1st gesis summer school in survey methodology cologne ... · in survey methodology cologne, germany...

GESIS EvEntS

Leibniz Institute for the Social Sciences

1st GESIS Summer School in Survey Methodology

Cologne, GermanyAugust 9 to 25, 2012

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Prof. Dr. Jürgen H. P. Hoffmeyer-ZlotnikSummer School Director (until December 2011)

Email: [email protected]

Website: www.gesis.org/summerschool

Address: GESIS - Leibniz Institute for the Social Sciences Unter Sachsenhausen 6-8 50667 Köln Germany

Contact Details

The GESIS Summer School Team

Angelika Ruf Administrative Coordinator

Dr. Silke Schneider Summer School Director

Prof. Dr. York Sure-VetterGESIS President

Welcome from GESIS

the GESIS Summer School is a new endeavour by GESIS - Leibniz Institute for the Social Sciences. We hope it will well complement the existing spring, summer and winter schools in Europe and possibly beyond. this Summer School is unique in Europe with its focus on Survey Methodology and data collection. It is the right place to go for PhD students and junior researchers planning to run their own survey, but also those who analyse secondary data and want to know more about how the data came about and how to assess their quality. the Summer School will be hosted in a new building at the University of Cologne.

the GESIS Summer School will not only give a broad overview of survey methods, but provide an opportu-nity to deeply engage with the different tasks of survey design and implementation (such as measurement, questionnaire design, sampling, nonresponse and fieldwork monitoring), with different survey modes (such as mail and web surveys), and research designs involving surveys (such as mixed methods, vignette studies, and cross-national surveys). the courses offer engaging instruction in state-of-the-art knowledge and application oriented skills, provided by an international team of survey specialists. Our instructors come from a diverse set of countries and fields, and we welcome applicants from all countries and fields.

In the evenings, survey practitioners and leaders in the field of survey research methodology will give an insight into their individual perspectives of the survey research landscape. We have invited speakers to present three well-known cross-national surveys, the International Social Survey Programme, European values Study and European Social Survey, as well as practitioners from the areas of sampling, fieldwork, online research and official statistics.

We are very thankful for the cooperation with and support by the Universities of Cologne and Mannheim. We also gratefully acknowledge the contributions made by our sponsors to a social and cultural programme for the weekend between the two course weeks. It is greatly important to us that participants can meet outside the seminar rooms to have a good time and find new research collaborators and, indeed, friends. We aim to provide participants with a supportive social environment, a stimulating and academically rigorous programme, and an exciting time in Cologne.

We hope you enjoy reading the programme, and hope to see you in Cologne in August 2012!

the Summer School team and the GESIS President:

Prof. Dr. Jürgen H. P. Hoffmeyer-Zlotnik Dr. Silke Schneider Angelika Ruf Prof. Dr. York Sure-vetter

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Sponsors

Partner Universities

GfK-nürnberg e.v. nordwestring 101D-90319 nürnberg

infas Institut für angewandte Sozialwissenschaft GmbHFriedrich-Wilhelm-Str. 18D-53113 Bonn

tnS Infratest Sozialforschung Landsberger Straße 28480687 München

USUMA GmbH Markt-, Meinungs- und SozialforschungBerliner Allee 96D-13088 Berlin

Universität zu KölnAlbertus-Magnus-Platz50923 Köln

Universität MannheimSchloss68131 Mannheim

General Information...................................................................................................................................................................................6

Application, Fees and Payment Details .............................................................................................................................................. 7

Certificates and ECtS Credits ................................................................................................................................................................8

timetable ........................................................................................................................................................................................................9

GESIS Summer School Programme ...................................................................................................................................................10

Refresher Courses ..................................................................................................................................................................................11 A Survey Design ............................................................................................................................................................................12 B Structure Equation Models with Mplus .........................................................................................................................13 C Bayesian Inference using R .................................................................................................................................................14

Main Courses ............................................................................................................................................................................................15 1 Questionnaire Design ..............................................................................................................................................................16 2 Complex Survey Sampling, Weighting, variance Estimation and Design Effects in Cross-Sectional Surveys ...........................................................................................................18 3 Measurement Interview and Data Quality 3.1 Measurement Models, Error and Equivalence .................................................................................................. 20 3.2 Data Collection and Interviewer training ........................................................................................................... 22 4 Mail Surveys ...............................................................................................................................................................................24 5 Web Surveys ...............................................................................................................................................................................26 6 Mixed Methods .........................................................................................................................................................................28 7 vignette Analysis - Factorial Survey and Scenario Approach ............................................................................. 30 8 nonresponse 8.1 Unit nonresponse ..........................................................................................................................................................32 8.2 Item nonresponse and Multiple Imputation ................................................................................................... 34 9 Cross-national Comparative Surveys .............................................................................................................................36 10 translation and Harmonisation in Cross-national Surveys 10.1 translation of Questionnaires ................................................................................................................................ 38 10.2 Harmonisation of Socio-demographic variables .......................................................................................... 40

Alphabetical List of Instructors ...........................................................................................................................................................42

Campus and travelling Information ................................................................................................................................................ 44

About Cologne ........................................................................................................................................................................................... 46

Accommodation ........................................................................................................................................................................................47

About GESIS ...............................................................................................................................................................................................51

Research Methods training at GESIS ................................................................................................................................................52

Sponsors´ Pages ........................................................................................................................................................................................ 54

Content

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General Information

the first GESIS Summer School in Survey Metho-dology will take place between August 9 and 25, 2012. It is a joint venture of GESIS and the University of Mannheim.

ObjectivesSurveys are the main method of systematic data collection in the Social Sciences. Surveys provide empirical data for researchers to analyse, and are an important source of information for business, charities and policy makers. there are numerous types of surveys suited for different purposes. Given the variety and complexity of survey research, designing and conducting a survey that effectively and efficiently serves a specific purpose requires specialised expertise and skill (as well as a good team). the GESIS Summer School offers high quality training in state of the art techniques and methods of survey research. It aims to equip participants with essential skills in the design, planning, execution, documentation and quality assurance of surveys of households, individuals or organisations.

StructureIn this first year, three two-day refresher courses, seven two-week and six one-week main courses are on offer. Each course is a full-time course. the one-week courses can be booked as a two-week theme or independently. the courses cover themes related to survey methodology reflecting the whole survey life-cycle as well as different research designs, e.g. questionnaire design, mail and web surveys, sampling, mixed methods, vignette studies and cross-national surveys. Please see the full list of courses on page 10. to ensure high course quality, the number of places on each main course is limited to 20 participants. We therefore recommend early application.

Courses consist of a variable mix of lectures, workshops, project work and practical exercises in

small groups. Please note that for the exercises, participants may have to bring their own laptop computers. In the evenings, there will be plenary sessions and round-tables with invited speakers discussing various topics related to survey metho-dology throughout the two weeks. In the later evenings, there will also be the opportunity to meet up for networking and culinary explorations in a Cologne brewery. A cultural programme is finally scheduled for the weekend between the two course weeks.

Target audiencethe summer school is designed for advanced graduate and PhD students as well as post-docs and junior researchers interested in improving their knowledge and skills in survey methodology from all relevant fields, such as Political Science, Sociology, Economics, Education Science, Communication Science, Epidemiology, Demography etc. Professionals from outside academic research who are working with surveys are welcome to apply.

PrerequisitesParticipants benefit most from a course that matches their prior skills and knowledge. We thus recommend strongly that applicants check the course require-ments indicated in the detailed description of every individual course. All prospective participants need to have basic knowledge of empirical research methods and the survey process, such as from a Social Science Master’s degree class on empirical research methods. Some courses may have higher specific requirements that will be indicated in the detailed programme. In order to meet those requirements, participants can brush up their knowledge and skills in intensive two-day refresher courses on August 9 and 10 (see pages 11 to 14). the Summer School will entirely be held in English; thus a good command of English is also expected.

Application: to apply for the summer school, please complete the application form at www.gesis.org/summerschool (available from late 2011).

Fees: Refresher courses: ..................................................................................................................................... € 120 Main courses: two week courses (courses 1, 2, 4, 5, 6, 7, 9): ................................................ € 400 One week courses (courses 3.1, 3.2; 8.1, 8.2; 10.1, 10.2): .......................... € 200 Examination: .............................................................................................................................................€ 50

Registration and payment: Full payment must be made by July 1, 2012. Places cannot be guaranteed if payment has not been received by this date.

Cancellation Policy: Cancellations made prior to July 1, 2012 will receive a refund of 100 % of the registration fee.Cancellations made between July 1 and August 1, 2012 will receive a refund of 50 % of the registration fee.Cancellations made after August 1, 2012 will receive no refund.

Usually, GESIS events fill up quickly and we operate waiting lists. We thus recommend early application and payment. If you realise after having been accepted that you cannot make it, we would very much appreciate if you would let us know as soon as possible so that we can reallocate your place to another applicant.

Financial support:GESIS currently does not have any bursaries for Summer School participants.

Students and Ph.D. candidates who receive a scholarship can often apply to their scholarship organisation or research council for a grant to subsidise or cover the costs of attending events like the GESIS Summer School. Please enquire with your scholarship organisation or research council.

Some university departments, graduate schools, doctoral programmes and research training groups may also provide small-scale grants for attending training events. Please ask your local mentor or supervisor. Research projects funded through third-party funds may also include a budget to cover members‘ training needs.

Please regularly check the Summer School website for up-to-date information since more information on funding opportunities may be available in the future.

Application, Fees and Payment Details

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Refresher course participants will receive a certificate of attendance. there is no formal exam and we cannot give European Credit transfer System (ECtS) points for these courses.

For the main courses, each participant will receive a certificate of attendance before leaving the GESIS Summer School. Participants have the opportunity to gain ECtS points from the Center for Doctoral Studies in Social and Behavioral Sciences at the University of Mannheim. For participation in the summer school, 3 ECtS points can be obtained. those who also take part in the optional exam can gain further 2 ECtS points. ECtS certificates are sent to participants after the Summer School has ended. Applicants are advised to contact their home institution to enquire about the recognition of ECtS points from the University of Mannheim.

Certificates and ECTS Credits Timetable

Refresher Courses:

Registration: Wednesday, August 8 – 17:00 to 20:00 Refresher courses: thursday, August 9 and Friday, August 10Morning sessions: 09:00 to 12:00 Lunch break: 12:00 to 14:00Afternoon sessions: 14:00 to 17:00

Main Courses:

Registration: Sunday, August 12 – 14:00 to 18:00 Introductory meeting and Sunday, August 12 welcome reception: from 18:00 Main courses: Week 1: Monday, August 13 to Friday, August 17 Week 2: Monday, August 20 to Friday, August 24Examination: Saturday, August 25 Morning sessions: 09:00 to 12:00 Lunch break: 12:00 to 14:00 Afternoon sessions: 14:00 to 17:00 Evening sessions: 18:30 to 20:00

Cultural Programme:

Summer School excursion: Sunday, August 19 Summer School farewell party: Friday, August 24

Evening Programme:

In the evenings, there will be plenary talks by researchers and professionals from survey research and methodology, who will share their extensive experience and sometimes personal views from outside the seminar room from a wide range of angles. Personalities who have promoted different large-scale cross-national survey programmes, online research, official statistics and commercial survey institutes are represented. We are looking forward to lively and inspiring discussions.

