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Progress in International Reading Literacy Study 2016 (IGLU/PIRLS 2016)

Inhaltsverzeichnis

Projektbeschreibung

Leerdatensätze

Dokumentation

Weiterführende Informationen

Hinweise zur Nutzung der Daten

Literatur

> Scientific Use Files beantragen

Projektbeschreibung

Das Projekt IGLU (International: Progress in Reading Literacy Study [PIRLS]) 2016 untersucht auf Basis repräsentativer Daten die Lesekompetenz von Schüler*innen am Ende der vierten Grundschulklasse in Deutschland. Durch die internationale Ausrichtung des Projekts können die Schulleistungen in Deutschland mit denen in anderen Ländern und Regionen der Welt verglichen werden. IGLU ist ein zentrales Element des Bildungsmonitorings in Deutschland und fand im Jahr 2016 zum vierten Mal statt. Die Erhebung ermöglicht Aussagen über Trends im Schulsystem. Im Fokus der IGLU Studie steht die Lesekompetenz. Darüber hinaus werden zentrale Merkmale von Schüler*innen, ihres Unterrichts, ihrer Schulen und ihrer Familien betrachtet. Eine Besonderheit in IGLU 2016 stellt die Erweiterungsstudie PIRLS Literacy dar. In der Vergangenheit zeigte sich, dass in den Staaten, in denen die Lesekompetenzen der Viertklässler*innen deutlich unterhalb des internationalen Niveaus lagen, die Kompetenzen der leistungsschwächeren Kinder mit den regulären IGLU-Instrumenten nicht angemessen erfasst werden konnten. Deutschland hat NICHT an PIRLS Literacy teilgenommen. (Projekt/IQB)

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Blank data sets

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

• students and parents data (SPSS)
• students and teachers data (SPSS)
• teachers data (SPSS)
• school principals data (SPSS)
• trackinglist data (SPSS)

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Documentation

Here, you can find the Report for IGLU 2016 (in German)

Here, you can find the Scale Manual for IGLU 2016 (in German).

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Further information

Further information on IGLU/PIRLS 2016 can be found on the websites of the IEA and the TIMSS & PIRLS International Study Center.

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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 achievement tests used in German large scale assessment studies (PISA, IGLU and IQB studies) correlate highly, but the underlying competence 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 (Kultusministerkonferenz, KMK) and as a result more closely aligned with the German school curriculum than 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 trends on a common metric.

How can I link the partial data sets?

A linked teacher-student data set already exists. There is also a data set containing only the teacher data. However, the teacher dataset does not allow for statements that are representative on a national level.

There are 79 duplicate cases in the teacher-student data set. These are automatically taken into account when weighted analyses are performed. Linking the teacher-student data set to the tracking data set by student ID is only possible after the duplicate cases have been treated (e.g. exclusion, random selection or combination).

Why do the numbers of students differ between the student-parent data set and the tracking data set?

The tracking data set contains all students who were included in the sample. In the student-parent data set, on the other hand, only those who actually took part in the test are included. Non-participation can be explained by various reasons, which are summarised in the tracking data set in the variable "TR_EXCLUSION_FDZ". The sample size of n = 3959 in the student-parent data set is also used in the results report, so it is recommended to work with this sample size.

How was the age variable calculated?

The reference time for the age calculation (variable "ASDAGE") is the test date, which has been emptied due to data protection. If you have questions regarding the age at a different point in time, please contact us. We will be happy to advise you on how to use our data in accordance with data protection regulations.

Scales on classroom management

What is provided in the data and reports?

The student-parent data (SEFB) and the teacher-student data (LSFB) each contain four assessment scales on classroom management in the broader sense (classroom management/discipline (CM), cognitive activation (CA), social climate/support from the teacher (SC) and structuring (STR)). The constituent items were asked of the students and teachers, respectively, and are documented in the respective sections of the scale manual.

A sum score of the recoded items was formed for each scale. The coding instructions are described in chapter 9 of the results report (Stahns et al., 2017, pp. 264). The (four-level) items were first dichotomised by assigning 1 as the new value to the responses 3 and 4 in most cases. The other two responses were assigned the value 0.  If the response to an item was missing because it was either omitted (missing by omission) or answered invalidly (missing invalid response), 0 was also assigned as the new value. A sum score was then calculated for the items of the scale.

Only people who had not been presented with the questions on classroom management were given empty/missing values on the individual items and sum scores. This means there is a valid sum score for every person who had the opportunity to answer the questions. However, this also means that people without valid answers to the individual items still have a valid sum score of 0. This applies to between 80 and 200 cases per scale.

What does this mean for data users?

The sum scores calculated in this way are included in the data and were the basis for the analyses in the IGLU 2016 report. To reproduce these results, the available sum scores have to be used. Data users who wish to conduct secondary analyses and use the constructs for classroom management in a different coding can use the individual unchanged items also included in the data to develop their own coding rules. To facilitate this, we provide an overview of the items, their assignment to the scales and the coding rules of the report chapter: Table (in German)

Reference

Stahns, R., Rieser, S., & Lankes, E.-M. (2017). Unterrichtsführung, Sozialklima und kognitive Aktivierung im Deutschunterricht in vierten Klassen. In A. Hußmann, H. Wendt, W. Bos, A. Bremerich-Vos, D. Kasper, E.-M. Lankes, et al. (Eds.), IGLU 2016: Lesekompetenzen von Grundschulkindern in Deutschland im internationalen Vergleich (pp. 251–277). Münster, New York: Waxmann. available at https://www.waxmann.com/index.php?eID=download&buchnr=3700

What else must be taken into account during data analysis?

You will find more detailed methodical information on calculating with data from large scale assessment surveys in this tutorial (english).

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Literature

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

2022

Bütje, L. (2022). Exam Timing and the Socioeconomic Achievement Gap - unveröffentlichte Masterarbeit. Universität Konstanz, Konstanz.

2020

Hussmann, A., Wendt, H., Bos, W., Bremerich-Vos, A., Kasper, D., Lankes, E.-M. et al. (2020). Internationale Grundschul-Lese-Untersuchung 2016 (IGLU 2016) (Version 3) [Data set]. Berlin: IQB - Institut zur Qualitätsentwicklung im Bildungswesen. https://doi.org/10.5159/IQB_IGLU_2016_v3

Hussmann, A., Wendt, H., Bos, W. & Rieser, S. (2020). IGLU 2016: Skalenhandbuch zur Dokumentation der Erhebungsinstrumente und Arbeit mit den Datensätzen: Waxmann. Verfügbar unter https://books.google.de/books?id=jhsDEAAAQBAJ

2019

Hussmann, A. (2019). Unter der Norm - Kompetenz und Diagnostik in IGLU 2016. Below the norm – competence and diagnosis in PIRLS Germany 2016. Lengerich: Pabst Science Publishers.

2017

Hussmann, A., Wendt, H., Bos, W., Bremerich-Vos, A., Kasper, D., Lankes, E.-M. et al. (Hrsg.). (2017). IGLU 2016. Lesekompetenzen von Grundschulkindern in Deutschland im internationalen Vergleich. Münster: Waxmann. Verfügbar unter https://www.waxmann.com/index.php?eID=download&buchnr=3700

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