Search
GESIS Training Courses
user_jsdisabled

Wiss. Koordination

Alisa Remizova

Administrative Koordination

Claudia O'Donovan-Bellante

Please wait...

Survey Data Integration: Combining (Non)probability, Administrative, and Digital Trace Data

About
Location:
Online via Zoom
 
General Topics:
Data Analysis, Computational Social Science
Course Level:
Advanced
Format:
Workshop
Software used:
R
Duration:
3 days
Language:
English
Fees:
Students: 330 €
Academics: 495 €
Commercial: 990 €
 
Keywords
Additional links
Lecturer(s): Camilla Salvatore

About the lecturer - Camilla Salvatore

Course description

Social science researchers increasingly rely on multiple data sources because no single source provides a complete or fully accurate picture of the population or the phenomena of interest. Combining data allows us to improve the quality and efficiency of estimates, and to analyse relationships that cannot be investigated using one source alone.
 
This workshop will introduce advanced methods for integrating traditional probability sample surveys with multiple data sources, including nonprobability samples, administrative data, and digital trace data. You will learn how to design research projects that rely on integrated data, critically assess the assumptions underlying different integration methods, and select and apply appropriate techniques for specific research questions.
 
To provide you with a broad perspective and practical tools applicable to different situations, each day will focus on a specific data integration problem: (1) inference for nonprobability samples, (2) record linkage and statistical matching, and (3) the integration of surveys and digital traces. Through group work and R exercises, you will gain practical experience applying these methods to real-world examples.
 
By the end of the workshop, you will be able to implement state-of-the-art data integration techniques, evaluate data quality issues, and effectively present their results. You are welcome to bring your own data and research ideas to work on during the workshop.
 
Organizational structure of the course
The workshop will be delivered online and consist of a mix of theoretical lectures, R exercises, and group activities. Individual consultations will be offered on the last day of the workshop.


Target group

You will find the course useful if:
  • You are a social scientist or statistician at the advanced Master's or PhD-level (and beyond), working on academic research projects, or a researcher working in the survey field.


Learning objectives

By the end of the course, you will:
  • Understand the data integration problem.
  • Identify challenges related to data quality and errors.
  • Design research projects centered on survey data integration.
  • Select appropriate methodologies for survey data integration.
  • Implement these methods in R.
  • Present and visualize the results of your analyses.


Prerequisites

  • Basic knowledge of regression models, statistical inference, and survey statistics and methodology is assumed; key concepts will be revised at the beginning of Day 1.
  • Basic knowledge of R language is required; interactive notebooks will be provided to support hands-on practice.
 
Software requirements
You need to install R (version 4.5.0 or higher) and be able to install packages. The full list of required R packages will be communicated before the start of the course. Analysis methods are introduced using R, supported by interactive workbooks that help you run code and explore results, even with limited prior experience in R.


Schedule