 at special schools out of a total of n= 6,504 students
- School data set: n= 8 vocational schools, n= 12special schools out of a total of n= 205 schools

PISA 2018 E:
- 9th grade: no students at vocational schools, n= 115 at special schools out of a total of n= 3,567 students
- 15-year-olds: n= 184 students at vocational schools, n= 98 at special schools out of a total of n= 5,451 students
- School data set: n= 10 vocational schools, n= 7 special schools out of a total of n= 191 schools

Can teacher and student data be linked in PISA?

Unfortunately, linking is only possible for the partial data sets of 9th graders (the data sets of 15-year-olds include cross-school samples). In most PISA waves, two 9th graders were drawn, but the partial data sets often lack a unique class ID.

Here is an overview in bullet points of the individual PISA waves:

- PISA 2000: no teacher questionnaire was used here.

- PISA 2003: partial data set "PISA-I-9th grade": teacher questionnaires contain questions at school level, not at class level; a link via the variable [idclass_FDZ] is possible, but in the teacher data set there is a high proportion of missing values on this variable (presumably because many teachers were surveyed per school); partial data set "PISA-E": no teacher questionnaires available

- PISA Plus 2003-2004: a linkage is possible in principle, but teacher data would have to be imported from PISA 2003 data and are only available at the first measurement point.

- PISA 2006: partial data set "PISA-E": no teacher data set for 9th grades available, linkage only possible at school level; partial data set "PISA-I": no clear linkage possible, as the teacher data set does not contain a class ID.

- PISA 2009: also no class ID in the teacher data set, but linking via idsch and variable [LF39M01] (German taught in PISA class: yes vs. no) partially possible; however, two 9th grades were drawn from each school.

- PISA 2012: Linking is possible in principle via class name variables (teacher data set: class_FDZ; student data set: ClassName_FDZ) but difficult to achieve in practice, as the metric of the school ID does not correspond between the two sub-data sets and there is a high proportion of missing values on class name variables (I interpret reports from PISA staff that linking is not successful in the majority of cases).

- PISA 2015: Linkage is not directly possible, as all teachers in the drawn schools were surveyed.

- PISA 2018: A link between teachers and students via the variable "TEACHCLASS_ID" is not possible until the end of 2022 due to a blocking notice. However, this variable also only contains the information whether the teacher has taught a ninth grade or not. This is because almost all teachers in the drawn school were surveyed. Alternatively, the variable "TEACHERID" can be used, but this variable also does not allow a clear assignment between students and the corresponding teacher.

For which PISA data is a repeated measures data set available?

A repeated measures data set is available for PISA-2003 (PISA-Plus 2003, 2004) and PISA-2012 (PISA-Plus 2012, 2013).

How were the science literacy tests developed in PISA?

In contrast to the IQB National Assessment Studies, the science tests in PISA are not curricularly anchored or subject-specifically designed. Therefore, there are no subtests for biology, physics and chemistry in PISA. Instead, PISA tests scientific literacy (see e.g. OECD, 2006). This involves skills that are significant in situations in which one is confronted with science and technology. These situations relate to physical systems, living systems, earth and space systems and technological systems. Specifically, the following competencies are tested:

(a) recognise scientific issues

b) describe, explain and predict scientific phenomena

c) use scientific evidence to make decisions.

More information on the concept and the test (including sample items) can be found here:

• OECD 2015 Assessment / Framework
• OECD: How PISA measures science literacy
• in this results report (for Germany) and here
• Prenzel, M., Artelt, C., Baumert, J., Blum, W., Hammann, M., Klieme, E., Pekrun, R. (Hrsg.) (2008). PISA 2006 in Deutschland: Die Kompetenzen der Jugendlichen im dritten Ländervergleich. Münster: Waxmann.

Were the teachers also interviewed again at the 2nd measurement point?

Yes, but unfortunately this information is not included in the data set. They are part of the COACTIV project, but unfortunately this data has not (yet) been made available for secondary analyses.

Are there variables that include the subjects taught?

