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Programme for International Student Assessment 2012 (PISA 2012)

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

The fifth survey of PISA in 2012 in Germany was carried out by a working group at the Centre for International Student Assessment (ZIB e.V.). PISA aims to measure how well students at age 15 are prepared to meet the challenges of today's knowledge societies and lifelong learning. Thus, the PISA test items are not directly linked to national school curricula but are designed to assess the skills and competences required for the acquisition of knowledge and its application to real-life situations. In this study, as in the previous PISA surveys, students were tested in the domains of reading literacy, mathematical literacy, scientific literacy and problem solving. In PISA 2012 the focus was on mathematical literacy for the second time since 2003. In addition to assessing students' competencies, PISA 2012, like its forerunners, gathered information about students' attitudes, their school context, demographic characteristics and students' social, cultural and family background. Furthermore, questionnaires for teachers, school principals and parents were used for the detection of other relevant background characteristics. (IQB)

Please note:
Germany's national supplementary surveys independent since 2009

Successful applicants for the data of PISA 2000, 2003 or 2006, respectively, will receive the German data from both the international survey (PISA-I) and the national supplementary survey (PISA-E). Starting with PISA 2009, however, the national supplementary surveys (PISA-E) have since been replaced by the IQB Ländervergleiche (National Assessment Studies), which are carried out parallel to the international PISA surveys. Therefore, the data from the IQB National Assessment Studies since 2009 are not provided automatically together with the German data of the corresponding international PISA surveys. Nevertheless, they, too, are available from the Research Data Centre (FDZ) at the Institute for Educational Quality Improvement (IQB) upon application. Further information on the IQB National Assessment Studies is to be found here and here.

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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 2012

• PISA 2012 parents data (SPSS)
• PISA 2012 teachers data (SPSS)
• PISA 2012 students data (9th grade) (SPSS)
• PISA 2012 students data (15 years) (SPSS)
• PISA 2012 school principals data (SPSS)
• PISA 2012 matching data (SPSS)

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Documentation

Here you can find further documentation:

• Scaling Manual PISA 2012
  

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Further information

• Here you can find key results of PISA 2012 (German only).
• A comprehensive report is available for download here.
• Reports in English and German are also available for download on the PISA website of the OECD.
• Information about the data (German only)

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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
• PISA Scientific Literacy
• 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.

Is there a possibility to get the federal state identifier in PISA 2012?

The federal state identifier is included in the data sets. We will gladly make this data set version available to you via the controlled remote computer access JoSuA. However, for this we would have to conclude an additional agreement with you on the use of this data version. Please contact us for this purpose. The data sets of the PISA 2015 study also contain the federal state identifier.

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Literature

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

2022

Brunner, M., Stallasch, S. E. & Lüdtke, O. (2022, 31. March) 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

2020

Camarero Garcia, S. (2020) Inequality of Educational Opportunities and the Role of Learning Intensity (ZEW Discussion Paper 18-021). Mannheim: Zentrum für Europäische Wirtschaftsforschung. Accessed 09.05.2022. Retrieved from http://ftp.zew.de/pub/zew-docs/dp/dp18021.pdf

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. Bielefeld: wbv. https://doi.org/10.3278/6001820fw

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

Mang, J., Ustjanzew, N., Schiepe-Tiska, A., Prenzel, M., Sälzer, C., Müller, K. & Gonzaléz Rodríguez, E. (2018). PISA 2012 Skalenhandbuch. Dokumentation der Erhebungsinstrumente. Münster: Waxmann. Verfügbar unter https://www.pisa.tum.de/fileadmin/w00bgi/www/Berichtsbaende_und_Zusammenfassungungen/PISA_2012_Skalenhandbuch_final-openaccess.pdf

Mora-Ruano, J. G., Gebhardt, M. & Wittmann, E. (2018). Teacher Collaboration in German Schools: Do Gender and School Type Influence the Frequency of Collaboration Among Teachers? Frontiers in Education, 3. https://doi.org/10.3389/feduc.2018.00055

Pluschnikov, M., Gianneres, S. & Sicking, T. (2018). Eine empirische Analyse des einschulungsbedingten Geburtsmonatseffektes auf die kognitive Leistungsfähigkeit und den beruflichen Erfolg in Deutschland - Unveröffentlichte Seminararbeit. Westfälische Wilhelms-Universität Münster, Münster.

2017

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

2016

Andrietti, V. (2016) The causal effects of an intensified curriculum on cognitive skills : Evidence from a natural experiment (UC3M WP Economic Series 16-06). Madrid: Universidad Carlos III de Madrid. Accessed 11.08.2021. Retrieved from http://hdl.handle.net/10016/22880

Huebener, M., Kuger, S. & Marcus, J. (2016) Increased Instruction Hours and the Widening Gap in Student Performance (1^st ed.) (DIW Discussion Paper 1561). Berlin: Deutsches Institut für Wirtschaftsforschung.

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 - Unveröffentlichte Masterarbeit. Universität Kassel, Kassel.

Stöhr, S. C. (2016). Gender Effects on Achievement - Self-concept Relationships. Girls and Mathematics - Why not? - Unveröffentlichte Masterarbeit. Ludwig-Maximilians-Universität München, München.

2015

Prenzel, M., Sälzer, C., Klieme, E., Köller, O., Mang, J., Heine, J.-H., Schiepe-Tiska, A. & Müller, K. (2015). Programme for International Student Assessment 2012 (PISA 2012) (Version 5) [Datensatz]. Berlin: IQB - Institut zur Qualitätsentwicklung im Bildungswesen. https://doi.org/10.5159/IQB_PISA_2012_v5

2013

Prenzel, M., Saelzer, C., Klieme, E. & Koeller, O. (Hrsg.). (2013). PISA 2012. Fortschritte und Herausforderungen in Deutschland. Münster: Waxmann. Verfügbar unter https://www.pisa.tum.de/fileadmin/w00bgi/www/Berichtsbaende_und_Zusammenfassungungen/PISA_2012_EBook_ISBN3001.pdf

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