The KODAQS Toolbox – Assessing and Mitigating Data Quality Issues – Part 1: Survey Data

Chances are, you’ve run into this before. Regression results that change depending on how you handle missing values in your dependent variable. A long item battery where many respondents select the same response option all the way through. A set of survey items that do not seem to measure the concept you had in mind. Or a panel dataset that gradually loses entire demographic subgroups over time. These issues are common in real-world survey data. They may not trigger error messages or stand out immediately in our analyses, but they can quietly and systematically bias our findings if we ignore them.

Wahrscheinlich sind Sie schon einmal auf dieses Problem gestoßen. Regressionsergebnisse, die sich je nach Umgang mit fehlenden Werten in Ihrer abhängigen Variablen ändern. Eine lange Reihe von Fragen, bei denen viele Befragte durchgehend dieselbe Antwortoption auswählen. Eine Itembatterie, die offenbar nicht das von Ihnen beabsichtigte Konzept misst. Oder ein Panel-Datensatz, bei dem im Laufe der Zeit nach und nach ganze demografische Untergruppen verloren gehen. Diese Probleme treten in realen Umfragedaten häufig auf. Sie lösen möglicherweise keine Fehlermeldungen aus und fallen in unseren Analysen nicht sofort ins Auge, aber wenn wir sie ignorieren, können sie unsere Ergebnisse still und systematisch verfälschen.

DOI: 10.34879/gesisblog.2025.114

Understanding Data Quality Issues Through the Total Survey Error Framework

How can we make sense of these different data quality issues? Rather than treating them as separate challenges, we can classify these issues using a single, well-established framework: the Total Survey Error framework.

In survey research, random errors and systematic biases that can affect the quality of survey data are usually described by looking at where they arise in the survey process. Each stage – from sampling and recruiting participants to collecting and processing responses – can introduce its own challenges. The Total Survey Error (TSE) framework (Biemer, 2010, Groves & Lyberg, 2010) maps these challenges onto two broader error dimensions: representation errors and measurement errors– that is, errors arising from units not being observed and errors arising from how observed units are measured.

Representation bias, or non-observation error, arises when a survey fails to “observe” the population it intends to study. This can happen due to coverage errors, for example through over- or undercoverage of specific subgroups due to the survey design, or through sampling errors that result from drawing one out of many potential samples of the target population. Nonresponse error is the most common source of representation bias, occurring when individuals who do not respond to the survey are systematically different from those who do. Even post-survey corrections of non-observation errors can introduce additional bias, known as adjustment errors.

Measurement bias, or observation error, on the other hand, refers to inaccuracies that arise when collecting information from the respondents. It includes specification errors that occur when survey instruments fail to accurately capture the theoretical construct, as well as measurement errors that describe the general deviation of recorded answers from respondents’ true values. Finally, processing errors can emerge during data handling, cleaning, or coding.

From Theory to Practice: Finding and Using Tools for Data Quality Assessment

Understanding the theory behind survey errors is important, but the real challenge is putting that knowledge into practice. Where can researchers find tools and resources that help them assess and improve data quality in their own projects?

A good starting point is the survey research literature, which offers key indicators to identify and assess issues such as nonresponse error or response bias. But even if researchers know which indicators to use, the question remains how to implement them in practice and how to ensure they are correctly computed for a specific data set?

Searching for practical resources, researchers might come across tools in R or Stata designed to compute key quality indicators or to support data quality analyses. However, assessing data quality issues is only the first step – the greater challenge is figuring out how to address them effectively. This leads to a number of questions. How to make sure the indicators truly suit a specific case? How should they be interpreted? And perhaps most importantly, how do researchers move from identifying quality issues in their data to addressing them?

The KODAQS Data Quality Toolbox: A Practical Resource

Fortunately, you do not have to start from scratch. The KODAQS Data Quality Toolbox bridges the gap between theoretical knowledge and the practical implementation of data quality analyses. It guides you through different case scenarios and provides recommendations on how to handle specific data quality issues.

Developed by the Competence Center for Data Quality in the Social Sciences (KODAQS), the Toolbox is an educational resource designed to help researchers build the skills needed to assess and improve data quality in their own projects. It provides step-by-step tutorials and hands-on coding examples for calculating key quality indicators across various data types, from survey data to digital trace data and linked data.