The corresponding data for measurement time point (MZP) 1 can be taken from the teacher data set of the PISA 2003 study (data set name: PISA2003-I_Datensatz_Lehrkraft_9Kl) and transferred to the PISA I-Plus 2003-4 data set via the variable [idclass_FDZ]. However, the PISA 2003 teacher data set only contains information on whether any of the subjects taught are mathematics ([fama_all]), German ([fadeu_al]), physics ([faphy_al]), biology ([fabio_al]) or chemistry ([fache_al) (1 = yes).

Does the variable [fama_all] include the indication whether it is a mathematics teacher? If yes, which specification (0/1)? Is it possible to find out the associated subject-specific final grade (state examination/main examination)?

The variable [fama_all] indicates whether the teacher teaches mathematics (1 = yes). Unfortunately, it is not possible to find out which final grade corresponds to the subject mathematics on the basis of the data set available at the FDZ at IQB.

Is there a variable on the teachers' parental occupation?

In the teacher data set of the PISA 2003 study, the following grouped information on the occupational status of parents is available:

- bmut11_1: Occupational status of mother: civil servant (1 = yes).

- bmut12_1: Occupational status of mother: Employee (1 = yes)

- bmut13_1: Occupational status of mother: Self-employed (1 = yes)

- bvat11_1: Occupational status of father: Civil servant (1 = yes)

- bvat12_1: Occupational status of father: Employee (1 = yes)

- bvat13_1: Occupational status of father: Self-employed (1 = yes)

In addition, the ISCO codes (grouping of occupations) of the parents are also included in the data set:

- iscolfm1: ISCO code occupation mother

- iscolff1: ISCO code occupation father

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Literature

By kind permission of Waxmann, excerpts from the books shown can be viewed by clicking on the following links (German only):

• PISA 2003 - Results
• PISA 2003 - What do young people know and can do?
• PISA 2003 - Studies on competence development

Selected literature is listed  here (as of March 2023).

2022

Brunner, M., Stallasch, S. E., & Lüdtke, O. (2022, March 31). Empirical Benchmarks to Interpret Intervention Effects on Student Achievement in Elementary and Secondary School: Meta Analytic Results from Germany: Department of Educational Sciences, University of Potsdam, Germany; Leibniz Institute for Science and Mathematics Education, Germany; Centre for International Student Assessment, Germany. https://doi.org/10.35542/osf.io/39gbq

Camarero Garcia, S. (2022). Inequality of Educational Opportunities and the Role of Learning Intensity. Labour Economics, 74(4), 102084. https://doi.org/10.1016/j.labeco.2021.102084

Gutfleisch, T., & Kogan, I. (2022). Parental occupation and students’ STEM achievements by gender and ethnic origin: Evidence from Germany. Research in Social Stratification and Mobility, 82(100735). https://doi.org/10.1016/j.rssm.2022.100735

2021

Holzberger, D., & Schiepe-Tiska, A. (2021). Is the school context associated with instructional quality? The effects of social composition, leadership, teacher collaboration, and school climate. School Effectiveness and School Improvement, 64(8), 1–21. https://doi.org/10.1080/09243453.2021.1913190

2020

Camarero Garcia, S. (2020). Inequality of Educational Opportunities and the Role of Learning Intensity (ZEW Discussion Paper No. 18-021). Mannheim: Zentrum für Europäische Wirtschaftsforschung. Retrieved from Zentrum für Europäische Wirtschaftsforschung website: http://ftp.zew.de/pub/zew-docs/dp/dp18021.pdf   

Roller, M., & Steinberg, D. (2020). The distributional effects of early school stratification - non-parametric evidence from Germany. European Economic Review, 125, 103422. https://doi.org/10.1016/j.euroecorev.2020.103422

2019

Andrietti, V., & Su, X. (2019). Education curriculum and student achievement: Theory and evidence. Education Economics, 27(1), 4–19. https://doi.org/10.1080/09645292.2018.1527894

2018

Autorengruppe Bildungsberichterstattung. (2018). Bildung in Deutschland 2018: Ein indikatorengestützter Bericht mit einer Analyse zu Bildung und Migration [Education in Germany 2018. An indicator-based report including an analysis of education and migration]. Bielefeld: wbv. https://doi.org/10.3278/6001820fw

