GESIS EVENTS

Leibniz Institute for the Social Sciences

3rd GESIS Summer School in Survey Methodology

Cologne, GermanyAugust 7-29, 2014

the

About the GESIS Summer School

Why Survey Methodology? Surveys are the main method of systematic data collection in the Social Sciences. Surveys provide empirical data for researchers to analyze 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 specialized expertise and skill (as well as a good team).

Objectives The GESIS Summer School in Survey Methodology offers high quality training in state of the art techniques and methods of survey research. It aims to:• give a broad overview of survey methods;• equip participants with essential skills in the design, planning, execution, documentation and quality

assurance of surveys;• provide an opportunity to deeply engage with the different tasks of survey design and implementation

(such as questionnaire design, sampling, nonresponse and fieldwork monitoring), different survey modes (such as personal interviews, web surveys, and mixed-mode), and research designs involving surveys (such as mixed methods, factorial surveys, longitudinal surveys and cross-national surveys);

• provide participants with good networking opportunities.

Target audience The Summer School is designed for advanced graduate and PhD students, post-docs, junior researchers, as well as survey professionals from all relevant fields:• interested in improving their general knowledge and skills in survey methodology;• planning to run their own survey or working for a large-scale survey project;• desiring to update their methodological expertise in some specific area of survey methodology;• wishing to engage in methodological research;• wanting to know more about how the data they analyze came about and how to assess their quality.

We are very thankful for the cooperation with and support by the Center for Doctoral Studies in Social and Behavioral Sciences (CDSS) of the University of Mannheim, German Academic Exchange Service (DAAD), and GESIS Panel. We also gratefully acknowledge the contributions made by our sponsors - TNS Infratest and infas - to the social and cultural program. 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 academi-cally rigorous program, and an exciting time in Cologne. We hope you enjoy reading the program, and hope to see you in Cologne in August 2014!

The Summer School team and the GESIS president

Email: summerschool@gesis.org

Website: www.gesis.org/summerschool

Facebook page: www.facebook.com/GESISSummerSchool

Contact Details

The GESIS Summer School Team

Angelika Ruf Administrative Coordinator

Evgenia Samoilova Summer School Deputy Scientific Coordinator

Prof. Dr. York Sure-VetterGESIS President

Dr. Silke Schneider Head of the GESIS Knowledge Transfer DepartmentSummer School Scientific Coordinator

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

5

General Information...................................................................................................................................................................................6Fees and Payment Details ........................................................................................................................................................................7Certificates and ECTS Credits ................................................................................................................................................................8Timetable ........................................................................................................................................................................................................9GESIS Summer School Program 2014 ........................................................................................................................................10Short Courses .............................................................................................................................................................................................12 A Introduction to Research Data Management for Social Scientists .....................................................................12 B Qualitative Methods ...............................................................................................................................................................14

One-Week Courses ...................................................................................................................................................................................16 1 Introduction to Survey Design ............................................................................................................................................16 2 Design and Implementation of Surveys in Developing Countries .......................................................................18 3 Mixed Methods ......................................................................................................................................................................... 20 4 Introduction to the Structural Equation Modeling Framework ........................................................................... 22 5 Introduction to Web Surveys ..............................................................................................................................................24 6 Introduction to Data Analysis Using R ...........................................................................................................................26 7 Understanding and Modeling Measurement Error in Social Surveys ................................................................28 8 Experimental Techniques in Survey Research ............................................................................................................. 30 9 Web Survey Instrument Design .........................................................................................................................................32 10 Item Nonresponse and Multiple Imputation ................................................................................................................ 34 11 Mixed-Mode Surveys .............................................................................................................................................................36 12 Questionnaire Design ............................................................................................................................................................. 38 13 Factorial Survey Designs ........................................................................................................................................................41 14 Design and Implementation of Longitudinal Surveys ..............................................................................................42 15 Data Quality and Fieldwork Management ................................................................................................................... 44 16 Sampling, Weighting, and Estimation ............................................................................................................................ 46 17 Questionnaires for Cross-Cultural Surveys ................................................................................................................ 48 18 Surveying Large-Scale Political Events ......................................................................................................................... 50Alphabetical List of Instructors ...........................................................................................................................................................52Practical Information .......................................................................................................................................................................... 54 Venue .............................................................................................................................................................................................. 54 How to reach Cologne .................................................................................................................................................................... 55 About Cologne .................................................................................................................................................................................... 56 Accommodation ..................................................................................................................................................................................57 About GESIS .............................................................................................................................................................................................. 62Research Methods Training at GESIS ............................................................................................................................................... 63Sponsors´ Pages ........................................................................................................................................................................................ 64

Sponsors:

Partner University: Contents

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

Center for Doctoral Studies in Social and Behavioral Sciences (CDSS)Graduate School of Economic & Social SciencesUniversity of MannheimL 9, 7, Rooms: 4.01 to 4.10D - 68131 Mannheim

Page

The GESIS Summer School is supported by:

The GESIS Panel offers the academic social science community a possibility to collect primary data within a probability-based omnibus access panel free of charge.

TNS Infratest Sozialforschung Landsberger Straße 284D-80687 München

6 7

General Information

StructureThe Summer School 2014 comprises a three-week (August 11-29) intensive program with 18 one-week courses, and 2 short courses running in the preceding week (August 7-8).

Short courses are two-day courses aimed at updating participants’ skills to better fulfill the entrance requirements of some one-week courses or at providing instruction in a specialized area. Short courses consist of about 6 hours of instruc-tion per day.

One-week courses consist of 4 hours of in-class instruction a day, either in the morning (9:00-13:00) or in the afternoon (14:00-18:00). Outside of these hours, participants are expected to take part in guided individual or group study time for around 2 hours per day (details for each course are provided in the course descriptions). Given the workload of both in-class instruction and study time, partici-pants can register only for one course per week.

To ensure high course quality, the number of places on each course is limited to 20-25 participants. We therefore recommend early registration.

In addition to the courses, participants are welcome to attend evening talks with experts in survey methodology as well as present their own research.

On Mondays, there will be welcome receptions followed up by end-of-the-week parties on Thursdays and cultural/social activities on Saturdays. You can find more information on the Summer School website.

PrerequisitesParticipants benefit most from a course that matches their prior skills and knowledge. For each course, a syllabus with detailed information on the content of the course, topics, day-to-day schedule and the list of reading materials is provided. The syllabus is there to help you find out the course requirements and preparatory options.

Some course syllabi have a section on preparatory readings to help you prepare or brush up your knowledge. The content of the preparatory readings is not obligatory. The purpose of the preparatory readings is to help participants familiarize themselves with the topic prior to the start of the course.

In addition to the prerequisites mentioned on the web pages and in the syllabi of the courses, all Summer School participants are expected to meet the following general criteria:• basic knowledge of empirical research

methods and the survey process, such as from a Social Science Master’s degree class on empirical research methods. (Exception: participants in the course Introduction to Survey Design and Design and Implementation of Surveys in Developing Countries)

• the summer school will be entirely held in English; thus a good command of English is required.

Please note that for some of the courses, partici-pants may have to bring their own laptop compu-ters (please check course descriptions).

To sign up for the Summer School, please go to www.gesis.org/summerschool and select Registration and Fees in the left-hand menu. The registration deadline is May 31st.

Fees: The Summer school provides reduced rates for participants from academic institutions (including the public and non-profit sector) and gives a further reduction to students. If you want to take advantage of the student rate, please send your proof of status to summerschool@gesis.org when registering online.

Student rate Academic rate Commercial rate

Short courses: 100 € 140 € 280 €

One-week courses (normal rate): 250 € 350 € 700 €

One-week courses (early-bird rate*):

200 € 300 € 650 €

2 ECTS: 20 € 20 € 20 €

4 ECTS: 50 € 50 € 50 € *If you book and pay for a course by April 30, you will receive a 50 Euro discount off the one-week course fees!

Cancellation Policy: If you realize after registration that you cannot make it, please email us as soon as possible so that we can reallocate your place to another applicant. Please see the Terms and Conditions on the Summer School website for our cancellation and reimbursement policy.

Financial support:Conditional on final confirmation by DAAD, Summer School participants from outside Germany can apply for one of 10 available travel bursaries funded by the German Academic Exchange Service (DAAD) through means of the Federal Foreign Office. Bursaries are calculated on the basis of the following rates:• a flat-rate subsistence and accommodation allowance of 250 EUR per week up to a maximum of

750 EUR, i.e. for all three weeks of the summer school.• a flat-rate travel allowance depending on the country from which the participant travels to the

Summer School.

Also, your course fees amounting to a maximum of 500 EUR will be covered. The application deadline is April 30. For details, please see Summer School Bursaries on the Summer School website.

Fees and Payment Details

8 9

Participants will receive a certificate of attendance at the end of their stay, provided they have not missed a substantial part of the course.

Participants completing a one-week course have the opportunity to gain European Credit Transfer System (ECTS) points from the Graduate School of Economic and Social Sciences at the University of Mannheim. When registering for the Summer School, you can choose:• no ECTS points required (i.e. certificate of attendance is sufficient);• 2 points can be obtained for regular attendance and satisfactory work on daily assignments;• 2 further points can be gained by submitting a paper/report of about 5000 words to the instructors

up to 4 weeks after the end of the summer school, after agreeing upon a topic with the instructors.

Certificates and ECTS Credits Timetable

Week 0 (August 7-9): Course days: Thursday to Friday Registration: Thursday, 08:00-09:00 Course timing: 09:00-16:00/09:30-17:30 (see course descriptions)Cultural/Leisure Outing: Saturday

Weeks 1 to 3 (August 11-29) Course days: Mondays to FridaysRegistration: Mondays, 08:00-09:00 and 13:00-14:00Course timing: 09:00 - 13:00/14:00-18:00 (see course descriptions)Welcome meeting: Mondays, at 18:30Welcome reception: Mondays, at 20:30Participants‘ Presentations: Tuesdays, 18:30-20:00Evening Talks: Thursdays, 18:30-20:00End-of-week party: Thursdays, 20:00Cultural/Leisure Outing: Saturdays (but not on August 30th)

10 11

GESIS Summer School Program 2014

Week 1

Week 2

Week 0

Week 3

Cour

se

Desc

riptio

ns

Short Courses:

One-week courses:

A Introduction to Research Data Management for Social Scientists

B Qualitative Methods

1 Introduction to Survey Design

2 Design and Implementation of Surveys in Developing Countries

3 Mixed Methods

4 Introduction to the Structural Equation Modeling Framework

5 Introduction to Web Surveys

6 Introduction to Data Analysis Using R

7 Understanding and Modeling Measurement Error

8 Experimental Techniques in Survey Research

9 Web Survey Instrument Design

10 Item Nonresponse and Multiple Imputation

11 Mixed-Mode Surveys

12 Questionnaire Design

13 Factorial Survey Designs

14 Design and Implementation of Longitudinal Surveys

15 Data Quality and Fieldwork Management

16 Sampling, Weighting, and Estimation

17 Questionnaires for Cross-Cultural Surveys

18 Surveying Large-Scale Political Events

12 13

Course content: If you use or create data you need to take research data management seriously. Data itself is now seen as an asset in its own right. Not only are funding bodies pushing requirements related to retaining, sharing, and citing of research data, journals are also requiring data underpinning publications be accessible to the research community.

Therefore, you need to be aware of how to look after data from the planning stages of research, through the lifecycle of a project, and beyond. Good research data management practice minimizes the risk of data loss, ensures research integrity and facilitates replication; it enhances data security, research efficiency and reliability, and over the long-term, saves time and resources.

Taught by the International Data Infrastructures team at GESIS, this course covers the critical areas related to research data management: licensing and intellectual property, metadata and contextu-al description, obtaining informed consent for participation in research, anonymizing research data for reuse, data archiving and long-term preservation, data security and storage, and handling data management in a collaborative setting.

Teaching is delivered through a combination of presentations, exercises, discussions, and group work. Through the course, participants will have the opportunity to work on a data management plan for their own research projects grounded in practical information and advice developed by the research data management community.

Target group: Participants will find the course useful if they:• are social science researchers working with

qualitative or quantitative data (principal investigators, researchers who are parts of project teams, individual researchers, and PhD students)

Course and learning objectives: By the end of the course participants will:• have gained a basic understanding of research

data management in social science research (e.g. general rules, tools, role, benefits);

• be able to write and implement a research data management plan;

• be familiar with the roles and responsibilities for research staff regarding research data management within the larger data lifecycle;

• be aware of data reuse in the social sciences and ways to enhance your own research.

Course prerequisites: • none

Course participants will not need to bring a laptop computer for this course.

Organizational structure of the course: The course is a full-time course, consisting of 7 hours of group instruction per day (including a one-hour lunch break).

About the instructors:Laurence Horton is a Data Librarian at the London School of Economics and Political Science. Before this he worked in the GESIS International Data Infrastructures Team constructing a training

Week 0 Introduction to Research Data Management

12

Instructors: Laurence Horton, Astrid Recker, Date: August 7-8, 2014 Alexia Katsanidou, Jessica Trixa Time: 9:00-16:00

Short Course A

13

center in research data management and archiving for European social science research. Prior to this he was employed at UK Data Archive on a project supporting the data infrastructure of large-scale research centers and prior to that worked in data acquisition and preparing data collections for the archive.

