Programme for International Student Assessment 2000 (PISA 2000)
Table of contents
> Link to application form (Scientific Use Files)
| Data Set Published on | 01.01.2009 |
|---|---|
| Current Version Available Since | 01.01.2009 |
| Survey Period | 2000 |
| Sample | Students (15-year old and in grade 9) (N= circa 36,000) in General Education Schools (N=1,480) |
| Survey Unit | Parents Principals Students |
| 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 | Baumert, Prof. Dr. Jürgen |
| Data Producers | Baumert, Prof. Dr. Jürgen 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-2000/ |
| Related Studies | PISA 2003 (DOI: 10.5159/IQB_PISA_2003_v1), PISA 2006 (DOI: 10.5159/IQB_PISA_2006_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 | Baumert, J., Artelt, C., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., Schneider, W., Tillmann, K.-J., & Weiß, M. (2009). Programme for International Student Assessment 2000 (PISA 2000) (Version 1) [Data set]. Berlin: IQB – Institut zur Qualitätsentwicklung im Bildungswesen. http://doi.org/10.5159/IQB_PISA_2000_v1 |
| Restriction Notice | Cognitive abilities must not be used as a dependent variable in the analyses. Users of the data set should always cite the folowing publications: Baumert, J., Artelt, C., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., Schneider, W., Schümer, G., Stanat, P., Tillmann, K.-J., & Weiß, M. (Hrsg.). (2002). Pisa 2000 - Die Länder der Bundesrepublik Deutschland im Vergleich. Opladen: Leske + Budrich. Baumert, J., Artelt, C., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., Schneider, W., Tillmann, K.-J., & Weiß, M. (Hrsg.). (2003). Pisa 2000 - Ein differenzierter Blick auf die Länder der Bundesrepublik Deutschland. Opladen: Leske + Budrich. Baumert, J., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., Schneider, W., Stanat, P., Tillmann, K.-J. , & Weiß, M. (2001). PISA 2000: Basiskompetenzen von Schülerinnen und Schülern im internationalen Vergleich. Opladen: Leske + Budrich. Any publication using the German PISA 2000 data set (I or E) must carry the following notice: "PISA 2000 was designed in Germany as a national research programme by the German PISA Consortium (Jürgen Baumert, Eckhard Klieme, Michael Neubrand, Manfred Prenzel, Ulrich Schiefele, Wolfgang Schneider, Klaus-Jürgen Tillmann, Manfred Weiß). It was lead-managed by Professor Dr. Jürgen Baumert, Max-Planck Institute for Human Development, Berlin. Primary research results have been published, e.g., in Baumert et al. (2001, 2002, 2003). Survey questionnaires have been documented in Kunter et al. (2002). We thank the German PISA Consortium and the Research Data Center (Forschungs¬datenzentrum, FDZ) in Berlin for granting permission to conduct this secondary analysis and for their support." |
Project description
In three assessment cycles starting in the year 2000 and carried out at three-year intervals, the PISA study assessed the performance of 15-year-old students in the domains of reading, mathematics and science. It also looked at cross-curricular competences such as prerequisites for self-regulated learning as well as aspects of students' ability to cooperate and communicate. The study operates within the framework of an internationally agreed concept of "literacy". Rather than focusing on the assessment of students' factual knowledge, PISA evaluates the basic knowledge and skills required to participate in social, economic and political life. It examines the extent to which young adults have acquired these broader competences and gauges social disparities in educational achievement. The PISA 2000 survey focused on the subject area of reading competence while also assessing student performance in mathematics and science on a smaller scale. The PISA study aimed at comparing Germany's outcomes with those of other participating countries (PISA-International; PISA-I) was supplemented in 2000 by a series of representative surveys at Länder level (PISA-Ergänzung; PISA-E). In Germany, PISA 2000 was lead managed by Prof. Dr. Jürgen Baumert, director at the Max Planck Institute for Human Development, Berlin. (IQB)
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.
