The DZA Research Data Centre provides researchers with access to the studies German Ageing Survey (DEAS) and Old Age in Germany (D80+) in the form of Scientific Use Files. We prepare the data so that they can be analysed directly. To support this, we provide extensive documentation (e.g. codebooks, survey instruments, methodological reports and further accompanying materials). We also offer advisory support to data users - for example on the application process, access and conditions of use, and working with the datasets.
The German Ageing Survey (DEAS) is a nationally representative survey of people in the second half of life, i.e. adults aged 40 and over in Germany. DEAS combines both a cross-sectional perspective (a snapshot at a given point in time) and a longitudinal panel design (tracking developments over time). It is funded by the Federal Ministry of Education, Family Affairs, Senior Citizens, Women and Youth (BMBFSFJ).
DEAS provides microdata (individual-level survey data) that can be used for social and behavioural science research as well as for social reporting. The data help to better understand living conditions and life-course developments in midlife and older age and serve as an information base for research, the public, and policy-makers.
The first wave was conducted in 1996. Further waves took place in 2002, 2008, 2011, 2014, 2017, 2020, 2020/2021 and 2023. The next wave will start in 2026. This means that societal and individual changes can now be studied over a period of 27 years.
Further information on DEAS, data use, and the services of the Research Data Centre is available on the DZA website under “Research Data Centre – German Ageing Survey”.
The “Old Age in Germany (D80+)” study is a nationally representative survey of people aged 80 and over in Germany. Unlike many ageing studies, D80+ includes not only people living in private households, but also those living in residential long-term care facilities.
The study was designed to provide reliable information on living conditions and quality of life in very old age across Germany. It covers key life domains such as housing, family and household, health and cognitive status, everyday activities, social relationships, life satisfaction/well-being, and financial situation. Where necessary, proxy interviews were conducted so that a close person could provide information on the respondent’s behalf.
Due to the COVID-19 pandemic, the study design had to be adapted and face-to-face interviews were not possible. Instead, data collection was carried out in two phases: a self-administered written survey (Module 1) and a telephone interview (Module 2). The data are available for scientific research via the FDZ-DZA as a Scientific Use File; accompanying documentation materials are available online.
The German Survey on Volunteering (FWS) is a representative survey on voluntary engagement in Germany, targeting people aged 14 years and older. The 1999, 2004 and 2009 surveys were carried out by TNS Infratest. From 2011 to 2021, scientific leadership of the FWS was held by the German Centre of Gerontology (DZA).
All Scientific Use Files of the FWS (1999–2019) prepared by the FDZ-DZA are now available only from the GESIS Data Archive for the Social Sciences. The FDZ-DZA no longer provides user support or individual analyses for the FWS.
All active data users may continue to use the data for as long as agreed in their contract.
In connection with the transfer of the FWS to GESIS, a long-prepared publication has been released that should serve as a reference paper for all publications based on the FWS. Users are asked to cite the following paper (open access) to describe the data basis:
- Nicole Hameister, Nadiya Kelle, Corinna Kausmann, Nora Karnick, Céline Arriagada & Julia Simonson (2023). Monitoring Civil Society. The German Survey on Volunteering 1999–2019. Soziale Welt, 74(2), 294–314. DOI: 10.5771/0038-6073-2023-2-294
The SUFs are provided by GESIS together with all documentation materials under the following study numbers and DOIs for scientific use:
- ZA3350: German Survey on Volunteering 1999, dx.doi.org/10.4232/1.14128
- ZA4331: German Survey on Volunteering 2004, dx.doi.org/10.4232/1.14129
- ZA5433: German Survey on Volunteering 2009, dx.doi.org/10.4232/1.14130
- ZA5713: German Survey on Volunteering 2014, dx.doi.org/10.4232/1.14131
- ZA5714: German Survey on Volunteering 2019, dx.doi.org/10.4232/1.14132
- ZA5715: German Survey on Volunteering Trend 1999–2014, dx.doi.org/10.4232/1.14133
A list of all publications known to the FDZ-DZA that are based on the FWS (2010–2023) is available on the FWS information page.
A Scientific Use File makes high-quality research data accessible to researchers without compromising the protection of the people surveyed. The Research Data Centre applies a range of measures to ensure that respondents’ anonymity is preserved and that no conclusions can be drawn about identifiable individuals. A data use agreement additionally specifies who may use the data and for what purpose, how the data may be stored and analysed, and which security and citation rules apply.