Böker, K., King, V., Koller, H.‑C., & Tressat, M. (2018). Migrationsgeschichte, Familienbeziehungen und Adoleszenz: (Bildungs-)biographische Entwicklungen junger Männer aus italienischen Familien. In M. S. Baader, P. Götte, & W. Gippert (Eds.), Migration und Familie: Historische und aktuelle Analysen (1st ed., pp. 207–221). Wiesbaden: Springer Fachmedien. https://doi.org/10.1007/978-3-658-15021-1_12

Huebener, M., Kuger, S., & Marcus, J. (2018). G8-Schulreform verbessert PISA-Testergebnisse: Insbesondere leistungsstarke SchülerInnen profitieren. DIW-Wochenbericht, 85(13/14), 265–275. https://doi.org/10.18723/diw_wb:2018-13-1

Jores, D., & Leiss, L. M. (2018). Die Kontroversen um PISA und Co. sowie eine Untersuchung zur Einstellung von Lehrkräften und Schulleitern zu Large Scale Assessments (Unveröffentlichte Bachelorarbeit): Johannes-Gutenberg-Universität Mainz, Mainz.

Lippmann, Q., & Senik, C. (2018). Math, girls and socialism. Journal of Comparative Economics, 46(3), 874–888. https://doi.org/10.1016/j.jce.2018.07.013

Lippmann, Q., & Senik, C. (2018). Math, Girls and Socialism (IZA Discussion Paper Series No. 11532). Bonn: Institut zur Zukunft der Arbeit.

2017

Bartig, S., Bosswick, W., & Heckmann, F. (2017). Vielfaltsmonitor: Studie zum Umgang mit ethnischer und religiöser Vielfalt in Deutschland. Bericht an die Bertelsmann Stiftung. Bamberg: Europäisches Forum für Migrationsstudien.

Dustmann, C., Puhani, P. A., & Schönberg, U. (2017). The long-term effects of early track choice. The Economic Journal, 127(603), 1348–1380. https://doi.org/10.1111/ecoj.12419

Homuth, C. (2017). Die G8-Reform in Deutschland: Auswirkungen auf Schülerleistungen und Bildungsungleichheit (1st ed.). Wiesbaden: Springer Fachmedien. Retrieved from https://link.springer.com/content/pdf/10.1007%2F978-3-658-15378-6.pdf  https://doi.org/10.1007/978-3-658-15378-6

Horlboge, J. C. (2017). Elterliche Bildungsaspiration und soziokulturelle Herkunft in der PISA-Studie: Eine Sekundäranalyse des Zusammenhangs zweier Konstrukte im Kontext sozialer Herkunft (Unveröffentlichte Masterarbeit): Universität Göttingen, Göttingen.

Huebener, M., Kuger, S., & Marcus, J. (2017). Increased instruction hours and the widening gap in student performance. Labour Economics, 47, 15–34. https://doi.org/10.1016/j.labeco.2017.04.007

Lippmann, U. (2017). Muttersprache und die Wahrnehmung der Lehrkraft: Der Zusammenhang zwischen sprachlich-kulturellem Hintergrund von Schülerinnen und Schülern und ihrer Wahrnehmung der Unterstützung durch die Lehrkraft im Mathematikunterricht (Unveröffentlichte Masterarbeit): Humboldt-Universität zu Berlin, Berlin.

Roller, M., & Steinberg, D. (2017). The Distributional Effects of Early School Stratification - Non-Parametric Evidence from Germany (WWZ Working Paper No. 2017/20). Basel: Center of Business and Economics - University of Basel. Retrieved from Center of Business and Economics - University of Basel website: https://ideas.repec.org/p/bsl/wpaper/2017-20.html  

2016

Andrietti, V. (2016). The causal effects of an intensified curriculum on cognitive skills : Evidence from a natural experiment (UC3M WP Economic Series No. 16-06). Madrid: Universidad Carlos III de Madrid. Retrieved from Universidad Carlos III de Madrid website: http://hdl.handle.net/10016/22880  

