Programme for International Student Assessment 2006 (PISA 2006)

 

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)

Data Set Published on 01.04.2010
Current Version Available Since 01.04.2010
Survey Period 2006
Sample Students (15-year old and in grade 9) (N=circa 39,000) in General Education Schools (N=1,448); Teachers (N=14,572)
Survey Unit Parents
Principals
Students
Teachers
Measured Competencies German - Reading Comprehension, Mathematics, Natural Sciences
Region Germany, Baden-Wuerttemberg, Bavaria, Berlin, Brandenburg, Bremen, Hamburg, Hesse, Mecklenburg-Western Pomerania, Lower Saxony, North Rhine-Westphalia, Rhineland-Palatinate, Saarland, Saxony, Saxony-Anhalt, Schleswig-Holstein, Thuringia
Principal Investigators Prenzel, Prof. Dr. Manfred
Data Producers Konferenz der Kultusminister (KMK)
Funded by Standing Conference of the Ministers of Education and Cultural Affairs of the Länder in the Federal Republic of Germany, Organization for Economic Cooperation and Development (OECD)
Link https://www.pisa.tum.de/pisa/pisa-2000-2018/pisa-2006/
Related Studies PISA 2000 (DOI: 10.5159/IQB_PISA_2000_v1), PISA 2003 (DOI: 10.5159/IQB_PISA_2003_v1), PISA 2009 (DOI: 10.5159/IQB_PISA_2009_v1), PISA 2012 (DOI: 10.5159/IQB_PISA_2012_v5), PISA 2015 (DOI: 10.5159/IQB_PISA_2015_v3), PISA 2018 (DOI: 10.5159/IQB_PISA_2018_v1), PISA-I-Plus 2003-4 (DOI: 10.5159/IQB_PISA_I_Plus_v1), PISA Plus 2012-13 (DOI: 10.5159/IQB_PISA_Plus_2012-13_v2)
Suggested Citation Prenzel, M., Artelt, C., Baumert, J., Blum, W., Hammann, M., Klieme, E., & Pekrun, R. (2010). Programme for International Student Assessment 2006 (PISA 2006) (Version 1) [Data set]. Berlin: IQB – Institut zur Qualitätsentwicklung im Bildungswesen. http://doi.org/10.5159/IQB_PISA_2006_v1
Restriction Notice No federal state identifier variable is available for the PISA-I data sets.

Cognitive abilities must not be used as a dependent variable in the analyses.

 

Project description

The Programme for International Student Assessment enters the third round of surveys with PISA 2006, hereby completing the first survey cycle. As in PISA 2000 and PISA 2003, the domains of reading, mathematics, and science were assessed. In line with the rotating focus, PISA 2006 concentrated on the assessment of scientific subjects. As previously, the study aimed to assess how well fifteen-year-old students are prepared for the demands of the knowledge society and lifelong learning. In Germany, the option of an additional grade-based sample was taken up. As a result, in addition to the internationally standard sample of 15-year-old school students drawn from the entire student body, a further sample comprising two grade 9 classes per school surveyed in the international sample is available. In addition to the domains of reading, mathematics, and science, further data on student attitudes, the school context, demographic features, and information on social, cultural, and family background were collected once more in 2006. As in the years 2000 and 2003, the PISA study 2006 aimed at comparing Germany's outcomes with those of other participating countries (PISA-International; PISA-I) was supplemented by a series of representative surveys at Länder level (PISA-Ergänzung; PISA-E). (IQB)

back to overview

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 2006 E

PISA 2006 I

back to overview

Documentation

 

PISA06 Bücherbild

 

Here you can find further documentation:

back to overview

Further information

General information, the technical report, and the data of the PISA 2006 international survey can be found on the homepage of the Organisation for Economic Co-operation and Development (OECD).

back to overview

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

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:

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:

back to overview

Literature

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

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

2022

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

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

Güntherodt, S. (2019). Resilienz beeinflussende Faktoren im Unterricht von Schülerinnen und Schülern mit sonderpädagogischem Förderbedarf in der Lernentwicklung unter Bezugnahme der PISA Forschung (Unveröffentlichte Staatsexamensarbeit): Universität Leipzig, Leipzig.

