Search
GESIS Training Courses
user_jsdisabled

Wiss. Koordination

Alisa Remizova

Administrative Koordination

Claudia O'Donovan-Bellante

Please wait...

Data Quality Assessment for Survey Responses: Be Careful of the Careless

About
Location:
Online via Zoom
General Topics:
Data Analysis, Research Data Management, Survey Methodology
Course Level:
Beginner
Format:
Workshop
Software used:
R, Stata
Duration:
2 days
Language:
English
Fees:
Students: 220 €
Academics: 330 €
Commercial: 660 €
Keywords
Additional links
Lecturer(s): Julian Urban, Thomas Knopf

About the lecturer - Julian Urban

About the lecturer - Thomas Knopf

Course description

Registration deadline: 06.11.2026  
Registration after this date might be possible, but is subject to sufficient demand and availability of places.
 
In survey research, some of the collected responses may be of low quality. This can be attributed to various reasons, such as survey design issues, respondent fatigue, lack of motivation or understanding, carelessness, and response biases.
Low-quality responses can threaten the validity and reliability of research findings and data analysis. Low-quality responses can also affect the effectiveness of statistical analyses, leading to inaccurate interpretations and misleading conclusions. Thus, identifying and addressing low-quality responses is crucial for ensuring the integrity and robustness of research outcomes.
This is an introductory workshop on detecting and handling quality issues in data collected from survey research. The primary focus will be on the quality of the responses to the Likert scale questions. After the workshop, you will have an overview of response quality issues in survey research and know the indicators to diagnose and deal with quality issues.
 
Specifically, this workshop will cover the following topics.
  • Introduction to response quality in survey research;
  • Introduction to a wide range of response quality indicators, both theoretical and practical. In more detail:
    • Paradata - usage of, for example, timing variables, device and software usage;
    • Check items - usage of, for example, directed and undirected attention checks (Bogus items) and similar   items;
    • Item responses - usage of three classes of response quality indicators that address outliers, consistency, and   response patterns;
  • Discussion and recommendations on how to flag and filter respondents that do not meet quality standards, and how to report and document findings and decisions.
 
We will provide software demonstrations and hands-on exercises on data quality assessment using R. For some selected topics, we will also provide  Stata do-files. You are welcome to bring your own data to work during the exercises, and an example dataset will be available as well.
 
Organisational structure of the course
The workshop will include lectures and practical assignments, carried out individually or in small groups, consisting of participants' projects, group discussions, and hands-on exercises with the provided cases. Lecturers will be available for consultations on Day 2, support work on assignments, and facilitate discussions within group assignments.


Target group

The workshop is designed for researchers and practitioners who work with Likert-scale or single/multiple-choice survey data. Specifically, you will find the course useful if:
  • You are planning or have already conducted surveys, such as social science surveys, customer surveys, or employee surveys;
  • You analyze quantitative data from surveys, which include single items or/and multiple-item scales, and want to improve the data quality;
  • You aim to acquire robust skills that are necessary to understand, evaluate, and handle data quality issues in surveys.


Learning objectives

By the end of the course, you will:
  • know data quality issues and concepts;
  • know how to evaluate the quality of survey responses and detect and handle low-quality data;
  • gain hands-on experience in data quality assessment in R, using commonly applied data quality indicators in survey research.


Prerequisites

  • Familiarity with basic survey research terminology: survey mode, questionnaire, item, response format;
  • Basic understanding of descriptive statistics: counts, proportions, means, standard deviations, correlations;
  • Basic skills in R (RStudio) for data handling and analysis (above all: path and package handling, data import, and data     wrangling/inspection of data frames);
  • Note: Stata users are highly welcome! Core indicator concepts are software-agnostic, and Stata do-files are available    for selected topics. The hands-on exercises and provided code are mostly in R.
  • Own data: You are strongly encouraged to bring your own survey datasets. The dataset should be in a wide format    (one row per respondent) and include at least one multi-item scale. The lecturers will also provide a learning dataset     on Day 1.
  •  
Software and hardware requirements
Please install the following software prior to the workshop:
R version ≥ 4.2, for convenience, ideally in combination with the most recent RStudio version.
R-packages for data import: readr, sjmisc, etc.; for data wrangling: tidyverse; for data analysis: psych.
 
When bringing your own data, please prepare a wide-format data file (one row per participant).
 
Important: Ensure that you have administrator rights to install R packages autonomously on your computer during the workshop!


Schedule

Recommended readings