The PIAAC longitudinal study in Germany: rationale and design
Rammstedt et al. Large-scale Assess Educ
The PIAAC longitudinal study in Germany: rationale and design
Beatrice Rammstedt beatrice.rammstedt@gesis 0
Silke Martin 0
Anouk Zabal 0
0 GESIS - Leibniz Institute for the Social Sciences , B2, 1, 68159 Mannheim , Germany
In Germany, the respondents who had participated in the 2012 survey of the Programme for the International Assessment of Adult Competencies (PIAAC) were re-approached for the panel study PIAAC-L. PIAAC-L aims at investigating the longitudinal effects of skill outcomes over the life course and the development of the key skills assessed in PIAAC. Moreover, additional and alternative background information was collected and analyzed within PIAAC-L. PIAAC-L consists of three follow-up waves to the initial PIAAC 2012 survey. The present paper describes the rationale for PIAAC-L and the benefits of conducting a longitudinal PIAAC follow-up study in Germany. In addition, we outline the general design of PIAAC-L and the specific design of the three waves of data collection. Finally, we address the analytic potential of PIAAC-L data set and its availability to the scientific community.
PIAAC; PIAAC-L; Large-scale assessment; Basic skills; Longitudinal
sampling approach was implemented. Compared to other large-scale surveys conducted
in Germany in recent years, fieldwork for PIAAC in Germany was quite successful,
achieving a response rate of 55%. Approximately 5400 interviews were carried out as
computer-assisted personal interviews (background questionnaire) followed by a
selfadministered skills assessment (in the presence of the interviewer), which was usually
computer-based, but with an optional paper-based version. On average, the
administration of the background questionnaire and the cognitive assessment lasted 1 h and 40 min
in Germany (see Zabal et al. 2014). In order to obtain permission to re-contact German
PIAAC respondents, at the end of the interview they were asked whether they would be
tentatively willing to participate in a follow-up study.
In summary, the resulting data in each country participating in PIAAC provide
estimates of the literacy, numeracy and problem-solving (in technology rich environments)
skills of the adult population and offer the possibility of comparing and contrasting these
skills with sociodemographic and other background variables as well as with skills use in
the workplace and in daily life. The international data from this first round of PIAAC are
available as public use files via the OECD website.2 In addition, more comprehensive
data sets are available as national scientific use files for several countries, including
The motivation and context of the German longitudinal follow‑up
The PIAAC data provide detailed information about the level and distribution of the
basic skills in the adult population in the participating countries. However, due to
the fact that limited time was available for the PIAAC interview and thus also for the
administration of the background questionnaire, only a somewhat restricted amount of
background information could be collected. Thus, while the PIAAC background
questionnaire included the key predictors and social and economic outcomes of the basic
skills measured in the assessment, the number of background variables was by necessity
limited. These limitations constrain the extent to which the PIAAC data allows to
investigate key predictors and outcomes of the PIAAC skills and how extensively research
and policy questions can be addressed. Hence, central survey questions, for example (a)
how competences are acquired, (b) how skill use helps individuals to maintain and
further develop skills, (c) how skills shape labor market outcomes and job mobility over the
life course, and (d) whether adults are prepared for the challenges of modern knowledge
societies (see OECD 2013a) can be answered only partially on the basis of these data.
In addition to the need to further enrich the background information for the PIAAC
data, the investigation of skill maintenance and development versus skill loss can be
thoroughly investigated only by using longitudinal data. Only a longitudinal design can
disentangle whether self-selection into certain professions might be a crucial
determining factor in the correlation between earnings and competences.