At the same time, this also means that the original data may differ from the SUF, because some variables have been edited or are not included in the SUF. Researchers who require exactly this more detailed information can access it for analysis at a specially protected guest researcher workstation at the DZA in Berlin.
If you would like to use data from the German Ageing Survey (DEAS) or the D80+ study, please complete the application form in full. You will receive a copy of your entries by email. Please read our data use conditions carefully in advance.
The anonymised and processed data from the German Ageing Survey (DEAS) and Old Age in Germany (D80+) are available free of charge via the DZA Research Data Centre (FDZ-DZA) as Scientific Use Files for non-commercial scientific research purposes. For data protection reasons, access to the data requires the conclusion of a data use agreement. Analyses of the Scientific Use Files may be carried out only by the data user themselves (and, where applicable, their registered co-users). Users therefore need proficiency in statistical software such as SPSS, R or Stata.
Researchers at universities and non-university research institutions, as well as university graduates and students using the data for qualification projects leading to an academic degree (e.g. a Master’s thesis, doctoral dissertation or habilitation), are permitted to work with the data. After submitting an application for data use and signing the data use agreement, the data are provided free of charge via download.
Students who have not yet obtained a Bachelor’s degree are not (yet) considered researchers and therefore must submit the data use application together with a supervising member of their university. For use of the SUF in teaching, specific modifications are required; these are described in detail in the data use agreement.
A guest researcher workstation is a controlled environment that enables research with sensitive data without compromising respondents’ data protection. It is located at the DZA Research Data Centre in Berlin and allows analyses of data that cannot be provided in the Scientific Use File for data protection reasons, or that are available only in a more detailed form (e.g. fine-grained regional data or sensitive additional variables).
Use is on-site only and subject to defined security rules:
- Access is restricted to a secure computer on the premises of the Research Data Centre.
- No data may be taken away (no copying to USB drives, no cloud uploads, etc.).
- Results may leave the workstation only as checked output (e.g. tables/figures after disclosure control), so that no conclusions about identifiable individuals are possible.
See also the FAQ entry on the option to supply additional data to the DEAS.
For an initial introduction, the FDZ-DZA recommends the short descriptions available on the DEAS documentation pages and the D80+ study pages. These provide first orientation and a good overview of contents and survey specifics.
There you will also find survey instruments (German and partly English), codebooks, the variable correspondence list, methodological reports from the survey institute, and further wave-specific and wave-overarching documents that facilitate working with the data.
If you have further questions, you are welcome to contact the FDZ-DZA team.
In DEAS, missing values are coded using a standardised scheme (from wave 4 onwards). This scheme supports both automated handling of missings and a substantive interpretation of why information is missing.
Missing codes (SPSS/Stata) and meaning:
- -1 / .a = refused
- -2 / .b = don’t know
- -3 / .c = filtered out (question-level routing)
- -4 / .d = filtered out (sample-level; e.g. panel-only or baseline-only; not used in 2020/21)
- -5 / .e = no drop-off available
- -6 / .f = no answer (drop-off questionnaire)
- -7 / .g = deleted during data processing (e.g. where drop-off information had to be removed due to identity/matching issues)
Practical guidance for analysis
In most cases, these missing codes are already defined in the dataset as missing values (user-defined missings).
Important for early waves (1996/2002/2008): These waves do not yet fully follow the standardised missing scheme. Before analysing, check frequency tables and value labels to identify any “missing codes” that may still lie within the valid value range (e.g. 8/9/98) and set them to missing accordingly.
For analyses comparing multiple waves, ensure that missing-value definitions are applied consistently—especially when pooling data across waves or when creating your own recodes.
Further information: For more details, see the DEAS User Manual.
DOI stands for Digital Object Identifier and refers to a permanent, persistent identifier used to cite and link electronic resources (texts, research data or other content). DOIs have been registered for all Scientific Use Files and survey instruments of the German Ageing Survey (DEAS) and the D80+ study.
If you cite the data using the corresponding DOI, you automatically refer to the exact dataset version you used for your analyses. The same applies to the documentation materials. This makes it easy for readers of your publication to identify and obtain a clear overview of your data source with minimal effort.
Yes. Master’s students (who already hold a Bachelor’s degree) can apply for the data in their own right via the regular procedure.
For Bachelor’s students, there are often easier options:
Campus file for teaching: The instructor applies for the data and prepares a campus file for the course. The required modifications are usually straightforward to implement.