Dr. Astrid Recker joined the Data Archive for the Social Sciences and its Data Management Training Center at GESIS in July 2012. She has a master’s degree in Library and Information Science and specializes in questions of digital preservation. She has previously worked for the German Federal Institute for Vocational Education and Training, where she coordinated the library and bibliogra-phic services team. In addition, she also teaches modules and courses on digitization, digital preservation, and models of (open) access to digital content at the Institute of Information Science at Cologne University of Applied Science.

Dr. Alexia Katsanidou is the team leader of the GESIS International Data Infrastructures Team. Her responsibilities include managing projects on data harmonization, research, data management and archiving particularly for large-scale data collections. Her current research deals with differences in congruence between national and EU level representation in EU countries and Individual political behavior in a comparative perspective focusing on the influence of valence politics in different political systems.

Jessica Trixa joined the GESIS International Data Infrastructures Team in January 2014. In particular, she focuses on the data management of audiovi-sual data and its specific challenges. Previously she was employed at the GESIS Data Archive as research assistant on the GESIS Panel project and prior to that she worked as a student assistant for the European Data Laboratory for Comparative Social Research (EUROLAB).

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

for Social Scientists

14 15

Course content: A piece of art, an event or a text do not, as it is commonly assumed, „speak for themselves.“ Rather, to systematically explore their meaning(s), researchers turn to qualitative methods because a key characteristic of qualitative methods and methodology is a preoccupation with and the reconstruction of meaning.

This course provides an introduction to the rich field of qualitative methods and methodology (partly in contrast to quantitative research). It examines the methodological foundations of qualitative research, discusses how researchers in the qualitative tradition acquire their data (via, for instance, interviews, documents or observation), and reviews methods for the analysis of qualitati-ve data (like, for instance, content or discourse analysis). The course also discusses the “quality of qualitative analysis.”

Given the short time frame of the course, not all methods and steps in the qualitative research process can be discussed in-depth. Instead, the course puts special emphasis on methods to collect written material (documents, newspaper articles, speeches etc.), on coding (also as a way to move from data collection to analysis), discourse and content analysis as a method of data analysis. The data analysis exercises will be accompanied by a short introduction into the first steps of working with MAXQDA, a qualitative data analysis software tool that can also be used for working on mixed methods projects. The course also aims to help those interested in attending the Mixed Methods course fill the gap in their knowledge of qualitative research.

The course is an introduction which implies limitations on what can be covered. Questions concerning the philosophy of science cannot be covered. Next to methods to collect written material, various methods of data collection are discussed but not examined in detail. The same counts for the methods of data analysis as only discourse and content analysis are the focus of the course. Furthermore, the course cannot go beyond a short introduction into MAXQDA; it only discusses basic functionality but highlights possible ways how participants might include the program in their own research. Overall, this being an introductory course, the two day program aims at pointing out potential avenues to the partici-pants for their current or future qualitative research.

Target group: Participants will find the course useful if they:• want to acquire basic knowledge of qualitative

methodology and methods;• want to refresh their knowledge of qualitative

methodology and methods;• are considering to apply qualitative methodo-

logy and methods in the future and would like to find out whether this would be a suitable approach;

• are already planning to use qualitative methodology and methods in their research and would like some general input on different ways to do this;

• wish to prepare for the Mixed Methods course offered at the GESIS Summer School.

Week 0 Qualitative Methods

14

Instructor: Eva Herschinger Date: August 7-8, 2014 Time: 9:30-17:30

Short Course B

15

Course and learning objectives: By the end of the course participants will:• be familiar with the key characteristics and

rationales of doing qualitative research;• have an overview over the most frequently

used qualitative methods of data collection and data analysis;

• have gained some insight into the hands-on application of some of these methods;

• be able to select the suitable qualitative methods for their own research;

• have gained some insights of how to move from data collection to data analysis;

• be familiar with the basic functions of MAXQDA;

• have gained some insights on the quality criteria of qualitative research.

Course prerequisites: • interest in getting to know qualitative

methodology and methods;• openness to learn new or unfamiliar approa-

ches to research;• willingness to participate in some (yet not

excessive!) group work on data collection.

The participants need to bring their laptops only for the MAXQDA session on the second day of the course.

Organizational structure of the course: The course is a full-time course, consisting of 8 hours of group instruction per day (including a one-hour lunch break). For more details see day-today schedule in the course syllabus. The instructor will be available for individual consulta-tions on participants’ projects after the course.

About the instructor:Dr. Eva Herschinger is a lecturer in International Politics and Conflict Studies at the Bundeswehr University Munich. Next to qualitative methods and methodology, in particular discourse analysis, her research interests include international relations theory, (critical) security studies and the empirical study of terrorism, drug prohibition and migration. Her most recent publications in the field of methods and methodology include: “The matter of method: analysing discourses and materialities of (in)security” (2014, Routledge, with Claudia Aradau, Martin Coward, Owen Thomas, Nadine Voelkner) and “Diskursforschung in den Internationalen Beziehungen” (2014, Nomos, with Judith Renner).

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

16 17

Course content: Setting up a survey may seem to be a relatively simple process. In practice however, conducting a survey often turns out be highly complicated, for various reasons. First of all, the number of choices for the basic design of your survey can seem overwhelming. You have to think about the choice of survey mode, obtaining a good sample, limiting nonresponse, asking good questions and analyzing data, all within time and costs constraints.

This course gives an overview of the design and implementation of surveys from the initial planning phase to the data preparation as a final step. Topics include survey mode assessment and selection, sampling frames and designs, nonres-ponse, interviewer effects, questionnaire design, cognitive pretesting, assessing measurement errors and data editing. The course is taught from a Total Survey Error perspective weighing up data quality at each step of the process against associated costs.

The course is taught through formal lectures in which the theoretical foundation in the literature is discussed, less formal presentations and discussions of survey design in existing survey research, as well as personal tutorial meetings that give participants the opportunity to discuss exercises and their own survey designs. Each day will comprise a specific topic which will focus on one or more aspects of survey design within the Total Survey Error framework. First, the choice for the survey mode is discussed, and how different ways to sample respondents follow from that choice. On the second day, the issue of survey nonresponse is treated. How to prevent, analyze

and correct for it. On the third and fourth day, the actual survey content is discussed. How to write survey questions, make sure they measure what they intend to measure, test them, and finally, how to assess whether survey data are of good quality. On the final day, we focus on assessing measurement error after data collection. We conclude with an overview of all survey errors and their interaction with survey costs.

The course is tailored to those relatively new to the area of survey methodology, possibly planning to later follow more advanced and specialized courses at the Summer School.

This course will not cover the analysis of survey data, i.e. how to analyze your survey data once you have collected them. Neither will it cover working with software for implementing Internet surveys or surveys in other modes or conduncting qualitative interviews.

Target group: Participants will find the course useful if they:• are thinking about conducting a quantitative

survey themselves;• use survey data and wish to understand its

potential errors; • are students preparing their own survey;• are researchers who collaborate within a

survey research project;• are relatively new to the area of survey

methodology and plan to attend more advanced and specialised courses at the GESIS Summer School.

Week 1 Introduction to Survey Design

16

Instructors: Annelies Blom, Peter Lugtig Date: August 11-15, 2014 Time: 9:00-13:00

Course 1

17

Course and learning objectives: By the end of the course participants will:• have a good grasp of the complexities of

interacting survey errors;• be able to design a survey project themselves

taking these into account.

Course prerequisites: • no previous experience in survey research is

needed, however, some practical experience in conducting surveys and analysing data will be beneficial;

• a basic understanding of statistics is assumed, at the level of the Analysis of Variance (ANOVA) and multiple regression;

• during practicals, students can either work with the Stata or SPSS packages. We therefore expect participants to have experience in one of those statistical packages. If participants have no experience at all in either of these packages, please contact the instructors well before the start of the course.

Course participants will not need to bring a laptop computer for this course. This course will take place in a computer lab.

Organizational structure of the course: The course days contain 4 hours of classroom instruction and 2 hours of tutored individual and group exercises, during which the instructors are available for support. The exercises will take place either in the GESIS computer pool or in the open study areas.

About the instructors:Annelies Blom, PhD, is assistant professor at Mannheim University and founder of Survex - Survey Methods Consulting. At Mannheim University she is principal invesigator of the German Internet Panel, a longitudinal online survey of the general population. In her research she looks into the representativeness of online and offline data collection methods. She has published on nonresponse processes in cross-national surveys and interviewer effects. Peter Lugtig, PhD, works as an assistant professor and senior research officer at Utrecht University and the University of Essex. 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 and mixed-device surveys, attrition, and data collection techniques.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

18 1918

Target group: Participants will find the course useful if they:• are conducting surveys in developing or

transitional countries and would like a forum to discuss challenges and issues and how those might be addressed;

• are planning to conduct surveys in developing or transitional countries and would like to learn about the process of survey research and issues specific to such cultures;

• are interested in conducting research on sources of survey error relevant to crosscultu-ral research especially in developing or transitional countries.

Course and learning objectives: By the end of the course participants will:• have gained an overview of the survey

lifecycle;• have gained an understanding of survey

design issues relevant to conducting surveys in developing and transitional countries;

• have gained an understanding of operational issues relevant to conducting surveys in developing and transitional countries;

• have gained an overview of the total survey error framework, but especially sources of error relevant to cross-cultural research;

• be familiar with the literature on cross-cultural survey research.

Course content: This course is designed to provide an overview of the theory and practice of survey research methods with a focus on the unique challenges faced in the design and implementation of surveys in developing and transitional countries. Issues related to letting tenders, evaluating bids and establishing contracts, sample design and sample management, question-naire development and testing, response processes, interviewer recruitment and training, field opera-tions, and quality monitoring will be discussed with examples drawn from surveys conducted in Africa, the Middle East, and Southeast Asia.

The class will also cover cultural dimensions and how these interact with the survey process and survey error. The class will be a mixture of lecture (morning session) and group practice (afternoon session). Each day will focus on a set of topics following the survey lifecycle. To simulate real-life survey contexts, students will be divided into groups and will be asked to respond to a mock request for proposal (RFP) or tender. This group practice will provide students with an opportunity to apply the course material in an integrated fashion. Each group will formally present their study design to the class on the last day. Students who are working in the field of survey research in developing countries are also encouraged to share their experiences and present any challenges they are facing.

While the class briefly touches on the following topics in the context of the survey lifecycle: data processing, data preparation, editing, coding, weighting, imputation, disclosure analysis, data dissemination, data documentation, and data analysis, these topics will not be addressed in details.

Week 1 Design and Implementation of Surveys in Developing Countries Instructors: Zeina Mneimneh, Beth-Ellen Pennell Date: August 11-15, 2014

Time: 9:00-13:00

Course 2

19

Course prerequisites:• no previous experience in survey research is

needed, however basic knowledge about the survey research process might be beneficial.

Course participants will need to bring a laptop computer with an installed web browser for performing the practical exercises for this course.

Organizational structure of the course: The course is structured around 4 hours of classroom instruction (9:00-13:00) and 2 hours of group work (14:00-16:00). Each afternoon’s session will focus on the topic(s) addressed in the morning session. During those afternoon sessions, students will also be asked to discuss their survey design and implementation decisions in groups with their team members. Instructors will be available to answer any questions students might have regarding the project. The last afternoon session will be dedicated to group presentations and discussions, where each team will be asked to present their overall survey design plans. Students who would like to present their own experiences are asked to submit a one page summary of the survey they are working on and the challenges they would like to address to the instructors by July 14.

About the instructors:Zeina Mneimneh, PhD, is a survey director and methodologist in Survey Research Operations, University of Michigan, USA. She has an MSc and a PhD in Survey Methodology from the University of Michigan. She has 15 years of experience in designing, conducting, and overseeing surveys in international settings - specifically developing

countries, and is a collaborator in the cross-natio-nal World Mental Health survey initiative. Her main operational interests include conducting surveys in areas affected by conflict, cross-cultu-ral survey design and implementation, and international capacity building. Her substantive methodological interests include interview privacy, social desirability effects, and adaptive measurement design.

Beth-Ellen Pennell is the Director of International Survey Operations, University of Michigan, USA. She has an MA in Applied Social Research from the University of Michigan and has more than 30 years of experience in survey research operations and methods. Ms. Pennell serves as the Director of the Data Collection Coordination Centre for the cross-national World Mental Health Survey Initiative. Most recently, she became the Mana-ging Director of the International Statistical Institute-University of Michigan Center for Capacity Building in Survey Methods and Statistics. She also led the development of the Cross-cultural Survey Guidelines and is one of the editors of Survey Methods in Multinational, Multiregional, and Multicultural Contexts.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

20 2120

Target group: Participants will find the course useful if they:• are considering doing mixed methods research

in the future and would like to find out whether this would be a suitable approach;

• are planning to use mixed methods in their research and would like some input on how to do this;

• have started working on mixed methods research and are unsure about their data analysis or other elements;

• have used mixed methods in their research and would like some feedback.

