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Related Concept Videos

Archival Research01:40

Archival Research

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Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Dropout analysis: A method for data from Internet-based research and dropR, an R-based web app and package to analyze

Ulf-Dietrich Reips1, Annika T Overlander2, Matthias Bannert3

  • 1Department of Psychology, University of Konstanz, Universitätsstr. 10, 78464, Konstanz, Germany. reips@uni-konstanz.de.

Behavior Research Methods
|July 18, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces dropR, a free R package and web application designed to analyze and visualize participant dropout in internet-based research. dropR simplifies complex dropout data analysis for researchers, offering publication-ready visualizations and statistical insights.

Keywords:
AtrittionDropoutMortalityNon-responseR packageShinySurvival analysisWeb appiScience

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Area of Science:

  • Psychology
  • Computer Science
  • Statistics

Background:

  • Internet-based research presents unique challenges with participant non-response, including item non-response and dropout.
  • The voluntary nature and large sample sizes in online studies necessitate robust methods for analyzing dropout.
  • Dropout analysis is crucial for understanding data integrity and research validity in digital environments.

Purpose of the Study:

  • To develop and discuss methodologies for analyzing dropout in Internet-based research.
  • To introduce dropR, an R package and web service for analyzing and visualizing dropout data.
  • To provide researchers with accessible tools for understanding and presenting participant attrition.

Main Methods:

  • Development of the dropR R package and Shiny web application.
  • Utilizing R for statistical computing and graphical representation of dropout curves.
  • Implementation of statistical tests including chi-square, odds ratios, Kaplan-Meier survival analysis, Kolmogorov-Smirnov, and rho-family statistics.

Main Results:

  • dropR generates accessible, publication-ready visualizations of dropout curves.
  • The package calculates key dropout parameters like chi-square values and odds ratios.
  • Automated inferential components identify critical dropout points and differences between experimental conditions.

Conclusions:

  • dropR offers a user-friendly solution for analyzing and visualizing dropout in internet-based research, requiring no programming knowledge.
  • The tool enhances data interpretation by providing statistical insights and visual displays.
  • Freely available as a web application and R package, dropR supports researchers in addressing participant attrition.