Autorengruppe Bildungsberichterstattung. (2016). Bildung in Deutschland 2016: Ein indikatorengestützter Bericht mit einer Analyse zu Bildung und Migration [Education in Germany 2016. An indicator-based report including an analysis of education and migration]. Bielefeld: Bertelsmann. Retrieved from http://www.oapen.org/search?identifier=640941  https://doi.org/10.3278/6001820ew

Lorenz, C.‑V. (2016). A tree must be bent while it is young?: The effect of age at school entrance on school performance in Germany (Unveröffentlichte Bachelorarbeit): Universität Mannheim, Mannheim.

Nikolaus, J. (2016). Soziale Ungleichheit im deutschen Bildungssystem: Welche herkunftsspezifischen Einflüsse auf die Lesekompetenz von 15-jährigen Schülerinnen und Schülern gibt es in Deutschland und haben sie sich im Zeitverlauf verringert?: Eine Analyse der PISA-Daten im Zeitvergleich [Social inequality in the German education system: What are the (family) background-specific influences on the reading skills of 15-year-old students in Germany and have they decreased over time?] (Unveröffentlichte Masterarbeit): Universität Kassel, Kassel.

Reinders, H., Fresow, M., & Fresow, P. (2016). Kompetenzunterschiede und Bildungsgangwechsel bei Schülern mit Migrationshintergrund - Ergebnisse zur Vorhersage der Mathematikleistungen durch individuelle Voraussetzungen. In Bundesministerium für Bildung und Forschung (Ed.), Bildungsforschung: Vol. 44. Forschungsvorhaben in Ankopplung an Large-Scale-Assessments (1st ed., pp. 4–22). Bielefeld: Bundesministerium für Bildung und Forschung.

Voß, H. (2016). Zum Einfluss der Schulpolitik der Bundesländer auf den Schulerfolg: Eine bildungsökonomische Analyse. Schriftenreihe Volkswirtschaftliche Forschungsergebnisse: Vol. 211. Hamburg: Verlag Dr. Kovac.

2015

Plieninger, H., & Dickhäuser, O. (2015). The female fish is more responsive: Gender moderates the BFLPE in the domain of science. Educational Psychology, 35(2), 213–227. https://doi.org/10.1080/01443410.2013.814197

Reinders, H., Fresow, P., Wixler, L., May, N., Endreß, J., & Fresow, M. (2015). KuBiS - Kompetenzunterschiede und Bildungsgangwechsel bei Schülerinnen und Schülern mit Migrationshintergrund : Abschlussbericht: TIB - Technische Informationsbibliothek Universitätsbibliothek Hannover. https://doi.org/10.2314/GBV:864354584

Tressat, M., Böker, K., King, V., & Koller, H.‑C. (2015). Vater-Sohn-Dynamiken im Kontext von Migration: Adoleszente Entwicklung und Bildungsverläufe am Beispiel von Söhnen aus italienischen Migrantenfamilien. In K. Bueschges (Ed.), Internationale Frauen- und Genderforschung in Niedersachsen: Teilband 17. Bildung - Selbst(bild) - Geschlechterbilder (pp. 249–277). Berlin, Münster: LIT.

2014

Tatsi, E. (2014). Essays in Social and Spatial Interactions (Dissertation): Johann Wolfgang Goethe-Universität Frankfurt am Main, Frankfurt, Main.

2013

Kiss, D. (2013). Are Immigrants and Girls Graded Worse? Results of a Matching Approach. Education Economics, 21(5), 447–463. https://doi.org/10.1080/09645292.2011.585019

Wolff, C. A. (2013). Education, Development and Labor Markets (Dissertation): Stockholm School of Economics Göteborg, Stockholm.

2012

Faber, S. (2012). Der Einfluss des Fernsehkonsums auf die Lesekompetenz von Jugendlichen in Deutschland: Eine Sekundäranalyse der PISA-Daten (Unveröffentlichte Masterarbeit): Universität Leipzig, Leipzig.