Course and learning objectives: By the end of the course participants will:• be familiar with the current discussion

surrounding mixed methods;• be familiar with common reasons underlying

the choice of mixed methods research;• have gained an overview of different mixed

methods designs and strategies for mixing;• be able to select and implement suitable design

elements in their own research.

Course prerequisites:• participants should have basic knowledge

about the qualitative and the quantitative tradition in empirical social science research;

• for participants with insufficient background knowledge in qualitative research methods, we recommend the short course on Qualitative Methods;

• participants accepted for this course will have to submit a two-page summary of their current research project to the instructors before July

Course content: 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. The course will start out with a recap of the main characteristics of quantitati-ve and qualitative research. Next, the types of research questions that lend themselves to a mixed methods approach will be examined. We will then look at mixed methods designs in terms of criteria for differentiating between different kinds of mixed methods designs and at some 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 ways of mixing during the phase of sampling, data collection, and data analysis.

The format of the course is interactive, combining short lectures with activities as well as applications to participants‘ own research examples and projects. In addition to class hours, participants will work on selected examples for each topic in small groups, and each day of the course will start with a discussion of these examples.

Due to time limitations, the course will not cover questions concerning the philosophy of science underlying mixed methods („paradigm wars,“etc.). Nor will there be enough time to deeply look at and apply the data analysis techniques used in Mixed Methods studies.

Week 1 Mixed Methods

Instructors: Margrit Schreier, Evgenia Samoilova Date: August 11-15, 2014

Time: 9:00-13:00

Course 3

21

13 2014 or else inform the instructors that they do not bring a specific project to the course.

Course participants will need to bring a laptop only on one day of the course (Thursday). Participants should make sure to install a (free) trial version of the text analysis software MaxQDA on their computer.

Organizational structure of the course:The course is structured around 4 hours of classroom instruction (9:00-13:00) and 2 hours of group work (14:00-16:00). Within the two hours of group work, we expect the participants to apply learned material to their own projects, which they will be asked to present on the next day. The assignment will be implemented in groups. Participants who wish to profit from work on their examples during the course are therefore asked to submit a 2-page summary of their research to the instructors by July 13. Participants who do not have their own projects will be able to work with the materials of one of our studies. As the time of the course is limited to 5 days, only 10 projects will be selected for the assignments. The group work on these projects is an important part of the course where participants can reflect upon and apply the learned material to a concrete research problem. We therefore expect all of the participants to take part in these 2 hours of group work outside of the lecture. If for some reason a participant cannot take part in this group activity, they are required to inform the instructors before the beginning of the course.

About the instructors: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 divergences” of Forum: Qualitative Social Research (2001; with Nigel Fielding) and co-author of “Forschungsmethoden in Psychologie und Sozialwissenschaften” (2nd ed. 2013; Springer; with Walter Hussy and Gerald Echterhoff).

Evgenia Samoilova is a PhD candidate in Bremen International Graduate School of Social Science. In her PhD project she addresses the issue of naturali-zation policies and integration experiences of immigrants via combining representative survey data and qualitative biographical interviews. Since 2013, she is a deputy scientific coordinator of the GESIS Summer School in Survey Methodology at the knowledge transfer department at GESIS – Leibniz Institute for the Social Sciences. Along with her PhD project and interest in migration research, she has been actively involved in teaching qualitative, quanti-tative, and mixed methods to BA, MA and PhD students at Jacobs University Bremen. Her primary research interests include mixed methods, migration, ethnicity, and methods education.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

22 23

22

• want to get an introduction into Structural Equation Model (SEM)-Framework;

• have had prior experience with SEM, but no formal training;

• have had prior training, but still find the whole matter rather complicated;

• want to get acquainted with Mplus;• wish to attend the course Understanding and

Modeling Measurement Error in Social Surveys in the subsequent week.

The theoretical input of the course is dense enough, so that advanced users can also find it useful to brush up their knowledge.

Course and learning objectives: By the end of the course participants will:• know how to apply SEM in the context of a

research question;• comprehend the mathematical and

statistical foundation of SEM;• have an understanding of the Mplus

command structure and practice with the program’s basic commands;

• be able to read, understand and interpret an Mplus output;

• be able to transfer the theoretical knowledge to applied research projects;

• acquire the set of skills they need for their individual projects within the course content.

Course prerequisites:• participants need to be familiar with and

have a conceptual/mathematical understan-ding of variance, covariance, correlation, standardization, hypothesis testing, and regression analysis;

Course content: This course will introduce the participants to the techniques of causal modelling and model estimation through structural equation models (also referred to as „SEM models“) and will show how a theoretical model represented through measurement models and possibly causal relationships can be applied to empirical data. Our experience has shown that Confirmatory Factor Analysis (CFA) as an important aspect of the SEM-framework seems to be a good starting point for an introduction. Therefore, this course revolves around concepts of the CFA such as proving the construct validity of a measurement model. The topics addressed in the course include different modeling techniques of CFA: Simultaneous CFA (SCFA), the Multiple Group Comparison of the CFA (MGCFA), the higher-order CFA and the longitudinal CFA. The course will introduce partici-pants to practical examples involving one of the commonly used SEM software packages, Mplus. The course is also recommended for those who wish to prepare themselves for the course Understanding and Modeling Measurement Error in Social Surveys.

Target group: Participants will find the course useful if they:(on the level of their research questions)• work with models that involve a complex

structure of variables and their relationships to each other;

• have a strong deductive framework and want to verify theoretical assumptions derived from substantive theories;

• need information on measurement quality (validity and reliability testing). (on a more general level)

Week 1 Introduction to the Structural Equation Modeling Framework Instructors: Jost Reinecke, Georg Kessler Date: August 11-15, 2014

Time: 14:00-18:00

Course 4

23

• basic knowledge of matrix notation;• basic knowledge of solving a linear equation

system;• familiarity with writing syntax (Mplus input is

syntax only);• handling of system files and transformation

to portable or ASCII-data files.

Course participants will not need to bring a laptop computer for this course. This course will take place in a computer lab. Organizational structure of the course: The course is structured around 4 hours of classroom instruction (14:00-18:00) and 2 hours of assisted learning practice (10:00-12:00). During the 2 hours of assisted learning practice, we expect the participants to apply learned material in their assignments and/or their own projects. Instructors will be present during these sessions. Teaching will take place as a combination of lectures on the theory of SEM, training in the program Mplus, and application of the theory in practice. To increase the learning curve, participants are encouraged to bring and discuss their own projects (e.g. research questions) to get feedback from their peers and the instructors. The debate will enhance the transfer of theoretical learning to applied knowledge. We therefore expect all of the partici-pants to take part in these 2 hour activity outside of the lecture to benefit from the exchange and the extra practice.

All the participants will have an opportunity to consult the instructors individually within the mentioned 2 hours or by appointment. The room for individual consultations will be announced in class.

About the instructors:Prof. Dr. Jost Reinecke is professor of quantitative methods of empirical social research at the Faculty of Sociology at the University of Bielefeld, Germany. 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 behavior and relationships of group-focused enmity to individual and contextual variables.

Mag. Georg Kessler graduated in Sociology at the University of Vienna, Austria. He specialized in methodology and wrote his master thesis on a mixed methods design, applying SEM and Cognitive Interviews on the Schwartz Value Scale used in the ESS. He received his SEM-training from Prof. Reinecke and Prof. Schmidt from the University of Gießen. Currently, he works as a lecturer at the University of Vienna in the department of Sociology and as a freelancer at a consulting firm in Vienna.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

24 2524

Target group: Participants will find the course useful if they:• have some basic knowledge of survey

methodology and would like to extend it to web surveys;

• want to conduct a web survey for their research or study purposes;• would like to improve the quality of web surveys they already conduct or plan to conduct in the future;• consider switching a currently existing survey to the web mode;• wish to prepare themselves for the course Web Survey Instrument Design.

Course and learning objectives: By the end of the course participants will:• obtain the practical and methodological knowledge to conduct high-quality web surveys;• learn the basics of web survey software tools and understand the principles of selecting the most appropriate tool for specific purposes;• understand possible deficiences and mistakes

in conducting web surveys;• be able to conduct all stages of their own web survey projects;• be able to evaluate quality of data obtai- ned with a web survey.

Course prerequisites:• participants are expected to be familiar with the basics of survey methodology (key terminology and basic principles of writing survey questions).

Course content: The course deals with the challenge of using the World Wide Web for survey data collection. Web surveys are technically easy to implement, however the understanding of underlying methodological principles is crucial for successful application in practice. Throughout the course, participants will learn the basics of these principles for all stages of the web survey process: pre-fielding (choosing the mode, sampling design, questionnaire preparation and testing, managerial aspects), fielding (nonresponse reduction, measurement, monitoring), and post-fielding (editing, archiving). Throughout the course, we will put a special emphasis on error sources that can compromise the quality of web survey data. Participants will also be introduced to 1KA, a free open-source software, for implementing web surveys. They are encouraged to bring their own web survey projects to the class.

The course will offer participants general basic knowledge needed for implementation of high-quality web surveys. It may also serve as an optional introduction to the course Web Survey Instrument Design, which will provide more comprehensive knowledge on appropriate web questionnaire design and its influence on measurement errors.

Participants are strongly encouraged to bring their own research projects on which they will work during the course. Participants who do not have their own projects will be provided with a survey example.

Week 1 Introduction to Web Surveys

Instructors: Katja Lozar Manfreda, Nejc Berzelak Date: August 11-15, 2014

Time: 14:00-18:00

Course 5

25

Course participants will need to bring a laptop computer for performing the practical exercises for this course. The laptop should have a web browser installed.

Organizational structure of the course: The classroom time will consist of about 3 hours of lectures with practical examples and discus-sions, followed by about 1 hour of group work on participants’ own research problems. Those without own research project will be provided with a practical example by instructors. Partici-pants will be expected to perform some work on their projects outside the classroom time. The instructors will be available each day for additional consultations.

About the instructors:Katja Lozar Manfreda, PhD, is an assistant professor of statistics at the University of Ljubljana, Faculty of Social Sciences. She is one of the pioneers in the field of web survey methodo-logy (since 1998, holds the first PhD on this topic from 2001) and part of the WebSM group, who is maintaining the largest information source on the methodology of web surveys, the websm.org. She has published on this topic (especially on web survey nonre-sponse, questionnaire design, mixed-mode surveys, software) in several scientific journals and with international book publishers. She is a co-author of a forthcoming book Web Survey Methodology (Callegaro, Lozar Manfreda, Vehovar, published in 2014 by Sage). She is an associate editor of the Survey Research Methods (ESRA) and member of the Scientific Committee of the Bulletin of Sociological Methodology (ISA-RC33). She is core group

member of the Cost Action WebDataNet which is dedicated to the web-based data collection methods and analysis.

Nejc Berzelak is a researcher at the Faculty of Social Sciences, University of Ljubljana. His research work is focused on measurement issues in web surveys, questionnaire design, and mixed-mode surveys. He also conducted three large-scale evaluations of software tools for web surveys. He was a methodological consultant for several surveys conducted by academic, govern-mental and private organizations. He has lectured several courses on web surveys and teaches topics related to survey methodology, web analytics, and new technologies in social science research.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

26 2726

Course content: This course provides an introduction to the statistical programming language R. Since R has gained a lot of attention among data analysts in general and social scientists in particular, we will emphasize the particularities of survey data analysis using R. The objective of the course is to enable attendants to write their own project syntax ranging from importing data into R to applying standard and more advanced analysis methods. We expect participants to be already familiar with data analysis in general, and ideally with some other statistical analysis software such as Stata or SPSS.

Although R is a genuine object oriented statistical programming language, the emphasis of this course will be on the analysis of survey data. After a general introduction of the architecture of R the course will therefore be loosely structured like a data analysis project, starting with importing data from external sources (SPSS, Stata, Excel, Web). The next objective will be getting summary statistics from the data and assigning results to different values. At this point, we will introduce the various data structures available in R. Some of them are unique to R, whereas others will be known from other software packages. After covering the basics, we give a brief introduction to one of R’s most powerful features: data visualiza-tion. Dealing with repeated tasks, loops and self-written functions will allow users to keep away from copy and paste syntax.

More advanced topics, such as multilevel models, text mining or advanced graphics in R, might be covered on the last day of the class depending on

the preferences of the participants. The final part of this course will also be covering an outlook to project organization when working collaboratively with R.

The course also aims to help those interested in attending the course Item nonresponse and multiple imputation acquire a sufficient level of R knowledge.

Target group: Participants will find the course useful if they:• want to benefit from recent implementa- tions of statistical algorithms and methods;• have to do a lot of data analysis, and create graphics or (standardized) reports;• need a powerful tool at hand for various tasks;• want to become part of a fast-growing user community;• have been wondering if R is hard to learn (No, it is not!);• wish to acquire sufficient knowledge of R language for the course Item nonresponse and multiple imputation.

Course and learning objectives: By the end of the course participants will:• be able to conduct sophisticated (survey) data analysis in R;• know how to create publishable graphics and tables;• be familiar with “programming” statistical analysis;• be able to structure workflow in research projects.