- Students data set, 15-year-olds (SPSS)
- Students data set, 9th graders (SPSS)
- Schools data set (SPSS)
Documentation
Here you can find further documentation:
Handbook of scales (in German)- Supplement to the handbook of scales (in German)
- List of variables students
- List of variables schoolID
To be noted
Please observe the following conditions for using the PISA 2000 data:
1. Any publication using the German PISA 2000 data sets (PISA-I or PISA-E) must carry the following notice: "PISA 2000 was designed in Germany as a national research programme by the German PISA Consortium (Jürgen Baumert, Eckhard Klieme, Michael Neubrand, Manfred Prenzel, Ulrich Schiefele, Wolfgang Schneider, Klaus-Jürgen Tillmann, Manfred Weiß). It was lead-managed by Professor Dr. Jürgen Baumert, Max-Planck Institute for Human Development, Berlin. Primary research results have been published, e.g., in Baumert et al. (2001, 2002, 2003). Survey questionnaires have been documented in Kunter et al. (2002). We thank the German PISA Consortium and the Research Data Centre (Forschungsdatenzentrum, FDZ) in Berlin for granting permission to conduct this secondary analysis and for their support." The references cited in this footnote must be listed in your bibliography in the usual style.
2. In line with parents' consent to having their children tested in the PISA 2000 Assessment, information on students' basic cognitive skills (KFT; kognitive Grundfähigkeiten) can only be used as covariates, not as target variables. The descriptive representation of distributions, especially the publication of group comparisons, as well as the use of the aggregate KFT test or of test parts as dependent variables is strictly prohibited. In the event of a violation of this agreement the publication concerned must be withdrawn, with its author acknowledging his or her infringement of the right of consent of the persons involved.
3. As the contracting authorities and the Consortium have agreed to preserve the anonymity of the schools participating in the PISA Assessment, a ban has been placed on publishing any analyses in which individual schools can be identified or made identifiable.
4. When the Federal Government and the Federal States awarded the contract for the PISA 2000 Assessment to the PISA 2000 Consortium, this was done with the proviso that no school-type related performance comparisons between Federal States shall be drawn. This agreement also applies to any and all secondary analyses.
Further information
General information, the Technical Report and the data of the international assessment study PISA 2000 I are to be found on the website of the Organisation for Economic Co-Operation and Development (OECD).
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.
There are different PV variables for mathematics in the 9th grade student data set. What is the difference between the PV variable groups?
- pvxnatm]: These PVs are the national mathematics tests and their results are reported on a national metric with a mean of 100 and a standard deviation of 30.
- [pvxnatmi]: These PVs are also based on the national test items, but are reported on the international metric with mean 500 and standard deviation 100 (therefore they correlate to one with the PVs [pvxnatm]).
- [pvxmg]: This is the combined performance score. All international and all national items are included in this score. According to the scale manual for the PISA 2000 study (Kunter et al., 2002; p. 77ff.; in the scale manual these items are labelled [NPV1MG1D]), it is recommended to use these PVs: "Results of various dimensional analyses show that it is appropriate to map the international and the national mathematics test on an overall dimension of basic mathematical literacy" (Kunter et al., 2002; p. 78). You can download the scale manual here ; if necessary, the supplements to the scale manual will also help you.
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
Helm, F., Wolff, F., Möller, J., Zitzmann, S., Marsh, H. W., & Dicke, T. (2022). Individualized teacher frame of reference and student self-concept within and between school subjects. Journal of Educational Psychology. Advance online publication. https://doi.org/10.1037/edu0000737
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
Baden, G. (2018). Das akademische Selbstkonzept im Kontext primärer und sekundärer Herkunftseffekte: Eine quantitative Querschnittsstudie (Unveröffentlichte Masterarbeit): Universität Bremen, Bremen.
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.
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
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.
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
Schmid, C. (2015). Lernen von älteren oder Lernen durch jüngere Geschwister? Effekte der Geschwisterkonstellation auf die Lesekompetenz und Hausaufgabenhilfe in PISA 2000-E. Zeitschrift für Erziehungswissenschaft, 18(3), 591–615. https://doi.org/10.1007/s11618-015-0635-5
2014
Penk, C., Pöhlmann, C., & Roppelt, A. (2014). The role of test-taking motivation for students’ performance in low-stakes assessments: an investigation of school-track-specific differences. Large-scale Assessments in Education, 2(1), 1–17. https://doi.org/10.1186/s40536-014-0005-4
Tatsi, E. (2014). Essays in Social and Spatial Interactions (Dissertation): Johann Wolfgang Goethe-Universität Frankfurt am Main, Frankfurt, Main.