Each tool in the KODAQS Toolbox focuses on a practical application case. It demonstrates how to detect, evaluate, and interpret data quality issues for different research questions and contexts. Along with concrete workflows, it also gives researchers recommendations on how to avoid common pitfalls in data quality analyses and handle data quality issues to ensure valid and unbiased outcomes of substantial analyses.

Exploring KODAQS Tools for Survey Data

How can the Toolbox be used in practice? The KODAQS Toolbox includes four tools that focus on different quality aspects of survey data and offer a hands-on introduction to identifying and addressing common data quality issues.

Assessing Response Quality in Multi-Item Scales with RESQUIN

The RESQUIN tool helps researchers evaluate the response quality in multi-item scales. In doing so, it contributes to identifying and mitigating measurement errors which arise during the response process and can be the result of systematic response biases or other response patterns. Based on the analysis of political trust and environmental attitude scales, the tool illustrates how key quality indicators reveal low-quality responses in real survey data. It provides a guided R workflow for the response quality analyses along with example code for calculating and interpreting indicators, such as response distributions, response styles, and other response patterns like straightlining. These indicators help reveal when respondents may not be fully engaged with the question content and when response patterns may systematically distort results.

Beyond key indicators, RESQUIN also discusses the caveats and suitability of each indicator for different question types and response scales and offers suggestions for what to do if poor-quality responses are detected.

Examining Sample Composition with SAMPCOMPR

The SAMPCOMPR tool helps researchers evaluate how well their survey sample represents the target population. It addresses representation errors and biases that occur when the sample does not accurately capture the intended study population. Using a demographic benchmark example, the tool illustrates how differences in age, education, or gender composition between the sample and the target population can be identified and highlights typical cases of under- and overrepresentation. It provides R-based workflows to calculate and visualize sample composition indicators, allowing users to see where their realized sample deviates from population benchmarks. Through simple visualizations and transparent reporting, SAMPCOMPR makes it easy to spot under- or overrepresented subgroups and to document these findings in a reproducible way.

SAMPCOMPR offers guidance on how to systematically compare data to external benchmarks, compute key composition indicators such as absolute and relative deviations, and summarize results to strengthen survey quality documentation.

Evaluating and Validating Scales with SCALEARCH

The SCALEARCH tool guides researchers through the basic analyses needed to evaluate and document the validity of a scale as a research instrument. With this, the tool directly addresses specification errors and supports researchers in ensuring their scales accurately capture the theoretical constructs they are intended to measure. Based on an applied example of mental ability test scores from schoolchildren, the tool demonstrates each step of the scale validation workflow. Using an example scale with continuous indicators, it provides hands-on guidance for computing descriptive statistics, exploring the dimensionality of items, fitting confirmatory factor models, and estimating reliability coefficients.

The tutorial walks users through each step – from calculating item means and composite scores to assessing model fit and interpreting different reliability estimates. It also highlights procedures that require caution and helps users decide which analytic approaches best suit their data and measurement goals.

Predicting Measurement Quality for Questionnaire Development with SQP

The Survey Quality Predictor (SQP) helps researchers evaluate the expected measurement quality of survey questions before data collection even begins. By coding formal and linguistic features, such as wording, response scale, and mode of administration, SQP predicts key quality components like reliability, validity, and overall measurement quality. By doing so, SQP helps researchers design survey questions that are both clearer and more reliable to address measurement error prior to collecting data. The SQP tool demonstrates this with an applied example that compares two versions of the same question on satisfaction with the country’s economy using different response scales, showing how design choices lead to different predicted quality.

The tool first introduces the logic behind SQP and shows, step by step, how to use its online interface to generate and interpret quality predictions for the user’s own items. It also explains how question characteristics influence these predictions and how the results can inform better questionnaire design and item selection.

… If you are working with digital trace data and wondering what kinds of quality issues can arise and how to handle them, stay tuned for our next blog post in the KODAQS Toolbox Series where we introduce tools to help you tackle data quality challenges in digital trace data.

About KODAQS: Improving Data Quality in the Social Sciences

The Competence Center for Data Quality in the Social Sciences (KODAQS) is a collaboration between GESIS – Leibniz Institute for the Social Sciences, the University of Mannheim, and LMU Munich. Its mission is to support researchers in evaluating and improving the quality of social science data through a combination of training opportunities at the KODAQS Data Quality Academy, open-educational resources like the KODAQS Data Quality Toolbox, and collaborative research as part of the Guest Researcher Progr am.

Curious to learn more or get involved? Explore the KODAQS Toolbox and our related KODAQS services and join us in advancing data quality in the social sciences.