Week 1: Monday, August 13 GESIS and the cooperation universities Cologne and Mannheim tuesday, August 14 European Social Survey – Prof. Roger Jowell (London City University) Wednesday, August 15 Fieldwork – Doris Hess (Infas) thursday, August 16 Official Statistics – thomas Körner (German Federal Statistical Office)

Week 2: Monday, August 20 Online Research – Dr. Lorenz Gräf (Globalpark AG) tuesday, August 21 International Social Survey Programme – Prof. Dr. Peter Mohler (University of Mannheim) and European values Study – Prof. em. Dr. Wolfgang Jagodzinski (University of Cologne) Wednesday, August 22 Sampling – Christiane Heckel (BIK ASCHPURWIS + BEHREnS GmbH)

http://en.wikipedia.org/wiki/European_Credit_Transfer_and_Accumulation_System

http://gess.uni-mannheim.de/CDSS/

http://gess.uni-mannheim.de/CDSS/

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Refresher Courses:

A Survey Design (Peter Lugtig, PhD)

B Structural Equation Modelling with Mplus (Prof. Dr. Jost Reinecke)

C Bayesian Inference using R (Dr. Florian Meinfelder)

Main Courses:

1 Questionnaire Design (Prof. Dr. Marek Fuchs)

2 Complex Survey Sampling, Weighting, Variance Estimation and Design Effects in Cross-Sectional Surveys (Dr. Matthias Ganninger, Prof. Dr. Ralf Münnich)

3 Measurement Interview and Data Quality3.1 Measurement Models, Error and Equivalence (Prof. Jaak Billiet, PhD, Georg Datler)3.2 Data Collection and Interviewer training (Dr. Remco Feskens)

4 Mail Surveys (Dr. Hans-Jürgen Hippler)

5 Web Surveys (Prof. Katja Lozar Manfreda, PhD, Prof. Mick P. Couper, PhD)

6 Mixed Methods (Prof. Dr. Margrit Schreier, Prof. Dr. Udo Kelle)

7 Vignette Analysis: Factorial Survey and Scenario Approach (Prof. Dr. Stefanie Eifler, Dr. Katrin Auspurg, Carsten Sauer, Dr. Lisa Wallander)

8 Nonresponse8.1 Unit nonresponse (Dr. Ineke Stoop, Prof. Dr. Jelke G. Bethlehem) 8.2 Item nonresponse and Multiple Imputation (Prof. Dr. Susanne Rässler, Dr. Florian Meinfelder)

9 Cross-National Comparative Surveys (Prof. Dr. Dominique Joye, tom W. Smith, PhD, Prof. Dr. Christof Wolf)

10 Translation and Harmonisation in Cross-National Surveys 10.1 translation of Questionnaires (Dr. Dorothée Behr, Brita Dorer)10.2 Harmonisation of Socio-demographic variables (Prof. Dr. Jürgen H.P. Hoffmeyer-Zlotnik, Dr. Uwe Warner)

GESIS Summer School Programme

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Instructor: Peter Lugtig

Course content: this refresher course revises the basics of survey methodology. It will discuss the process of designing and setting up a survey in five short parts that take participants through the whole survey process:

1. Why do a survey? A survey is only one of the tools available to social scientists to conduct an empirical study. the advantages and disadvantages of surveys will be compared with those of other research designs.

2. For any survey, one or more research questions and a definition of the target population need to be developed. these early-stage decisions guide the researcher

in all further steps of survey design.

3. How to get from a research question to a questionnaire, and how to design a plan of analyses that will answer the research

question.

4. How is fieldwork organised and conducted? thinking about survey modes, mode choices, mixed-mode surveys, working with inter-viewers and minimising nonresponse error.

5. time, survey quality and survey costs: thinking about constraints in the survey process, and achieving the best data quality within them. How to analyse survey data quality.

the course will include short lectures, combined with practical exercises that train participants in the various aspects of designing a survey.

Refresher Course A Survey Design

Course and learning objectives: the objective of this course is for participants to refresh their knowledge about the fundamentals of survey design and methodology. After the course, participants are ready for a more advanced training in research methodology or survey design.

Course prerequisites: One basic course in social science research methodology.

Recommended reading:

1. Czaja, R. & J. Blair (2005). Designing Surveys: A Guide to Decisions and Procedures. London.

2. De Leeuw, E. D., J. J. Hox & D. Dillman (2008). International Handbook of Survey Methodology. new York. Chapters 1, 2, 3, 5-14 and 16.

Peter Lugtig, PhD, works as an assistant professor in the Department of Methods and Statistics at Utrecht University, the netherlands. His research focuses on improving and evaluating data quality in surveys, particularly longitudinal surveys. He has published articles on the measurement of change, mixed-mode surveys, attrition, and data collection techniques, and completed his PhD in survey methodology in 2011.

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3. Bollen, K. A. & J. S. Long (Eds.). (1993). testing Structural Equation Models. newbury Park.

4. Kline, R. B. (2004). Principles and Practice of Structural Equation Modelling. 2nd edition. new York.

5. Mulaik, S. A. (2009). Linear Causal Modeling with Structural Equations. Boca Raton.

6. Muthén, L. & B. O. Muthén (1998-2010). Mplus Users. Los Angeles.

7. Raykov, t. & G. A. Marcoulides (2006). A first Course in Structural Equation Modelling. Mahwah.

8. Schumacker, R. E. & R. G. Lomax (2010). A Beginners Guide to Structural Equation

Modelling. 3rd edition. Mahwah.

Optional reading:1. Reinecke, J. (2005). Strukturgleichungsmodelle

in den Sozialwissenschaften. München.

Prof. Dr. Jost Reinecke is professor of quantitative methods of empirical social research at the Faculty of Sociology at the University of Bielefeld. His research focuses on the methodology and applica-tion of structural equation models and latent class analysis, both, cross-sectionally and longitudinally. His current methodological research focuses on growth curve and growth mixture models and the development of techniques related to multiple imputation of missing data in complex survey designs. His current substantive research focuses on the longitudinal development of adolescents‘ delinquent behaviour and relationships of group-focused enmity to individual and contextual variables.

Instructor: Jost Reinecke

Course content: the course will give a basic introduction into the techniques of causal modelling and shows how a theoretical model containing causal relationships can be represented by a path or structural equation model. Different specifications of measurement models are tested with confirmatory factor analysis. We will expand these models to allow measurement and structural relations in the model simultaneously. techniques of model estimation will be shown using the programme Mplus (www.statmodel.com).

the usefulness of such models for measuring attitudinal and behavioural aspects will also be addressed. Different aspects related to cross-sectional, cross-cultural and longitudinal designs are emphasized.

Course and learning objectives: this course aims to provide participants with the skills to build their own path and structural equation models including confirmatory factor analyses using the programme Mplus. this is an entry requirement for main course 3.1 (see page 20) for participants new to SEM and/or Mplus.

Course prerequisites: Participants should have some basic knowledge in statistical data analysis with programmes like SPSS and StAtA as e.g. required at Master degree levels.

Recommended reading:1. Bollen, K. A., & P. J. Curran (2006). Latent Curve Models: A Structural Equation

Perspective. new Jersey.

2. Bollen, K. A. (1989). Structural Equations with Latent variables. new York.

Refresher Course B Structural Equation Modelling with Mplus

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Course and learning objectives: this refresher course is particularly designed to prepare partici-pants for Main Course 8.2 on item non-response and multiple imputation (MI, see page 34). therefore, the focus is on those aspects of Bayesian inference which are required for the understanding of MI, whereas other subjects such as Bayesian model selection or decision theory are neglected.

Course prerequisites: Basic knowledge of probability theory and (generalized) linear modelling is required for the ‘Bayesian‘ part of this course. Basic knowledge of R or some other statistical software is desirable but not necessary.


1. Gelman, A., J. B. Carlin, H. S. Stern & D. B. Rubin (2004). Bayesian Data Analysis. 2nd edition.

Boca Raton.

2. Albert, J. (2007). Bayesian Computation with R. new York.

3. venables, v. n. & B. D. Ripley (2002). Modern Applied Statistics with S. 4th edition. new York.

4. Carlin, B. P. & t. A. Louis (2000). Bayes and Empirical Bayes Methods for Data Analysis.

2nd edition. Boca Raton.

5. Iversen, G. R. (1984). Bayesian Statistical Inference. newbury Park.

Dr. Florian Meinfelder has written his PhD thesis on topics related to Multiple Imputation, supervised by Prof. Susanne Rässler and Prof. trivellore E. Raghunathan. He worked as a Research Manager for GfK SE for several years, and is author of the MI R package ‘BaBoon’.

Instructor: Florian Meinfelder

Course content: Bayesian inference has enjoyed increasing popularity over the last fifteen to twenty years, accompanied by an eased access to computing power. this eased access allowed a much broader community of researchers the implementation of MCMC algorithms such as Metropolis Hastings or Gibbs Sampling, and is at the heart of the revival of Bayesian inference. Although specialized software for Bayesian inference is available (e.g. BUGS), we will use R for this refresher course, since it is the predominant language for statistical computing. the course will start with an introduction to the programming language R, giving simple hands-on advice on how to use R efficiently, with an emphasis on differences to data analysis software such as SPSS, Stata or SAS. the objective of this first part of the course is the comprehension of loops and functions in R, which will both be needed for the introduction to Bayesian inference. the second day of the course starts with an introduction to Bayesian inference and its differences to classical (‘frequentist’) statistics. We will give examples of how to derive Bayesian estimators from posterior distributions using conjugate priors, where analytical solutions are available, and move on to situations where solutions rely on simulations using MCMC algorithms. For the remainder of this course we will focus on such MCMC applications using our acquired knowledge of R.

Refresher Course C Bayesian Inference using R

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Instructor: Marek Fuchs

Course content: In traditional text books, questionnaire design is typically treated as an „art“. However, the rules given in such books are either too specifically concerned with particular substantive questions (and generalisation of such rules to other questions is not trivial), or the advice given is too general and it is left to the reader to apply the rules to specific question-naires. this course aims to avoid this dilemma by means of two complementary strategies: On the one hand, basic concepts relevant to survey measurement will be discussed in order to make participants aware of the mechanisms underlying survey responses. On the other hand, the course will discuss results of field experiments testing various aspects of survey questions in order to demonstrate the impact of design decisions on the answers provided by respondents.

the course starts with an introduction into the total survey error concept as a theoretical frame-work for the integrated assessment of survey quality. A special focus will be on measurement error and its components and particularly on the impact of the questionnaire on data quality. the course then provides a description of the question-answer process. Based on this concept we will discuss problems in response behaviour like satisficing and social desirability and how to address these problems during questionnaire design.

Using this theoretical framework, we will then assess various types of questions (e.g. closed-ended vs. open-ended questions; multiple response questions) and focus in greater detail on requirements for questions regarding factual information, attitudes and behaviours. Based on

field experiments concerning question wording, question order, response order, and other aspects of survey questions, the consequences of design decisions for the answers provided by respondents will be discussed. In addition, advanced measure-ment tools like event history calendars, diaries and visual analogue scales will be covered.

Since survey research makes use of various survey modes (face-to-face, telephone, mail, and web), the implications of these survey modes for questionnaire design will be highlighted. Further-more, the course will address the visual design language of questionnaires and we will demonst-rate that visual aspects of questionnaires have an effect on survey responses similar to the wording of questions and response categories. the course concludes with an overview of pre-testing methods for survey questionnaires.

the course will be composed of lectures and group work. During group work, participants will work on a questionnaire that will be tested and fielded using an online-access panel in the final days of the course.

Course and learning objectives: the course provides scientific background knowledge for questionnaire design and educates participants in their professional reasoning when designing survey questions and a questionnaire as a whole. At the end of the course, participants should be able to anticipate potential data quality problems during the design stage of a questionnaire, and make use of the acquired knowledge in order to develop high-quality questionnaires.

Course 1 Questionnaire Design

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Course prerequisites: Basic understanding of survey research (introductory course in social science research methods or similar).


1. Dillman, D. A., J. D. Smyth & L. M. Christian (2008). Internet, mail, and mixed-mode surveys: the tailored design method.

new York.

2. Fuchs, M. (2010). Improving research Governance through use of the total survey error framework. In German Data Forum (Ed.). Building on progress. Expanding the research infrastructure for the social, economic, and behavioral sciences. vol. 1, Pp. 471-486). Leverkusen Opladen.

3. Groves, R. M., F. J. Fowler, M. P. Couper, J. M. Lepkowski, E. Singer & R. tourangeau (2010). Survey Methodology. 2nd edition. Hoboken, n.J.

4. Presser, S., J. M. Rothgeb, M. P. Couper, J. t. Lessler, E. Martin, J. Martin et al. (2004).

Methods for testing and evaluating survey questionnaires. Hoboken, n.J.

5. Sudman, S., n. Bradburn & n. Schwarz (1996). thinking about answers. the application of cognitive Processes to survey methodology. San Francisco.

6. tourangeau, R., L. Rips & K. Rasinski (2010). the Psychology of Survey Response.

Cambridge.