The cross-sectional design used in PIAAC allows an initial exploration of the
associations between background variables and the cognitive skills assessed. However,
finding, for example, an association between skills and participation in continuing education
might indicate that participation in continuing education helps to maintain cognitive
skills. Another interpretation could be that such an association indicates that higher
skilled people have a greater tendency to participate in continuing education (without
this necessarily having any further effect on their cognitive skill level). To investigate
which of the two hypotheses is more appropriate, a longitudinal design is needed. Only a
prospective panel design can provide insights into the effects of basic skills for example
on (a) participation and opportunities or risks in the labor market, e.g. income or
unemployment, (b) participation in continuing education as outlined above, and (c) causes for
skill maintenance and development.
As mentioned above, fieldwork in PIAAC Germany was quite successful as evidenced
by the response rate of 55%. Although Germany ranks in the lower midfield compared
to other countries participating in PIAAC (OECD 2013b), in comparison to other
largescale face-to-face surveys in Germany conducted in recent years the achieved response
rate of 55% is extraordinarily high. For example, the German General Social Survey
(ALLBUS) obtained response rates between 34 and 38% in the last years (Wasmer et al.
2012, 2014). Similarly in the last rounds of the European Social Survey (ESS) Germany
realized response rates of 33% on average (European Social Survey 2012, 2014). Hence,
as the methodological standards for, and the costs of, PIAAC sampling and fielding
were very high, and the German PIAAC sample is of extremely high quality compared
to other German samples, it would have been a waste of a high-potential sample not
to make use of it for further investigations. This is even more the case due to the fact
that 98% of PIAAC respondents could be re-contacted and this was considered to be an
excellent starting point for a follow-up study. Finally, re-using the PIAAC sample also
incurred substantially lower costs.
Given the need to enhance the background information within PIAAC and the interest
in obtaining longitudinal follow-up data on the PIAAC participants, a PIAAC
longitudinal study (PIAAC-L) was initiated in Germany. Funded by the German Federal Ministry
of Education and Research, PIAAC-L has been implemented as a joint research project
on the part of the following three key institutions in the field of social sciences in
Germany who have substantial experience in one or more areas central to PIAAC-L, namely
the assessment of adult skills, the collection of background information, and conducting
GESIS—Leibniz Institute for the Social Sciences has first-hand expertise in PIAAC,
as it was commissioned by the Federal Ministry of Education and Research to act as
National Project Manager for PIAAC Germany. In addition, GESIS conducts several
large-scale national and, in particular, international surveys on a regular basis.
The German Institute for Economic Research (DIW Berlin) has coordinated the key
longitudinal household survey of income and living conditions in Germany, the German
Socio-Economic Panel (SOEP), since 1984.
The Leibniz Institute for Educational Trajectories (LIfBi) is the coordinator of the
National Educational Panel Study (NEPS), a project that collects longitudinal data on
educational processes from early childhood to adulthood in Germany.
In order to combine expertise from these three central longitudinal and
cross-sectional surveys, a consortium was constituted under the aegis of GESIS. All three
institutes are experienced in research data infrastructures and are members of the
Some of the other countries that participated in the first round of PIAAC have also
followed up on their PIAAC respondents. In Canada, a subset of the sample persons for
the Canadian longitudinal social survey Longitudinal and International Study of Adults
(LISA) also participated in PIAAC and these respondents are being re-interviewed
biennially as part of the longitudinal household survey. There was also a Polish follow-up
study on PIAAC (postPIAAC), which collected longitudinal and additional background
information on the PIAAC respondents in one additional wave of data collection
(2014/2015) and also implemented some basic cognitive skills tests as well as a basic ICT
skills test. Finally, a follow-up of PIAAC in Italy (also in 2014/2015) gathered
longitudinal information on Italian PIAAC respondents and focused on obtaining more data on
their non-cognitive skills. However, of all the PIAAC panel surveys, only the German
PIAAC-L has carried out a second assessment of literacy and numeracy using PIAAC
instruments (see description of Wave 2).