Fresow, M., Rettich, P., Gniewosz, B., & Reinders, H. (2012). Individuelle Bedingungen für erfolgreiche Bildungskarrieren bei Schülerinnen und Schülern mit Migrationshintergrund. Diskurs Kindheits- und Jugendforschung, 7(4), 473–480. Retrieved from http://www.budrich-journals.de/index.php/diskurs/article/view/10387/8957

Helbig, M., & Leuze, K. (2012). Ich will Feuerwehrmann werden!: Wie Eltern, individuelle Leistungen und schulische Fördermaßnahmen geschlechts(un-)typische Berufsaspirationen prägen. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 64(1), 91–122. https://doi.org/10.1007/s11577-012-0154-9

Homuth, C. (2012). Der Einfluss des achtjährigen Gymnasiums auf den Kompetenzerwerb: Bamberg Graduate School of Social Sciences Working Paper.

Legewie, J. (2012). Die Schätzung von kausalen Effekten: Überlegungen zu Methoden der Kausalanalyse anhand von Kontexteffekten in der Schule. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 64(1), 123–153. https://doi.org/10.1007/s11577-012-0158-5

Legewie, J., & DiPrete, T. A. (2012). School Context and the Gender Gap in Educational Achievement. American Sociological Review, 77(3), 463–485. https://doi.org/10.1177/0003122412440802

Prokic-Breuer, T., & Dronkers, J. (2012). The high performance of Dutch and Flemish 15-year-old native pupils: Explaining country differences in math scores between highly stratified educational systems. Educational Research and Evaluation, 18(8), 749–777. https://doi.org/10.1080/13803611.2012.727359

Schlicht-Schmälzle, R. (2012). Efficiency and Equality of Education: The Effectiveness of Education Policy in the German Federal States. Politische Vierteljahresschrift, 53(2), 265–290. https://doi.org/10.5771/0032-3470-2012-2-265

2011

Block, R., Klein, E. D., van Ackeren, I., & Kühn, S. M. (2011). Leistungseffekte des Zentralabiturs? Eine kritische Auseinandersetzung mit bildungsökonomischen Interpretationen zu den Effekten der Prüfungsorganisation auf der Basis von PISA-E-2003-Daten. Bildungsforschung, 8(1), 215–238. https://doi.org/10.25656/01:4700

Ponomarenko, V. (2011). Determinanten der Bildungsaspirationen von jugendlichen Migranten in Deutschland (Unveröffentlichte Diplomarbeit): Otto-Friedrich-Universität Bamberg, Bamberg.

Prokic-Breuer, T., & Dronkers, J. (2011). Why 15-year old pupils in the Netherlands and Flanders have such high scores in international comparisons of educational outcomes? A first analysis with one German educational system, Maastricht.

Schlicht-Schmälzle, R. (2011). Determinanten der Bildungsungleichheit: Die Leistungsfähigkeit von Bildungssystemen im Vergleich der deutschen Bundesländer (1st ed.). Wiesbaden: Springer VS. Retrieved from https://link.springer.com/book/10.1007%2F978-3-531-92626-1 https://doi.org/10.1007/978-3-531-92626-1

Walter, O. (2011). Der Schulerfolg vietnamnesischer und philippinischer Jugendlicher in Deutschland: Eine Analyse auf der Grundlage der Erweiterungsstichprobe von PISA 2003. Zeitschrift für Erziehungswissenschaft, 14, 397–419.

2010

Bos, W., Klieme, E., & Köller, O. (Eds.). (2010). Schulische Lerngelegenheiten und Kompetenzentwicklung. Münster, München, Berlin: Waxmann.

Kiss, D. (2010). Are immigrants graded worse in primary and secondary education?: Evidence for German schools (Ruhr economic papers No. 223). Essen: RWI. Retrieved from RWI website: https://ssrn.com/abstract=1711873  https://doi.org/10.2139/ssrn.1711873

Kleeberger, F. (2010). Bildung im Elementarbereich: Institutionelle Rahmenbedingungen und Effekte vorschulischer Kinderbetreuung (Unveröffentlichte Diplomarbeit): Ludwig-Maximilians-Universität München, München.