Prof. Dr. Marek Fuchs is full professor for social science research methods at Darmstadt University of technology, Germany. Ho obtained his PhD from Kassel University in 1993 and conducted post-doctoral work at the University of Michigan, Ann Arbor (USA). Since then, he has been the principal investigator of several large scales surveys. His methodological research is particu-larly concerned with methodological aspects of survey measurement. Over the course of the past 20 years, he has published on laboratory and field-experimental studies concerning question-naire design for face-to-face surveys, telephone surveys and self-administered surveys (paper & pencil as well as web surveys). He has a long standing experience in teaching courses on survey methodology at the PhD and Master levels to an international audience.

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precision of estimators. treating the sample data naively as having arisen from a simple random sample can lead to an underestimation of the variance of an estimator. In comparative studies, the design effect can give additional information to simply using variance estimation. Different approaches for the estimation of design effects exist from which different estimators can be derived. Which estimator to choose depends on the structure of the data at hand.

the lectures will be accompanied by practical computer lab sessions where participants will deepen their knowledge by analysing real-world data sets (e.g. ESS, EU-SILC) using R, an open source statistical software package. A strong emphasis will be given to the practical aspects of variance estimation techniques and different approaches of the estimation of design effects.

Course and learning objectives: Participants will get a theoretical introduction to sample designs like for example one- and two-stage sampling, stratified, and unequal probability sampling. Participants will learn the distinction between design weighting and adjustment weighting and calibration by means of real-world examples. In the second week, participants will gain an overview of recent findings on the use of variance estimation techniques in data analysis. the course will help participants understand the impact of sample design on estimators and show pathways to improve the quality of their research results. In computer lab sessions, participants will learn how the specifics of the sample design and the estimator at hand determine the choice of a variance estimation technique in a real-world data analysis setting.

Instructors: Matthias Ganninger Ralf Münnich

Course content: this course will give an overview of the most important topics of survey sampling as applied in multi-national sample survey projects like the European Social Survey (ESS) or the International Social Survey Programme (ISSP). Surveys of this kind often apply complex sample designs. these sample designs have their merits and drawbacks, which must be taken into account in the data analysis process.

the informed choice of a sample design requires prior knowledge about possible estimators and statistics that are to be achieved through data analysis after data collection. today, data analysts can choose from a wide range of data analysis techniques that consider the specifics of a given sample design. the informed choice of a specific technique requires detailed knowledge about its appropriateness for the data to be analysed. the first part of the course will give an overview of a number of frequently used estimation techniques such as design weighting, calibration and weighted least squares regression. the interaction between sample designs and estimators will be discussed in detail.

then, the focus will move on to variance estimation techniques. nowadays, an assessment of the accuracy of a given statistic becomes more and more important. In general, accuracy measurement is based on the use of variance estimation methods. Although some modern statistical software packages like SAS, Stata and R provide users with a range of variance estimation methods, the decision for or against an adequate technique is left to the data analyst. A thorough knowledge of the methods’ functioning should guide this decision as well as potential peculiarities in the data.

Furthermore, with complex sample designs, design effects can have a negative influence on the

Course 2 Complex Survey Sampling, Weighting, Variance Estimation and Design Effects in Cross-Sectional Surveys

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Course prerequisites: Basic skills in statistics. Basic knowledge of survey design is also helpful. no prior knowledge in using R is required for this course. Participants should download and install R on their laptops before coming to the summer school. the software is available for free at www.r-project.org.


1. Wolter, K. M. (2007). Introduction to variance estimation. new York.

2. Lohr, S. (2010). Sampling: Design and Analysis. Boston.

3. Cochran, W. G. (1977). Sampling techniques. new York.

4. Särndal, C.-E., B. Swensson & J. Wretman (2003). Model Assisted Survey Sampling. new York.

Dr. Matthias Ganninger is researcher at the GESIS – Leibniz Institute for the Social Sciences in Mannheim, Germany. For his doctorate from the University of trier, he has worked on design effects and published many articles in internatio-nal scientific journals. He co-authored a textbook on data analysis in the social sciences. He has also taught specialized courses on survey methodology in Germany and elsewhere in Europe.

Prof. Dr. Ralf Münnich is full professor at the Economic and Social Statistics Department of the University of trier. His main activities focus on small area statistics and their application, on variance estimation methods with recent applications to census methods and poverty measurement, as well as on survey statistical simulations. Prof. Münnich was responsible for several large projects like DACSEIS, KEI, and AMELI (EU projects) as well as for the German Census sampling project. He has published many articles in national and international scientific journals. He has taught specialized courses on survey statistics and variance estimation methods in PhD programmes.

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Finally, the course will investigate the assessment of equivalent measurement in cross-cultural research. the different kinds of equivalence and procedures to assess them will be introduced. We will discuss what to do in the case of single indicators and social-background variables, as well as the assessment of equivalence for multiple indicator latent variables. the course will also consider what to do if some indicators are not equivalent. the project exercises in the afternoon sessions are focused on practical measurement problems in large datasets and will guide participants to apply the concepts and procedures they have learned in the theoretical morning sessions.

Course and learning objectives: the course provides a thorough understanding of the different kinds of measurement error related to the instrument (questionnaire) in social surveys, and the ways in which errors can be detected, measured and controlled for. Participants will become familiar with the most recent developments and literature in the field of measurement error, especially in the context of large-scale cross-cultural surveys. Participants will be able to apply the strategies in practice, and they will become familiar with the best practices in the field.

Course prerequisites: Basic knowledge of statistical analysis and structural equation modelling. the examples in the theoretical part are based on LISREL_8® and participants should be able to interpret the parameters. the afternoon sessions assume practical knowledge of Mplus. this can be acquired in Refresher Course B (see page 13).

Instructors: Jaak Billiet Georg Datler

Course content: this course provides a comprehen-sive introduction to measurement error in survey research. to start with, the basic concepts and sources of measurement error as well as the concepts of validity and reliability will be introduced in the context of measurement quality, measurement validity, and conceptual (construct) validity. We will discuss exam-ples of and ways to reduce measurement error, such as basic rules for question wording as well as strategies for single indicators. then the course will introduce participants to the measurement of measurement error. In this context, the use and findings of ‘split ballots’ will be discussed and critically reviewed. Measurement error will be related to the concept of ‘attitude strength’, classical as well as recent cognitive models of the question-answer process (e.g. Zaller’s RAS model) and the concept of response instability. the next step concerns the modelling of measurement error using models for assessing reliability and validity such as the test-retest model, simplex model, RMM model, congeneric model, and the MtMM model as well as their application. We will study their advan-tages and disadvantages as well as the true-score MtMM model. the course also covers meta analysis of MtMM models and how to adjust their correlations.

Because it is one important source of measurement error, the course will have a detailed look at response styles in survey questions: What are response styles, and what are the different kinds of response styles? We will use acquiescence as a generic example and model it using Structural Equation Modelling (SEM). What are the advantages of modelling acquiescence in a cross-cultural context? Do we measure an acquiescence method effect? We will also examine whether this kind of modelling is useful for other response styles.

Week 1: Measurement Models, Error and Equivalence (Course 3.1)Course 3 Measurement, Interview and Data Quality

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1. Foddy, W. (1993). An Initial Statement of the Problem (chapter 1). Pp. 1-11 in W. Foddy.

Constructing Questions for Interviews and Questionnaires. theory and Practice in Social Research. Cambridge.

2. Saris, W. E. & I. n. Gallhofer (2007). Concepts- by-Intuition and Concepts-by-Postulation in W. E. Saris & I. n. Gallhofer. Design, Evaluation,

and Analysis of Questionnaires for Survey Research. Hoboken, n.J.

3. Schwarz, n. et al. (2008). the Psychology of Questions. Pp. 18-34 in E. De Leeuw, J. Hox, &

D. A. Dillman (Eds.). International Handbook of Survey Methodology. new York.

4. De Wit, H. & J. Billiet (1995). the MtMM Design: Back to the Founding Fathers. Pp. 39-60 in

W. E. Saris & A. Münnich (Eds.). the Multitrait-Mutlimethod Approach to Evaluate Measure-ment Instruments. Budapest.

5. Bollen, K. A. (1989). Structural Equations with Latent variables. new York. Pp. 1-39 (chapters

1 and 2).

Optional reading:

1. Scherpenzeel, A. & W. Saris (1997). the validity and Reliability of Survey Questions: A Meta Analysis of MtMM studies”. Sociological Methods & Research, 25: 341-383.

2. Billiet, J. & J. McKee McClendon (2000). Modeling Acquiescence in Measurement Models for two Balanced Sets of Items. Structural Equation Modeling. An Interdisciplinary Journal, 7 (4): 608-629.

3. Billiet, J. & E. Davidov (2008). testing the Stability of an Acquiescence Style Factor Behind two Interrelated Substantive variables in a Panel Design. Sociological Methods and Research,

36 (4): 542-562 (paper 7).

4. Billiet, J. (2003). Cross-cultural Equivalence with Structural Equation Modeling. Pp. 247-265

in J. Harkness, F. van de vijver &. P. Mohler (Eds.). Cross-cultural Survey Methods. new York.

5. Billiet, J. & J. Welkenhuysen-Gybels (2004). Assessing Cross-national Construct Equivalence in the ESS: the Case of Six Immigration Items”

in C. van Dijkum, J. Blasius & C. Durand (Eds.). Recent Developments and Applications in Social Research Methodology. Opladen, 19 pp.

Prof. Dr. Jaak Billiet was full professor in social methodology at the Katholieke Universiteit Leuven, Belgium, and is now professor emeritus with research tasks. His research interests include validity assess-ment and modelling measurement error in social surveys as well as longitudinal and comparative research in ethnocentrism, political attitudes and religious orientations.

Georg Datler is a PhD candidate in sociology at the University of Zurich. His research interests include the political sociology of Europeanization, the methodology of comparative research, and the application of structural equation modelling.

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In the morning lectures will be given, and in the afternoon students will work on exercises individually and in small groups using SPSS and Mplus.

Course and learning objectives: Participants will become familiar with the basic concepts around total survey error, and learn how to analyse and evaluate data quality. they will also acquire the practical skills of how to train interviewers and how to monitor fieldwork.

Course prerequisites: Basic understanding of survey methodology.

Instructor: Remco Feskens

Course content: this course will introduce participants in how to prepare, conduct and evaluate survey fieldwork. It starts with an introduction into the concept of total survey error. Survey errors can be introduced by not represen-ting the target population accurately (selection errors); not representing the target variables from research questions accurately (measurement errors) and survey administration issues. One important aim in survey design is to minimize survey errors within a given budget.

A good design however does not guarantee a good survey, and here interviewers play an important role. Interviewer training and fieldwork monitoring will be the main topics of this course. Well-trained interviewers have the skills to persuade reluctant respondents to participate in the survey, obtain better quality responses and reduce item nonresponse, thereby optimizing the quality of data. In contrast, untrained interviewers may induce serious interviewer effects, which may jeopardize data quality. Interviewer training is therefore an essential part in obtaining data that can be used to answer research questions in a meaningful way.

the third topic of the course will be the evaluation and analysis of the quality of the collected data. Preferably, this quality check is performed during the data collection period in order to guide the survey process in such a way that survey errors will be discovered early and can thus be minimized. this is an important part of fieldwork monitoring, the fourth topic of the course. Part of this is also the supervision of interviewers.

Week 2: Data Collection and Interviewer Training (Course 3.2)Course 3 Measurement, Interview and Data Quality

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1. Biemer, P. P. & L. E. Lyberg, (2003). Introduction to Survey Quality. Hoboken, new Jersey.

2. Couper, M. (2000). Web Surveys: A Review of Issues and Approaches. Public Opin Q, 64 (4): 464-494.

3. De Beuckelaer, A., & F. Lievens (2009). Measurement Equivalence of Paper-and-Pencil and Internet Organisational Surveys: A Large Scale Examination in 16 Countries. Applied Psychology: An International Review, 58 (2): 336–361.

4. Dillman, D. A., J. D. Smyth & L.M. Christian (2009). Internet, Mail and Mixed Surveys:

the tailored Design Method. Hoboken, n.J.

5. Fowler, F. L. & t. W. Mangione (1990). Standardized Survey Interviewing: Minimizing Interviewer Related Error. newbury Park, CA.

6. Groves, R. M., F. J. Fowler, M. P. Couper, J. M. Lepkowski, E. Singer & R. tourangeau

(2010). Survey Methodology. 2nd edition. Hoboken, n.J.