The design of the German PIAAC longitudinal study
PIAAC-L was designed to follow up the German PIAAC 2012 respondents that could be
re-contacted for a prospective PIAAC-related survey (n = 5225 individuals referred to in
PIAAC-L as “anchor persons”)4 over three additional waves of data collection,
combining research questions and measurement instruments from PIAAC, NEPS, and SOEP,
and extending the focus by also including adult members of the anchor persons’
households. Each of the three waves had a particular focus (cf. Table 1).
The first follow-up wave was conducted in 2014, and thus approximately two years
after the PIAAC 2012 assessment. It focused on collecting additional background
information. All respondents (anchor persons and additional household members aged
18 years and older) were administered a comprehensive CAPI questionnaire (average
duration: approximately 45 min) mainly consisting of items and questions regularly
collected in the SOEP (see Wagner et al. 2007). It collected, among other individual
characteristics, additional information on education and work history, employment situation,
income, family background, place of origin, health status, religious affiliation, and time
use (an overview of the concepts assessed in each of the three waves is provided in
“Appendix”). To obtain more context information on the household, a separate
household questionnaire (duration: 15 min on average)—similar to that applied in the SOEP—
was administered. This questionnaire collected information on household income, living
situation, living conditions and costs, as well as varied information on household
The second follow-up wave, which was conducted in 2015, concentrated on the
assessment of basic skills and the repetition of several questionnaire items from PIAAC (full
interview duration: 100 min on average). Therefore, the central skills assessed in PIAAC,
namely literacy and numeracy, were re-assessed for all anchor persons.5 In addition, as
4 As outlined below, a household design was adopted in PIAAC-L. In order to differentiate between respondents ini
tially recruited within PIAAC and the additional participating household members, the original PIAAC respondents are
referred to as “anchor persons”.
5 PIAAC-L does not include the domain problem solving in technology-rich environments in its cognitive assessment.
Table 1 PIAAC‑L design and sample size for each of the three follow ‑up waves
Extensive background information
Extensive background information
one of the central methodological aims of PIAAC-L was to compare the different
approaches to skills assessment and their operationalization in PIAAC and NEPS, the
corresponding competence tests from NEPS for reading (comparable to literacy in
PIAAC) and mathematics (comparable to numeracy in PIAAC) were also administered
in the 2015 wave. Hence, both the PIAAC and the NEPS assessment instruments were
administered in parallel using a randomized design with eight test conditions for the
anchor persons. This design is illustrated in Table 2. As can be seen from the table, each
anchor person completed either (a) the PIAAC numeracy and literacy instruments, or
(b) the NEPS math and reading instruments, or (c) either the PIAAC literacy instrument
and the NEPS reading instrument or the PIAAC numeracy instrument and the NEPS
Besides the anchor persons, their spouses or partners living in the same household
were interviewed and tested in the 2015 wave. However, spouses and partners were
administered only the NEPS instruments for reading and mathematics, with just two
different assessment conditions (in the second condition, the instruments were
administered in reverse order). The resulting data thus enable similarities and differences in
competences within couples to be compared.
Both skills assessments were administered under the same conditions as in PIAAC or
NEPS, respectively. While the PIAAC assessment was computer-based by default, with
an optional paper-based version for a small number of respondents who lacked
1. Measure Reading (NEPS)
2. Measure Math (NEPS) 408 64
computer experience, the NEPS assessment was paper-based only.6 Respondents taking
the PIAAC assessment had unlimited time to solve the tasks, whereas respondents
taking the NEPS assessment were timed by the interviewer and were allowed up to 28 min
per domain. The administration of both timed and untimed tasks within the cognitive
assessment is an innovative component of PIAAC-L.
In addition to the cognitive instruments, all respondents were administered a
comparatively short background questionnaire which focused on central questions from the
PIAAC questionnaire, for example on education, work status, and occupation. Some
alternative measures to those implemented in PIAAC were also included, for example
a module on skills use at work, which was conceptually similar to that administered
in PIAAC but with a different operationalization (taken from the NEPS). In addition,
efforts were made to collect a wider range of information relevant to the cognitive
assessment, for example more information on computer experience and health. Given
the design of the second wave of data collection, the questionnaire also included a
module administered to the spouse or partner of the anchor person. A set of plausible values
for the competence data will provide the basis for comparing PIAAC test performance
over time, for comparing the actual test instruments from PIAAC and NEPS, and for
analyzing the competence data within couples.