Piopiunik, M., & Wößmann, L. (2010). Volkswirtschaftliche Folgekosten unzureichender Bildung: Eine makroökonomische Projektion. In G. Quenzel & K. Hurrelmann (Eds.), Bildungsverlierer: Neue Ungleichheiten (1st ed., pp. 463–473). Wiesbaden: Springer VS. https://doi.org/10.1007/978-3-531-92576-9_21

Schubert, F., & Becker, R. (2010). Social inequality of reading literacy. Research in Social Stratification and Mobility, 28(1), 109–133. https://doi.org/10.1016/j.rssm.2009.12.007

2009

OECD. (2009). Pisa data analysis manual. Paris: OECD Publishing.

Rauschenbach, T., Betz, T., Borrmann, S., Müller, M., Pothmann, J., Prein, G., . . . Züchner, I. (2009). Prekäre Lebenslagen von Kindern und Jugendlichen - Herausforderungen für die Kinder- und Jugendhilfe: Expertise zum 9. Kinder- und Jugendbericht des Landes Nordrhein-Westfalen (1st ed.). Dortmund, München: Forschungsverbund DJI & TU Dortmund. Retrieved from http://www.aba-fachverband.org/fileadmin/user_upload/user_upload%202010/berichte/Expertise_Rauschenbach_9.KJB_NRW.pdf ; https://nbn-resolving.org/urn:nbn:de:0111-pedocs-118365   https://doi.org/10.25656/01:11836

Wößmann, L., & Piopiunik, M. (2009). Was unzureichende Bildung kostet: Eine Berechnung der Folgekosten durch entgangenes Wirtschaftswachstum (Wirksame Bildungsinvestitionen). Gütersloh: Bertelsmann Stiftung. Retrieved from Bertelsmann Stiftung website: https://www.bertelsmann-stiftung.de/fileadmin/files/BSt/Presse/imported/downloads/xcms_bst_dms_30242_31113_2.pdf 

2008

Hinz, T., & Groß, J. (2008). Der Einfluss sozialer Herkunft und des Migrationshintergrunds unter Berücksichtigung von Schulmerkmalen auf Bildungskompetenzen von Schülerinnen und Schülern in Bayern. Retrieved from http://www.sepp-duerr.de/wp-content/uploads/2015/08/080918-Bildungsstudie.pdf 

2007

Gebhardt, E., & Adams, R. J. (2007). The influence of equating methodology on reported trends in PISA. Journal of Applied Measurement, 8, 305–322.

Prenzel, M., Baumert, J., Blum, W., Lehmann, R., Leutner, D., Neubrand, M., . . . Schiefele, U. (2007). Programme for International Student Assessment 2003 (PISA 2003) (Version 1) [Data set]. Berlin: IQB - Institut zur Qualitätsentwicklung im Bildungswesen. https://doi.org/10.5159/IQB_PISA_2003_v1

2006

Burba, D. (2006). Leistungen von Jungen und Mädchen bei PISA 2003 - bedeutsame Unterschiede? (Dissertation): Christian-Albrechts-Universität zu Kiel, Kiel. Retrieved from http://e-diss.uni-kiel.de/diss_1855/

Ehmke, T., & Siegle, T. (2006). Mathematical Literacy bei Erwachsenen. Über welche Kompetenz verfügen die Eltern von PISA - Schülerinnen und Schülern? In M. Prenzel & L. Allolio-Näcke (Eds.), Untersuchungen zur Bildungsqualität von Schule: Abschlussbericht des DFG-Schwerpunktprogramms (pp. 84–99). Münster, München: Waxmann.

Klusmann, U., Kunter, M., Trautwein, U., & Baumert, J. (2006). Lehrerbelastung und Unterrichtsqualität aus der Perspektive von Lehrenden und Lernenden. Zeitschrift für Pädagogische Psychologie, 20(3), 161–173.

Prenzel, M., Baumert, J., Blum, W., Lehmann, R., Leutner, D., Neubrand, M., . . . Schiefele, U. (Eds.). (2006). PISA 2003: Untersuchungen zur Kompetenzentwicklung im Verlauf eines Schuljahres [PISA 2003: Studies on competence development in the course of a school year]. Münster, New York, München, Berlin: Waxmann.