7. Lyberg, L. E., P. E. Biemer, M. Collins, E. D. De Leeuw, C. Dippo, n. Schwarz & D. trewin. (1997). Survey Measurement and Process Quality. new York.

8. Schouten, B., F. Cobben & J. Bethlehem (2009). Indicators of Representativeness of Survey nonresponse. Survey Methodology, 35:101-113.

Remco Feskens, PhD, is researcher at the Department of Methodology and Statistics of the Utrecht University. He is also associated to Cito, the national Institute for Educational Measure-ment in the netherlands. His main research interest is survey design and data collection issues.

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In practical exercises (working groups), the theoretical ideas and different rules are applied to selected case studies, checked together with the participants and tested with “real participants” (other course participants).

Course and learning objectives: Participants will learn how to conduct mail surveys in an optimal way regarding high data quality and a minimised nonresponse error. they will be prepared for understanding the basic operations in conducting self-administered surveys. the main target is the development of a comprehensive understanding for the methodological requirements of mail surveys.

Course prerequisites: Basic experiences in survey research and social science research.

Instructor: Hans-Jürgen Hippler

Course content: In this practically oriented course, the optimal procedures for mail surveys in different contexts are presented. After a general introduction into the history of postal surveys, the advantages and disadvantages of mail surveys are discussed. the main focus of this part of the course will be why and how people respond to postal or mail surveys and the principles and practices for an optimal procedure. Here, Don Dillman’s „total Design Method“ will be presented and discussed in depth. the considerations in regard of types of information, information quality, question structure and the construction of mail questionnaires will be described in detail. We will then focus on the necessities for the visual presentation of questionnaires and the accompa-nying material (cover letters, envelopes, instruc-tions etc.) as well as the whole implementation procedure.

In a second section, these principles are applied to the potential use of mail surveys as part of „mixed-mode designs“. Here we will discuss how to choose the most appropriate mode and avoid typical mode effects (written vs. verbal informa-tion). the essential issues are discussed in the context of the „tailored Design Method” by Dillman. to conclude, the course will consider the pros and cons of self-administered surveys in regard to e.g. effects of the absence of interview-ers and other methodological effects in compari-son to interviewer-based data collection modes (telephone or personal interview). the problems of mail surveys in regard to coverage, sampling and response rates will also be discussed.

Course 4 Mail Surveys

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1. Bishop, G. F., H.-J. Hippler, n. Schwarz & F. Strack (1988). A Comparison of Response

Effects in Self Administered and telephone Surveys. Pp. 321-340 in R. M. Groves (Ed.). telephone Survey Methodology. new York.

2. De Leeuw, E. D., J. J. Hox, & D. A. Dillmann (Eds.) (2008). International Handbook of Survey Methodology. new York, London.

3. Dillman, D. A. (1978). Mail and telephone Surveys. the total Design Method. new York.

4. Dillman, D. A. (1983). Mail and other Self-Administered Questionnaires. Pp. 359-376 in P. H. Rossi, J. D. Wright &

A. B. Anderson (Eds.). Handbook of Survey Research. new York.

5. Dillman, D. A., J. D. Smyth & L. M. Christian (2009). Internet, Mail, and Mixed-Mode Surveys. the tailored Design Method.

3rd edition, new York.

6. Sudman, S. & n. M. Bradburn (1983). Asking Questions. A Practical Guide to Questionnaire Design. 2nd edition, San Francisco.

7. Sudman, S., n. M. Bradburn & n. Schwarz (1996). thinking about Answers. the Application of Cognitive Processes to Survey Methodology. San Francisco.

Dr. Hans-Jürgen Hippler studied Sociology, Social Psychology and Political Science at the University of Mannheim and received his PhD in Sociology and Political Science with a publication on judgmental processes in surveys. From 1977 to 1992, he was employed as Project Director at Zentrum für Umfragen, Methoden und Analysen, ZUMA (the former GESIS-branch on survey methodology) in Mannheim. From 1993 to 2006, he worked for the ZMG (German newspaper Marketing Organization) as Research Director in Frankfurt. From 2006 to 2010 he was employed as Chief Executive Officer for the MARPLAn Group (a German Survey Research Company) in Offenbach and concurrently was Professor for Survey Methods at Calw University of Applied Sciences. Since January 2011 he has worked as Professional Advisor for Survey Research and Marketing in Mannheim.

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Instructors: Week 1: Katja Lozar Manfreda Week 2: Mick Couper

Course content: the course deals with the challenge of using the World Wide Web for survey data collection. Although Web surveys are some-times perceived as a questionable survey mode, the experiences over the 15 years of their usage show that they can be used in a valid and reliable way for many research purposes. the instructors will provide participants with the basic principles of implementing Web surveys in order to collect survey data of adequate quality.

In the introduction, participants will be presented with the history of computer-assisted survey data collection and its varieties, the typology of Web surveys as one of the recently developed computer-assisted modes, and the advantages and disadvantages of Web surveys in the context of their usage. then, principles of implementing Web surveys will be presented through the steps of the internet survey process. Problems of coverage and sampling, nonresponse, and measurement issues will be addressed accordingly. Special attention will be given to aspects of instrument design for Web surveys, drawing on empirical results from experiments and practical experience.

Students will also be introduced to the variety of survey packages for implementing Web surveys and have the opportunity to conduct their own survey project using one of these packages. the course will conclude by addressing ethical issues in conducting surveys on the Web.

Course and learning objectives: the objective of the course is to provide participants with the practical knowledge and tools to conduct Web surveys and to provide them with an understan-ding of possible deficiencies and mistakes (problems of noncoverage and sampling, nonresponse, measurement error). After the course participants should be able to conduct their own Web survey projects.

Course prerequisites: Basics of survey methodo-logy are recommended, however not required since basic principles will be presented during the course when needed.

Course 5 Web Surveys

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1. Fricker, R. D. & M. Schonlau (2002). Advanta-ges and Disadvantages of Internet Research Surveys. Evidence from the Literature. Field Methods, 14 (4): 347-365.

2. Couper, M. P. (2000). Web Surveys. A Review of Issues and Approaches. Public Opinion Quarterly, 64, 4: 464-495.

3. vehovar, v. & K. Lozar Manfreda (2008). Internet Surveys. Pp. 264-284 in E. D. de Leeuw, J. J. Hox & D. A. Dillman (Eds.). International Handbook of Survey Metho-dology. new York et al.

4. Special issue of Public Opinion Quarterly, 72 (5), published December 2008. Available

free at http://poq.oxfordjournals.org/content/vol72/issue5

5. Couper, M. (2008). Designing Effective Web Surveys. Cambridge.

6. Dillman, D. A. et al. (2009). Internet, Mail, and Mixed-Mode Surveys. the tailored Design Method. 3rd edition, new York.

Prof. Katja Lozar Manfreda, PhD, is an assistant Professor of Statistics at the Faculty of Social Sciences, University of Ljubljana. She has been researching the methodology of Internet surveys since 1998 and has several publications on this topic. She is also part of the team establishing and maintaining websm.org, the main web site dedicated to the methodology of web surveys.

Prof. Mick P. Couper, PhD, is a research professor at the Survey Research Center, University of Michigan and the Joint Program in Survey Methodology. He has published extensively on Web survey design and implementation issues, including Designing Effective Web Surveys published by Cambridge University Press (2008). His current research interests focus on aspects of technology use in surveys, whether by interviewers or respondents.

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Instructors: Week 1: Margrit Schreier Week 2: Udo Kelle

Course content: the course is divided into two parts. Part 1 (Week 1) will focus on the choice and on the construction of a mixed methods design; part 2 (Week 2) will show how research results obtained within a mixed methods design can be interpreted and how the combination of methods in such a design can be used to cope with typical limitations, weaknesses and problems of both qua-litative and quantitative methods. We will work with examples from our own empirical research and with participants’ own research proposals, and the concepts discussed in the course will be applied to participants’ own work.

the first week of the course will take participants through the various stages of the research process, the associated methods and decisions to be taken in the context of mixed methods research. We will start out with the research question and the types of research questions that lend themselves to a mixed methods approach. Our next stop along the way will be mixed methods designs. We will look at current typologies of research designs; at the same time, we will also examine in what ways typologies may restrict researchers’ choice of design type. the design a researcher chooses will in turn influence the selection of other design elements and of research methods. On the following days, we will continue by taking a look at sampling strategies, methods for data collection, and methods for data analysis (including interpretation) in the context of mixed methods designs.

In week 2 of the course, participants will learn how qualitative and quantitative findings coming from mixed methods studies have to be interpreted and how they can be integrated to achieve meaningful

and valid research results. For this purpose we will discuss typical problems and limitations of qualitative and quantitative “mono” methods and common misinterpretations of qualitative and quantitative data and results. Drawing on examples from research practice, it will be shown how the specific problems and limitations of qualitative and quantitative research can be overcome in mixed methods designs by using methods and results of the respective other tradition. It will be discussed how complemen-tarity of qualitative and quantitative findings can be obtained and how divergent findings in mixed methods studies have to be treated. Questions of generalizability, of (causal) explanation, of theory testing and theory generation in Mixed Methods research will be examined in detail.

Course and learning objectives: Week 1 of the course will familiarise participants with purposes underlying mixed methods designs, the most common kinds of mixed methods designs, and issues of sampling, data collection, and data analysis in the context of mixed methods designs. Participants will also be enabled to apply these considerations to their own research. Week 2 will qualify participants to relate qualitative and quantitative findings in a mixed methods study to each other in a meaningful way. Participants will become aware of typical methodological problems and limitations of findings from qualitative and quantitative methods in a mixed methods study; and acquire strategies to cope with these problems and limitations within a mixed methods design. they will also learn what to do if divergent and contradictory findings occur in a mixed methods study.

Course 6 Mixed Methods

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Course prerequisites: Basic knowledge about the qualitative and the quantitative tradition in empirical social science research. Participants accepted for this course will have to submit a two-page summary of their current research project to the instructors before July 15th 2012.


1. Erzberger, Ch. & U. Kelle (2002). Making Inferences in Mixed Methods. the Rules of Integration. Pp. 457-490 in A. tashakkori & Ch. teddlie (Eds.). Handbook of Mixed Methods for the Social and Behavioural Sciences. thousand Oaks, CA.

2. Greene, J. C. (2007). Mixed Methods in Social Inquiry. San Francisco, CA.

3. Kelle, U. (2006). Combining Qualitative and Quantitative Methods in Research Practice? Purposes and Advantages. In L. Gürtler & G. L. Huber (Eds.). Special Guest Issue on Mixed Methods. Qualitative Research in Psychology, 3 (4): 293-311.

4. teddlie, Ch. & A. tashakkoris (2009). Foundations of Mixed Methods Research. thousand Oaks et al.

Prof. Dr. Margrit Schreier is Professor of Empirical Research Methods at Jacobs University Bremen. Her research interests include qualitative methods and methodology, mixed methods, media psychology, and the empirical study of literature. She is co-editor of the issue “Qualitative and quantitative research: Conjunctions and divergen-ces” of Forum: Qualitative Social Research (2001; with nigel Fielding) and co-author of “Forschungs-methoden in Psychologie und Sozialwissenschaf-ten” (2009; Springer; with Walter Hussy and Gerald Echterhoff).

Prof. Dr. Udo Kelle is Professor of Research Methods and Statistics at Helmut-Schmidt University Hamburg. His research interests cover the fields of qualitative and quantitative methods in social research and their methodological and epistemological underpinnings. He has written and edited several books (e.g. “Die Integration qualitativer und quantitativer Methoden in der empirischen Sozialforschung”, 2nd edition 2008, vS verlag, or “vom Einzelfall zum typus”, with S. Kluge, 2nd edition 2010, vS verlag) and numerous articles about social research methods, especially about the relationship between qualitative and quantitative methods.

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Instructors: Stefanie Eifler Katrin Auspurg

Course content: this course deals with the application of vignettes, which are short descriptions of hypothetical situations, within the framework of surveys. there are two traditions of applying vignettes in survey research, firstly, the so-called Factorial Survey Approach, and secondly, the so-called Scenario Approach. Both branches of empirical social research are addressed when talking about vignette analysis.

the Factorial Survey Approach goes back to the work of Rossi (1979). It integrates survey metho-dology and experimental designs. It aims at an assessment of judgments of objects where objects may be ideas, actions, institutions, oneself or other people. Relevant dimensions of the objects under consideration are varied in their levels according to experimental designs. thereby their exact impact on respondents’ judgments can be estimated. Classical applications are the measure-ment of norms, values, attitudes and justice principles.

the Scenario Approach is based on a number of cognitive assumptions. From the perspective of Abelson‘s Script theory, the respondent‘s knowledge on social situations and their typical structures and courses of action is cognitively organized and available in the form of scripts. vignettes refer to respondent‘s knowledge of social situations and their typical structures and courses of action. In particular, it is assumed that scripts can be evoked through the presentation of vignettes.