The third follow-up wave, which was conducted in 2016, again included all members
of the anchor person’s household who were aged 18 years or over. In this wave, detailed
background information—especially updates of previous information—was collected.
The average duration of the person questionnaire and of the household questionnaire
was 45 and 10 min, respectively. In addition, alternative operationalizations of
particular constructs initially assessed in PIAAC were tested in this wave. They included
an extended and re-worked module for measuring continuing education and revised
questions to assess skill mismatch (Perry 2016; see also McGowan and Andrews 2015).
These modules, and a number of other new items, were developed by researchers from
the PIAAC Leibniz Network, a collaborative project of eight German Leibniz institutes;
6 In PIAAC, computer-based and paper-based administration could also occur within a single interview: respondents
with low literacy and numeracy skills in the computer-based assessment subsequently worked on paper-based Reading
Components tasks (OECD 2013b). Mixing modes in the assessment in this way thus proved to be feasible and
the PIAAC Leibniz Network aims at further analyzing, advancing, and disseminating
PIAAC, and thus also PIAAC-L.
In addition to the background information, basic general cognitive skills of all
respondents (i.e., anchor persons and all other participating adult household members) were
assessed. For this purpose, short general cognitive tasks used in the SOEP were
administered. The two ultra-short cognitive performance tasks allow for a reliable assessment of
general intellectual ability and distinguish between two components of intellectual
functioning: cognitive mechanics and pragmatics (see Richter et al. 2013).
To facilitate analyses of the competence data and the repeated measures, plausible
values, which are the state of the art in analysis techniques for large-scale assessment
data, will be provided, adopting the procedures from PIAAC as far as possible. Plausible
values capture latent estimates for relations between competence measures and context
data (OECD 2013b). With the additional data assessed in PIAAC-L, additional sets of
plausible values will be needed for analyses. As a first step, the additional context data
will be taken into account (first set of plausible values). As a second step, the repeated
measurement of PIAAC literacy and numeracy will be included in an extended
scaling and analysis model to enable longitudinal analyses (second set of plausible values).
Furthermore, the analysis of the dyadic competence and context data will also require
another extended item response model (third set of set of plausible values). For all three
additional models presented, the complexity and the amount of context data in
PIAACL constitutes a methodological challenge that will have to be addressed, as the standard
techniques for generating plausible values might not work. Thus, alternative approaches
in drawing plausible values by reducing the number of context variables either
statistically or by theoretical selection and combinations thereof will be evaluated.
To account for selectivity, in each PIAAC-L wave weighting factors (nonresponse and
cross-sectional) for each participating anchor person are computed and provided to the
data users (see Bartsch and Poschmann 2016, for documentation on weighting in wave 1).
The extended and longitudinal data based on the German PIAAC 2012 sample provide
researchers with a vast range of opportunities for addressing different research
questions regarding skill development and sustainability—in particular skill development in
adulthood as a function of central social and demographic circumstances, the effects of
basic skills on labor market participation, careers, and unemployment, and, finally, the
effects of basic skills on educational and employment mobility over the life course. The
extended background information collected in the three waves of PIAAC-L also enables
the investigation of the relationship between basic skills assessed in PIAAC and various
constructs, such as central non-cognitive skills (see Rammstedt et al. 2016a, 2017), life
satisfaction (Danner et al. 2016), and health.