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.

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.

Chevalier, A., & Marie, O. (2017). Economic Uncertainty, Parental Selection, and Children’s Educational Outcomes. Journal of Political Economy, 125(2), 393–430. https://doi.org/10.1086/690830

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

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

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

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.

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

Chevalier, A., & Marie, O. (2015). Economic Uncertainty, Parental Selection, and Children’s Educational Outcomes (IZA Discussion Paper Series No. 9004). Bonn: Institut zur Zukunft der Arbeit.

Korthals, R. A. (2015). Tracking Students in Secondary Education: Consequences for Student Performance and Inequality. Dissertation (1st ed.). ROA Dissertation Series. Maastricht: ROA. Retrieved from https://cris.maastrichtuniversity.nl/en/publications/tracking-students-in-secondary-education-consequences-for-student  https://doi.org/10.26481/dis.20150618rk

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

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

Debuschewitz, P., & Bujard, M. (2014). Determinanten von Bildungsdifferenzen in Deutschland: Lehren und Grenzen der PISA-Studie. Bildungsforschung, 11(1), 1–16. https://doi.org/10.25656/01:11396

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

2013

Bachsleitner, A. (2013). Welchen Einfluss hat die Zusammensetzung der Schülerschaft auf die Schulleistung im Lesen und in Mathematik (Unveröffentlichte Masterarbeit): Universität zu Köln, Köln.

Lüdemann, E., & Schwerdt, G. (2013). Migration Background and Educational Tracking: Is there a Double Disadvantage for Second-Generation Immigrants? Journal of Population Economics, 26(2), 455–481. https://doi.org/10.1007/s00148-012-0414-z

Nikolai, R., & Helbig, M. (2013). Schulautonomie als Allheilmittel? Über den Zusammenhang von Schulautonomie und schulischen Kompetenzen der Schüler. Zeitschrift für Erziehungswissenschaft, 16(2), 381–403. https://doi.org/10.1007/s11618-013-0359-3

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

2012

Berkemeyer, N., Bos, W., & Manitius, V. (2012). Chancenspiegel: Zur Chancengerechtigkeit und Leistungsfähigkeit der deutschen Schulsysteme (1st ed.). Chancenspiegel: Vol. 1. Gütersloh: Bertelsmann Stiftung.

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

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.

Jungbauer-Gans, M., Lohmann, H., & Spieß, C. K. (2012). Bildungsungleichheiten und Privatschulen in Deutschland. In R. Becker & H. Solga (Eds.), Kölner Zeitschrift für Soziologie und Sozialpsychologie Sonderhefte: Vol. 52. Soziologische Bildungsforschung (1st ed., pp. 64–85). Wiesbaden: Springer Fachmedien. https://doi.org/10.1007/978-3-658-00120-9_3

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

2011

Lüdemann, E., & Schwerdt, G. (2011). Zuwanderer der zweiten Generation: Im deutschen Schulsystem doppelt benachteiligt? ifo Schnelldienst, 64(4), 19–25. Retrieved from http://hdl.handle.net/10419/164923

Möller, G. (2011). Jeder Vierte gehört zu den Risikoschülern in Mathematik oder Lesen. Verteilung der Leistungsschwächsten in Lesen, Mathematik und Naturwissenschaften in PISA 2006. Schulverwaltung. Nordrhein-Westfalen, 22(2), 60–62.

OECD. (2011). Quality Time for Students: Learning In and Out of School. Paris: OECD Publishing.

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.

2010

Lüdemann, E., & Schwerdt, G. (2010). Migration Background and Educational Tracking: Is there a Double Disadvantage for Second-Generation Immigrants? (CESifo Working Paper No. 3256).

Lüdemann, E., & Schwerdt, G. (2010). Migration Background and School Tracking: Is there a Double Disadvantage for Second-Generation Immigrants? (Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Empirical Studies of Training and Education No. D10-V3). Frankfurt am Main: Verein für Socialpolitik. Retrieved from Verein für Socialpolitik website: https://www.econstor.eu/handle/10419/37284  

Penk, C. (2010). Effekte von Varianzheterogenität bei der Erzeugung von Plausible Values im Rasch-Modell: Eine Simulationsstudie (Unveröffentlichte Masterarbeit): Humboldt-Universität zu Berlin, Berlin.