In the first week of this course, the basics of research design will be presented in a brief overview. We will then introduce both branches of vignette analysis: Firstly, the Factorial Survey Approach, its history, basic ideas and its advantages as well as aspects of experimental designs and data management will be presented. then, we will introduce the Scenario Approach and its basic ideas and cognitive assumptions.

the second week of the course is devoted to applications of the Factorial Survey Approach (e. g. the study of professional judgments, the analysis of justice and fictive decisions like moving for job-related reasons) and applications of Scenario Methods (the study of intentions). the potential of this research perspective is to be illuminated. In this part of the course, we will also deal with certain methodological problems of vignette analysis and how to avoid them. In particular, limitations of the validity of this technique are addressed which may arise from response sets or learning effects.

Practical exercises will deal with the appropriate composition of dimensions and vignettes; experimental designs; creation of vignettes to be included in online or paper-and-pencil questionnaires; techniques for data management and preparation for data analysis.

Course 7 Vignette Analysis: Factorial Survey and Scenario Approach

Carsten SauerLisa Wallander

31

Course and learning objectives: Participants will acquire basic knowledge of vignette analyses - the Factorial Survey Approach and Scenario Methods - as well as their main methodological difficulties. they will also be enabled to apply these tech-niques in their own research.

Course prerequisites: For this course, basic knowledge of questionnaire design, data analysis and statistical software (e.g. Stata) is required.


1. Jasso, G. (2006). Factorial Survey Methods for Studying Beliefs and Judgements. Sociological Methods & Research, 34: 334-423.

2. Rossi, P. H. (1979). vignette Analysis. Uncovering the normative Structure of Complex Judgments. Pp. 176-186 in R. K. Merton, J. S. Coleman & P. H. Rossi (Eds.). Qualitative and Quantitative Social Research. Papers in Honor of Paul F. Lazarsfeld.

new York.

3. Wallander, L. (2009). 25 Years of Factorial Surveys in Sociology. A Review. Social Science Research, 38: 505-520.

4. Eifler, S. (2007). Evaluating the validity of Self-Reported Deviant Behavior Using vignette Analyses. Quality and Quantity,

41: 303-318.

Prof. Dr. Stefanie Eifler holds a chair in sociology and quantitative methods in social research at the Institute of Sociology, Martin-Luther-University of Halle-Wittenberg, Germany and is director of the Centre for Social Science Methodology at the Faculty for Philosophy. Her main research interests are the sociology of crime and deviance, and measurement and scaling techniques in the social sciences.

Dr. Katrin Auspurg is a researcher at the department of History and Sociology at the University of Konstanz, Germany. She studied in Munich and completed her PhD at the University of Konstanz. Her main research interests are survey research, labour market research, social inequality and analytical sociology.

Carsten Sauer is doing his PhD at the Collabora-tive Research Centre (SFB 882): “From Hetero-geneities to Social Inequalities”, Project: “Structu-ral Conditions of Justice Attitudes over the Lifespan” at the University of Bielefeld, Germany. His main research interests are labour market research, social inequality and justice, as well as quantitative research methods.

Lisa Wallander, PhD, is a senior lecturer in Health and Society, Faculty of Health and Society, at Malmö University, Sweden. Her main research interests are the factorial survey approach, sociology of professions, judgement and decision making in social work, substance misuse and treatment, and quantitative research methods.

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Instructors: Ineke Stoop Jelke Bethlehem

Course content: Unit nonresponse is a serious concern because it can affect survey results. this course describes two general approaches to deal with the unit nonresponse problem. One is to reduce nonresponse in the fieldwork as much as possible. the other is to correct for the negative aspects as much as possible.

Survey response rates can be increased through a wide range of tactics and strategies, partly related to the fieldwork mode. the course will discuss in detail how to enhance contact rates and how to minimize refusal. It will pay special attention to the risk of unbalanced response rates (i.e. high response rates among one group and low among another) and how to tackle this. the course discusses several indicators for the quality of the survey response. One is the response rate. this is an imperfect indicator. therefore also another indicator is proposed: the R-indicator.

notwithstanding all efforts to reduce nonresponse as much as possible, there will always remain an amount of nonresponse. to avoid biased estimates of population characteristics, some adjustment procedure must be carried out. An overview of adjustment techniques will be given. this includes adjustment weighting, use of propensity scores, a follow-up survey among nonrespondents and the Basic Question Approach. For the analysis and correction of nonresponse, auxiliary variables are of the utmost importance. this is stressed throughout the course. Attention is paid to sources of auxiliary variables, including paradata.

Week 1: Unit Nonresponse (Course 8.1)

the course will focus on nonresponse in a cross-national survey, the European Social Survey. this means that evidence from a wide range of European countries will be available and that the main emphasis is on face-to-face studies. Some attention will be paid to the issue of nonresponse in online panel surveys.

the course will include two types of exercises: computer exercises and practical assignments. In computer exercises, participants will carry out a nonresponse analysis of a real survey data set. they will do this using the R-language. In addition, participants will have to prepare short presenta-tions on nonresponse issues, based on the literature and if possible their own experience.

Course and learning objectives: the course caters to the needs of several target audiences. After following the course, substantive researchers will be more aware of the possible impact of nonresponse on their survey outcomes, and how to handle this; survey designers will be familiar with strategies to enhance response rates and to minimize nonresponse bias; and survey methodo-logists will get a concise overview of current developments in the area of nonresponse research.

Course prerequisites: General knowledge of survey research is required, especially on sampling and survey modes. Some basic knowledge of R helps, but is not required.

Course 8 Nonresponse

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1. Bethlehem, J. G., F. Cobben & B. Schouten (2011). Handbook of nonresponse in Household Surveys. Hoboken, nJ.

2. Billiet, J., H. Matsuo, K. Beullens & v. vehovar (2009). non-Response Bias in Cross-national Surveys. Designs for Detection and Adjust-ment in the ESS. Institute of Philosophy and Sociology Publishers (Ed.). ASK. Research & Methods, 18 (1): 3-43.

3. Billiet, J., M. Philippens, R. Fitzgerald & I. Stoop (2007). Estimation of nonresponse

Bias in the European Social Survey. Using Information from Reluctant Respondents. Journal of Official Statistics, 23 (2): 135-162.

4. Groves, R. M. (2006). nonresponse Rates and nonresponse Bias in Household Surveys. Public Opinion Quarterly, 70: 646-675.

5. Koch, A., A. G. Blom, I. Stoop & J. Kappelhof (2009). Data Collection Quality Assurance in Cross-national Surveys. the Example of the ESS. Methoden Daten Analysen. Zeitschrift für Empirische Sozialforschung, 3 (2): 219-247. URL: http://tinyurl.com/4y3ejoc

6. Stoop, I. (2005). the Hunt for the Last Respondent. nonresponse in Sample Surveys. the Hague, www.scp.nl/default/root/English_website/Publications/Publications_by_year/Publications_2005/the_Hunt_for_the_Last_Respondent

7. Stoop, I., J. Billiet, A. Koch & R. Fitzgerald (2010). Improving Survey Response. Lessons Learned from the European Social Survey. Chichester.

Dr. Ineke Stoop is head of the Department of Data Services and It, the netherlands Institute for Social Research/SCP. She studied psychology at Leiden University and obtained her PhD at Utrecht University for a thesis on survey nonresponse. She is a member of the European Statistical Advisory Committee (ESAC) and of the Central Coordinating team of the European Social Survey, Scientific Secretary of the International Association of Survey Statisticians, and a Laureate of the 2005 Descartes Prize for Excellence in Scientific Collaborative Research. Her main research interests are comparative social surveys and nonresponse. She has taught courses on compara-tive surveys and nonresponse as part of the ECPR summer school, as ESS training courses, and at Dutch universities, and recently co-authored a book on nonresponse in the European Social Survey.

Prof. Dr. Jelke Bethlehem is Senior Survey Methodologist in the Methodology Department of Statistics netherlands. He is also part-time professor in Survey Methodology at the University of Amsterdam. He studied mathematics and statistics, also at the University of Amsterdam. From 1974 to 1978 he worked at the Mathematical Centre in Amsterdam, a research centre for mathematics and computer science. Since 1978 he is involved in research and development at Statistics netherlands. He obtained his PhD on the treatment of nonresponse in surveys in 1986. Other research topics are adjustment weighting, disclosure control and web surveys. He published two books: “Applied Survey Methods” and “Handbook of nonresponse in Household Surveys”.

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Instructors: Susanne Rässler Florian Meinfelder

Course content: this course introduces multiple imputation (Rubin 1987) as a general method to analyse incomplete data sets. By multiply imputing missing data in general or data missing due to item nonresponse in particular, valid statistical inference can be achieved using standard statistical software packages. Multiple imputation (MI) is based on a Bayesian framework.

Apart from MI itself, we will also cover the theoretical foundations of incomplete data analysis and introduce participants to underlying assumptions such as Missing At Random (MAR) or ignorability. In order to appreciate the benefits of MI, we will also give an overview of inadequate ways of handling missing data, and describe their respective shortcomings. the statistical software R will be used for small simulations demonstrating the quality of the discussed imputation methods, as well as for strategies of handling M>1 different data sets, thus showing how Rubin’s combining rules can be applied to estimates for various quantities of interest.

Another important issue in imputation is the assessment of the imputation quality. therefore, a part of the course is devoted to diagnostics and visualization.

Week 2: Item Nonresponse and Multiple Imputation (Course 8.2)

MI routines have been implemented during the last couple of years in many standard statistical packages. therefore, we will discuss the benefits and drawbacks of various MI algorithms and give some examples for using available routines (in R and SPSS). the final part of this course comprises a small case study, where all the elements of the previous parts are rehearsed – from the analysis of missingness patterns over multiply imputing the missing data to the correct MI analysis.

Course and learning objectives: the course aims to convey a fundamental understanding of inferential statistics for partially unobserved data. Participants will learn both theory as well as application of MI to empirical data.

Course prerequisites: Good knowledge of (generalized) linear models and empirical data analysis. Basic knowledge of Bayesian inference and the software R is recommended. this can be acquired in Refresher Course C (see page 14).

Course 8 Nonresponse

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1. Rubin, D. B. (1987). Multiple Imputation for nonresponse in Surveys. new York.

2. Little, R. J. A. & D. B. Rubin (2002). Statistical Analysis with Missing Data. 2nd edition,

new York.

3. Allison, P. (2002). Missing Data. Quantitative Applications in the Social Sciences. Sage University Papers 136. thousand Oaks et al.

4. Rässler, S., D. B. Rubin & E. R. Zell (2008). Imputation. Pp. 322-327 in P. J. Lavrakas (Ed.). Encyclopaedia of Survey Research Methods. thousand Oaks et al.

Prof. Dr. Susanne Rässler holds the Chair of Statistics and Econometrics at the Otto-Friedrich-University of Bamberg, Germany, and is speaker of the methods group of the national Educational Panel Study. Her research interest involves methods for handling missing data in complex surveys, multiple imputation, Bayesian and computational statistics as well as matching techniques for causal analysis and marketing research.

Dr. Florian Meinfelder has written his PhD thesis on multiple imputation related topics, supervised by Prof. Susanne Rässler and Prof. trivellore E. Raghunathan. He worked as a Research Manager for GfK SE for several years, and is author of the MI R package ‘BaBoon’.