In addition, the PIAAC-L data also enable methodological research questions to be
addressed. For example, the PIAAC-L design makes it possible to compare the
competence measures used in PIAAC, NEPS, and the SOEP. PIAAC-L was also set up to
investigate alternative operationalizations of central constructs assessed in the PIAAC
background questionnaire, such as skill mismatch and participation in continuing
education. In addition, the longitudinal design allows panel attrition to be investigated,
especially with regard to the anchor persons’ competence levels. Preliminary results
provide first indications of possible nonresponse bias within PIAAC-L with regard to this
key outcome variable (Rammstedt et al. 2016b).
Finally, the extension of the design to include the entire household and to measure
the competences of the anchor person’s spouse or partner offers additional insights, for
example into skill formation (Cunha et al. 2010) in the course of professional careers and
educational trajectories and into assortative mating.
The above list of potential research questions is by far not exhaustive nor does it
predetermine any prioritizing of the different endeavors.
Data from the three waves of the PIAAC-L study as well as accompanying
documentation—including the questionnaires (in German), codebooks (in English), and technical
reports (English)—are, or will be, available to researchers in the form of scientific use
files through the PIAAC Research Data Center and the Data Archive at GESIS.7 Both
German and international researchers interested in the data can submit a short
application. Users will be required to sign a data user contract and will then obtain access to the
data free of charge.8 Data from the first PIAAC-L wave were published at the end of the
first quarter of 2016;9 data from the second wave was published in an aggregated form
with the first wave by the end of 2016,10 and the third wave will be available by the end of
2017. For anchor persons, it will be possible to match data from all three PIAAC-L waves
to data from the German PIAAC scientific use file.11 In addition, it is planned to link
data of consenting anchor persons from PIAAC and PIAAC-L to microdata from the
Federal Employment Agency.12 Access to these data will be made available through the
Secure Data Center of the Statistics Department of the Federal Employment Agency.
From 2017 onwards, it is intended to include the remaining PIAAC-L participants
and all other household members in the SOEP as a new refresher sample. In this
course, information on the respondents will be updated according to the SOEP
standardly assessed household and person information. In 2017, it is planned that PIAAC-L
respondents will provide data on their current wealth situation. The future follow-ups
will also be made available to the research community through the SOEP Research Data
In sum, the resulting data from the German PIAAC longitudinal study allow a wide range
of content-related and methodological research questions in the field of competence
development and maintenance to be addressed, which would not be possible on the basis
8 There is only a small handling fee of 20€ for 5 data sets.
9 GESIS—Leibniz Institute for the Social Sciences, German Socio-Economic Panel (SOEP) at DIW Berlin & LIfBi—
Leibniz Institute for Educational Trajectories (2016a).
10 GESIS—Leibniz Institute for the Social Sciences, German Socio-Economic Panel (SOEP) at DIW Berlin & LIfBi—
Leibniz Institute for Educational Trajectories (2016b).
11 Rammstedt et al. (2016c).
12 Data stored at the Federal Employment Agency include detailed information on income and occupational biography
on an individual basis. In order to allow to link individual data from PIAAC and PIAAC-L with the Federal Employment
Agency data, anchor persons had to give their informed consent.
of PIAAC data only. Results of these analyses provide insights into the quality and
predictive power of the concepts assessed in PIAAC. These results furnish crucial information
for the revision of the instruments for upcoming PIAAC cycles and also provide
empirical evidence on alternative measures and their quality. Finally, and very importantly, the
design of PIAAC-L also provides empirical evidence on additional concepts that could be
included in upcoming cycles of PIAAC. For example, the Big Five personality domains—
the most highly used and most extensively researched personality approach (see for
example John et al. 2008; McCrae and Costa 1999)—were assessed within PIAAC-L.13 In
a recent analysis of the PIAAC-L data we were able to show that personality in terms of
the Big Five and other concepts of non-cognitive skills prove to be highly predictive of
numerous outcome variables measured in PIAAC (see Rammstedt et al. 2017).