2013
Horschig, K. (2013). Die Bedeutung des Zusamenhangs von Selbstkonzept und Motivation für die Schulleistungen hochbegabter Underachiever (Unveröffentlichte Masterarbeit): Technische Universität Berlin, Berlin.
Wolff, C. A. (2013). Education, Development and Labor Markets (Dissertation): Stockholm School of Economics Göteborg, Stockholm.
2012
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
2011
Georg, W. (2011). Soziale Ungleichheit und kulturelles Kapital in der PISA-2000-Studie - eine latente Klassen-Analyse. Zeitschrift für Soziologie der Erziehung und Sozialisation, 31(4), 393–408. https://doi.org/10.3262/ZSE1104393
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
Szczesny, M., & Watermann, R. (2011). Differenzielle Einflüsse von Familie und Schulform auf Leseleistung und soziale Kompetenzen. Journal for Educational Research Online, 3(1), 168–193. https://doi.org/10.25656/01:4687
Walper, S., Köller, O., Lewalter, D., & Spangler, G. (2011). Psychologie in Erziehung und Unterricht. Zeitschrift für Forschung und Praxis, 48(4).
2010
Brunner, M., & Krauss, S. (2010). Modellierung kognitiver Kompetenzen von Schülern und Lehrkräften mit dem Nested-Faktormodell. In W. Bos, E. Klieme, & O. Köller (Eds.), Schulische Lerngelegenheiten und Kompetenzentwicklung (pp. 105–125). Münster, München, Berlin: Waxmann.
Deininger, V. (2010). Messung der Fähigkeit zum selbstregulierten Lernen bei Auszubildenden des Berufsfeldes Elektroniker in Baden-Württemberg (Unveröffentlichte Masterarbeit): Pädagogische Hochschule Heidelberg, Heidelberg.
Hegelow, M. (2010). Vorhersage der Leistungsvarianz auf Schulebene durch das Konstrukt erwartungswidriger Leistungen (Unveröffentlichte Diplomarbeit): Freie Universität Berlin, Berlin.
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
2009
Baumert, J., Artelt, C., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., . . . Weiß, M. (2009). Programme for International Student Assessment 2000 (PISA 2000) (Version 1) [Data set]. Berlin: IQB - Institut zur Qualitätsentwicklung im Bildungswesen. https://doi.org/10.5159/IQB_PISA_2000_v1
OECD. (2009). Pisa data analysis manual. Paris: OECD Publishing.
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
2006
Becker, R., & Schubert, F. (2006). Soziale Ungleichheit von Lesekompetenzen: Eine Matching-Analyse im Längsschnitt mit Querschnittsdaten von PIRLS 2001 und PISA 2000. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 58(2), 253–284. https://doi.org/10.1007/s11575-006-0055-0
2005
Neubrand, M. (2005). Impulse aus PISA für die mathematikdidaktische Forschung. Der Mathematikunterricht, 51, 23–35.
Watermann, R., Thurn, S., Tillmann, K.-J., & Stanat, P. (Eds.). (2005). Die Laborschule im Spiegel ihrer PISA-Ergebnisse: Pädagogisch-didaktische Konzepte und empirische Evaluation reformpädagogischer Praxis. Weinheim: Juventa.
2004
Krohne, J. A., Meier, U., & Tillmann, K.‑J. (2004). Sitzenbleiben, Geschlecht und Migration - Klassenwiederholungen im Spiegel der PISA-Daten. Zeitschrift für Pädagogik, 50(3), 373–391. Retrieved from https://nbn-resolving.org/urn:nbn:de:0111-opus-48161
Lenzen, D., Baumert, J., Watermann, R., & Trautwein, U. (Eds.). (2004). Zeitschrift für Erziehungswissenschaft: Beiheft 3. PISA und die Konsequenzen für die erziehungswissenschaftliche Forschung. Wiesbaden: Springer VS.
Neubrand, M. (Ed.). (2004). Mathematische Kompetenzen von Schülerinnen und Schülern in Deutschland: Vertiefende Analysen im Rahmen von PISA 2000. Wiesbaden: Springer VS.
Rost, J., Prenzel, M., Carstensen, C. H., Senkbeil, M., & Groß, K. (2004). Naturwissenschaftliche Bildung in Deutschland: Methoden und Ergebnisse von PISA 2000. Wiesbaden: Springer VS.