Week 1 Introduction to Data Analysis Using R

Instructors: Florian Meinfelder, Date: August 11-15, 2014

Hans Walter Steinhauer Time: 14:00-18:00

Course 6

27

Course prerequisites:• prior experience with statistical software;• prior experience with data analysis;• basic knowledge of the linear model;• basic concept of matrix algebra recommded.

Course participants will need to bring a laptop computer for performing the practical exercises for this course. The laptop should have the following software installed: R and Rstudio (free open-source software).

Organizational structure of the course: The course is structured around 4 hours of classroom instruction (14:00-18:00) and 2 hours of group work in the morning. During group work hours we expect participants to work individually or in groups on provided tutorial sheets to become more familiar with R. Besides we offer specializati-on on topics participants are interested in. Additionally, we provide individual support or consulting for particular data analysis projects (please contact either of the instructors for an appointment slot by July 31).

About the instructors:Dr. Florian Meinfelder is a lecturer at the Depart-ment for Statistics and Econometrics at the University of Bamberg, where he teaches, among others, statistical programming using R, Bayesian inference and statistical analysis with missing data. He has written his PhD thesis on Multiple Imputation related topics, supervised by Prof. Susanne Rässler and Prof. Trivellore E. Raghu-nathan, while working as a Research Manager for GfK SE. He is also author of the R package „BaBooN.“

Hans Walter Steinhauer is a research assistant at the Leibniz Institute for Educational Trajectories (LIfBi), working on sampling designs and weighting strategies for complex survey designs. Additio-nally, he is a PhD candidate and works as a lecturer at the Department of Statistics and Econometrics at the University of Bamberg teaching sampling techniques and applied statistics using R.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

28 2928

Course content: This course provides a comprehensive introduction to measurement error in survey research. This is a crucial topic because there are always errors in the measurement and if we do not take care of them, substantive results may be completely wrong. First, we will discuss examples and ways to reduce measurement error, such as basic rules for question wording. Then, findings of „split ballots“ will be critically reviewed. The next step concerns the modeling of measurement error using models for assessing reliability and validity such as the MTMM model.

The course will also have a detailed look at response styles in survey questions, as it is an important source of measurement error. We will use acquiescence as a generic example and model it using Structural Equation Modeling (SEM). Finally, the course will investigate the assessment of equivalent measurement in cross-cultural research, which is a key requirement if one wants to compare data across countries or groups. The different kinds of equivalence and procedures to assess them will be introduced. The practical exercises will guide participants to apply the concepts and procedures learned in the theoreti-cal sessions. The software Mplus and data from the ESS will be used.

Target group: Participants will find the course useful if they:• are PhD students who will use survey data in

their work; • employees in survey/marketing institutes

who are engaged in the analysis of survey data, or in preparation of questionnaires;

• are exceptionally specialized research master students interested in measurement error if this topic is not covered in their program.

Course and learning objectives: By the end of the course participants will:• have acquired a thorough understanding

of the different kinds of measurement error related to the instrument (questionnaire) in social surveys,

• be aware of ways to detect, measure and control for errors;

• be familiar with the most recent develop ments and literature in the field of measure-ment error, especially in the context of large scale cross-cultural surveys;

• be familiar with the best practices in the field;

• be able to apply the strategies in practice.

Course prerequisites:• students must be motivated to apply what

they will learn in their own research and should be interested in the quality of survey data;

• basic knowledge of statistical analysis and structural equation modeling. The examples in the theoretical part are based on LISREL_8® and participants should be able to interpret the parameters. The practical exercises assume practical knowledge of Mplus. These require-ments can be fulfilled by attending course Introduction to the Structural Equation Modeling Framework in the previous week.

Week 2 Understanding and Modeling Measurement Error in Social SurveysInstructors: Jaak Billiet, Melanie Revilla Date: August 18-22, 2014

Time: 9:00-13:00

Course 7

29

Course participants will not need to bring a laptop computer for this course. This course will take place in a computer lab.

Organizational structure of the course: The course structure includes 4 hours of in-class instruction in the morning, of which 3 hours will be a lecture (9:00-12:00) and 1 hour will comprise practical exercises (12:00-13:00). In addition, the participants are expected to take part in 2 hour guided exercises in presence of a course instructor (14:00-16:00), which will take place in the PC-pool. In the afternoon, participants can also make appointments with the course instructors for individual consultations. The presence in the PC-pool during this time is considered to be an important part of the learning for this course, as we will learn how to work with different software than the one used for the lectures.

About the instructors:Prof. Billiet Jaak, PhD, is emeritus professor in social methodology at KU Leuven, Belgium since 2007. He is a member of the central co-ordination team of the European Social Survey. Methodologi-cal research: validity assessment, interviewer and response effects, and the modeling of measure-ment error in social surveys. Substantive research: longitudinal and comparative research in the domains of ethnocentrism, political attitudes and religious orientations. He played a central role in the implementation of the fourth wave of the European Value Study in 2008. Jaak Billiet has published widely with research fellows from various countries in peer-reviewed journals and specialized books on survey methodology and cross-cultural analysis.

Melanie Revilla, PhD, is a postdoctoral researcher at the Research and Expertise Centre for Survey Methodology (RECSM) and associate professor at Universitat Pompeu Fabra (UPF, Barcelona, Spain). PhD from Universitat Pompeu Fabra (2012) in the areas of statistics and survey methodology, under the supervision of professors Willem Saris (UPF) and Peter Lynn (Essex University). Dissertation dealt with the effects of different modes of data collection on the quality of survey questions. Besides the impact of the mode of data collection, she is interested in all aspects of survey methodo-logy.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

30 3130

Course content: Recently, experimental techniques have become more important in the field of social sciences. The implementation of experimental techniques into survey research required some modifications of the classical experimental approaches which refer-red to laboratory and field experiments.

The course will provide an overview of a variety of experimental techniques that have been imple-mented in survey research recently. We will start with the idea and principles of experimentation in the social sciences. The participants will obtain profound knowledge about the criteria that guide decisions for experimental techniques. The distinctive feature of experiments consists in the possibility to study causal relationships by manipulating the “causes” in order to analyse their “effects” upon the “outcomes.”

The course will cover a selection of the most important experimental techniques in survey research, i.e. split-sample experiments, multitrait-multimethod experiments, randomised response techniques, scenario-techniques and the factorial survey approach. The course will combine classroom instructions, group work and practical exercises. However, the format of all elements of the course is interactive, thereby relying upon the participants active contributions.

The course will not cover experimental approaches that are closely related to the factorial survey approach, e.g. choice experiments or conjoint analyses. Participants who want to learn how to set up a factorial survey should attend the course Factorial Survey Designs in addition to this course.

Target group: Participants will find the course useful if:• they are considering using experimental techniques in a survey and need more background knowledge in order to arrive at a decision;• they are planning to use experimental techniques in their current or future survey research projects and would like to get advice on how to do it.

Course and learning objectives: By the end of the course participants will:• have obtained an overview over experimental

techniques in survey research;• be familiar with the main reasons guiding the

choice of experimental techniques for surveys;• able to select the appropriate experimental

technique for their own survey research;• able to select the suitable experimental design

for their own research.

Course prerequisites:• the principles of questionnaire design;• the major types of research designs;• the software Stata;• to fill the gap in basic knowledge of survey

methodology, participants are recommended to attend the course Introduction to Survey Design;

• participants will have to submit a one-page summary of their current research or the research they are planning to the instructors before July 15th 2014.

Week 2 Experimental Techniques in Survey Research

Instructors: Stefanie Eifler; Lisa Wallander Date: August 18-22, 2014

Time: 9:00-13:00

Course 8

31

Course participants will need to bring a laptop computer for performing practical exercises for this course.

Organizational structure of the course: The course is structured around 4 hours of classroom instruction and 2 hours of group work or practical exercises. Within the 2 hours of group work, participants are asked to present their current or planned research project with special emphasis on their research design or the factorial survey approach. Participants who want to profit from discussions of their examples are asked to submit a one-page summary of their current or planned research to Stefanie Eifler and Lisa Wallander by July 15th. Four projects will be selected for each group work. The practical exercises will refer to split-sample experiments and scenario-techniques and will use data sets from the instructor’s current research.

About the instructors:Prof. Dr. Stefanie Eifler is a full professor of Sociology and Empirical Social Research at the Catholic University of Eichstätt-Ingolstadt, Germany. Her research interests cover the implementation of experimental techniques into survey research and the interplay of rational choice and norms in the explanation of prosocial and criminal behavior.

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, judgment and decision making in social work, substance misuse and treatment, and quantitative research methods.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

32 3332

Course content: The course will focus on the design of Web survey instruments and procedures, based on theories of human-computer interaction, interface design, cognitive and social psychological theories, and research on the effect of Web survey design on measurement error. The course will cover all aspects of instrument design for Web surveys, including the appropriate use of widgets (e.g., radio buttons, check boxes) for Web surveys, general formatting and layout issues (including typeface, color, layout and alignment), movement through the instrument (action buttons, navigati-on, error messages), and so on. The course will draw on empirical results from experiments on alternative design approaches as well as practical experience in the design and implementation of Web surveys.

Issues related to the increased use of mobile devices (smart phones and tablets) for competing surveys will be addressed. Issues of representation (i.e., sampling, coverage and nonresponse) will not be addressed in this course, but will be the focus of the course Introduction to Web Surveys in the previous week.

The course is designed for students and resear-chers new to this topic (but with prior survey experience) as well as for those who already have some knowledge or experience in Web survey design and deployment. While some universities offer courses on questionnaire design and on general survey design, there are no courses focusing on specific issues related to Web survey design. As is clear from the large and growing literature on the topic, Web survey design brings

special challenges and opportunities, but the knowledge and skills gained will apply in other design settings too. In addition to learning about how to design surveys, participants will also learn how to conduct methodological studies on Web survey design, and identify gaps in the literature to which they may contribute.

Target group: Participants will find the course useful if they:• are designing or conducting online surveys or

planning to do so;• have some experience with (or knowledge of)

questionnaire design;• course will be particularly relevant for those

with some prior experience with Web surveys.

Course and learning objectives: By the end of the course participants will:• be able to design a Web survey to maximize

the quality of the survey responses collected;• be familiar with the research literature and

relevant theories on measurement error in Web surveys;

• understand the role that design plays in the quality of data obtained from Web surveys.

Course prerequisites:• familiarity with survey research methods;• ideally, students will have taken the course

Introduction to Web surveys the previous week, but this is not required.

Course participants will need to bring a laptop computer for performing practical exercises for this course. The laptop should have a web browser installed.

Week 2 Web Survey Instrument Design

Instructors: Mick P. Couper, Frederik Funke Date: August 18-22, 2014

Time: 9:00-13:00

Course 9

33

Organizational structure of the course: The classroom time will consist of about 3 hours of lecture and demonstration on various aspects of Web survey design, followed by about 1 hour of structured time working with the LimeSurvey software to implement these ideas. In addition: • participants are expected to work on their own

design projects during “free study time.” If they do not have a project, the instructors will provide an example survey to work on;

• the instructors will be available for individual and group consultations on participants’ projects, and provide support on using LimeSurvey to develop and deploy a Web survey.

About the instructors: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) and (With Roger Tourangeau and Frederick Conrad), The Science of Web Surveys (Oxford, 2013). His current research interests focus on aspects of technology use in surveys, whether by interviewers or respondents.

Frederik Funke, PhD, is a survey methodologist who works in Germany as a freelance scientist, consultant, and statistics trainer. Furthermore, he is a lecturer at the University of Kassel and senior project manager for online research at the LINK Institut, Frankfurt. Frederik received a Master’s

degree in sociology and a PhD in psychology, both in the area of online research methods. His research focuses on rating scales in Web surveys and he published on the topic in Behavior Research Methods, Field Methods, and Social Science Computer Review.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

34 3534

Course content: This course provides an introduction to Multiple Imputation (MI) (Rubin 1987) which has become a very popular way for handling missing data, because it allows for correct statistical inference in the presence of missing data. With the advent of MI algorithms implemented in statistical standard software (SAS, Stata, SPSS), the method has become more accessible to data analysts. Since we consider it essential for potential users of MI techniques to understand its underlying principles, we will introduce some statistical theory on analysis of incomplete data at the beginning of this course. For didactic purposes, some naïve ways of handling missing data are introduced as well, in order to convey what constitutes a „good“ imputation method.

The statistics software R has become a lingua franca for statistical analysis and is predominantly used for examples and demonstrations in this course, but the purpose of the course is not to teach doing MI using R, and while we recommend basic R skills for this course, it can be attended without prior knowledge in R. We hope that by the end of the course people are able to use MI algorithms in general, and to apply Rubin’s combining rules for estimators, as well as to explain to readers of their work how and why they used the method.

This one-week course can be loosely categorized into three parts. The first part of the course introduces the notation and assumptions used in missing-data analysis, where small simulation studies are used to demonstrate how MI allows for correct inferences if certain assumptions hold. The

second part of the course gives an overview of available options of MI algorithms, discussing their strengths and weaknesses as well as their applicability to specific data situations.

Target group: Participants will find the course useful if they:• are researchers working with incomplete data;• want to learn more about the analysis of

incomplete data in general;• are already aware of MI and its benefits, but

feel insecure about the available parameter settings in MI algorithms implemented in their preferred statistical software.