From a national point of view, PIAAC-L has merged for the first time three distinct
and very heterogeneous large-scale surveys conducted by three research data
infrastructure institutes within the Leibniz Association in Germany. A direct comparison of
measures such as the competence tests used in the different studies is thus possible using the
PIAAC-L data. From a more general perspective, PIAAC-L can be regarded as a
milestone on the road to stronger collaboration between, and perhaps a partial integration of,
different surveys studying overlapping concepts. For data users, this can, in the medium
term, improve concept harmonization and operationalization and thus yield higher data
quality and new insights into causal mechanisms of skill formation and the outcomes of
cognitive skills over the life course.
BR had the initial idea for the manuscript. BR, AZ and SM wrote and revised major parts of the manuscript. SM computed
the data for the tables, AZ contributed the information in the appendix. CC added the section on the scaling and the
computation of plausible values. JS contributed to the section on analytical potential of the longitudinal data. All authors
read and approved the final manuscript.
1 GESIS – Leibniz Institute for the Social Sciences, B2, 1, 68159 Mannheim, Germany. 2 University of Bamberg, Wilhelmsplatz 3, 96047 Bamberg, Germany. 3 German Institute for Economic Research, Mohrenstr. 58, 10117 Berlin, Germany.
The authors declare that they have no competing interests.
Household questionnaire (based on the SOEP core questionnaire)
• Person questionnaire (based on the SOEP core questionnaires):
• Family situation, family background, childhood
• Biographic calendar: education and employment history
13 For a critique of the Big Five approach see Block (1995).
Questionnaire (based on PIAAC and other questionnaires):
• Education, work status, work history, occupation etc. (based on PIAAC)
• Skills use at work (based on the NEPS questionnaires), and a self-assessment of
• Extended information on computer experience and use
• Languages, including mother tongue and foreign languages
• Module administered to spouse or partner of the anchor person
• Parental information
• Health, satisfaction, critical life events, leisure activities
• Cognitive assessment instruments
• PIAAC Literacy (per default computer-based, optionally paper-based)
• PIAAC Numeracy (per default computer-based, optionally paper-based)
• NEPS Reading (including Reading Speed in certain assessment conditions;
• NEPS Math (paper-based only)
Person questionnaire (similar to Wave 1 but somewhat abbreviated):
SOEP-based (longitudinal repeat of a subset of questions from Wave 1):
• Extended and improved module on continuing education
• Improved questions for skill mismatch
• SOEP basic cognitive skills scale
Bartsch , S. & Poschmann , K. ( 2016 ). Weighting for PIAAC-L 2014 . GESIS Papers 2016|xx: Köln .
Block , J. ( 1995 ). A contrarian view of the five-factor approach to personality description . Psychological Bulletin , 117 , 187 - 215 .
Cunha , F. , Heckman , J. , & Schennach , S. ( 2010 ). Estimating the technology of cognitive and noncognitive skill formation . Econometrica, Econometric Society , 78 , 883 - 931 .
Danner , D. , Luhmann , M. , & Rammstedt , B. ( 2016 ). Competencies as predictors of life satisfaction . (Manuscript in preparation).
European Social Survey . ( 2012 ). ESS6-2012 Fieldwork Summary and Deviations . Retrieved from http://www.europeansocialsurvey.org/data/deviations_6.html. October 16 , 2016 .
European Social Survey . ( 2014 ). ESS7-2014 Fieldwork Summary and Deviations . Retrieved from http://www.europeansocialsurvey.org/data/deviations_7.html. October 16 , 2016 .
GESIS-Leibniz Institute for the Social Sciences, German Socio-Economic Panel (SOEP) at DIW Berlin & LIfBi-Leibniz Institute for Educational Trajectories (2016a) . PIAAC-Longitudinal (PIAAC-L), Germany. GESIS Data Archive , Cologne. ZA5989 Data file Version 1 .1.0. doi:10.4232/1.12576.