Ramm, G., Prenzel, M., Baumert, J., Blum, W., Lehmann, R., Leutner, D., . . . Schiefele, U. (Eds.). (2006). PISA 2003: Dokumentation der Erhebungsinstrumente [PISA 2003: Documentation of the survey instruments]. Münster, New York, München, Berlin: Waxmann.

Rost, J., Walter, O., Eid, M., & Diener, E. (2006). Multimethod Item Response Theory. In M. Eid (Ed.), Handbook of multimethod measurement in psychology (pp. 249–268). Washington, DC: American Psychological Association.

Walter, O., Ramm, G., Zimmer, K., Heidemeier, H., & Prenzel, M. (2006). PISA 2003 - Kompetenzen von Jungen und Mädchen mit Migrationshintergrund in Deutschland. Ein Problem ungenutzter Potentiale? Unterrichtswissenschaft, 34(2), 146–169. Retrieved from https://nbn-resolving.org/urn:nbn:de:0111-opus-55134;   http://www.digizeitschriften.de/dms/img/?PPN=PPN513613439_0034&DMDID=DMDLOG_0013

Zimmer, K., Stick, A., Burba, D., & Prenzel, M. (2006). PISA 2003 - Kompetenzmuster von Jungen und Mädchen in den deutschen Ländern. Unterrichtswissenschaft, 34(4), 310–329. Retrieved from https://nbn-resolving.org/urn:nbn:de:0111-opus-55216 ;  http://www.digizeitschriften.de/dms/img/?PPN=PPN513613439_0034&DMDID=DMDLOG_0026

2005

Ferrini-Mundy, J., & Schmidt, W. H. (2005). International Comparative Studies in Mathematics Education: Opportunities for Collaboration and Challenges for Researchers. Journal for Research in Mathematics Education, 36, 164–175.

Krauss, S., & Brunner, M. (2005). Der naturwissenschaftliche Unterricht an der Laborschule: Empirische Befunde zu den naturwissenschaftlichen Kompetenzen. In R. Watermann, S. Thurn, K.-J. Tillmann, & P. Stanat (Eds.), Materialien. Die Laborschule im Spiegel ihrer PISA-Ergebnisse: Pädagogisch-didaktische Konzepte und empirische Evaluation reformpädagogischer Praxis (pp. 155–166). Weinheim u.a.: Juventa.

Kunter, M., & Stanat, P. (2005). Die Rolle von individuellen Eingangsvoraussetzungen und Gruppenmerkmalen beim kooperativen Lösen eines Problems. In E. Klieme (Ed.), Problemlösekompetenz von Schülerinnen und Schülern. Diagnostische Ansätze, theoretische Grundlagen und empirische Befunde der deutschen PISA-2000-Studie (pp. 99–115). Wiesbaden: Springer VS.

Prenzel, M., & Baumert, J. (2005). Editorial. Zeitschrift für Erziehungswissenschaft, 8(4), 485–486. https://doi.org/10.1007/s11618-005-0154-x

Prenzel, M., Baumert, J., Blum, W., Lehmann, R., Leutner, D., Neubrand, M., . . . Schiefele, U. (Eds.). (2005). PISA 2003: Der zweite Vergleich der Länder in Deutschland - was wissen und können Jugendliche? Münster: Waxmann.

Senkbeil, M., Rost, J., & Carstensen, C. H. (2005). Der nationale Naturwissenschaftstest PISA 2003: Entwicklung und empirische Überprüfung eines zweidimensionalen Facettendesigns. Empirische Pädagogik, 19(2), 166–189.

2004

Prenzel, M., Baumert, J., Blum, W., Lehmann, R., Leutner, D., Neubrand, M., . . . Schiefele, U. (Eds.). (2004). PISA 2003: Der Bildungsstand der Jugendlichen in Deutschland - Ergebnisse des zweiten internationalen Vergleichs. Münster: Waxmann.

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