Registration as co-users: Alternatively, the course supervisor can apply for the datasets as the data user and register the students as Bachelor’s candidates. In this case, the students also receive access to the data.
The data can be used for all qualification projects from the level of a Master’s degree onward. For Bachelor’s theses, the application must be submitted by the supervising university member.
To use DEAS or D80+ data, please complete the application form in full.
The data from the DEAS and the D80+ study can be used for teaching purposes. In this context, the FDZ-DZA follows a somewhat different approach from many other research data centres: lecturers and data users compile their campus file independently after applying for the data, signing the data use agreement, and downloading the Scientific Use Files (SUFs). Three criteria must be observed:
- A 50% random subsample must be drawn.
- All open-ended responses (string variables) must be removed.
- The dataset may contain no more than 100 variables.
At first glance, this procedure may appear more time-consuming, as the campus file is not provided in advance. However, the key advantage lies in its flexibility: data users can select variables according to their own substantive focus — for example, concentrating on health, housing environment, or employment rather than on intergenerational relationships or voluntary engagement.
The compilation of the campus file can also be meaningfully integrated into teaching. Using the freely available documentation materials (survey instruments, instrument documentation, codebooks), students can learn how to select appropriate variables for their research question and assess their quality and case numbers.
This process results in a customised campus file that is optimally aligned with the respective teaching and learning objectives.
No. Lecturers and data users work with their own Scientific Use File (SUF) and compile their campus file independently after applying for the data, signing the data use agreement, and downloading the SUFs. Three criteria must be observed:
- A 50% random subsample must be drawn.
- All open-ended responses (string variables) must be removed.
- The dataset may contain no more than 100 variables.
At first glance, this procedure may appear more time-consuming, as the campus file is not provided in advance. However, its key advantage lies in flexibility: data users can select variables according to their own substantive focus — for example, concentrating on health, housing environment, or employment rather than on intergenerational relationships or voluntary engagement.
The compilation of the campus file can also be meaningfully integrated into teaching. Using the freely available documentation materials (e.g. survey instruments, instrument documentation, codebooks), students can learn how to select appropriate variables for their research question and assess their quality and case numbers.
This results in a customised campus file that is optimally aligned with the respective teaching and learning objectives.
If the agreed period of data use expires, it cannot be extended. However, you may apply for the data again at any time. As we provide the data free of charge, the only additional effort required on your part is to complete the application form again and sign a new data use agreement.
If you wish to use the data for a new research project, please resubmit the completed application form for DEAS or D80+.
Important: A new data use agreement can only be concluded once we have received the data deletion confirmation for your previous contract.
No. The data deletion confirmation applies only to the datasets themselves. You may, of course, retain your do-files or syntax files, log files, and any analysis results, provided that they contain only aggregated information (e.g. means, variances, regressions, cross-tabulations, etc.) and no information on individual cases.
Yes, in principle it is possible to link or merge additional data to the DEAS, provided that doing so does not compromise or remove the anonymity of respondents. For example, additional information can be linked to the Scientific Use File (SUF) via the ISCO occupational classification (one example are job exposure indices, such as those made freely available by the BAuA).
In most cases, however, linking additional information to the SUF is not feasible because it would require unique regional identifiers that have been removed from the SUF for anonymity reasons. At the FDZ’s protected guest researcher workstation, by contrast, regional codes (e.g. district or municipality identifiers) can be used. This makes it possible to link custom indicator sets for contextual analyses—for instance from researchers’ own surveys/data collections or from official or established sources (e.g. the INKAR indicators provided by the BBSR).
In addition, the FDZ holds a number of datasets that can likewise be analysed only at the guest researcher workstation:
- Residential neighbourhood data (microm/infas360) on social milieus, purchasing power, age structure and more (for selected years/waves)
- More detailed housing price indices (RWI) at district/municipality level (including differentiation by forms of ownership and a rent index)
- Day-accurate dates of death
- Full methodology datasets as well as panel maintenance datasets
Important: If additional information is to be linked at municipality level or at an even finer geographic level, costs may arise. This is particularly the case if infas, acting as a data trustee, first needs to generate the required identifiers or update them to the current administrative boundary status (e.g. after territorial reforms).
More detailed information is available on our DEAS context data page.
If you use DEAS or D80+ data or documentation materials in publications (including theses or grey literature) or in presentations, we ask you to cite the sources in accordance with the principles of good scientific practice. Guidance on how to cite DEAS and D80+ data appropriately can be found both in the appendix to your data use agreement and here as a download.