Advanced Survey Design

Description

This five-day course in survey design takes students beyond the introductory courses offered in BA and MA programmes, and discusses current issues in one of the most important data collection methods: surveys. Specifically, it focuses on doing surveys in the Internet-era. It will show how to collect data in online surveys, using smartphones, and mixing surveys with Big data, such as digital behavioral data, smartphone sensors or administrative data. It combines short one-hour lectures with exercises on most of the topics discussed. Course participants must be proficient in working with statistical software. Course materials are prepared for working with R.

We present new ways to analyse modern surveys, including non-probability survey designs, smartphone data collection, digital trace data and data collected via apps. Course participants must be proficient working with the statistical software package R at the level of at least knowing Tidy and multivariate regression in R.

The course is interesting for social scientists or statisticians at the PhD level or beyond, working on academic research projectsThe course slightly overlaps with the course: S15 - Survey Research: Design, Implementation and Data Processing . The current course is however more advanced and more focused on survey research within the academic (university) setting, as well as focused on current issues related to the use of mobile phones and Big Data in social science research.

We expect students to have quite extensive knowledge of survey research (for example by using survey data extensively or conducting survey research in their daily work) and have knowledge of statistics at the MSc level for social scientists (the general linear model). Note that you have to upload a brief motivation letter (up to 300 words) with your application, in which you write a short list of your experience with surveys and statistical analyses. Please include in the motivation letter what software you are proficient in, and what courses related to survey design you have taken.

IOPS students do not have to do this.

Note: Participants need to bring a laptop computer to the course with R (https://www.r-project.org/) installed and the ability to download packages (Internet access is provided). Exercises are designed to be conducted in R. Users may use their own software of choice (e.g. Python, Stata, or SAS), but we do not provide solutions for these programmes.

Course outline:

Day 1: mixed device and push-to-web surveys (mode in the 21^st century)

• Total Survey Error in the context of the era of ‘Big data’
• Design-based and model-based inference
• Survey modes including mixed-mode and push-to-web surveys  
• Brief overview of probability sampling methods, and the survey package in R.
• Exercises on modern sampling with R

Day 2: Modern survey fieldwork

• Adaptive survey design
• Mixed-device surveys
• Mobile communications: text messaging, apps
• Ecological Momentary Assessment
• Exercises on designing data collection for mobiles.

Day 3: Designed big data: digital data

• Trust, consent, ethics
• Digital trace data and surveys
• Data donation
• Exercises on data donation (do it yourself), and working with digital trace data (in R)

Day 4: Designed big data: smart surveys, apps and sensors

• Browser-based sensor data collection
• Data collection through smartphone apps and wearables.
• Exercises on working with geo-location and other sensor data (e.g. pictures, accelerometer data)

Day 5:  Data integration

• Multi-source statistics
• Models of data integration and data fusion
• Consultations about your own research project

Lecturers

Dr. Bella Struminskaya, Dr. Peter Lugtig

Target audience

If you want to know the current state of the art in survey data and survey data analysis, this is a course for you. This course assumes minimally MSc level knowledge of methods and statistics. In the course R will be used extensively, and knowledge of R is presumed. The course is intended for PhD students, and others at the postgraduate level who wish to know more of survey data and its collection in the 21st century. A maximum of 50 participants will be allowed in this course. IOPS students will get priority.

 

For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here

Aim of the course

The aim of this course is to provide an overview of theory and practice of modern survey design and analysis, particularly focusing on modern methods of web surveys and the analysis of new types of data (e.g. sensor data). This course is useful for more experienced students. After the course, participants are ready to apply the learned towards their own data collection, are able to critically assess existing surveys and survey documentation and analyse survey data themselves successfully.

Learning goals: 

• Knowledge of current issues in data collection; data donation, digital behavioral data, smart surveys
• Knowledge of methods to integrate organic (Big) data into data collection
• Knowledge of conducting research in an era of mobile phones
• Skills in conducting analyses in R using probability-based sampling models
• Skills in processing and integrating digital behavioral and sensor data into data collection methods.

Study load

The course consists of one-hour lectures mixed with one-hour exercises using the computer, to apply the tools presented in the lectures. A typical course day starts at 9.30 and ends at 16.30 with breaks for coffee, lunch and tea (included in the course fee). Everyone actively participating on all five days of the course will qualify for ECs. (2 EC). Certificates of attendance will be handed out at the end of the course. There is no reading required before the start of the course. However, we will provide an overview of further reading with every formal lecture on a topic.

Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will however obtain a certificate upon completion of this course.

Tags

• big data
• Data Science
• M&S
• nonresponse
• sensors
• survey
• total survey error
• questionnaire design
• survey methods
• measurement error
• mobile web surveys