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Instructors: Week 1: tom W. Smith

Course content: this course provides an over-view of cross-national survey programmes, the challenges faced when conducting comparative surveys as well as those faced when analysing cross-national data. We begin the first week by introducing available survey data for comparative research comprising academic survey programs, such as SHARE, ESS and ISSP, and surveys from official statistics, for example EU-LFS. We continue by discussing issues of survey methodo-logy specifically relevant for comparative research, e.g. problems of translation, functional equiva-lence, (cognitive) pretesting, sampling, field work procedures, and harmonisation of data.

the second week will be devoted to issues of data quality and specific challenges of data analysis in cross-national survey research. We will begin by discussing response rate and non response bias as measures of data quality, and continue by introdu-cing ways to assess survey quality using paradata (i.e. data on the data gathering process) and metadata (i.e. data describing the data). Finally, we will discuss practical issues in data analysis for comparative research, including multilevel analysis, multi group confirmatory factor analysis and general problems and opportunities of secondary data analysis. We will also introduce participants to available sources for context/macro data.

Individual and group exercises will complement the lectures and allow participants to acquire the relevant practical skills.

Course and learning objectives: the course aims at introducing participants to data sources and methods for cross-national research. In addition to learning about the possibilities and limitations of available comparative surveys, participants will gain a thorough understanding of the different elements/aspects of comparative surveys and how these impact on data quality. Participants will also become familiar with specific methods and strategies of data analysis for comparative survey research.

Course prerequisites: We assume that partici-pants have basic knowledge of survey methodo-logy in a national context, in particular with respect to questionnaire design, sampling, survey implementation and data editing. For the data analysis part, basic knowledge in regression and factor analysis is also required.

Course 9 Cross-National Comparative Surveys

Weeks 1 and 2: Dominique Joye Christof Wolf

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1. Byrne, B. M. (2010). Structural Equation Modeling with AMOS. Basic Concepts, Applications, and Programming. 2nd edition, new York.

2. Davidov, E., P. Schmidt & S. H. Schwartz (2008). Bringing values Back In. the Adequacy of the European Social Survey to Measure values

in 20 Countries. Public Opinion Quarterly, published online Aug. 2008.

3. Harkness, J., M. Braun, B. Edwards, t. P. Johnson, L. Lyberg, P. Mohler et al. (Eds.). (2010). Survey Methods in Multinational, Multiregional, and Multicultural Contexts. Hoboken, n.J.

4. Harkness, J. A. (Ed.). (2006). Conducting Cross-national and Cross-Cultural Surveys. Mannheim.

5. Hoffmeyer-Zlotnik, J. H. P. & J. A. Harkness (Eds.). (2006). Methodological Aspects in Cross-national Research. Mannheim.

6. Hoffmeyer-Zlotnik, J. H. P. & C. Wolf (Eds.). (2003). Advances in Cross-national Comparison. An European Working Book for Demographic and Socio-Economic variables. new York.

7. Steinmetz, H., P. Schmidt, A. tina-Booh, S. Wieczorek & S. H. Schwartz (2009). testing

Measurement Invariance using Multigroup CFA. Differences between Educational Groups in Human values Measurement. Quality and Quantity, 43 (4): 599-616.

8. Hox, J. J. (2010). Multilevel Analysis. techniques and Applications. 2nd edition, Hove.

Prof. Dr. Dominique Joye is Professor for Sociology at the University of Lausanne, Switzer-land. He is involved in the ISSP, ESS and SHARE. His research interests include methodology, inequality and social mobility.

Tom W. Smith, PhD, is Senior Fellow at nORC and President of the World Association for Public Opinion Research. He is director of the U.S. General Social Survey and founding member of the ISSP. His research interests include survey methodology and public opinion research.

Prof. Dr. Christof Wolf is Scientific Director at GESIS and Professor for Sociology at the University of Mannheim. His current research interests include social network analysis and survey methodology with an emphasis on problems of cross-national comparability and harmonization.

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Week 1: Translation of Questionnaires (Course 10.1)

Instructors: Dorothée Behr Brita Dorer

Course content: this course provides an introduction to questionnaire translation for cross-cultural surveys. to begin with, the general context of questionnaire translation in cross-cultural research is presented. this entails the ‘ask-the-same-question’ (ASQ) approach where a source questionnaire is jointly developed in one language and then serves as the basis for translation into other languages. When presenting the ASQ approach, special emphasis is placed on the need for cross-cultural collaboration during questionnaire design.

the remainder of the course deals with various aspects of questionnaire translation. Different translation and assessment methods currently used in survey translation will be presented and discussed: e.g. single translation, back-translation, or external expert checks. the main focus, however, will be on the team approach, consisting in parallel translation, several review stages, and extensive team cooperation. the importance of qualified team members with knowledge in survey research, translation, questionnaire design, the topic at hand, and, in general, with cultural and linguistic skills, will be emphasized. then, specific translation issues will be covered, with guidance on which issues to consider in survey translation and typical pitfalls to avoid. Aspects discussed are e.g. translating meaning to produce functionally equivalent items, finding a balance between fidelity and freedom in translation, scale translation, or adaptation.

to conclude, various translators’ resources and support material for those involved in translation will be presented, e.g. checklists, survey-specific and item-specific translation instructions, and general web resources. Practical exercises throughout the course will allow participants to gain hands-on experience regarding various aspects of survey translation.

Course and learning objectives: the course aims at providing a comprehensive overview of questionnaire translation. Participants will become aware of the impact translation and culture have on questionnaire design as well as their impor-tance for overall comparability of cross-cultural survey data. they will also learn how to organise, perform and assess survey translations.

Course prerequisites: We strongly recommend knowledge of at least one language besides English in order to be fully able to participate in the practice sessions.

Course 10 Translation and Harmonisation in Cross-National Surveys

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1. European Social Survey (2010). ESS Round 5 translation Guidelines. European Social Survey, Mannheim.

2. Harkness, J. A. (2003). Questionnaire translation. Pp. 35-56 in J. A. Harkness, F. van de vijver & P. Ph. Mohler (Eds.). Cross-Cultural Survey Methods. Hoboken, nJ.

3. Smith, t. (2003). Developing Comparable Questions in Cross-national Surveys. Pp. 69-91. In J. A. Harkness, F. van de vijver & P. Ph. Mohler (Eds.). Cross-Cultural Survey Methods. Hoboken, nJ.

4. Harkness, J., B.-E. Pennell & A. Schoua-Glus-berg (2004). Survey Questionnaire translation and Assessment. Pp. 453-473 in S. Presser, J. M. Rothgeb, M. P. Couper, J. t. Lessler, E. Martin, J. Martin & E. Singer (Eds.). Methods for testing and Evaluating Survey Questionnaires. Hoboken, nJ.

5. Harkness, J. A., A. villar & B. Edwards (2010). translation, Adaptation, and Design. Pp. 117-140. in J. A. Harkness, M. Braun, B. Edwards, t. P. Johnson, L. Lyberg, P. Ph. Mohler, B.-E. Pennell & t. W. Smith. Survey Methods in Multinational, Multiregio-nal, and Multicultural Contexts. Hoboken, nJ.

6. Harkness, J. (2010). translation. Section vIII in Survey Research Center (Ed.). Guidelines for Best Practice in Cross-Cultural Surveys. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan. URL: http://ccsg.isr.umich.edu/translation.cfm.

Optional reading:

1. Behr, D. (2009). translationswissenschaft und international vergleichende Umfragefor-schung. Qualitätssicherung bei Fragebogen-übersetzungen als Gegenstand einer Prozessanalyse. Bonn. URL: www.ssoar.info/ssoar/view/?resid=26125.

Dr. Dorothée Behr is a researcher at GESIS – Leibniz Institute for the Social Sciences, previously responsible in the European Social Survey (ESS) and the Programme for the International Assessment of Adult Competencies (PIAAC) for the guidance of questionnaire translation activities. Her current research interests include question-naire translation, cross-cultural probing on the web, and differences in item interpretation across countries.

Brita Dorer is a researcher at GESIS – Leibniz Institute for the Social Sciences and heading the translation team of the European Social Survey (ESS). She has been translating questionnaires for different cross-cultural surveys such as ISSP, PIAAC, SHARE, and ESS. Her current research interests include quality enhancement of questionnaire translation, intercultural effects of translation in cross-cultural surveys, and improvement of the translatability of source questionnaires.

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Week 2: Harmonisation of Socio-demographic Variables (Course 10.2)

Instructors: Jürgen H. P. Hoffmeyer-Zlotnik Uwe Warner

Course content: When analysing attitudes, opinions and behaviours, socio-demographic background variables are essential. they are used to classify survey respondents into homogenous social groups; and they are used to explain social behaviour of observed population groups and types. However, the measurement of socio-demo-graphic variables is influenced by the national and cultural settings and thus differs across countries. In order to be able to compare across various (national) contexts, context specific socio-demo-graphic variables need to be harmonised by harmonising both the underlying concepts as well as their measurement.

this course provides an introduction to the comparative measurement of socio-demographic variables both from a theoretical as well as a practical point of view. to begin with, strategies and rules for national standardization and international harmonisation are introduced. then, the harmoni-sation of a number of socio-demographic variables and concepts commonly used in cross-national research will be discussed: Education, occupation, labour force status, income, ethnicity, migration background, as well as private household.

Education is one of the basic social facts setting up social stratification and inequality. the course will illustrate the process from concepts of national educational structures to a harmonized categorical system which may allow comparing education across countries. Occupation and labour force status are important for social status measure-ments and social stratification analyses. For income and private household, we propose a set of survey questions that take into account the national

income distributions and the national cultures that determine the household definitions and compositions across countries. Finally, studying respondents’ race, ethnicity, and migration back-ground across countries has become increasingly important. In comparative surveys, we need to identify nationality, citizenship, ethnicity, and the migration experience of survey respondents for the comparison of European countries.

through a mixture of lectures in the morning and practical exercises using SPSS during the afternoon, participants will become familiar with harmonisati-on techniques for social background variables and with the difficulties measuring social stratification across Europe.

Course and learning objectives: Knowledge and skills with respect to the harmonisation of socio-demographic variables allow researchers to contribute to comparative surveys in international research networks. they also increase researchers’ capability to judge the comparative potential of survey data, and to evaluate and potentially improve the quality of survey data for comparative analyses.

Course prerequisites: Participants should be familiar with questionnaire design; alternatively they should study Part III: Methodology, in: Sage Handbook of Public Opinion Research (2008).Furthermore, participants should get a quick overview of the data documentation of the European Social Survey, the International Social Survey Programme, and the European values Study. Finally, students should be familiar with the basics of SPSS.

Course 10 Translation and Harmonisation in Cross-National Surveys

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1. Hoffmeyer-Zlotnik, J. H. P. (2008). Harmoni-sation of Demographic and Socio-Economic variables in Cross-national Survey Research. Bulletin de Méthodologie Sociologique, 98: 5-24.

2. Hoffmeyer-Zlotnik, J. H. P. & C. Wolf (Eds.) (2003). Advances in Cross-national Compari-son. An European Working-Book for Demo-

graphic and Socio-Economic variables. new York.

3. Hoffmeyer-Zlotnik, J. H. P. & U. Warner (2008). Private Household Concepts and their Operationalisation in national and Internatio-nal Social Surveys. GESIS-Forschungsberichte, Reihe: Survey Methodology 1. Mannheim. URL: www.ssoar.info/ssoar/view/?resid=26234

Prof. Dr. Jürgen H. P. Hoffmeyer-Zlotnik is head of the Knowledge transfer Unit at GESIS – Leibniz Institute for the Social Sciences and Professor at the Institute for the Political Sciences at Justus-Liebig University of Giessen. His current research interests include standardisation and harmonisation of socio-demographic variables for national and cross-national comparative research.

Dr. Uwe Warner is scientific consultant at CEPS/InStEAD (Centre d‘Etudes de Populations, de Pauvreté et de Politiques Socio-Economiques/International network for Studies in technology, Environment, Alternatives, Development) in Esch/Alzette, Luxembourg. His current research interests include harmonisation of socio-demographic variables for cross-national comparative research.