Schiefele, U., Artelt, C., & Schneider, W. (Eds.). (2004). Struktur, Entwicklung und Förderung von Lesekompetenz: Vertiefende Analysen im Rahmen von PISA 2000. Wiesbaden: Springer VS.
Schümer, G., Tillmann, K.-J., & Weiß, M. (Eds.). (2004). Die Institution Schule und die Lebenswelt der Schüler: Vertiefende Analysen der PISA-2000-Daten zum Kontext von Schülerleistungen (1. Aufl.). Wiesbaden: Springer VS.
Watermann, R., & Stanat, P. (2004). Schulrückmeldungen in PISA 2000: Sozialnorm- und kriteriumsorientierte Rückmeldeverfahren. Empirische Pädagogik, 18, 40–61.
2003
Artelt, C., Baumert, J., Mc Elvany, N., & Peschar, J. (2003). Learners for life: Student approaches to learning. Results from PISA 2000. Paris: OECD.
Baumert, J., Artelt, C., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., . . . Weiß, M. (Eds.). (2003). PISA 2000: Ein differenzierter Blick auf die Länder der Bundesrepublik Deutschland. Opladen: Leske & Budrich.
Baumert, J., Watermann, R., & Schümer, G. (2003). Disparitäten der Bildungsbeteiligung und des Kompetenzerwerbs. Zeitschrift für Erziehungswissenschaft, 6(1), 46–71. https://doi.org/10.1007/s11618-003-0004-7
Klieme, E. (2003). Benotungsmaßstäbe an Schulen: Pädagogische Praxis und institutionelle Bedingungen: Eine empirische Analyse auf der Basis der PISA-Studie. In H. Döbert, B. von Kopp, R. Martini, & M. Weiß (Eds.), Bildung vor neuen Herausforderungen: historische Bezüge, rechtliche Aspekte, Steuerungsfragen, internationale Perspektiven (pp. 195–210). Neuwied: Luchterhand.
Schümer, G. (2003). Soziale Herkunft, Bildungsbeteiligung und Schulleistungen von Jugendlichen aus Migrantenfamilien: Ergebnisse der OECD-Studie PISA 2000. In S. Handschuck (Ed.), Interkulturelle Verständigung: Fachtagung Bildung und Chancengleichheit 2002. Dokumentation (pp. 7–19). München.
Watermann, R., Stanat, P., Kunter, M., Klieme, E., & Baumert, J. (2003). Schulrückmeldungen im Rahmen von Schulleistungsuntersuchungen: Das Disseminationskonzept von PISA-2000. Zeitschrift für Pädagogik, 49(1), 92–111. Retrieved from https://nbn-resolving.org/urn:nbn:de:0111-opus-38708
Weiß, M., & Preuschoff, C. (2003). Sind mehr Privatschulen eine Antwort auf PISA?: Ergebnisse einer explorativen Analyse von Daten aus PISA-E. Recht der Jugend und des Bildungswesens, 51, 231–238.
Wirth, J., & Klieme, E. (2003). Computer-based assessment of problem solving competence. Assessment in Education: Principles, Policy & Practice, 10, 329–345.
2002
Artelt, C., Schiefele, U., Schneider, W., & Stanat, P. (2002). Leseleistungen deutscher Schülerinnen und Schüler im internationalen Vergleich (PISA): Ergebnisse und Erklärungsansätze. Zeitschrift für Erziehungswissenschaft, 5(1), 6–27. https://doi.org/10.1007/s11618-002-0002-1
Baumert, J., Artelt, C., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., . . . Weiß, M. (Eds.). (2002). PISA 2000: Die Länder der Bundesrepublik Deutschland im Vergleich. Opladen: Leske & Budrich.
Baumert, J., & Stanat, P. (2002). PISA 2000: Erste Ergebnisse und die Identifikation von Handlungsfeldern. Schulmanagement, 33, 30–32.
Kunter, M., Schümer, G., Artelt, C., Baumert, J., Klieme, E., Neubrand, M., . . . Weiß, M. (2002). PISA 2000: Dokumentation der Erhebungsinstrumente [PISA 2000: Documentation of survey instruments]. Materialien aus der Bildungsforschung: Vol. 72. Berlin: Max-Planck-Institut für Bildungsforschung. Retrieved from https://www.iqb.hu-berlin.de/fdz/studies/PISA-2000/PISA2000Dokument.pdf
Kunter, M., & Stanat, P. (2002). Soziale Kompetenz von Schülerinnen und Schülern: Die Rolle von Schulmerkmalen für die Vorhersage ausgewählter Aspekte. Zeitschrift für Erziehungswissenschaft, 5(1), 49–71.