Prenzel, M., Artelt, C., Baumert, J., Blum, W., Hammann, M., Klieme, E., & Pekrun, R. (2010). Programme for International Student Assessment 2006 (PISA 2006) (Version 1) [Data set]. Berlin: IQB - Institut zur Qualitätsentwicklung im Bildungswesen. https://doi.org/10.5159/IQB_PISA_2006_v1

2009

Frey, A., Taskinen, P. H., Schütte, K., Prenzel, M., Artelt, C., Baumert, J., . . . Drechsel, B. (Eds.). (2009). PISA 2006 Skalenhandbuch: Dokumentation der Erhebungsinstrumente [PISA 2006 scaling manual. Documentation of the survey instruments]. Münster: Waxmann.

OECD. (2009). Green at fifteen?: How 15-year-olds perform in environmental science and geoscience in PISA 2006. Paris: OECD Publishing.

OECD. (2009). Pisa 2006 Technical Report. Paris: OECD Publishing.

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

2008

Carstensen, C. H., Prenzel, M., & Baumert, J. (2008). Trendanalysen in PISA: Wie haben sich die Kompetenzen in Deutschland zwischen PISA 2000 und PISA 2006 entwickelt? In M. Prenzel & J. Baumert (Eds.), Zeitschrift für Erziehungswissenschaft: Sonderheft 10. Vertiefende Analysen zu PISA 2006 (Zeitschrift für Erziehungswissenschaft: Sonderheft No. 10) (pp. 11–34). Wiesbaden: Springer VS.

Hammann, M., & Prenzel, M. (2008). Ergebnisse des internationalen PISA Naturwissenschaftstests 2006. Der mathematische und naturwissenschaftliche Unterricht, 61(2), 67–74.

Prenzel, M., Artelt, C., Baumert, J., Blum, W., Hammann, M., Klieme, E., & Pekrun, R. (Eds.). (2008). PISA 2006 in Deutschland: Die Kompetenzen der Jugendlichen im dritten Ländervergleich. Münster, München: Waxmann. Retrieved from http://archiv.ipn.uni-kiel.de/PISA/Zusfsg_PISA2006_national.pdf

Prenzel, M., & Baumert, J. (2008). Unbekanntes PISA: Über den Nutzen der internationalen Vergleichsstudie für die Hochschulen. Forschung & Lehre, 15(3), 168–169.

Prenzel, M., & Baumert, J. (Eds.). (2008). Zeitschrift für Erziehungswissenschaft: Sonderheft 10. Vertiefende Analysen zu PISA 2006 (Zeitschrift für Erziehungswissenschaft: Sonderheft No. 10). Wiesbaden: Springer VS.

2007

OECD. (2007). Pisa 2006: Schulleistungen im internationalen Vergleich. Naturwissenschaftliche Kompetenzen für die Welt von morgen. Bielefeld: Bertelsmann.

OECD. (2007). Pisa 2006: Science Competencies for Tomorrow's World: Volume 2: Data. Paris: OECD Publishing.

OECD. (2007). Pisa 2006: Science Competencies for Tomorrow's World: Volume 1: Analysis. Paris: OECD Publishing.

Prenzel, M., Artelt, C., Baumert, J., Blum, W., Hammann, M., Klieme, E., & Pekrun, R. (Eds.). (2007). PISA 2006: Die Ergebnisse der dritten internationalen Vergleichsstudie. Münster, München: Waxmann.

back to overview

Postal Address

Humboldt-Universität zu Berlin
Institute for Educational Quality Improvement (IQB)
Unter den Linden 6
D-10099 Berlin
Follow @iqbmedia

Offices

Luisenstr. 56
10117 Berlin
Tel: +49 (30) 2093-46500
Fax: +49 (30) 2093-46599
Email: iqboffice@iqb.hu-berlin.de

Secondary Offices

Hannoversche Straße 19
10115 Berlin
Tel: +49 (30) 2093-46552


Schönhauser Allee 10
10119 Berlin