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Alphabetical List of Instructors

A

Dr. Katrin Auspurg: Dept. of History and Sociology, University of Konstanz, Germany (Course 7) B

Dr. Dorothée Behr: Dept. Survey Design and Methodology, GESIS – Leibniz Institute for the Social Sciences, Germany (Course 10.1)

Prof. Dr. Jelke G. Bethlehem: Dept. of Quanti-tative Economics, University of Amsterdam and Dept. Methodology, Statistics netherlands, netherlands (Course 8.1)

Prof. Jaak Billiet, PhD: Centre for Sociological Research, Katholieke Universiteit Leuven, Belgium (Course 3.1)

C

Prof. Mick P. Couper, PhD: Survey Research Center, Institute for Social Research, University of Michigan, USA (Course 5)

D

Georg Datler: Institute of Sociology, University of Zurich, Switzerland (Course 3.1)

Brita Dorer: Dept. Survey Design and Methodo-logy, GESIS – Leibniz Institute for the Social Sciences, Germany (Course 10.1)

E

Prof. Dr. Stefanie Eifler: Institute of Sociology, Martin-Luther-University Halle-Wittenberg, Germany (Course 7)

F

Dr. Remco Feskens: Dept. of Methodology and Statistics, Utrecht University, netherlands (Course 3.2)

Prof. Dr. Marek Fuchs: Institute of Sociology, technical University Darmstadt, Germany (Course 1)

G

Dr. Matthias Ganninger: Dept. Survey Design and Methodology, GESIS – Leibniz Institute for the Social Sciences, Germany (Course 2)

H

Dr. Hans-Jürgen Hippler, Professional Advisor for Survey Research and Marketing, Mannheim, Germany (Course 4)

Prof. Dr. Jürgen H.P. Hoffmeyer-Zlotnik: Dept. Knowledge transfer, GESIS – Leibniz Institute for the Social Sciences, Germany (Course 10.2)

J

Prof. Dr. Dominique Joye: Institut des Sciences Sociales, University of Lausanne, Switzerland (Course 9)

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K

Prof. Dr. Udo Kelle: Chair of Methods of Empirical Social Research, Helmut Schmidt University/University of the Federal Armed Forces Hamburg, Germany (Course 6)

L

Prof. Katja Lozar Manfreda, PhD: Centre for Social Informatics, University of Ljubljana, Slovenia (Course 5)

Peter J. Lugtig, PhD: Dept. of Methods and Statistics, Utrecht University, netherlands (Refresher Course A)

M

Dr. Florian Meinfelder: Statistics and Econo-metrics, University of Bamberg, Germany (Refresher Course C and Course 8.2)

Prof. Dr. Ralf Münnich: Economic and Social Statistics Dept., University of trier, Germany (Course 2)

R

Prof. Dr. Susanne Rässler: Chair of Statistics and Econometrics, University of Bamberg, Germany (Course 8.2)

Prof. Dr. Jost Reinecke: Faculty of Sociology, University of Bielefeld, Germany (Refresher Course B)

S

Carsten Sauer, M.A.: Graduate School in History and Sociology, University of Bielefeld, Germany (Course 7)

Prof. Dr. Margrit Schreier: School of Humanities and Social Sciences, Jacobs University Bremen, Germany (Course 6)

Tom W. Smith, PhD: national Opinion Research Center, University of Chicago, USA (Course 9)

Dr. Ineke Stoop: Data Services and It Dept., the netherlands Institute for Social Research, the Hague, netherlands (Course 8.1)

W

Dr. Lisa Wallander: Faculty of Health and Society, Malmö University, Sweden (Course 7)

Dr. Uwe Warner: CEPS/InStEAD (International network for Studies in technology, Environment, Alternatives, Development), Luxembourg (Course 10.2)

Prof. Dr. Christof Wolf: Dept. Monitoring Society and Social Change, GESIS – Leibniz Institute for the Social Sciences, Germany (Course 9)

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Campus and Travelling Information

GESIS Summer School venue

University of CologneGebäude 106 (Seminargebäude – “Seminar building“)Universitätsstraße 3550931 KölnGermany

How to reach the venue

Air: Cologne/Bonn Airport is a hub for several low-cost carriers. Cologne is also within less than an hour‘s reach to Frankfurt/Main and Düsseldorf airports. there are good connections (every 10 to 20 minutes) between Cologne airport and Cologne central station, and the travel time is only 15 minutes. Continue to the venue by local transport.

www.koeln-bonn-airport.de

Rail: High-speed trains offer smooth connections between many German and European cities and Cologne. the high-speed thalys train connects the city with Brussels (1 h 47) and Paris (3 h 14); the ICE directly connects Amsterdam and Utrecht with Cologne in 2 h 38 and 2 h 13 respectively. Cologne central station is located in the city centre and has good and quick connections by tram to the venue. Continue to the venue by local transport. If you arrive by local train, the nearest local train station is Bahnhof Süd/Dasselstraße at just 900 metres from the venue.

www.bahn.de

Intercity Bus: the bus station is located in the centre of Cologne next to the central train station. travelling by bus is often cheaper than travelling by train. Connections are especially good between Eastern European cities and Cologne. Continue to the venue by local transport.

Car: GESIS Summer School can be reached via Autobahn A1, A3, A4, A57, A59 and A555. Finding a parking place free of charge and close to the venue may be rather difficult. However, there are several public car parks (chargeable) within walking distance of the university.

Local transport: “neumarkt” and “Dom/Hbf” are the largest tram stations where to change from one line to the other. tram stop “Universität“, line 9 is a 5 minute walk from the venue. Bus stop “WiSo-Fakultät“, lines 136 and 146 are a 10 minute walk from the venue (all stop at “neumarkt”). Please check the timetable information at

http://auskunft.kvb-koeln.de/

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About Cologne

Cologne is centrally located in the west of the Federal Republic of Germany and right in the heart of Europe.

Founded more than 2000 years ago, Cologne is one of Central Europe’s oldest cities. With more than one million inhabitants it is Germany‘s fourth largest city. People from 181 countries with more than 250 cultural backgrounds add to an open and multicultural atmosphere.

Cologne hosts thirteen public and private universities. Its 72,000 students make it the most populous and also one of the most popular university towns in Germany. the University of Cologne, partner of GESIS, and the University of Applied Sciences are the two largest of these institutions.

36 museums, more than 120 art galleries, 200 music ensembles, 60 theatres and numerous international festivals make Cologne one of Europe‘s leading cities for art and culture. the Roman heritage, a captivating array of 12 Romanesque churches, the Cologne Cathedral (UnESCO World Heritage) and many other historic sites hold something for every taste … and of course the “Kölsch”, both idiom and local brew (we will offer a room in a typical Cologne brewery for your networking and leisure) complete what the city is most proud of: Cologne is a feeling!

More information is available from the Cologne tourist office.

www.cologne-tourism.com

Accommodation

All accommodation is located close to the venue and/or well connected via public transport. Cologne tends to be very well booked all over the year. We urgently recommend making your reservation directly after you have been accepted by the GESIS Summer School. Please consider that the gamescom, one of Colognes most popular trade fairs, will take place from August 15-19, 2012. During trade fairs, considerably higher rates will be charged at almost all hotels (apart from the ones we could negotiate special rates with).

If you think of sharing your hotel room or apartment with another participant of the GESIS Summer School, we will have pages on both Facebook and LinkedIn to help participants coordinate their plans. Please search for “GESIS Summer School 2012”.

All rates given are as of September 2011. For a list of further accommodation please contact the Cologne tourist office at

www.koelntourismus.de

or check the following websites:

www.hrs.de/web3www.deutsche-pensionen.de/pension-koelnwww.koeln-lodge.dehttp://koeln.homecompany.dewww.zwischenmiete.dewww.mitwohnzentrale-koeln.comwww.studenteninserate.de/zwischenmiete/koeln.d/www.studenten-wg.de

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Accommodation without Trade Fair Rates (please book quickly)(incl. breakfast, Wi-Fi and tax)

Booking code: GESIS Summer School (unless indicated otherwise)

Pension Otto Richard-Wagner-Str. 18, 50674 Kölnwww.pensionotto.de, email: [email protected]€ 25-45 (single), 60 (double).

Hotel Chelsea Jülicher Str. 1, 50674 Kölnwww.hotel-chelsea.de/main.html, email: [email protected]€ 34,30 (single) without breakfast.

Weltempfänger Hostel & Cafévenloer Str. 196, 50823 Köln-Ehrenfeldwww.koeln-hostel.de, email: [email protected]€ 37,62 (single), 52,47-58,41 (double), 20,79-23,76 (4 beds, per person), 18,81 (6 beds, per person)Registration only via email with booking code: GESIS

Pension Jansen Richard-Wagner-Str. 18, 50674 Kölnwww.pensionjansen.de€ 42-80 (single), 64-90 (double).

Haus Kronenburg Kronenburger Str. 19, 50935 Köln www.hauskronenburg.net, email: [email protected]€ 45 (day), 35 (>10 days).

FeWo 123Sülzgürtel 9, 50937 Kölnwww.ferienwohnung-in-koeln.com, email: [email protected]€ 50-65 (day), 320-410 (week) – 3 persons: € 90 (day)-550 (week)

Simply Beds - Monika Steinkötter Brüsseler Str. 54, 50674 Kölnwww.simply-beds.de, email: [email protected]€ 50 (single or double).

Hotel Haus SchwanDürener Str. 235, 50931 Kölnwww.haus-schwan.de€ 60-70 (single), 70-90 (double).

Pension in Köln - Barbara AmelungBirresborner Str. 53, 50935 Kölnwww.pensioninkoeln.de, email: [email protected]€ 59-68 (single), 79-88 (double). Conti Hotel***Brüsseler Str. 40-42, 50674 Kölnwww.conti-hotel.de, email: [email protected]€ 60,83 (single), 79,53 (double).

Antik Hotel Bristol***Kaiser-Wilhelm-Ring 48, 50672 Kölnwww.antik-hotel-bristol.de, email: [email protected]€ 62,50 (single), 90 (double).

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Hotels with Special Rates(incl. breakfast, Wi-Fi and tax)

Hotel ibis Köln Centrum**neue Weyerstrasse 4, 50676 Kölnwww.ibishotel.com/de/hotel-1449-ibis-koeln-centrum/€ 71 (single), 81 (double), booking code: SC332619717

Hotel Flandrischer Hof*** Flandrische Str. 3-11, 50674 Kölnwww.flandrischerhof.de/, email: [email protected]€ 70,10 (single), 98,20 (double), booking code: BUnD 2011

Maternushaus*** tagungszentrum des Erzbistums KölnKardinal-Frings-Str. 1-3, 50668 Kölnwww.maternushaus.de, email: [email protected]€ 72,16 (single), 102,16 (double), booking code: GESIS

Hopper Hotel et cetera*** Brüsseler Str. 26, 50674 Kölnwww.hopper.de, email: [email protected]€ 84,02 (single), 110,10 (double), booking code: GESIS

Hotel Chelsea*** Jülicher Str. 1, 50674 Kölnwww.hotel-chelsea.de, email: [email protected]€ 85.33 (single), 92,71-126,42 (double) without breakfast Booking code: GESIS Summer School

Ameron Hotel Regent ****Melatenguertel 15, 50933 Kölnwww.hotelregent.de, email: [email protected]€ 79 (single), 106 (double), booking code: GESIS Summer School.