Lüdtke, O., Köller, O., Artelt, C., Stanat, P., & Baumert, J. (2002). Eine Überprüfung von Modellen zur Genese akademischer Selbstkonzepte: Ergebnisse aus der PISA-Studie. Zeitschrift für Pädagogische Psychologie, 16(3/4), 151–164.
Neubrand, M., Klieme, E., Lüdtke, O., & Neubrand, J. (2002). Kompetenzstufen und Schwierigkeitsmodelle für den PISA-Test zur mathematischen Grundbildung. Unterrichtswissenschaft, 30, 116–135.
Prenzel, M., Häußler, P., Rost, J., & Senkbeil, M. (2002). Der PISA-Naturwissenschaftstest: Lassen sich die Aufgabenschwierigkeiten vorhersagen? Unterrichtswissenschaft, 30, 100–115.
Stanat, P., Watermann, R., Baumert, J., Klieme, E., Artelt, C., & Schümer, G. (2002). Rückmeldung der PISA 2000-Ergebnisse an die beteiligten Schulen. Berlin: Max-Planck-Institut für Bildungsforschung.
Struck, P. (2002). TIMSS, PISA und LAU lassen viele Fragen offen und werfen neue auf. Pädagogikunterricht, 22(1), 41–42.
Wirth, J., & Klieme, E. (2002). Computer literacy im Vergleich zwischen Nationen, Schulformen und Geschlechtern. Unterrichtswissenschaft, 30(2), 136–157. Retrieved from https://nbn-resolving.org/urn:nbn:de:0111-opus-76835
2001
Artelt, C., Baumert, J., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., . . . Weiß, M. (Eds.). (2001). PISA 2000: Zusammenfassung zentraler Befunde. Berlin: Max-Planck-Institut für Bildungsforschung. Retrieved from http://www.mpib-berlin.mpg.de/pisa/ergebnisse.pdf
Artelt, C., Schiefele, U., & Schneider, W. (2001). Predictors of reading literacy. European Journal of Psychology of Education, 16(3), 363–384.
Baumert, J., Artelt, C., Klieme, E., & Stanat, P. (2001). PISA - Programme for International Student Assessment: Zielsetzung, theoretische Konzeption und Entwicklung von Messverfahren. In F. E. Weinert (Ed.), Leistungsmessungen in Schulen (pp. 285–310). Weinheim: Beltz.
Baumert, J., Klieme, E., Neubrand, M., Prenzel, M., Schiefele, U., Schneider, W., . . . Weiß, M. (Eds.). (2001). PISA 2000: Basiskompetenzen von Schülerinnen und Schülern im internationalen Vergleich. Opladen: Leske & Budrich.
Bundesministerium für Bildung und Forschung. (2001). Schülerleistungen im internationalen Vergleich. OECD PISA. Bonn: Bundesministerium für Bildung und Forschung.
Klieme, E., Funke, J., Leutner, D., Reimann, P., & Wirth, J. (2001). Problemlösen als fächerübergreifende Kompetenz: Konzeption und erste Resultate aus einer Schulleistungsstudie. Zeitschrift für Pädagogik, 47(2), 179–200. Retrieved from https://nbn-resolving.org/urn:nbn:de:0111-opus-52723
Neubrand, M. (2001). PISA - "Mathematische Grundbildung" beschreiben und testen. Die Grundschulzeitschrift, 147, 58–59.
Neubrand, M., Biehler, R., Blum, W., Cohors-Fresenborg, E., Flade, L., Knoche, N., . . . Wynands, A. (2001). Grundlagen der Ergänzung des internationalen PISA-Mathematik-Tests in der deutschen Zusatzerhebung. Zentralblatt für Didaktik der Mathematik, 33, 45–59.
OECD. (2001). Lernen für das Leben: Erste Ergebnisse der internationalen Schulleistungsstudie PISA 2000. Paris: OECD Publishing.
1999
Artelt, C., & Stanat, P. (1999). Schülerleistungen im internationalen Vergleich: Die OECD-Studie PISA (Programme for International Student Assessment). Erziehungswissenschaft, 10(8-14).