Course and learning objectives: By the end of the course participants will:• be familiar with the theoretical implications of

the MI framework and will be aware of the explicit and implicit assumptions (e.g. will be able to explain within an article why MAR was assumed, etc.);

• know when to use MI (and when not);• be aware how to specify a „good“ imputation

model and how use diagnostics;• be familiar with the availability of the various

MI algorithms;• be able to not only replicate situations akin to

the case studies covered in the course, but also know how to handle incomplete data in general.

Course prerequisites:• basic understanding of sampling theory;• an advanced understanding of the (genera-

lized) linear model;• familiarity with statistical distributions;

Week 2 Item Nonresponse and Multiple Imputation

Instructors: Susanne Rässler, Date: August 18-22, 2014

Florian Meinfelder Time: 14:00-18:00

Course 10

35

• basic knowledge of the Bayesian paradigm;• basic knowledge of matrix algebra;• solid skills in either R, SPSS or Stata (recom-

mended for exercises).

Knowledge of R can be acquired in the course Introduction to Data Analysis Using R in the previous week.

Course participants will not need to bring a laptop computer for this course. This course will take place in a computer lab.

Organizational structure of the course: The course is structured around 4 hours of classroom instruction (14:00-18:00) and 2 hours of group work in the morning. Within the 2 hours of group work, participants can either work on tutorial sheets uploaded to ILIAS, or work on their research projects. As of day three we provide consulting time for research projects during group work hours. Please contact either of the instruc-tors by July 31 for an appointment slot (if possible, send also the data set that is suffering from item nonresponse and/or a project description).

About the instructors:Prof. Dr. Susanne Rässler holds the Chair of Statistics and Econometrics at the Otto-Friedrich-University of Bamberg, Germany. She was a member of the German census board, and is speaker of the methods group of the National Educational Panel Study. Her research interest comprises methods for handling missing data in complex surveys, multiple imputation, Bayesian and computational statistics as well as matching techniques for causal inference.

Dr. Florian Meinfelder is a senior lecturer at the Department for Statistics and Econometrics at the University of Bamberg, where he teaches, among others, statistical programming using R, Bayesian inference and statistical analysis with missing data. He has written his PhD thesis on Multiple Imputation related topics, supervised by Prof. Susanne Rässler and Prof. Trivellore E. Raghu-nathan, while working as a Research Manager for GfK SE. He is also author of the R package ‘BaBooN’.

On the Summer School website, you can find the full syllabus of the course with complete informa-tion on the topics, literature, and day-to-day schedule.

36 3736

Course content: Currently, a large variety of data modes such as telephone interview, personal interview, mail or web survey are available for social surveys, which leads to methodological questions, such as, which mode is best? Each mode has its advantages and disadvantages; each mode also makes different logistical demands. Sometimes the choice for a particular data collection is easy and straightfor-ward. But often the situation is more complex and one single mode will not suffice. Therefore multiple modes of data collection or mixed modes have become more and more popular in survey practice.

In this course, we give an overview on the current state of the art in designing, implementing, and evaluating mixed-mode surveys. In the first part of the course, we address the major variants of mixed-mode data collection designs, issues in mixed-mode questionnaire design, and manage-ment of mixed-mode projects. In the second part, we discuss issues in the analysis of mixed-mode surveys, going from an introduction to more advanced statistical techniques.

The objective is to provide the participants with a thorough background on mixed-mode methodo-logy and with an empirical knowledge base on the implications of mixed-mode for questionnaire design, total survey error and logistics.

Target group:Participants will find the course useful if they:• are considering doing mixed-mode research in

the future and would like to find out whether this would be a suitable approach;

• are planning to use mixed-mode in their research and would like some input on how to do this;

• have used mixed-mode in their research and would like some feedback;

• think about adding another mode to their web survey to improve data quality, e.g. representa-tiveness;

• have concerns with mixed-device surveys, e.g. the fact that respondents to web surveys can complete their surveys on different devices (desktop, tablet, mobile phone);

• have started on mixed-mode research and are unsure about their data analysis or other elements.

Course and learning objectives:By the end of the course participants will:• be familiar with the current discussion

surrounding mixed-mode surveys;• be familiar with common reasons underlying

the choice of mixed-mode research;• have gained an overview of different mixed-

mode designs and strategies for mixing;• know the advantages and disadvantages of

different modes;• be able to select suitable design elements for

their own research;• be able to analyse mixed-mode surveys;• be able to make an informed judgement about

mixed-mode or mixed-device surveys.

Course prerequisites:• participants should have basic knowledge

about survey methodology and statistics;• basic knowledge of survey methodology could

be gained in the course Introduction to Survey Design;

Week 2 Mixed-Mode Surveys

Instructors: Vera Toepoel, Edith de Leeuw, Date: August 18-22, 2014

Thomas Klausch Time: 14:00-18:00

Course 11

37

• participants can submit a two-page summary of their current research project to the instructors before July 14th 2014 or else inform the instructors that they are not bringing a specific project to the course.

Course participants will need to bring a laptop computer with an installed web browser for performing the practical exercises for this course.

Organizational structure of the course: The course is structured around 4 hours of classroom instruction (14:00-18:00) and 2 hours of (group) work in the morning. Within the 2 hours of (group) work, we expect the participants to apply learned material in their own projects or provided assignments (practical exercises), which they will be asked to present on the next day. Participants who wish to profit from work on their examples during the course are therefore asked to submit a 2-page summary of their research to the instructors by July 14.

About the instructors:Dr. Vera Toepoel is Assistant Professor at the Department of Methodology & Statistics at Utrecht University in the Netherlands. She teaches courses in survey methodology and her research focuses on: web surveys, panel surveys, mixed-device surveys etc. Vera previously worked at the Dutch Tourism Centre, CentERdata and Tilburg University. She is the chair of the Dutch and Belgian Platform for Survey Research and is currently working on the book “Doing Surveys Online” which will be published by Sage.

Prof. Dr. Edith de Leeuw is a full professor in survey methodology and statistics at the University of Utrecht. She is an associate editor for the Journal of Official Statistics (JOS) and editor of the international journal Methods, Data, Analysis (MDA) and is on the editorial boards for leading journals in the field of survey methods. Edith organized and taught international workshops and seminars on mixed-mode, meta-analysis, nonresponse, computer assisted data collection, questionnaire design and data quality in surveys and was the editor of 3 internationally renowned books on survey methodology (The International Handbook of survey methodology, Advances in telephone methodology, and Survey Measurement and Process Quality). At present her research focuses on total survey error and mixed-mode data collection.

Thomas Klausch is a post-doc at the Department of Methodology & Statistics at Utrecht University. His main research interest concerns the analysis and adjustment of selection and measurement error in mixed-mode surveys. He completed his PhD research on mixed-mode surveys at Utrecht University and currently develops methodology for the adjustment of mode effects in mixed-mode surveys.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

38 3938

Course content: In traditional text books questionnaire design is typically treated as an „art.“ Designing questions and questionnaires is broadly described as an important step when planning a survey. However, little advice is provided on how to phrase individual questions and how to design a good questionnaire as a whole. The rules and instruc-tions given in such texts are either too specifically concerned with particular substantive questions—and accordingly those rules cannot be generalized to other types of questions-or the advice given is too broad and general and it is left to the reader to apply the general rules to his or her specific survey questions.

This course on questionnaire design will avoid this dilemma. Instead of providing general or specific rules on how to design a good survey question and a questionnaire as a whole, the course will approach the science of questionnaire design by means of two alternative strategies. On the one hand, basic concepts relevant to survey measure-ment will be discussed (e.g. mode differences, question-answer-process, satisficing or social desirability) in order to make participants aware of the mechanisms underlying survey responses. On the other hand, participants will be introduced to results of field-experimental studies testing various aspects of a survey question and a questionnaire as a whole (e.g. question wording, response order or the visual design of a question). The discussion of these studies will highlight the implication of various design aspects of a survey question for the responses provided by respon-dents.

Thus, the lectures provide scientific background knowledge and educate participants in their professional reasoning when designing survey questions and a questionnaire as a whole.

Based on the theoretical concepts and experi-ments discussed in the lectures, participants will be guided and supported in designing a joint topical survey questionnaire during practical sessions and by means of assignments. The course is not restricted to a specific survey mode.

Target group:Participants will find the course useful if they:• plan to or are about to conduct a survey; • would like to supplement their initial experi-

ence in designing questionnaires with practical advice based on a sound theoretical basis concerning the underlying mechanisms.

Course and learning objectives:By the end of the course participants will:• have an overview concerning the various

components of survey data quality in general and questionnaire quality in particular;

• understand the cognitive processes underlying survey measurement for the various survey modes;

• be able to design simple survey questions of various types and to combine them in an integrated survey instrument.

Course prerequisites: • basic knowledge in quantitative social science

research methods is required; • basic knowledge concerning survey design and

data quality is advisable;

Week 2 Questionnaire Design Instructors: Marek Fuchs, Tanja Kunz Date: August 18-22, 2014

Time: 14:00-18:00

Course 12

39

• participants not familiar with the total survey error framework are encouraged to consider the preparatory reading mentioned in the course syllabus;

Course participants will need to bring a laptop computer for performing the practical exercises in this course. The laptop should have the following software installed: Office package.

Organizational structure of the course: The 4 hours of classroom instruction combining lecture and group work are supplemented with 2 hours of on-site study assisted by the instructors where participants are expected to work on their assignments in due consideration of the stated literature. Additional time of 1-2 hours a day has to be scheduled for reading further course literature.

About the instructors: Prof. Dr. Marek Fuchs is full professor of social science research methods at Darmstadt University of Technology, Germany. He 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 particular-ly 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.

Tanja Kunz is a research associate in social science research methods at Darmstadt University of Technology (Germany) since 2010. Her main research interest focuses on improvement of data quality by experi-mental survey design, in particular by visual design in Web surveys.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

40 4140

Course content:The factorial survey („vignette analyses“) is a method that integrates multi-factorial experimental set-ups into surveys. Respondents are asked to evaluate fictitious situations, objects or persons. By systematically varying attributes of the descriptions it is possible to determine their influence on respondents’ stated attitudes, decisions, or choices. The experimental variation of stimuli allows estimating the influence of each attribute on the evaluation; the factorial survey is therefore a theory-testing method. As the experiment is embedded in a survey questionnaire, it is possible to reach heterogeneous sample populations.

This course gives a theoretical and practical overview on factorial survey methods. Participants will get practical insights into all single steps that are necessary to design factorial survey experi-ments: (1) construction of vignettes, (2) selection of an experimental design, (3) drafting and program-ming of questionnaires (for online and paper and pencil surveys), (4) data management, and (5) data analysis techniques (like multilevel analyses, estimations of willingness to pay). Participants might select a research question related to their own research for practical exercises. For most practical analyses the statistical software package Stata will be used (prior knowledge needed). For setting up experimental designs and programming of questionnaires we use the software packages SAS and QuestBack (no prior knowledge is needed). The method is not connected to (confirmatory and explorative) factor analysis. Moreover, the course does not include anchoring and video vignettes.

Target group: Participants will find the course useful if they:• have initial ideas for their own research

questions that could be realized by means of a factorial survey;

• plan to conduct a factorial survey in their projects;

• want to deepen their knowledge of experimen-tal designs and quantitative statistical methods;

• want to learn about special methods for analyzing data stemming from these methods or evaluating the quality of these data.

Course and learning objectives: By the end of the course participants will:• have learned and discussed the features,

typical applications, advantages, and shortco-mings of factorial survey methods;

• have acquired practical insights into all single steps that are needed to set up factorial survey designs, to implement them into (computer assisted) questionnaires, to analyze resulting data, and report on results;

• be familiar with practical methods to evaluate data quality gained by factorial survey methods;

• have gained some insights into related experimental survey methods like conjoint ana-lyses and choice experiments;

• be able to apply factorial survey methods on their own.

Course prerequisites:• good knowledge of the statistical software

package Stata (or similar software, like SPSS or R);

Week 3 Factorial Survey Designs

Instructors: Katrin Auspurg, Carsten Sauer Date: August 25-29, 2014

Time: 9:00-13:00

Course 13

41

• participants should have basic knowledge of questionnaire designs and experimental methods (this could be acquired in the course Experimental Techniques in Survey Research in the previous week);

• good knowledge of data management and quantitative data analyses (e.g. linear regression techniques, coding of variables, merging of data sets).

Course participants will not need to bring a laptop computer for this course. This course will take place in a computer lab.

Organizational structure of the course: The course consists of 4 hours of classroom instruction (9:00-13:00) and individual exercises in the afternoon. Classroom instruction: The instructors provide an overview and the theoretical background of the method, and explain the assignments for the exercises. Individual exercises: In each exercise, participants are expected to work on assignments in the PC pool. They can also work on their own projects and discuss them with other participants and the instructors. The instructors are available in the PC pool for 2 hours to help with the exercises or to discuss specific research problems. If the time allows, instructors can consult participants on their projects in more details.

