Programme for International Student Assessment 2009 (PISA 2009)
Table of contents
> Link to application form (Scientific Use Files)
| Data Set Published on | 01.07.2013 |
|---|---|
| Current Version Available Since | 01.07.2013 |
| Survey Period | 2009 |
| Sample | Students (15-year old and in grade 9) (N=circa 10,000) in General Education Schools (N=214); Teachers (N=2,201); Principals (N=226) |
| 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 | Klieme, Prof. Dr. Eckhard |
| 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-2009/ |
| Related Studies | PISA 2000 (DOI: 10.5159/IQB_PISA_2000_v1), PISA 2003 (DOI: 10.5159/IQB_PISA_2003_v1), PISA 2006 (DOI: 10.5159/IQB_PISA_2006_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 | Klieme, E., Artelt, C., Hartig, J., Jude, N., Köller, O., Prenzel, M., Schneider, W., & Stanat, P. (2013). Programme for International Student Assessment 2009 (PISA 2009) (Version 1) [Data set]. Berlin: IQB – Institut zur Qualitätsentwicklung im Bildungswesen. http://doi.org/10.5159/IQB_PISA_2009_v1 |
| Restriction Notice | Cognitive abilities must not be used as a dependent variable in the analyses. |
Project description
The German Institute for International Educational Research (DIPF) at Frankfurt/Main is in charge of the PISA 2009 German national assessment (PISA 2009). As previous PISA waves, PISA 2009 aimed to measure how well students at age 15 are prepared to meet the challenges of today's knowledge societies and lifelong learning. Therefore, the PISA test tasks are based on knowledge and skills required for the application and acquisition of knowledge in private and professional daily life rather than on specific curricula. As PISA 2000, PISA 2003 and PISA 2006, the PISA 2009 study assessed students' performance in the domains of reading, mathematics and science. The main focus of PISA 2009 was on reading comprehension. In addition to performance data assessing students' competencies in reading, mathematics and science, further data were collected on student attitudes, the school context, demographic features, and information on social, cultural, and family background, just as in previous PISA assessments. Questionnaires to be filled in by teachers, school principals and parents were used to gather further relevant background characteristics. Germany used the option of an additional grade-based sample. As a result, in addition to the internationally standard sample of 15-year-old school students drawn from the entire student body of a sample school, a further sample comprising two grade 9 classes per school is available. (IQB)
To be noted:
national supplementary study independent as of 2009
Whereas successful applicants for the data of PISA 2000, 2003 or 2006, respectively, will receive the German data from both the international study and the national supplementary study, this will not be the case with PISA 2009 and subsequent PISA waves. As of PISA 2009 the national supplementary study was replaced by the IQB Ländervergleich. The data from the IQB Ländervergleich will not automatically be provided together with the German data of the international PISA 2009 study, but is also available from the Research Data Centre (FDZ) at the Institute for Educational Quality Improvement (IQB) upon application. Further information on the IQB Ländervergleich is to be found here and here.
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 2009
- PISA 2009 Students and Parents data (SPSS)
- PISA 2009 School principals data (SPSS)
- PISA 2009 Teachers data (SPSS)
Documentation
Here you can find further documentation:
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.
Why are there no sub-data sets for 15-year-olds and 9th graders in the PISA 2009 study as in the other PISA studies?
Unfortunately, we have only received the PISA 2009 data for ninth graders from the data-providing consortium. The PISA 2009 data for 15-year-olds are freely available on the OECD website.
When was the PISA 2009 test conducted in Germany?
In Germany, the PISA 2009 survey was conducted in April and May 2009; further information on the implementation can be found in the Results Report on PISA 2009 in Germany (p. 16ff.).
Literature
Selected literature is listed 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 18-021). Mannheim: Zentrum für Europäische Wirtschaftsforschung. Accessed 09.05.2022. Retrieved from 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. 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
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 (1 Aufl.). Bamberg: Europäisches Forum für Migrationsstudien.
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
Roller, M. & Steinberg, D. (2017) The Distributional Effects of Early School Stratification - Non-Parametric Evidence from Germany (WWZ Working Paper 2017/20). Basel: Center of Business and Economics - University of Basel. Accessed 19.08.2021. Retrieved from 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 16-06). Madrid: Universidad Carlos III de Madrid. Accessed 11.08.2021. Retrieved from http://hdl.handle.net/10016/22880
Autorengruppe Bildungsberichterstattung. (2016). Bildung in Deutschland 2016. Ein indikatorengestützter Bericht mit einer Analyse zu Bildung und Migration. Bielefeld: Bertelsmann. 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 - Unveröffentlichte Masterarbeit. Universität Kassel, Kassel.
Schoor, C. (2016). Utility of reading — Predictor of reading achievement? Learning and Individual Differences, 45, 151–158. https://doi.org/10.1016/j.lindif.2015.11.024
2014
Hauschildt, J. (2014). The Effects of Ability Grouping within Schools on Educational Outcomes - an Empirical Approach - Unveröffentlichte Masterarbeit. Universität Hamburg, Hamburg.
Hertel, S., Hochweber, J., Mildner, D., Steinert, B. & Jude, N. (2014). PISA 2009 Skalenhandbuch. Münster: Waxmann. Verfügbar unter https://nbn-resolving.org/urn:nbn:de:0111-opus-95542
2013
Jude, N. & Klieme, E. (Hrsg.). (2013). PISA 2009 - Impulse für die Schul- und Unterrichtsforschung (Zeitschrift für Pädagogik Beiheft 59). Weinheim u.a.: Beltz.
Klieme, E., Artelt, C., Hartig, J., Jude, N., Köller, O., Prenzel, M., Schneider, W. & Stanat, P. (2013). Programme for International Student Assessment 2009 (PISA 2009) (Version 1) [Datensatz]. Berlin: IQB - Institut zur Qualitätsentwicklung im Bildungswesen. https://doi.org/10.5159/IQB_PISA_2009_v1
2010
Klieme, E., Artelt, C., Hartig, J., Jude, N., Köller, O., Prenzel, M. et al. (Hrsg.). (2010). PISA 2009. Bilanz nach einem Jahrzehnt. Münster: Waxmann. Verfügbar unter https://content-select.com/de/portal/media/view/519cc78b-758c-40d1-9c81-29105dbbeaba
OECD (Hrsg.) (2010). Pisa 2009 Ergebnisse. Zusammenfassung.