Holiday Inn Köln am Stadtwald****Dürener Str. 287, 50935 Kölnwww.koeln-hi-hotel.de, email: [email protected]€ 79 (single), 97 (double), booking code: 954289222

Best Western Hotel Brenner‘scher Hof****Wilhelm-von-Capitaine-Str. 15-17, 50858 Kölnwww.brennerscher-hof.de, email: [email protected]€ 93,50 (single), 114,32 (double), booking code: GESIS

Park Inn by Radisson Köln City-West****Innere Kanal Str. 15, 50823 Kölnwww.pikcw.de, email: [email protected]€ 103,74 (single), 120,24 (double), booking code: Universität zu Köln

Excelsior Hotel Ernst*****trankgasse 1-5 Domplatz, 50667 Kölnwww.excelsiorhotelernst.com, email: [email protected]€ 135 (single), 155 (double), booking code: Universität zu Köln

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5150

Rented Flats (with kitchenette)

Domicilium Serviced ApartmentsScheidtweilerstraße 15, 50933 Kölnwww.domicilium-koeln.de, email: [email protected]€ 800-850 (2 weeks), booking code: GESIS

Tourist Standard Hotels, Hostels and Guest Houses(rates incl. tax but partly without breakfast and with a shared bathroom)

Meininger Hotel Köln City Center Engelbert Str. 33-35, 50674 Kölnwww.meininger-hotels.com, email: [email protected]€ 39 (single), 25 (double, p.p.), 20,50 (shared room)

Hotel Elite garni Meister-Gerhardt-Straße 26, 50674 Kölnwww.hotel-elite-koeln.de/, email: [email protected]€ 58,50 (single), 74,50 (double), booking code: GESIS

Die Wohngemeinschaft - Hostel, Café, Bar & theater Richard-Wagner-Str. 39, 50674 Kölnwww.hostel-wohngemeinschaft.de/ € 59 (single) - 20 (shared room)

Hostel Köln Marsilstein 29, 50676 Kölnwww.hostel.ag/, email: [email protected]€ 59 (single), 39.50 (double, per person), booking code: GESIS

Hotel-Pension GoetheGoethestraße 5, 50858 Köln-Weidenwww.hotel-goethe-koeln.deemail: [email protected]€ 57,00 (single), 87,00 (double)booking code: GESIS Summer School

Guesthouse - Deutsche Sporthochschule KölnGuts-Muths-Weg 1, 50933 Köln-Müngersdorfwww.dshs-koeln.de/gaestehaus, email: [email protected]€ 46 (single), 71 (double), booking code: Universität zu Köln

Jugendherberge Pathpoint Cologne – Backpacker HostelMachabäer Str. 26, 50668 Kölnwww.pathpoint-cologne.de, email: [email protected]€ 42-57 (double), 14-18 (shared room)

Campingplatz der Stadt Köln – Campsite – Open: April 1- October 6Weidenweg 35, 51105 Köln-Pollwww.camping-koeln.de, email: [email protected]€ 10 upwards

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About GESIS

GESIS-Leibniz Institute for the Social Sciences is the largest social science infrastructure institution in Germany. Its mission is to independently, reliably and competently promote social science research. GESIS is therefore devoted to conducting state-of-the-art research in social science research methods and selected substantive areas, providing high-quality services such as informati-on and research tools, methodological expertise and consulting, data archiving, documentation and access, and organising a multitude of events, especially to promote knowledge and skills in empirical social science research methods amongst researchers and practitioners.

GESIS consists of five scientific departments supporting empirical social science research at every stage of the research process. the GESIS knowledge transfer unit (see also the following page) works closely with all scientific departments to set up a topical and diversified events programme. Several research data centres (RDC) focus on specific kinds of data (e.g. elections, comparative surveys, official micro data) and work across the departments Survey Design and Methodology (SDM) and Monitoring Society and Social Change (DBG).

GESIS is publicly funded and based on a registered non-profit association. the institute is headed by the president (currently Prof. Dr. York Sure-vetter) and governed through a board of trustees, a scientific advisory board, an annual general

meeting, and a user advisory council. GESIS is a member of the Leibniz Association, an alliance of research and infrastructure institutions united by the principle "theoria cum praxi - science for the benefit and welfare of people". GESIS is also a key player in the Council of European Social Science Data Archives (CESSDA). For many years, GESIS has been an important actor in a number of important cross-national survey programmes, such as the European Social Survey (ESS), the International Social Survey Programme (ISSP), and the European values Study (EvS). In the last 40 years it has also welcomed guest researchers from a large number of countries in the world.

Established in 1986 as the German Social Science Infrastructure Services, GESIS used to consist of the three independent institutes Social Science Information Centre (IZ) in Bonn, Central Archive for Empirical Social Research (ZA) in Cologne, which is the oldest archive of survey data in Europe, and Centre for Survey Research and Methodology (ZUMA) in Mannheim. Since 2007 GESIS has merged into one institute. In november 2008 GESIS has added "Leibniz Institute for the Social Sciences" to its name in order to emphasize its membership in the Leibniz Association. the Cologne and Bonn branches merged and moved to a new building in november 2011.

www.gesis.org/

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Research Methods Training at GESIS

Besides the GESIS Summer School in Survey Methodology, GESIS offers a range of other training events in the area of social research methods for researchers in and outside of academia in Germany and Europe (and beyond). they aim to develop participants’ methodological background knowledge and applied research methods skills in depth and breadth, covering the whole survey life cycle and more. the GESIS Spring Seminar (formerly ZA Spring Seminar) has been taking place in Cologne annually for more than 40 years. It offers three consecutive one-week courses in advanced methods of quantitative data analysis for social scientists. there is also a preparatory course in Mathematics for Social Scientists taking place in the autumn. Language of instruction of those two courses is English.

the GESIS Methodenseminar (formerly: ZHSF Herbstseminar) is conducted in German and annually conveys basic knowledge and skills in

quantitative data analysis for university graduates since 1980. It has a modular structure and follows an interdisciplinary approach, with a special focus on historical social research.

GESIS Workshops are short courses of one to three days in a large variety of social science research methods, ranging from qualitative interviews, questionnaire construction, sampling procedures and introduction to specific survey programmes to data archiving and data analysis. Most workshops are conducted in German, but some are offered in English.

In addition, GESIS organizes conferences on issues relating to the Social and Information Sciences. these especially include user conferences focussing on specific data sources and allowing an exchange of experiences and ideas amongst social science researchers using those data.

www.gesis.org/en/events

Complementing university education

ECTS points through cooperation with

universities

SPRING SEMINAR3 consecutive one-week courses in advanced data analysis techniques

WORKSHOPSOne- to three-day coursesintroduction, support, consolidation - International comparison- Methods and software for data analysis- Sampling- Instrument development- Qualitative methods

SUMMER SCHOOL IN SURVEY METHODOLOGY10-12 parallel two-week courses

METHODS SEMINARintroduction to statistical data analysis

with e-learning component

USERCONFERENCESsupporting data users

Advanced modules

based on university level education

Basic modules

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TNS Infratest Sozialforschung

Das bedeutet:

Professionelle Sozialforschung aus einer Hand

Durchführung auch von komplexen Datenerhebungen (ad hoc oder auch als Panel)

Full-Service: (auf Wunsch) von der Fragebogenentwicklung bis zur Datenanalyse und Präsentation

Methodenexpertise und praktisches Know-how

Offen für Neues: Social Media und Web 2.0, innovative Erhebungsverfahren und experimentelle Designs, Mixed Method und Mixed Mode

Das Institut:

TNS Infratest Sozialforschung gehört zur deutschen TNS Infratest-Gruppe innerhalb des weltweit tätigen Marktforschungsunternehmens TNS. Neben der Markt- und Medienforschung betreibt TNS Infratest bereits seit den 50er-Jahren Sozial- und Politikforschung. 1975 wurde dafür eine eigene Gesellschaft gegründet, die Infratest Sozialforschung GmbH, die heute auf dem Gebiet der sozialwissenschaftlichen Umfrageforschung das führende privatwirtschaftliche Institut in Deutschland ist.

Unsere Leistungen:

Alle Arbeitsschritte empirischer Erhebungen: von der inhaltlichen Konzeption über die statistische Anlage, die Wahl der Interviewmethode und die Fragebogenentwicklung bis zur Datenanalyse und Berichtserstellung. Durch unsere Einbindung in ein großes internationales Marktforschungsunternehmen stehen uns leistungsfähige Erhebungsressourcen zur Verfügung, wie CATI, CAPI, Online-Befragungen, Access-Panel. Wo nötig, verbinden wir quantitative und qualitative Methoden. Das Aufgabenspektrum, das wir in einem Forschungsprojekt übernehmen, wird mit dem Auftraggeber vereinbart. Kein Projekt ist bei uns "Standard", jedes braucht seine eigenen Lösungen. Wir beraten Sie gern.

Kontakt

Harald BielenskiTNS Infratest SozialforschungLandsberger Straße 28480687 Mü[email protected]

TNS Infratest Sozialforschung

Das bedeutet:

Professionelle Sozialforschung aus einer Hand

Durchführung auch von komplexen Datenerhebungen (ad hoc oder auch als Panel)

Full-Service: (auf Wunsch) von der Fragebogenentwicklung bis zur Datenanalyse und Präsentation

Methodenexpertise und praktisches Know-how

Offen für Neues: Social Media und Web 2.0, innovative Erhebungsverfahren und experimentelle Designs, Mixed Method und Mixed Mode

Das Institut:

TNS Infratest Sozialforschung gehört zur deutschen TNS Infratest-Gruppe innerhalb des weltweit tätigen Marktforschungsunternehmens TNS. Neben der Markt- und Medienforschung betreibt TNS Infratest bereits seit den 50er-Jahren Sozial- und Politikforschung. 1975 wurde dafür eine eigene Gesellschaft gegründet, die Infratest Sozialforschung GmbH, die heute auf dem Gebiet der sozialwissenschaftlichen Umfrageforschung das führende privatwirtschaftliche Institut in Deutschland ist.

Unsere Leistungen:

Alle Arbeitsschritte empirischer Erhebungen: von der inhaltlichen Konzeption über die statistische Anlage, die Wahl der Interviewmethode und die Fragebogenentwicklung bis zur Datenanalyse und Berichtserstellung. Durch unsere Einbindung in ein großes internationales Marktforschungsunternehmen stehen uns leistungsfähige Erhebungsressourcen zur Verfügung, wie CATI, CAPI, Online-Befragungen, Access-Panel. Wo nötig, verbinden wir quantitative und qualitative Methoden. Das Aufgabenspektrum, das wir in einem Forschungsprojekt übernehmen, wird mit dem Auftraggeber vereinbart. Kein Projekt ist bei uns "Standard", jedes braucht seine eigenen Lösungen. Wir beraten Sie gern.

Kontakt

Harald BielenskiTNS Infratest SozialforschungLandsberger Straße 28480687 Mü[email protected]

USUMA GmbH, Berliner Allee 96, 13088 Berlin Tel.: +49 (30) 92702810, Mail: [email protected]

USUMA arbeitet seit 1990 als unabhängiges Markt-, Meinungs- und Sozialforschungsinstitut nach anerkannten wissenschaftlichen qualitativen und quantitativen Methoden und bietet einen Rundum-service von der Fragebogenerstellung, über die Datenerhebung, die statistischen Datenanalyse bis zur Interpretation und Präsentation der Ergebnisse. Die Analyse der Daten erfolgt stets in anonymisierter Form, das heißt ohne Rückschlussmöglichkeit auf die befragten Personen. Wir betreiben als Forschungsinstitut keinerlei Werbung, Marketing oder Verkauf, dafür steht unsere Mitgliedschaft im Arbeitskreis Deutscher Marktforschungsinstitute e.V. (ADM) seit 1995. Mit derzeit 14 Mitarbeiten und einem geschulten Stab von Interviewern sind wir in der Lage, auch umfangreiche Studien in einem angemessenen Zeitrahmen durchzuführen.

Ein Schwerpunkt unserer Tätigkeit ist die empirische Sozialforschung. Um die Repräsentativität der Befragungen zu sichern, setzen wir ADM-Telefonstichproben, ADM-Face-to-Face Stichproben aber auch Adressstichproben - z.B. über die Einwohnermeldeämter - ein.Eine optimale Ausschöpfung der Stichprobe kann über Mixed Mode Studienansätze erreicht werden, unabhängig davon ob es sich um eine einmalige Befragung oder um eine Panelstudie handelt. In Kooperation mit universitären sozialwissenschaftlichen Einrichtungen ermöglich unsere Befragungssoftware die Umsetzung komplexer Befragungsmethoden wie z.B. Conjointanalysen, Vignettenanalysen und Randomized-Response-Techniken.

Die Daten erheben wir über unser Telefonstudio mit 100 CATI-Arbeitsplätzen über direkte persönliche Interviews vor Ort, über schriftliche Umfragen oder über Online-Befragungen. Wir führen über unsere moderne webbasierte Befragungssoftware Studien in Deutschland, Europa und weltweit in Privathaushalten und Firmen durch.

Freitag, 23. September 2011

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Address: GESIS - Leibniz Institute for the Social Sciences Unter Sachsenhausen 6-8 50667 Köln Germany

Email: [email protected]

Website: www.gesis.org/summerschool GESIS EvEntS

GESIS – Supporting social science research since 1986 in all phases of research:

• Research: Information on data, literature, research projects, important institutions and conferences

• Study planning: Consultation and services for planning and conducting a survey

• Data collection: Consultation and services during the data collection phase

• Data analysis: Support, consultation and data for secondary analysis and reference, analyzing tools

• Archiving and registering: Longterm permanent archiving and registration of data and publications

In addition, GESIS offers user conferences and research methods training events for all these phases.

GESIS – Leibniz Institute for the Social Sciences

www.gesis.org

1st gesis summer school in survey methodology cologne ... · in survey methodology cologne, germany...

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