About the instructors:Dr. Katrin Auspurg currently is an Assistant Professor at the Department of Sociology at the University of Konstanz and Research Associate at the Institute for Social & Economic Research, Essex. Her main research interests are in survey research methods, social inequality and labor market

research. Recent publications include: Auspurg, A., Hinz, Th. (2011) ‘What Fuels Publication Bias? Theoretical and Empirical Analyses of Risk Factors Using the Caliper Test.’ Journal of Economics and Statistics 231 (5-6), 636-660; Abraham, M., Auspurg, K., Hinz, Th. (2010) ‘Migration Decisions Within Dual-Earner Partnerships: A Test of Bargaining Theory. Journal of Marriage and Family 72 (4), 876-892.

Carsten Sauer is a researcher at the Collaborative Research Center (SFB) 882 at Bielefeld University. His research interests include the explanation of behavior, social inequality and justice and quantitative research methods (especially factorial surveys). Among his recent publications are “The justice of earnings in dual-earner house-holds.” In: Research in Social Stratification and Mobility. 30 (2012): 219 -232 (with Stefan Liebig and Jürgen Schupp); “The Application of Factorial Surveys in General Population Samples: The Effects of Respon-dent Age and Education on Response Times and Response Consistency.” In: Survey Research Methods 5 (2011): 89-102 (with Katrin Auspurg, Thomas Hinz, and Stefan Liebig).

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

42 43

Week 3 Design and Implementation of Longitudinal Surveys

Instructor: Corinna Kleinert, Katrin Drasch, Date: August 25-29, 2014

Nancy Kracke Time: 14:00-18:00

Course content: The collection and analysis of longitudinal data – data that contains information for several points in time for the same unit of analysis – has received much attention in the past years. Currently, it is regarded as the gold standard of empirical social research. However, less focus is often laid on how to carry out high-quality longitudinal surveys. Thus, the course aims at covering the main stages of designing and implementing longitudinal surveys, at revealing the challenges faced when conducting longitudinal surveys, and at offering state-of-the-art solutions for these challenges. Furthermore, important existing large-scale surveys form Germany and other countries are discussed, and the course introduces into analysing longitudi-nal data. The format of the course is interactive, combining short lectures with participants’ activities as well as applications to participants‘ own research examples and projects. In addition to class hours, participants will work on selected examples for each topic in small groups, and each day of the course will start with a discussion of these assignments. The software package Stata will be used for analysis of panel attrition as well as longitudinal data analysis of substantial research questions.

Target group: Participants will find the course useful if:• they work for or aim at working for a large-

scale longitudinal survey;• they intend to set up a longitudinal survey on

their own;• they develop or seek to develop survey

questions asking for retrospective information or being repeated in a panel survey;

• they are interested in methodological aspects of longitudinal data collection;

• they want to analyse longitudinal data with respect to methodological as well as substanti-al issues.

Course and learning objectives: By the end of the course participants will:• be familiar with important international and

German longitudinal surveys and their central design features;

• understand different strategies on how to collect longitudinal data;

• be familiar with the main steps of design and implementation of longitudinal surveys;

• be able to evaluate different quality aspects of a longitudinal survey;

• know how to design retrospective and complex panel instruments;

• be able to monitor nonresponse and prevent panel attrition;

• get a basic idea of cases where different longitudinal analyzing techniques are used.

Course prerequisites:• for this course, participants should have basic

knowledge of survey methodology from a cross-sectional perspective, in particular with respect to survey design, instrument develop-ment, and survey implementation (this could be gained in the course Introduction to Survey Design in the first week);

• participants accepted for this course are asked to submit a two-page summary of their current course-related research project to the instructors before July 31st 2014;

Course 14

42

• prior knowledge of Stata is suggested but not required. Students without prior Stata knowledge should be willing to learn some Stata during the course.

Course participants will need to bring a laptop computer for performing the practical exercises for this course.

Organizational structure of the course: The course is structured around four hours of classroom instruction (9:00-13:00) and two hours of participants’ group work (14:00-16:00). Here we expect the participants to work on practical exercises, which they will be asked to present on the next morning. Participants who wish to profit from work on their examples during the course are therefore asked to submit a two-page summary of their course-related research to the instructors by July 31.

About the instructors:Dr. Corinna Kleinert is directing the NEPS (National Educational Panel Study) research team at the Institute for Employment Research (IAB) in Nuremberg which is responsible for conducting NEPS stage 8 „Adult Education and Lifelong Learning.“ She received her training in sociology, communication science, political science and public law at the University of Munich (M.A. 1995, Dr. phil. 2003). She is working on issues related to survey methodology, school-to-work transitions, and gender inequality in the labour market. She has participated in the development of several large-scale surveys, among them the DJI Youth Survey and the IAB study ALWA.

Dr. Katrin Drasch is lecturer at the Institute of Sociology, Chair of Empirical Social Methods at the University of Erlangen-Nuremberg. From May 2007 until March 2012 she worked in the NEPS and ALWA research groups at the Institute for Employment Research (IAB). In 2006 she completed her M.Sc. at Utrecht University, Netherlands. She studied in Nuremberg gaining her diploma degree in 2007 (Diplom). In February 2013 she received her Doctorate from the Friedrich-Alexander University of Erlangen-Nuremberg. Her major research interests are issues of longitudinal data collection and analysis as well as employment careers of mothers.

Nancy Kracke is employed as researcher at the Institute for Employment Research (IAB) in the department „Education and Employment over the Life Course“ in Nuremberg. She is a member of the NEPS (National Educational Panel Study) research team and is involved in stage 8 „Adult Education and Lifelong Learning.“ In January 2013 she completed her Master of Science in Socio-Econo-mics (M.Sc.) at Friedrich-Alexander University Erlan-gen-Nuremberg. Her research interests include education and labour market opportunities, life course research and gender inequalities. In her Ph.D. project she examines overqualification with focus on social inequalities.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

43

44 45

Week 3 Data Quality and Fieldwork Management

Instructors: Remco Feskens, Ineke Stoop Date: August 25-29, 2014

Time: 09:00-13:00

Course content: Obtaining high data quality within given budget and time constraints is a very challenging task. All too often the focus on survey quality is narrowed down to one aspect of the total data quality. For example, if all efforts and funds are allocated to increasing response rates, while neglecting the timeliness of results, this may result in an unhappy survey sponsor. Or, if you choose to use interview-ers in the collection of your data you might get higher response rates and - more important - a better response composition, but you may also end up with lower measurement quality. We know that interviewers can be very good in convincing reluctant respondents to cooperate, but reluctant respondents may tend to satisfice and provide sloppy answers. Survey design thus has to acknowledge the need for trade-offs, and interviewers have to be trained in all aspects of recruitment and interviewing.

Important goals in survey design are thus to minimize survey errors within a given budget, acknowledging other quality aspects such as timeliness, relevance, consistency and comparabili-ty. Establishing effective and efficient fieldwork strategies will help to accomplish these goals. Optimal allocation of funds and total quality management are key issues in this area. The total survey error framework provides a useful tool to make choices while designing a survey.

This course will teach participants how to optimize and evaluate survey data quality. Important goals in survey design are to minimize survey errors within a given budget and to acquire timely collected high quality data. Establishing effective and efficient

fieldwork strategies will help to accomplish these goals. Optimal allocation of funds and total quality management are key issues in this area. Central in this course are all aspects of survey data collection. The course will cover the choices that have to be made related to the organization and the design of the fieldwork of survey data collection. Design and implementation choices cover the choice of data collection modes, efforts to enhance response rates, the quality of measurements, the collection of auxiliary data to adjust for nonresponse and the training of interviewers.

Students are encouraged to work on their own research project, both in the design phase of their project and in the analyses of their own data, if available. The latter is, however, not a requirement. Students can also work on exercises available for the course. The course will be applicable to surveys of individuals, and to a lesser degree to surveys of organisations.

Questionnaire design is not a part of this course, although elements in questionnaire design will be discussed when related to other survey quality criteria. The course will discuss the trade-off between different sources of errors (sampling error, coverage error, nonresponse error, and measure-ment error). If you are interested in the issue of questionnaire design specifically, we would recommend attending the course Questionnaire Design in the preceding week.

Target group: Participants will find the course useful if they:• are planning to conduct or to design a

(large-scale) survey;

Course 15

44

• wish to evaluate the quality of data collected by a survey;

• wish to take design issues into account when analysing survey data.

Course and learning objectives: By the end of the course participants will:• be familiar with the main survey design issues;• be familiar with ways to enhance response

rates;• be familiar with the main risks of nonresponse

bias;• be familiar with the basic concepts around

total survey error.

Course prerequisites:• basic understanding of survey methodology

(this could be gained in the course Introduc-tion to Survey Design in the first week);

• prior knowledge of SPSS is suggested but not required.

Course participants will need to bring a laptop computer for performing the practical exercises for this course.

Organizational structure of the course: The course is structured around 4 hours of classroom instruction and “free study time.” The course instructors will be available for group and/or individual support during the free study time. They will be available to discuss assignments, to provide additional detail on aspects of the course and to discuss individual research or data collection projects of the participants.

About the instructors:Remco Feskens, PhD, is a senior research scientist at the research department of Cito, the National Institute for Educational Measurement in the Netherlands. He obtained his PhD at the depart-ment of Methods and Statistics at Utrecht University, in collaboration with Statistics Netherlands. His main research interests are research methodology, survey design and data collection.

Dr. Ineke Stoop is a senior researcher at The Netherlands Institute for Social Research/SCP. She is also Deputy Director of Methodology of the European Social Survey. She obtained her PhD at Utrecht University with a thesis on nonresponse. Her main research interests are survey methodolo-gy, nonresponse and comparative surveys.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

45

46 47

Week 3 Sampling, Weighting and Estimation

Instructor: Stephanie Eckman Date: August 25-29, 2014 Time: 14:00-18:00

Course content: This course will cover three interrelated topics: methods of selecting complex samples, creation of analysis weights that adjust for nonresponse and undercoverage, and the analysis of data collected via complex weighted surveys. The emphasis of the course is more applied than theoretical, but students are expected to be comfortable with statistics and to have some experience with data analysis. In each section of the course, students will have ample time to apply the concepts and techniques learned in the lecture to exercises in Stata.

The first section, on sampling, will begin with simple random sampling and move onto stratified, clustered and multi-stage sampling. For each method, students will learn the relevant formulas for point estimates and variance estimates; however, the course will emphasize application over theoretical proofs of the formulas.

The second section will cover the calculation of sampling weights that adjust for unequal probabili-ties of selection and the ways in which such weights can be adjusted for nonresponse and undercoverage. Each adjustment method relies on assumptions, and students will learn the pros and cons of each method. Again, the course will involve several practical exercises so that students can practice the methods learned in class.

The third section will teach students how complex designs and weights alter the ways in which survey data should be analyzed. Traditional methods of analysis, usually taught in introductory statistics courses, do not apply to such data sets. There are

several different methods that can be used to analyze complex weighted survey data. Students will learn both why we need new methods and how the methods can be applied, as well as the advantages and disadvantages of each method.

Target group: Participants will find the course useful if they:• have some experience conducting surveys and/

or analyzing social science data, but have not yet studied sampling;

• plan, conduct or analyze complex sample surveys.

Course prerequisites:• introductory course in statistics. No prior

knowledge of sampling theory is assumed, but students should be comfortable with statistical concepts such as hypothesis testing, variance, standard errors, confidence intervals, etc;

• basic understanding of survey methodology (this could be gained in the course Introduc-tion to Survey Design in the first week);

• prior knowledge of Stata is required for this course;

• experience in handling survey data is helpful.

Course participants will need to bring a laptop computer for performing the practical exercises for this course.

Course and learning objectives: By the end of the course participants will:• have a sound understanding of the most

frequently used sample designs (one- and two-stage sampling, clustered sampling; stratified sampling, and related designs);

Course 16

46

• know how to create probability weights, and have experience with several methods for adjusting such weights for nonresponse and undercoverage;

• understand how and why the design of a sample survey affects the analysis of the data;

• know the appropriate methods to use in Stata to analyze complex survey data, and the pros and cons of each method.

Organizational structure of the course: The course will consist of 4 hours of lecture in the afternoon. Students will spend the mornings working on exercises designed to deepen their understanding of the material. The instructor will be available in the mornings to answer questions.

About the instructor:Dr. Stephanie Eckman is a Senior Researcher at the Institute for Employment Research in Nuremberg, Germany. Her research interests include underco-verage in household surveys and motivated underreporting by interviewers and respondents in survey interactions. She has taught specialized courses on survey methodology, weighting and data analysis at the University of Mannheim., Ludwig Maximilian University in Munich, and at the Joint Program in Survey Methodology at the University of Maryland.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

47

48 49

Week 3 Questionnaires for Cross-Cultural Surveys

Instructors: Dorothée Behr, Brita Dorer, Date: August 25-29, 2014

Silke Schneider Time: 14:00-18:00

Course content: Cross-cultural surveys are increasingly being set up in numerous disciplines and around the globe. They allow testing for the generality of hypotheses across cultures and systematic research into institutional and other contextual differences. While it is essential to have a sound knowledge of survey methodology in general when setting up these surveys, there are additional methodological aspects to take into account in order to collect cross-culturally comparable data. This course focuses on what it means to develop questionnaires for use in comparative research. It is organized around the following themes: measurement in comparative surveys, developing questionnaires for cross-cultural surveys, measu-ring background variables and their harmonization, and best practice in questionnaire translation and translation assessment. We will also touch upon pretesting methods for cross-cultural surveys and assessing comparability of survey data as well as cultural factors impacting on response processes and survey data. Class hours will be a mixture between lectures, short practical exercises and discussions. During the study hours outside class, students will work on their own projects (if applicable) and/or gain hands-on experience in designing source questions, translating and reviewing translations, and harmonizing variables, with support from the instructors.