GESIS-Leibniz Institute for the Social Sciences, German Socio-Economic Panel (SOEP) at DIW Berlin & LIfBi-Leibniz Institute for Educational Trajectories . ( 2016b ). PIAAC-Longitudinal (PIAAC-L), Germany. GESIS Data Archive , Cologne. ZA5989 Data file Version 2 .0.0. doi:10.4232/1.12707.
John , O. P. , Naumann , L. P. , & Soto , C. J. ( 2008 ). Paradigm shift to the integrative Big-Five trait taxonomy: History, measurement, and conceptual issues . In O. P. John, R. W. Robins , & L. A. Pervin (Eds.), Handbook of personality: Theory and research (3rd ed. , pp. 114 - 158 ). New York, NY: Guilford Press.
McCrae , R. R. , & Costa , P. T. ( 1999 ). A five-factor theory of personality . In L. A. Pervin & O. P. John (Eds.), Handbook of personality (pp. 139 - 153 ). New York : Guilford.
McGowan , M. A. , & Andrews , D. ( 2015 ). Skill mismatch and public policy in OECD countries . OECD Working Paper, ECO/ WKP ( 2015 )28.
OECD. ( 2013a ). OECD skills outlook 2013 : First results from the survey of adult skills . doi:10.1787/9789264204256-en.
OECD. ( 2013b ). Technical report of the survey of adult skills (PIAAC). Retrieved from OECD website : https://www.oecd.org/ skills/piaac/_ Technical%20Report_17OCT13 .pdf.
Perry , A. ( 2016 ). Developing a subjective skill mismatch measure for PIAAC and other surveys . (Manuscript in preparation).
Rammstedt , B. , Danner , D. , & Lechner , C. ( 2017 ). The association between personality and life outcomes-results from the PIAAC longitudinal study in Germany. Large-scale Assessment in Education.
Rammstedt , B. , Danner , D. , & Martin , S. ( 2016a ). The association between personality and cognitive ability: Going beyond simple effects . Journal of Research in Personality , 62 , 39 - 44 . doi:10.1016/j.jrp2016.03.005.
Rammstedt , B. , Lechner , C. , & Martin , S. ( 2016b ). Factors for selectivity in the transition from a cross-sectional assessment survey to a panel survey: Results from PIAAC 2012 and PIAAC-L. (Manuscript in preparation) .
Rammstedt , B. , Martin , S. , Zabal , A. , Konradt, I. , Maehler , D. , Perry , A. , Massing , N. , Ackermann-Piek , D. , & Helmschrott , S. ( 2016c ). Programme for the International Assessment of Adult Competencies (PIAAC), Germany-Reduzierte Version . GESIS Datenarchiv , Köln. ZA5845 Datenfile Version 2 .2.0. doi:10.4232/1.12660.
Richter , D. , Metzing , M. , Weinhardt , M. , & Schupp , J. ( 2013 ). SOEP scales manual . SOEP Survey Papers 138 : Series C. Berlin : DIW/SOEP.
Wagner , G. G. , Frick , J. , & Schupp , J. ( 2007 ). The German Socio-Economic Panel Study (SOEP)-Scope, evolution and enhancements . Schmollers Jahrbuch , 127 ( 1 ), 139 - 169 .
Wasmer , M. , Blohm , M. , Walter , J. , Scholz , E. , & Jutz , R. ( 2014 ). Konzeption und Durchführung der “Allgemeinen Bevölkerungsumfrage der Sozialwissenschaften“(ALLBUS) 2012 . GESIS Technical Report 2014 /22.
Wasmer , M. , Scholz , E. , Blohm , M. , Walter , J. , & Jutz , R. ( 2012 ). Konzeption und Durchführung der “Allgemeinen Bevölkerungsumfrage der Sozialwissenschaften” (ALLBUS) 2010 . GESIS Technical Report 2012 /12.
Zabal , A. , Martin , S. , Massing , N. , Ackermann , D. , Helmschrott , S. , Barkow, I. , et al. ( 2014 ). PIAAC Germany 2012 : Technical Report. Münster: Waxmann.