Target group: Participants will find the course useful if they:• are involved in developing a source question-

naire (attitude, personality, behaviour items, background variables) for several cultures and/or countries;

• want to understand the implications of questionnaire design and translation as well as variable harmonization for the comparability of cross-cultural survey data;

• organize questionnaire translation and assessment, or translate and review translated questionnaires themselves.

Course and learning objectives: By the end of the course participants will:• be aware of cross-cultural requirements during

source questionnaire development to ensure comparability;

• be able to take informed decisions about how to measure social background (“demographic”) variables to ensure comparability through design and harmonisation;

• be familiar with best practice in carrying out questionnaire translation and assessment;

• be able to better account for the needs of cross-cultural questionnaire design and translation in project proposals.

Course prerequisites:• knowledge of at least one language besides

English to be able to benefit from the practice sessions regarding translation;

• interest in the impact of linguistic, cultural and institutional contexts on cross-cultural survey research;

• knowledge of general questionnaire design principles is desirable (this could be gained in the course Questionnaire design in the preceding week ).

Course 17

48

Course participants will need to bring a laptop computer for performing the practical exercises for this course.

Organizational structure of the course: The course is structured around 4 hours of classroom instruction and 2 extra hours of individual and group work, either on student’s own projects or on assignments given by the course instructors. The assignments may include: • operationalizing a theoretical concept and

developing source questionnaire items suitable for a cross-cultural survey;

• harmonization exercises with background variables of interest to students;

• translating and assessing questionnaire items by different methods;

• discussing translation and harmonization resources;

• background reading.

About the instructors:Dr. Dorothée Behr is a senior researcher at GESIS – Leibniz Institute for the Social Sciences. She holds a degree in translation studies and a doctorate on questionnaire translation. Her current research focuses on assessing item equivalence using qualitative data from web probing, translation vs. adaptation and translation assessment methodolo-gy. Previous projects include, for instance, the European Social Survey, the Programme for the Inter-national Assessment of Adult Competencies and PISA 2015, where she was responsible for guiding translation activities and/or was involved in instrument design.

Brita Dorer is a researcher at GESIS – Leibniz Institu-te for the Social Sciences specialized in the field of questionnaire translation. She is heading the translation team of the European Social Survey (ESS). Her scientific interests are the evaluation and quality enhancement of questionnaire translation and adaptation, translatability of source question-naires / advance translations, and intercultural aspects of questionnaire translation. She holds a degree in English, French and Italian translation studies from Johannes-Gutenberg-University Mainz, FTSK Germersheim, where she also worked as a freelance lecturer for English-to-German and French-to-German translation. She has been involved in translating survey questionnaires into German, such as ESS, ISSP, PIAAC and SHARE.

Dr. Silke Schneider is head of the knowledge transfer department at GESIS – Leibniz Institute for the Social Sciences and engages in research on educational attainment and its measurement in cross-national surveys. Her main interests are in social stratification and social structure as well as survey methodology, especially measurement issues and cross-national comparability of socio-structural variables. She studied for her Master’s degree at the University of Cologne, after which she completed her doctorate at Nuffield College, University of Oxford. Silke is currently conducting a project on the development of a new instrument for measu-ring education in computer-assisted surveys.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

49

50 51

Week 3 Surveying large-Scale Political Events

Instructors: Jan Eichhorn, Ben Matthews Date: August 25-29, 2014

Time: 14:00-18:00

Course content: Crises, referenda, elections, global sports competi-tions, etc. Social scientists are often interested in people’s attitudes in relation to particular large scale events that carry a socio-political dimension. Conducting research into this is not easy, however: existing large-scale surveys are usually not event-specific, new surveys can be too expensive and online methods too unreliable. This course engages with the question how to best survey attitudes around such big events. It engages both with traditional cross-sectional surveys and more contemporary techniques such as online instru-ments and Experience Sampling Methods. Crucially, it discusses how surveying for specific events differs from traditional approaches, including how we have to engage differently with foundations for surveying, such as questionnaire design and adaptation to target audiences.

The course will place a focus on feasibility and takes into account issues such as time and resource cons-traints, as well as questions about the actual implementation of such projects. It will also enable participants to develop strategies for dissemination and engagement with results to inform public and/or policy debate. The course will be taught in a very interactive mode. Examples from existing research will be used as stimulus for discussions and group exercises in which participants can bring in their own experiences. Practical sessions will be used for participants to discuss their own research projects to develop strategies for their own work while receiving feedback throughout and potentially finding partners for collaboration.

Target group: Participants will find the course useful if they:• are currently conducting or planning to

conduct research on large scale socio-political events (e.g. referenda, crises, sport events, socio-cultural events);

• are interested in expanding their knowledge about survey methods to engage with short-term applications and topic-specific ways of canvassing opinions;

• are interested in surveying particular target groups for whom survey questions otherwise “tried and tested” may require substantial adaptation (e.g. surveying adolescents, people in specific interest groups);

• have particular ideas for projects and would benefit from space to develop their ideas into project plans receiving detailed feedback.

Course and learning objectives: By the end of the course participants will:• have a good overview of different ways of

canvassing attitudes of people in the context of large scale socio-political events;

• be able to adjust existing survey designs to match the needs of particular target groups;

• be familiar with more instantaneous tech-niques to surveying, such as Experience Sampling Methods;

• have engaged with strategies of piloting to ensure their research is valid despite potential time and cost restraints;

• have developed ideas and plans for own research projects.

Course 18

50

Course prerequisites:• engagement with results from social surveys;• understanding of concepts relating to social

and political attitudes/values/perceptions; • awareness of research processes and cycles; • ideally engagement with social surveys as user

(data analysis) or creator (survey design), though not a necessary requirement for course participation.

Course participants are recommended (but not required) to bring a laptop computer for performing the practical exercises for this course.

Organizational structure of the course: The course will be run in a very interactive and participant-engaged way. In addition to the instruc-tion sessions, there will be 2 hours of project work each day. In these sessions, participants are asked to develop plans for projects based on their own work and interests. There will be daily feedback from instructors on the progress within groups to strengthen the development of these research plans. If considered desirable, participants could use these plans as the foundation for genuine collaborative research projects or as the foundation for their own work. Project presentations will provide peer feedback and scrutiny in addition to the instructor advice.

About the instructors:Dr. Jan Eichhorn lectures in Social Policy at the University of Edinburgh, Scotland. He has run a wide range of research methods courses and workshops for academics and practitioners. He is co- or principal investigator on a range of Economic and Research Council funded projects

investigating people’s attitudes towards Scottish independence. Based on his work, he has been providing commentary for multiple media outlets (incl. BBC, Skynews, ZDF). He is also the co-founder and director of the think tank d|part and has provided expert advice to the German, Scottish and UK government on multiple issues.

Ben Matthews is a doctoral researcher in Crimino-logy at the University of Edinburgh, Scotland. Using his extensive experience in survey based analysis, he has been at the core of the development of instruction and teaching materials for school and university students in the context of the 2014 Scottish Independence referendum. He has also designed and delivered successful workshops for practitioners using a number of large-scale datasets. In addition to his academic activities, Ben is currently conducting analytical work for the Scottish government as part of their Reducing Reoffending Programme.

On the Summer School website, you can find the full syllabus of the course with complete information on the topics, literature, and day-to-day schedule.

51

52 53

Alphabetical List of Instructors

ADr. Katrin Auspurg: Dept. Sociology, University of Konstanz and Institute for Social and Economic Research, University of Essex (Course 13)

BDr. Dorothée Behr: Dept. Survey Design and Methodology, GESIS - Leibniz Institute for the Social Sciences, Germany (Course 17)

Nejc Berzelak: Faculty of Social Sciences, University of Ljubljana, Slovenia (Course 5)

Prof. em. Jaak Billiet, PhD: Centre for Sociological Research, Katholieke Universiteit Leuven, Belgium (Course 7)

Prof. Annelies Blom, PhD: Dept. of Political Science, University of Mannheim, Germany (Course 1)

CProf. Mick P, Couper: Survey Research Center, University of Michigan, USA (Course 9)

DBrita Dorer: Dept. Survey Design and Methodolo-gy, GESIS - Leibniz Institute for the Social Sciences, Germany (Course 17)

Dr. Katrin Drasch: Institute of Sociology, University of Erlangen-Nuremberg, Germany (Course 14)

EDr. Stephanie Eckman: Institute for Employment Research, Germany (Course 16)

Dr. Jan Eichhorn: School of Social and Political Science, University of Edinburgh, UK (Course 18)

Prof. Dr. Stefanie Eifler: Faculty of History and Social Science, Catholic University of Eichstätt-Ingolstadt, Germany (Course 8)

FRemco Feskens, PhD: National Institute for Educational Measurement (CITO), Netherlands (Course 15)

Prof. Dr. Marek Fuchs: Institute of Sociology, Darmstadt University of Technology, Germany (Course 12)

Frederik Funke, PhD: the University of Kassel and LINK Institut, Frankfurt, Germany (Course 9)

HLaurence Horton: London School of Economics, Library, UK (Short Course A)

Dr. Eva Heschinger: Dept. of International Politics and Conflict Studies, Bundeswehr University Munich, Germany (Short Course B)

KAlexia Katsanidou: Dept. Data Archive for the Social Sciences, GESIS - Leibniz Institute for the Social Sciences, Germany (Short Course A)

Georg Kessler: Goldemund Consulting, Vienna, Austria (Course 4)

Thomas Klausch: Dept. Methodology and Statistics, Utrecht University, Netherlands (Course 11)

Dr. Corinna Kleinert: Institute for Employment Research (IAB), Germany (Course 14)

Nancy Kracke: Dept. Education and Employment over the Life Course, Institute for Employment Research (IAB), Germany (Course 14)

52

Tanja Kunz: Institute of Sociology, Darmstadt University of Technology, Germany (Course 12)

LProf. Dr. Edith de Leeuw: Dept. Methodology and Statisticsat, University of Utrecht, Netherlands (Course 11)

Prof. Katja Lozar Manfreda, PhD: Faculty of Social Sciences, University of Ljubljana, Slovenia (Course 5)

Peter J. Lugtig, PhD: Dept. Methodology and Statistics, Utrecht University, Netherlands (Course 1)

MBen Matthews: School of Law, University of Edinburgh, UK (Course 18)

Dr. Florian Meinfelder: Dept. Statistics and Econometrics, University of Bamberg, Germany (Course 6 and Course 10)

Zeina Mneimneh, PhD: Institute for Social Research (ISR), Univerity of Michigan, USA (Course 2)

PBeth-Ellen Pennell: Institute for Social Research (ISR), University of Michigan, USA (Course 2)

RProf. Dr. Susanne Rässler: Dept. Social Sciences, Economics, and Business Administration, University of Bamberg, Germany (Course 10)

Dr. Astrid Recker: Dept. Data Archive for the Social Sciences, GESIS - Leibniz Institute for the Social Sciences, Germany (Short Course A)

Prof. Dr. Jost Reinecke: Faculty of Sociology, University of Bielefeld, Germany (Course 4)

Dr. Melanie Revilla: Research and Expertise Centre for Survey Methodology (RECSM), Universitat Pompeu Fabra (UPF), Barcelona, Spain (Course 7)

SEvgenia Samoilova: Dept. Knowledge Transfer, GESIS – Leibniz Institute for the Social Sciences and Bremen International Graduate School of Social Science, Germany (Course 3)

Carsten Sauer: Faculty of Sociology, University of Bielefeld, Germany (Course 13)

Dr. Silke Schneider: Dept. Survey Design and Methodology and Knowledge Transfer, GESIS – Leibniz Institute for the Social Sciences, Germany (Course 17)

Prof. Dr. Margit Schreier: School of Humanities and Social Sciences, Jacobs University Bremen, Germany (Course 3)

Dr. Ineke Stoop: The Netherlands Institute for Social Research/SCP, Netherlands (Course 15)

Hans Walter Steinhauer: Leibniz Institute for Educational Trajectories (LIfBi), Germany (Course 6)

TJessica Trixa: Dept. Data Archive for the Social Sciences, GESIS - Leibniz Institute for the Social Sciences, Germany (Short Course A)

Dr. Vera Toepoel: Dept. Methodology and Statistics, Utrecht University, Netherlands (Course 11)

WDr. Lisa Wallander: Dept. Health and Society, Malmö University, Sweden (Course 8)

53

54 55

Practical Information

Venue:GESIS – Leibniz Institute for the Social SciencesUnter Sachsenhausen 6-8 50667 Köln, Germany

From Cologne main station („Köln Hbf“), you can walk to GESIS in just 5 minutes (400 m). Cross “Bahnhofsvorplatz” to the right towards the street „An den Dominikanern“ and keep straight on. Follow „An den Dominikanern“, then named „Unter Sachsenhausen.“ At the crossroads „Tunisstraße,“ you will see the GESIS building right in front of you.

A – Cologne main station (Köln Hbf); B - GESIS - Leibniz Institute for the Social Sciences

Underground stop „Appellhofplatz“ is also very close to the venue. For local transport information, check Kölner Verkehrsbetriebe. http://auskunft.kvb-koeln.de

How to reach Cologne

Air: Cologne/Bonn Airport is a hub for several low-cost airlines. 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 main station, and the travel time is only 15 minutes.

http://www.koeln-bonn-airport.de/

Rail: High-speed trains offer smooth connections from many German and European cities and Cologne. The high-speed Thalys train connects the city with Brussels and Paris; the ICE directly connects Amsterdam and Utrecht with Cologne. Cologne central station is located in the city centre close to the venue.

http://www.bahn.de

Intercity Bus: The central bus station (ZOB) is located in the centre of Cologne next to the main station (Köln Hbf). Travelling by bus is often cheaper than travelling by train. Connections are especially good between Eastern European cities and Cologne.

http://www.eurolines.de

Car: Cologne 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 many public car parks (chargeable) within short walking distance of the venue: „Börsenplatz“ (Enggasse) and „Maternushaus“ (Auf dem Hunnenrücken).

56 57

About Cologne

Cologne is centrally located in the west of the Federal Republic of Germany and right in the heart of Europe. Entering Germany is facilitated by easy access regulations for most visitors.

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 popular university town in Germany. The University of Cologne 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

Summer school participants need to take care oftheir accommodation themselves - requirementsare too diverse for us to make central bookings.Cologne tends to be very well booked all over the year. It is especially the case during trade fairs (see http://www.koelnmesse.com), when considerably higher rates will be charged at almost all hotels.

We highly recommend that you book your accommodation as early as possible.

For accommodations listed with booking codes, special rates for GESIS are available. Please note that you will not be able to receive them when booking via online reservation systems. To make use of the reduced rates, please make your booking via the email addresses provided for the respective accommodation.

The City of Cologne generally charges an extra tax of 5% on the room accommodation rate. However, travelers, who have to stay overnight for essential professional or business-related reasons, are exempted from this tax. You can save money by handing in the filled in exemption request form to a hotel employee at the check-in upon your arrival. You will receive the form together with the list of accommodation options in your registration confirmation email.

All accommodations listed below are located close to the venue and/or can easily be reached via public transport (please see the timetable information of KVB - Kölner Verkehrsbetriebe, http://www.kvb-koeln.de).

The rates listed below are as of spring 2014 and therefore might change in rate or category.

To search for accommodation on your own, please check the following websites or contact the Cologne tourist office http://www.cologne-tourism.com. http://www.hrs.dehttp://www.9flats.comhttps://www.airbnb.dehttp://www.wimdu.dehttps://www.couchsurfing.org/http://www.arthouse-apartments.de/http://www.deutsche-pensionen.de/pension-koeln/http://www.wg-gesucht.de/en/http://www.zwischenmiete.dehttp://www.koeln-lodge.de/http://www.mitwohnzentrale-koeln.com/http://www.studenteninserate.de/zwischenmiete/koeln.d/

58 59

Close to GESIS (5-10 minutes walking distance)(rates incl. breakfast, tax and free WiFi)

Jugendherberge Köln-Pathpoint – Backpacker HostelAllerheiligenstr. 15, 50668 Kölnhttp://www.pathpoint-cologne.de, email: info@pathpoint-cologne.de€ 22,40 (4-8 beds), € 29,40 (double), incl. breakfast and bedding

Station Hostel – Hostel for BackpackersMarzellenstr. 44-56, 50668 Kölnhttp://www.hostel-cologne.de, email: station@hostel-cologne.de€ 17-24 p.p. (6-4 beds), € 66-73 (3 beds room), € 48-55 (double), € 32-39 (single)

Hotel Garni „Im Kupferkessel“**Probsteigasse 6, 50670 Kölnhttp://www.im-kupferkessel.de/de, email: info@im-kupferkessel.de€ 38-52 (single), € 80 (double or twin)Booking code: GESIS

Classic Hotel Harmonie*** Ursulaplatz 13-19, 50668 Kölnhttp://www.classic-hotel-harmonie.de/, email: harmonie@classic-hotels.com€ 53 (single), € 85 (double)Booking code: GESIS

Antik Hotel Bristol*** Kaiser-Wilhelm-Ring 48, 50672 Kölnhttp://www.antik-hotel-bristol.de, email: hotel@antik-hotel-bristol.de€ 60 (single), € 95 (double), for GESIS only - no higher rate during trade fairs!Booking code: GESIS

Hotel Domstern***Domstr. 26, 50668 Kölnhttp://www.hotel-domstern.de, info@hotel-domstern.de€ 69 (single), € 82 (double)Booking code: GESIS

Hotel Domspitzen***Domstr. 23-25, 50668 Kölnhttp://www.hotel-domspitzen.de, info@hotel-domspitzen.de€ 69 (single), € 82 (double) Booking code: GESIS

A&O Köln Dom (Hbf.)Komödienstraße 19-21, 50667 Kölnhttp://www.aohostels.com/de/koeln/koeln-dom/ € 69 (single), € 79 (double)

Motel One - Köln-Mediapark Am Kümpchenshof 2, 50670 Kölnhttp://www.motel-one.com/en/hotels/cologne/koeln-mediaparkemail: koeln-mediapark@motel-one.com€ 69 (single), 84 (double)Booking code: GESIS

Maternushaus***S Tagungszentrum des Erzbistums Köln Kardinal-Frings-Str. 1-3, 50668 Köln (direct neighbour of GESIS)http://www.maternushaus.de, email: info@maternushaus.de€ 69 (single), € 100 (double)Booking code: GESIS

58

Nearby (15-25 minutes walking distance or with public transport)(KVB week ticket - level 1a € 16,90 or 1b € 23,30)

Weltempfänger Hostel & CaféVenloer Str. 196, 50823 Köln-Ehrenfeldhttp://www.koeln-hostel.de, email: info@koeln-hostel.de€ 19 (6 beds), € 21-24 (4 beds), € 53-59 (double), € 38 (single)Booking code: GESIS

Die Wohngemeinschaft - Hostel Richard-Wagner-Str. 39, 50674 Kölnhttp://www.hostel-wohngemeinschaft.de/, Tel: +49-(0)221-39760904€ 20 (6-8 beds), € 25 (3 beds), € 59 (double), € 45-59 (single)

Jugendherberge Köln-Deutz - City-HostelSiegesstr. 5, 50679 Köln http://www.koeln-deutz.jugendherberge.de, email: koeln-deutz@jugendherberge.de € 31 (4-6 beds), € 38 (double), € 52 (single)

Hotel Chelsea Jülicher Str. 1, 50674 Kölnhttp://www.hotel-chelsea.de/main.html, email: mail@hotel-chelsea.de€ 33 (single), without breakfastBooking code: GESIS

Pension Otto Richard-Wagner-Str. 18, 50674 Kölnhttp://www.pensionotto.de, email: info@pensionotto.de€ 25-45 (single), € 60 (double)Booking code: GESIS

Pension Jansen Richard-Wagner-Str. 18, 50674 Kölnhttp://www.pension-jansen-koeln.de, email: pensionjansen@gmx.de€ 35-45 (single), € 70 (double), € 98 (3 beds)Booking code: GESIS

Hostel Köln Marsilstein 29, 50676 Kölnhttp://www.hostel.ag/, email: service@hostel.ag€ 33 (6 beds), € 36,50 (4 beds), € 40 (3 beds), € 44,50 (double), € 69 (single)

Simply Beds - Monika Steinkötter Brüsseler Str. 54, 50674 Kölnhttp://www.simply-beds.de, email: monikasteinko-etter@web.de€ 52 (single), € 62 (double)Booking code: GESIS

Motel One - Köln-Waidmarkt Tel-Aviv-Straße 6, 50676 Kölnhttp://www.motel-one.com/de/hotels/koeln, email: koeln-waidmarkt@motel-one.com€ 69 (single), 84 (double)Booking code: BUND 2013

Apartments(rates incl. tax, free WiFi)

Adagio Köln CityBlaubach 3, 50676 Kölnwww.adagio-city.com/Koln, email: H8288@adagio-city.com€ 89 upwards (double), without breakfast but with kitchenette

59

60 61

Hotel Chelsea***Art Hotel Jülicher Str. 1, 50674 Kölnhttp://www.hotel-chelsea.de/main.html, email: mail@hotel-chelsea.de€ 60(single), € 75 (French bed), without breakfastBooking code: GESIS

Conti Hotel***Brüsseler Str. 40-42, 50674 Kölnhttp://www.conti-hotel.de, email: info@conti-hotel.de€ 60 (single), € 80 (double) Booking code: GESIS

Hotel Casa Colonia*** Machabäerstraße 63, 50668 Kölnwww.casa-colonia.de, email: mail@casa-colonia.de€ 80 (single), € 99 (double)

Hotel Stern am Rathaus*** Bürgerstraße 6, 50667 Kölnhttp://www.stern-am-rathaus.de, email: info@stern-am-rathaus.de€ 85,00 (single), € 105,00 (double)

HOPPER Hotel et cetera***Brüsseler Str. 26, 50674 Kölnhttp://www.hopper.de, email: hotel@hopper.de€ 85,00 (single), € 110,00 (double) Booking code: GESIS

Azimut Hotel Cologne City Center****Hansaring 97, 50670 Köln http://www.azimuthotels.de, email: info.koeln@azimuthotels.com € 80 (single), € 95 (double)TMS booking code: BUND 2013

HOPPER Hotel St. Antonius ****SDagobertstr. 32, 50668 Kölnhttp://www.hopper.de/, email: st.antonius@hopper.de€ 85 (single), € 110 (double)Booking code: GESIS

Dorint Hotel am Heumarkt Köln *****Pipinstr. 1, 50667 Kölnhttp://www.dorint.com/koeln-city, email: reservierung.koeln-city@dorint.com€ 139 (single), € 174 (double)Booking code: Universität zu Köln (tax-exempt)

Excelsior Hotel Ernst *****STrankgasse 1-5 Domplatz, 50667 Kölnhttp://www.excelsiorhotelernst.com, email: info@excelsior-hotel-ernst.de€ 155 (single), € 175 (double)Booking code: Universität zu Köln (tax-exempt)

60

Hotels with 2** to 5*****S stars according to international classification (rates incl. breakfast, tax and free WiFi)

Campingplatz der Stadt Köln – Campsite – Open: March 28 - October 20Weidenweg 35, 51105 Köln-Pollhttp://www.camping-koeln.de, email: info@camping-koeln.de€ 11/day, upwards

Guesthouse - Deutsche Sporthochschule KölnGuts-Muths-Weg 1, 50933 Köln-Müngersdorfhttp://www.dshs-koeln.de/gaestehaus/index.html, email: gaestehaus@dpsg-koeln.de€ 46 (single), € 71 (double)Booking code: Universität zu Köln (tax-exempt)

Ameron Hotel Regent *****Melatenguertel 15, 50933 Kölnwww.hotelregent.de, email: info@hotelregent.de€ 79 (single), € 106 (double)TMS booking code: BUND 2013

Leonardo Royal Hotel Köln - Am Stadtwald*****Dürener Str. 287, 50935 Kölnhttp://www.leonardo-hotels.de/deutschland-hotels/hotel-koln/leonardo-hotel-koeln-am-stadt-wald, email: reservations.koeln@leonardo-hotels.com€ 80 (single), € 100 (double)TMS booking code: 954289222

Hotels, hostels, guest-houses and campsite in the environs

Park Inn by Radisson Köln City-West««««Innere Kanal Str. 15, 50823 Kölnhttp://www.pikcw.de, email: koeln@proventhotels.com€ 80 (single), € 100 (double)TMS booking code: 79507

Best Western Hotel Brenner‘scher Hof*****Wilhelm-von-Capitaine-Str. 15-17, 50858 Kölnhttp://www.brennerscher-hof.de, email: hotel@brennerscher-hof.de€ 96 (single), € 102 (double)

61

62 63

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 pages) works closely with the scientific depart-ments to set up a topical and diversified events program. Several research data centres (RDC) focus on specific kinds of data (e.g. elections, comparative surveys, official micro data) and work across the departments Data Archive for the Social Sciences (DAS) 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 programs, such as the European Social Survey (ESS), the Internati-onal 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/

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 course in Mathematics for Social Scientists taking place in the autumnm every year. Language of instruction of those 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 programs 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

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 als Teil der TNS Deutschland GmbH 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 MünchenHarald.Bielenski@tns-infratest.com

66 67

Notes: Notes:

68

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

Email: summerschool@gesis.org

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 hosts conferences and offers research methods training events for all these phases.

GESIS – Leibniz Institute for the Social Sciences

www.gesis.org